T cell balance gene expression, compositions of matters and methods of use thereof (2024)

This application is a continuation-in-part of International patent application Serial No. PCT/US2016/019949 filed Feb. 26, 2016 and published as PCT Publication No. WO2016/138488 on Sep. 1, 2016 and which claims priority to U.S. provisional patent application 62/176,796, filed Feb. 26, 2015; U.S. provisional patent application 62/181,697, filed Jun. 18, 2015 and U.S. provisional patent application 62/386,073, filed Nov. 16, 2015.

This invention was made with government support under Grant Nos. OD003958, HG006193, HG005062, OD003893, NS030843, NS045937, AI073748, A1045757 and AI056299 awarded by National Institutes of Health. The government has certain rights in the invention.

Reference is also made to PCT application PCT/US2015/017826, filed Feb. 26, 2015 and published on Sep. 3, 2015 as WO2015130968; WO/2012/048265; WO/2014/145631; WO/2014/134351; and U.S. provisional patent application 61/945,641, filed Feb. 27, 2014; and Wang et al., CD5L/AIM Regulates Lipid Biosynthesis and Restrains Th17 Cell Pathogenicity. Cell Volume 163, Issue 6, p 1413-1427, 3 Dec. 2015 and Gaublomme et al., Single-Cell Genomics Unveils Critical Regulators of Th17 Cell Pathogenicity. Cell Volume 163, Issue 6, p 1400-1412, 3 Dec. 2015, incorporated herein by reference.

The foregoing applications, and all documents cited therein or during prosecution (“appln cited documents”) and all documents cited or referenced in the appln cited documents, and all documents cited or referenced herein (“herein cited documents”), and all documents cited or referenced in herein cited documents, together with any manufacturer's instructions, descriptions, product specifications, and product sheets for any products mentioned herein or in any document incorporated by reference herein, are hereby incorporated herein by reference, and may be employed in the practice of the invention. Appln cited documents, herein cited documents, all documents herein referenced or cited, and all documents indicated to be incorporated herein by reference, are incorporated by reference to the same extent as if each individual document was specifically and individually set forth herein in full and indicated to be incorporated by reference when or where cited or referenced.

This invention relates generally to compositions and methods for identifying the regulatory network that modulates, controls or otherwise influences T cell balance, for example, Th17 cell differentiation, maintenance and/or function, as well compositions and methods for exploiting the regulatory network that modulates, controls or otherwise influences T cell balance in a variety of therapeutic and/or diagnostic indications. This invention also relates generally to identifying and exploiting target genes and/or target gene products that modulate, control or otherwise influence T cell balance in a variety of therapeutic and/or diagnostic indications.

Despite their importance, the molecular circuits that control the balance of T cells, including the differentiation of naïve T cells, remain largely unknown. Recent studies that reconstructed regulatory networks in mammalian cells have focused on short-term responses and relied on perturbation-based approaches that cannot be readily applied to primary T cells. Accordingly, there exists a need for a better understanding of the dynamic regulatory network that modulates, controls, or otherwise influences T cell balance, including Th17 cell differentiation, maintenance and function, and means for exploiting this network in a variety of therapeutic and diagnostic methods. Citations herein are not intended as an admission that anything cited is pertinent or prior art; nor does it constitute any admission as to the contents or date of anything cited.

The invention has many utilities. The invention pertains to and includes methods and compositions therefrom of Drug Discovery, as well as for detecting patients or subjects who may or may not respond or be responding to a particular treatment, therapy, compound, drug or combination of drugs or compounds; and accordingly ascertaining which drug or combination of drugs may provide a particular treatment or therapy as to a condition or disease or infection or infectious state, as well as methods and compositions for selecting patient populations (e.g., by detecting those who may or may not respond or be responding), or methods and compositions involving personalized treatment—a combination of Drug Discovery and detecting patients or subjects who may not respond or be responding to a particular treatment, therapy, compound, drug or combination of drugs or compounds (e.g., by as to individual(s), so detecting response, nor responding, potential to respond or not, and adjusting particular treatment, therapy, compound, drug or combination of drugs or compounds to be administered or administering a treatment, therapy, compound, drug or combination of drugs or compounds indicated from the detecting).

The invention provides a method of diagnosing, prognosing and/or staging an immune response involving T cell balance, comprising detecting a first level of expression, activity and/or function of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5 or one or more products of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l and comparing the detected level to a control of level of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l or gene product expression, activity and/or function, wherein a difference in the detected level and the control level indicates that the presence of an immune response in the subject.

The invention also provides a method of monitoring an immune response in a subject comprising detecting a level of expression, activity and/or function of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65. Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l at a first time point, detecting a level of expression, activity and/or function of one or more signature genes or one or more products of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Sc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l at a second time point, and comparing the first detected level of expression, activity and/or function with the second detected level of expression, activity and/or function, wherein a change in the first and second detected levels indicates a change in the immune response in the subject.

The invention also provides a method of identifying a patient population at risk or suffering from an immune response comprising detecting a level of expression, activity and/or function of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Sc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l or one or more products of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the patient population and comparing the level of expression, activity and/or function of one or more signature genes or one or more products of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in a patient population not at risk or suffering from an immune response, wherein a difference in the level of expression, activity and/or function of one or more of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Sc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l or one or more products of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the patient populations identifies the patient population as at risk or suffering from an immune response.

The invention also provides a method for monitoring subjects undergoing a treatment or therapy specific for a target gene selected from the group consisting of candidates Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino8Mc, Med2l, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l for an aberrant immune response to determine whether the patient is responsive to the treatment or therapy comprising detecting a level of expression, activity and/or function of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the absence of the treatment or therapy and comparing the level of expression, activity and/or function of Toso, advantageously Ctla2h, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the presence of the treatment or therapy, wherein a difference in the level of expression, activity and/or function of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13. Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l or products of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the presence of the treatment or therapy indicates whether the patient is responsive to the treatment or therapy.

In these methods the immune response is an autoimmune response or antiinflammatory response; or the inflammatory response is associated with an autoimmune response, an infectious disease and/or a pathogen-based disorder; or the signature genes are Th17-associated genes; or the treatment or therapy is an antagonist as to expression of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells; or the treatment or therapy is an agonist that enhances or increases the expression of Toso, advantageously Ctla2h, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce T cell differentiation toward Th17 cells; or the treatment or therapy is an antagonist of a target gene selected from the group consisting of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature; or the treatment or therapy is an agonist that enhances or increases the expression of a target gene selected from the group consisting of Toso, advantageously Ctla2h, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a non-pathogenic to a pathogenic signature; or the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent.

The invention also provides a method of modulating T cell balance, the method comprising contacting a T cell or a population of T cells with a T cell modulating agent in an amount sufficient to modify differentiation, maintenance and/or function of the T cell or population of T cells by altering balance between Th17 cells, regulatory T cells (Tregs) and other T cell subsets as compared to differentiation, maintenance and/or function of the T cell or population of T cells in the absence of the T cell modulating agent; wherein the T cell modulating agent is an antagonist for or of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5 in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells, or wherein the T cell modulating agent is an agonist that enhances or increases the expression of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65. Plzp, Toso or Cd5l in an amount sufficient to induce T cell differentiation toward Th17 cells, or wherein the T cell modulating agent is specific for a target gene selected from the group consisting of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5, or wherein the T cell modulating agent is an antagonist of a target gene selected from the group consisting of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr6S, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature, or wherein the T cell modulating agent is an agonist that enhances or increases the expression of a target gene selected from the group consisting of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a non-pathogenic to a pathogenic signature. In these methods the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent; or the T cells are naïve T cells, partially differentiated T cells, differentiated T cells, a combination of naïve T cells and partially differentiated T cells, a combination of naïve T cells and differentiated T cells, a combination of partially differentiated T cells and differentiated T cells, or a combination of naïve T cells, partially differentiated T cells and differentiated T cells.

The invention also provides a method of enhancing Th17 differentiation in a cell population, increasing expression, activity and/or function of one or more Th17-associated cytokines or one or more Th17-associated transcription regulators selected from interleukin 17F (IL-17F), interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-related orphan receptor C (RORC), and/or decreasing expression, activity and/or function of one or more non-Th17-associated cytokines or non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l.

In methods herein the agent enhances expression, activity and/or function of at least Toso. The agent can be an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist; advantageously an antibody, such as a monoclonal antibody; or an antibody that is a chimeric, humanized or fully human monoclonal antibody.

The invention comprehends use of an antagonist for or of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells for treating or Drug Discovery of or formulating or preparing a treatment for an aberrant immune response in a patient.

The invention comprehends use of an agonist that enhances or increases the expression of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65. Plzp, Toso or Cd5l in an amount sufficient to induce T cell differentiation toward Th17 cells for treating or Drug Discovery of or formulating or preparing a treatment for an aberrant immune response in a patient.

The invention comprehends use of an antagonist of a target gene selected from the group consisting of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature for treating or Drug Discovery of or formulating or preparing a treatment for an aberrant immune response in a patient.

The invention comprehends use of an agonist that enhances or increases the expression of a target gene selected from the group consisting of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a non-pathogenic to a pathogenic signature for treating or Drug Discovery of or formulating or preparing a treatment for an aberrant immune response in a patient.

The invention comprehends a treatment method or Drug Discovery method or method of formulating or preparing a treatment comprising any one of the methods or uses herein discussed.

The invention comprehends a method of drug discovery for the treatment of a disease or condition involving an immune response involving T cell balance in a population of cells or tissue which express a target gene selected from the group consisting of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l comprising the steps of (a) providing a compound or plurality of compounds to be screened for their efficacy in the treatment of said disease or condition; (b) contacting said compound or plurality of compounds with said population of cells or tissue; (c) detecting a first level of expression, activity and/or function of a target gene selected from the group consisting of Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l or one or more products of a target gene selected from the group consisting of Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med2l, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65.Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l; (d) comparing the detected level to a control of level of a target gene selected from the group consisting of Toso. Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med2l, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65. Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5 or gene product expression, activity and/or function; (e) evaluating the difference between the detected level and the control level to determine the immune response elicited by said compound or plurality of compounds.

The invention provides compositions and methods for modulating T cell balance. As used herein, the term “modulating” includes up-regulation of, or otherwise increasing, the expression of one or more genes, down-regulation of, or otherwise decreasing, the expression of one or more genes, inhibiting or otherwise decreasing the expression, activity and/or function of one or more gene products, and/or enhancing or otherwise increasing the expression, activity and/or function of one or more gene products.

As used herein, the term “modulating T cell balance” includes the modulation of any of a variety of T cell-related functions and/or activities, including by way of non-limiting example, controlling or otherwise influencing the networks that regulate T cell differentiation; controlling or otherwise influencing the networks that regulate T cell maintenance, for example, over the lifespan of a T cell, controlling or otherwise influencing the networks that regulate T cell function; controlling or otherwise influencing the networks that regulate helper T cell (Th cell) differentiation; controlling or otherwise influencing the networks that regulate Th cell maintenance, for example, over the lifespan of a Th cell; controlling or otherwise influencing the networks that regulate Th cell function; controlling or otherwise influencing the networks that regulate Th17 cell differentiation; controlling or otherwise influencing the networks that regulate Th17 cell maintenance, for example, over the lifespan of a Th17 cell; controlling or otherwise influencing the networks that regulate Th17 cell function; controlling or otherwise influencing the networks that regulate regulatory T cell (Treg) differentiation; controlling or otherwise influencing the networks that regulate Treg cell maintenance, for example, over the lifespan of a Treg cell; controlling or otherwise influencing the networks that regulate Treg cell function; controlling or otherwise influencing the networks that regulate other CD4+ T cell differentiation: controlling or otherwise influencing the networks that regulate other CD4+ T cell maintenance; controlling or otherwise influencing the networks that regulate other CD4+ T cell function; manipulating or otherwise influencing the ratio of T cells such as, for example, manipulating or otherwise influencing the ratio of Th17 cells to other T cell types such as Tregs or other CD4+ T cells; manipulating or otherwise influencing the ratio of different types of Th17 cells such as, for example, pathogenic Th17 cells and non-pathogenic Th17 cells; manipulating or otherwise influencing at least one function or biological activity of a T cell; manipulating or otherwise influencing at least one function or biological activity of Th cell; manipulating or otherwise influencing at least one function or biological activity of a Treg cell; manipulating or otherwise influencing at least one function or biological activity of a Th17 cell; and/or manipulating or otherwise influencing at least one function or biological activity of another CD4+ T cell.

The invention provides T cell modulating agents that modulate T cell balance. For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level(s) of and/or balance between T cell types, e.g., between Th17 and other T cell types, for example, regulatory T cells (Tregs), and/or Th17 activity and inflammatory potential. As used herein, terms such as “Th17 cell” and/or “Th17 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses one or more cytokines selected from the group the consisting of interleukin 17A (IL-17A), interleukin 17F (IL-17F), and interleukin 17A/F heterodimer (IL17-AF). As used herein, terms such as “Th1 cell” and/or “Th1 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses interferon gamma (IFNγ). As used herein, terms such as “Th2 cell” and/or “Th2 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses one or more cytokines selected from the group the consisting of interleukin 4 (IL-4), interleukin 5 (IL-5) and interleukin 13 (IL-13). As used herein, terms such as “Treg cell” and/or “Treg phenotype” and all grammatical variations thereof refer to a differentiated T cell that expresses Foxp3.

For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level of and/or balance between Th17 phenotypes, and/or Th17 activity and inflammatory potential. Suitable T cell modulating agents include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent.

For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level of and/or balance between Th17 cell types, e.g., between pathogenic and nonpathogenic Th17 cells. For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level of and/or balance between pathogenic and non-pathogenic Th17 activity.

For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to influence or otherwise impact the differentiation of a population of T cells, for example toward Th17 cells, with or without a specific pathogenic distinction, or away from Th17 cells, with or without a specific pathogenic distinction.

For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to influence or otherwise impact the differentiation of a population of T cells, for example toward a non-Th17 T cell subset or away from a non-Th17 cell subset. For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to induce T-cell plasticity, i.e., converting Th17 cells into a different subtype, or into a new state.

For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to induce T cell plasticity, e.g., converting Th17 cells into a different subtype, or into a new state.

For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to achieve any combination of the above.

In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.

The T cell modulating agents are used to modulate the expression of one or more target genes or one or more products of one or more target genes that have been identified as genes responsive to Th17-related perturbations. These target genes are identified, for example, by contacting a T cell, e.g., naïve T cells, partially differentiated T cells, differentiated T cells and/or combinations thereof, with a T cell modulating agent and monitoring the effect, if any, on the expression of one or more signature genes or one or more products of one or more signature genes. In some embodiments, the one or more signature genes are selected from those listed in Table 1 or Table 2 of WO/2014/134351, incorporated herein by reference; alone or with those of other herein disclosed methods.

In some embodiments, the target gene is one or more Th17-associated cytokine(s) or receptor molecule(s) selected from those listed in Table 3 of WO/2014/134351, incorporated herein by reference; alone or with those of other herein disclosed methods. In some embodiments, the target gene is one or more Th17-associated transcription regulator(s) selected from those shown in Table S3 (Gaublomme 2015) or listed in Table 4 of WO/2014/134351, incorporated herein by reference; alone or with those of other herein disclosed methods.

In some embodiments, the target gene is one or more Th17-associated transcription regulator(s) selected from those shown in Table S3 (Gaublomme 2015) or Table 5 of WO/2014/134351, incorporated herein by reference: alone or with those of other herein disclosed methods. In some embodiments, the target gene is one or more Th17-associated receptor molecule(s) selected from those listed in Table 6 of WO/2014/134351, incorporated herein by reference; alone or with those of other herein disclosed methods. In some embodiments, the target gene is one or more Th17-associated kinase(s) selected from those listed in Table 7 of WO/2014/134351, incorporated herein by reference; alone or with those of other herein disclosed methods. In some embodiments, the target gene is one or more Th17-associated signaling molecule(s) selected from those listed in Table 8 of WO/2014/134351, incorporated herein by reference; alone or with those of other herein disclosed methods. In some embodiments, the target gene is one or more Th17-associated receptor molecule(s) selected from those listed in Table 9 of WO/2014/134351, incorporated herein by reference; alone or with those of other herein disclosed methods. In some embodiments, the target gene is one or more target genes involved in induction of Th17 differentiation such as, for example one or more of the target genes listed in Table 2 herein or Table 5 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more target genes involved in onset of Th17 phenotype and amplification of Th17 T cells such as, for example, one or more of the target genes listed in Table 2 herein or Table 5 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more target genes involved in stabilization of Th17 cells and/or modulating Th17-associated interleukin 23 (IL-23) signaling such as, for example, one or more of the target genes listed in Table 2 herein or Table 5 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 6 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation. In some embodiments, the target gene is one or more of the target genes listed in Table 6 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 6 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 7 herein or Table 7 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 7 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 7 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 8 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 8 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 8 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table 9 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table S6 (Gaublomme 2015), Table 7 or in Table 9 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the target gene is one or more of the target genes listed in Table S6 (Gaublomme 2015), Table 7 or Table 9 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the late stage of Th17 differentiation, maintenance and/or function.

In some embodiments, the target gene is one or more target genes that is a promoter of Th17 cell differentiation. In some embodiments, the target gene is GPR65. In some embodiments, the target gene is also a promoter of pathogenic Th17 cell differentiation and is selected from the group consisting of CD5L, DEC1, PLZP and TCF4.

In some embodiments, the target gene is one or more target genes that is a promoter of pathogenic Th17 cell differentiation. In some embodiments, the target gene is selected from the group consisting of CD5L, DEC1, PUP and TCF4.

The desired gene or combination of target genes is selected, and after determining whether the selected target gene(s) is overexpressed or under-expressed during Th17 differentiation and/or Th17 maintenance, a suitable antagonist or agonist is used depending on the desired differentiation, maintenance and/or function outcome. For example, for target genes that are identified as positive regulators of Th17 differentiation, use of an antagonist that interacts with those target genes will shift differentiation away from the Th17 phenotype, while use of an agonist that interacts with those target genes will shift differentiation toward the Th17 phenotype. For target genes that are identified as negative regulators of Th17 differentiation, use of an antagonist that interacts with those target genes will shift differentiation toward from the Th17 phenotype, while use of an agonist that interacts with those target genes will shift differentiation away the Th17 phenotype. For example, for target genes that are identified as positive regulators of Th17 maintenance, use of an antagonist that interacts with those target genes will reduce the number of cells with the Th17 phenotype, while use of an agonist that interacts with those target genes will increase the number of cells with the Th17 phenotype. For target genes that are identified as negative regulators of Th17 differentiation, use of an antagonist that interacts with those target genes will increase the number of cells with the Th17 phenotype, while use of an agonist that interacts with those target genes will reduce the number of cells with the Th17 phenotype. Suitable T cell modulating agents include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent.

In some embodiments, the positive regulator of Th17 differentiation is a target gene selected from MINA, TRPS1, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3, and combinations thereof. In some embodiments, the positive regulator of Th17 differentiation is a target gene selected from MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS and combinations thereof.

In some embodiments, the negative regulator of Th17 differentiation is a target gene selected from SP4, ETS2, IKZF4, TSC22D3, IRF1 and combinations thereof. In some embodiments, the negative regulator of Th17 differentiation is a target gene selected from SP4, IKZF4, TSC22D3 and combinations thereof.

In some embodiments, the T cell modulating agent is a soluble Fas polypeptide or a polypeptide derived from FAS. In some embodiments, the T cell modulating agent is an agent that enhances or otherwise increases the expression, activity, and/or function of FAS in Th17 cells. As shown herein, expression of FAS in T cell populations induced or otherwise influenced differentiation toward Th17 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, these T cell modulating agents are useful in the treatment of an infectious disease or other pathogen-based disorders. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells. In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of FAS. Inhibition of FAS expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, these T cell modulating agents are useful in the treatment of autoimmune diseases such as psoriasis, inflammatory bowel disease (IBD), ankylosing spondylitis, multiple sclerosis, Sjögren's syndrome, uveitis, and rheumatoid arthritis, asthma, systemic lupus erythematosus, transplant rejection including allograft rejection, and combinations thereof. In addition, enhancement of Th17 cells is also useful for clearing fungal infections and extracellular pathogens. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells that express additional cytokines. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.

In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of CCR5. Inhibition of CCR5 expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, the T cell modulating agent is an inhibitor or neutralizing agent. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.

In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of CCR6. Inhibition of CCR6 expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.

In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of EGR1. Inhibition of EGR1 expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.

In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of EGR2. Inhibition of EGR2 expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.

For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the phenotype of a Th17 cell or population of cells, for example, by influencing a naïve T cell or population of cells to differentiate to a pathogenic or non-pathogenic Th17 cell or population of cells, by causing a pathogenic Th17 cell or population of cells to switch to a non-pathogenic Th17 cell or population of T cells (e.g., populations of naïve T cells, partially differentiated T cells, differentiated T cells and combinations thereof), or by causing a non-pathogenic Th17 cell or population of T cells (e.g., populations of naïve T cells, partially differentiated T cells, differentiated T cells and combinations thereof) to switch to a pathogenic Th17 cell or population of cells.

In some embodiments, the invention comprises a method of drug discovery for the treatment of a disease or condition involving an immune response involving T cell balance in a population of cells or tissue of a target gene comprising the steps of providing a compound or plurality of compounds to be screened for their efficacy in the treatment of said disease or condition, contacting said compound or plurality of compounds with said population of cells or tissue, detecting a first level of expression, activity and/or function of a target gene, comparing the detected level to a control of level of a target gene, and evaluating the difference between the detected level and the control level to determine the immune response elicited by said compound or plurality of compounds. For example, the method contemplates comparing tissue samples which can be inter alia infected tissue, inflamed tissue, healthy tissue, or combinations of tissue samples thereof.

In one embodiment of the invention, the reductase null animals of the present invention may advantageously be used to modulate T cell balance in a tissue or cell specific manner. Such animals may be used for the applications hereinbefore described, where the role of T cell balance in product/drug metabolism, detoxification, normal homeostasis or in disease etiology is to be studied. It is envisaged that this embodiment will also allow other effects, such as drug transporter-mediated effects, to be studied in those tissues or cells in the absence of metabolism, e.g., carbon metabolism. Accordingly the animals of the present invention, in a further aspect of the invention may be used to modulate the functions and antibodies in any of the above cell types to generate a disease model or a model for product/drug discovery or a model to verify or assess functions of T cell balance.

In another embodiment, the method contemplates use of animal tissues and/or a population of cells derived therefrom of the present invention as an in vitro assay for the study of any one or more of the following events/parameters: (i) role of transporters in product uptake and efflux; (ii) identification of product metabolites produced by T cells; (iii) evaluate whether candidate products are T cells; or (iv) assess drug/drug interactions due to T cell balance.

The terms “pathogenic” or “non-pathogenic” as used herein are not to be construed as implying that one Th17 cell phenotype is more desirable than the other. As described herein, there are instances in which inhibiting the induction of pathogenic Th17 cells or modulating the Th17 phenotype towards the non-pathogenic Th17 phenotype is desirable. Likewise, there are instances where inhibiting the induction of non-pathogenic Th17 cells or modulating the Th17 phenotype towards the pathogenic Th17 phenotype is desirable.

As used herein, terms such as “pathogenic Th17 cell” and/or “pathogenic Th17 phenotype” and all grammatical variations thereof refer to Th17 cells that, when induced in the presence of TGF-β3, express an elevated level of one or more genes selected from Cxcl3, IL22, IL3, Ccl4, Gzmb, Lrmp, Ccl5, Casp1, Csf2, Ccl3, Tbx21, Icos, IL17r, Stat4, Lgals3 and Lag, as compared to the level of expression in a TGF-β3-induced Th17 cells. As used herein, terms such as “non-pathogenic Th17 cell” and/or “non-pathogenic Th17 phenotype” and all grammatical variations thereof refer to Th17 cells that, when induced in the presence of TGF-β3, express a decreased level of one or more genes selected from IL6st, IL1rn, Ikzf3, Maf, Ahr, IL9 and IL10, as compared to the level of expression in a TGF-β3-induced Th17 cells.

In some embodiments, the T cell modulating agent is an agent that enhances or otherwise increases the expression, activity and/or function of Protein C Receptor (PROCR, also called EPCR or CD201) in Th17 cells. As shown herein, expression of PROCR in Th17 cells reduced the pathogenicity of the Th17 cells, for example, by switching Th17 cells from a pathogenic to non-pathogenic signature. Thus, PROCR and/or these agonists of PROCR are useful in the treatment of a variety of indications, particularly in the treatment of aberrant immune response, for example in autoimmune diseases and/or inflammatory disorders. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist.

In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of the Protein C Receptor (PROCR, also called EPCR or CD201). Inhibition of PROCR expression, activity and/or function in Th17 cells switches non-pathogenic Th17 cells to pathogenic Th17 cells. Thus, these PROCR antagonists are useful in the treatment of a variety of indications, for example, infectious disease and/or other pathogen-based disorders. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cell modulating agent is a soluble Protein C Receptor (PROCR, also called EPCR or CD201) polypeptide or a polypeptide derived from PROCR. In some embodiments, the invention provides a method of inhibiting Th17 differentiation, maintenance and/or function in a cell population and/or increasing expression, activity and/or function of one or more non-Th17-associated cytokines, one or more non-Th17 associated receptor molecules, or non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that inhibits expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments, the agent inhibits expression, activity and/or function of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the antibody is a chimeric, humanized or fully human monoclonal antibody. In some embodiments, the T cell is a naïve T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the T cell to become and/or produce a desired non-Th17 T cell phenotype, for example, a regulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. In some embodiments, the T cell is a partially differentiated T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the partially differentiated T cell to become and/or produce a desired non-Th17 T cell phenotype, for example, a regulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a CD4+ T cell phenotype other than a Th17 T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a shift in the Th17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17 cell phenotype.

In some embodiments, the invention provides a method of inhibiting Th17 differentiation in a cell population and/or increasing expression, activity and/or function of one or more non-Th17-associated cytokines, one or more non-Th17-associated receptor molecules, or non-Th17-associated transcription factor selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments, the agent enhances expression, activity and/or function of at least one of SP4, IKZF4, TSC22D3 or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the T cell is a naïve T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the T cell to become and/or produce a desired non-Th17 T cell phenotype, for example, a regulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. In some embodiments, the T cell is a partially differentiated T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the partially differentiated T cell to become and/or produce a desired non-Th17 T cell phenotype, for example, a regulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a CD4+ T cell phenotype other than a Th17 T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a shift in the Th17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17 cell phenotype.

In some embodiments, the invention provides a method of enhancing Th17 differentiation in a cell population increasing expression, activity and/or function of one or more Th17-associated cytokines, one or more Th17-associated receptor molecules, or one or more Th17-associated transcription regulators selected from interleukin 17F (IL-17F), interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-related orphan receptor C (RORC), and/or decreasing expression, activity and/or function of one or more non-Th17-associated cytokines, one or more Th17-associated receptor molecules, or one or more non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that inhibits expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments, the agent inhibits expression, activity and/or function of at least one of SP4, IKZF4, TSC22D3 or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the antibody is a chimeric, humanized or fully human monoclonal antibody. In some embodiments, the T cell is a naïve T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the T cell to become and/or produce a desired Th17 T cell phenotype. In some embodiments, the T cell is a partially differentiated T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the partially differentiated T cell to become and/or produce a desired Th17 T cell phenotype. In some embodiments, the T cell is a CD4+ T cell other than a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the non-Th17 T cell to become and/or produce a Th17 T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a shift in the Th17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17 cell phenotype.

In some embodiments, the invention provides a method of enhancing Th17 differentiation in a cell population, increasing expression, activity and/or function of one or more Th17-associated cytokines, one or more Th17-associated receptor molecules, and/or one or more Th17-associated transcription regulators selected from interleukin 17F (IL-17F), interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-related orphan receptor C (RORC), and/or decreasing expression, activity and/or function of one or more non-Th17-associated cytokines, one or more Th17-associated receptor molecules, or one or more non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments, the agent enhances expression, activity and/or function of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the antibody is a chimeric, humanized or fully human monoclonal antibody. In some embodiments, the agent is administered in an amount sufficient to inhibit Foxp3, IFN-γ, GATA3, STAT4 and/or TBX21 expression, activity and/or function. In some embodiments, the T cell is a naïve T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the T cell to become and/or produce a desired Th17 T cell phenotype. In some embodiments, the T cell is a partially differentiated T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the partially differentiated T cell to become and/or produce a desired Th17 T cell phenotype. In some embodiments, the T cell is a CD4+ T cell other than a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the non-Th17 T cell to become and/or produce a Th17 T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a shift in the Th17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17 cell phenotype.

In some embodiments, the invention provides a method of identifying genes or genetic elements associated with Th17 differentiation comprising: a) contacting a T cell with an inhibitor of Th17 differentiation or an agent that enhances Th17 differentiation; and b) identifying a gene or genetic element whose expression is modulated by step (a). In some embodiments, the method also comprises c) perturbing expression of the gene or genetic element identified in step b) in a T cell that has been in contact with an inhibitor of Th17 differentiation or an agent that enhances Th17 differentiation; and d) identifying a gene whose expression is modulated by step c). In some embodiments, the inhibitor of Th17 differentiation is an agent that inhibits the expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments, the agent inhibits expression, activity and/or function of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS or combinations thereof. In some embodiments, the inhibitor of Th17 differentiation is an agent that enhances expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments, the agent enhances expression, activity and/or function of at least one of SP4, IKZF4 or TSC22D3. In some embodiments, the agent that enhances Th17 differentiation is an agent that inhibits expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments, wherein the agent that enhances Th17 differentiation is an agent that enhances expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist.

In some embodiments, the invention provides a method of modulating induction of Th17 differentiation comprising contacting a T cell with an agent that modulates expression, activity and/or function of one or more target genes or one or more products of one or more target genes selected from IRF1, IRF8, IRF9, STAT2, STAT3, IRF7, STAT1, ZFP281, IFI35, REL, TBX21, FLI1, BATF, IRF4, one or more of the target genes listed in Table 2 herein or Table 5 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation, maintenance and/or function, e.g., AES, AHR, ARID5A, BATF, BCL11B, BCL3, CBFB, CBX4, CHD7, CITED2, CREB1, E2F4, EGR1, EGR2, ELL2, ETS1, ETS2, ETV6, EZH1, FLI1, FOXO1, GATA3, GATAD2B, HIF1A, ID2, IFI35, IKZF4, IRF1, IRF2, IRF3, IRF4, IRF7, IRF9, JMJD1C, JUN, LEF1, LRRFIP1, MAX, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF21A, PML, PRDM1, REL, RELA, RUNX1, SAP18, SATB1, SMAD2, SMARCA4, SP100, SP4, STAT1, STAT2, STAT3, STAT4, STAT5B, STAT6, TFEB, TP53, TRIM24, and/or ZFP161, or any combination thereof.

In some embodiments, the invention provides a method of modulating onset of Th17 phenotype and amplification of Th17 T cells comprising contacting a T cell with an agent that modulates expression, activity and/or function of one or more target genes or one or more products of one or more target genes selected from one or more of the target genes listed in Table 2 herein or Table 5 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating stabilization of Th17 cells and/or modulating Th17-associated interleukin 23 (IL-23) signaling comprising contacting a T cell with an agent that modulates expression, activity and/or function of one or more target genes or one or more products of one or more target genes selected from one or more of the target genes listed in Table 2 herein or Table 5 of WO/2014/134351 (alone or with those of other herein disclosed methods), incorporated herein by reference, as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table S6 (Gaublomme 2015), Table 7 or in Table 6 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table S6 (Gaublomme 2015), Table 7 herein or Table 6 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table S6 (Gaublomme 2015), Table 7 herein or Table 6 of WO/2014/134351 (alone or with those of other herein disclosed methods), incorporated herein by reference, as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 7 of WO/2014/134351 (alone or with those of other herein disclosed methods), incorporated herein by reference, as being associated with the early stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 7 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 7 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating is one or more of the target genes listed in Table 8 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 8 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 8 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 9 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the early stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 9 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 9 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), as being associated with the late stage of Th17 differentiation, maintenance and/or function. In some embodiments, the invention provides a method of inhibiting tumor growth in a subject in need thereof by administering to the subject a therapeutically effective amount of an inhibitor of Protein C Receptor (PROCR). In some embodiments, the inhibitor of PROCR is an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent. In some embodiments, the inhibitor of PROCR is one or more agents selected from the group consisting of lipopolysaccharide; cisplatin; fibrinogen; 1,10-phenanthroline; 5-N-ethylcarboxamido adenosine; cystathionine; hirudin; phospholipid; Drotrecogin alfa; VEGF; Phosphatidylethanolamine; serine; gamma-carboxyglutamic acid; calcium; warfarin; endotoxin; curcumin; lipid; and nitric oxide.

In some embodiments, the invention provides a method of diagnosing an immune response in a subject, comprising detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes selected from those listed in Table 1 or 2 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), and comparing the detected level to a control of level of signature gene or gene product expression, activity and/or function, wherein a difference between the detected level and the control level indicates that the presence of an immune response in the subject. In some embodiments, the immune response is an autoimmune response. In some embodiments, the immune response is an inflammatory response, including inflammatory response(s) associated with an autoimmune response and/or inflammatory response(s) associated with an infectious disease or other pathogen-based disorder.

In some embodiments, the invention provides a method of monitoring an immune response in a subject, comprising detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes, e.g., one or more signature genes selected from those listed in Table 1 or 2 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), at a first time point, detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes, e.g., one or more signature genes selected from those listed in Table 1 or 2 of WO/2014/134351 (alone or with those of other herein disclosed methods), incorporated herein by reference, at a second time point, and comparing the first detected level of expression, activity and/or function with the second detected level of expression, activity and/or function, wherein a change between the first and second detected levels indicates a change in the immune response in the subject. In some embodiments, the immune response is an autoimmune response. In some embodiments, the immune response is an inflammatory response.

In some embodiments, the invention provides a method of monitoring an immune response in a subject, comprising isolating a population of T cells from the subject at a first time point, determining a first ratio of T cell subtypes within the T cell population at a first time point, isolating a population of T cells from the subject at a second time point, determining a second ratio of T cell subtypes within the T cell population at a second time point, and comparing the first and second ratio of T cell subtypes, wherein a change in the first and second detected ratios indicates a change in the immune response in the subject. In some embodiments, the immune response is an autoimmune response. In some embodiments, the immune response is an inflammatory response.

In some embodiments, the invention provides a method of activating therapeutic immunity by exploiting the blockade of immune checkpoints. The progression of a productive immune response requires that a number of immunological checkpoints be passed. Immunity response is regulated by the counterbalancing of stimulatory and inhibitory signal. The immunoglobulin superfamily occupies a central importance in this coordination of immune responses, and the CD28/cytotoxic T-lymphocyte antigen-4 (CTLA-4):B7.1/B7.2 receptor/ligand grouping represents the archetypal example of these immune regulators (see e.g., Korman A J, Peggs K S, Allison J P, “Checkpoint blockade in cancer immunotherapy.” Adv Immunol. 2006, 90:297-339). In part the role of these checkpoints is to guard against the possibility of unwanted and harmful self-directed activities. While this is a necessary function, aiding in the prevention of autoimmunity, it may act as a barrier to successful immunotherapies aimed at targeting malignant self-cells that largely display the same array of surface molecules as the cells from which they derive. The expression of immune-checkpoint proteins can be dysregulated in a disease or disorder and can be an important immune resistance mechanism. Therapies aimed at overcoming these mechanisms of peripheral tolerance, in particular by blocking the inhibitory checkpoints, offer the potential to generate therapeutic activity, either as monotherapies or in synergism with other therapies.

Thus, the present invention relates to a method of engineering T-cells, especially for immunotherapy, comprising modulating T cell balance to inactivate or otherwise inhibit at least one gene or gene product involved in the immune check-point.

Suitable T cell modulating agent(s) for use in any of the compositions and methods provided herein include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent. By way of non-limiting example, suitable T cell modulating agents or agents for use in combination with one or more T cell modulating agents are shown in Table 10 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods), of the specification.

One skilled in the art will appreciate that the T cell modulating agents have a variety of uses. For example, the T cell modulating agents are used as therapeutic agents as described herein. The T cell modulating agents can be used as reagents in screening assays, diagnostic kits or as diagnostic tools, or these T cell modulating agents can be used in competition assays to generate therapeutic reagents.

In some embodiments, the invention provides a method of diagnosing, prognosing and/or staging an immune response involving Th17 T cell balance, comprising detecting a first level of expression of one or more of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) in Th17 cells, and comparing the detected level to a control level of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA), wherein a change in the first level of expression and the control level detected indicates a change in the immune response in the subject. In one embodiment, a shift towards polyunsaturated fatty acids (PUFA) and away from saturated fatty acids (SFA) indicates a non-pathogenic Th17 response.

In some embodiments, the invention provides a method for monitoring subjects undergoing a treatment or therapy involving T cell balance comprising, detecting a first level of expression of one or more of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) in Th17 cells in the absence of the treatment or therapy and comparing the detected level to a level of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) in the presence of the treatment or therapy, wherein a difference in the level of expression in the presence of the treatment or therapy indicates whether the subject is responsive to the treatment or therapy.

In another embodiment, the invention provides a method for monitoring subjects undergoing a treatment or therapy involving T cell balance comprising detecting a first level of expression of one or more of saturated fatty acids (SFA) and polyunsaturated fatty acids (PUFA) in Th17 cells in the absence of the treatment or therapy and comparing the ratio of detected level to a ratio of detected level of saturated fatty acids (SFA) and polyunsaturated fatty acids (PUFA) in the presence of the treatment or therapy, wherein a shift in the ratio in the presence of the treatment or therapy indicates whether the subject is responsive to the treatment or therapy. Not being bound by a theory, a shift in the ratio towards polyunsaturated fatty acids (PUFA) and away from saturated fatty acids (SFA) indicates a non-pathogenic Th17 response.

In another embodiment, the therapy may be a lipid, preferably a mixture of lipids of the present invention. The lipids may be synthetic. Not being bound by a theory, a treatment comprising lipids may shift T cell balance.

In another embodiment, the treatment or therapy involving T cell balance is for a subject undergoing treatment or therapy for cancer. Not being bound by a theory, shifting Th17 balance towards a pathogenic phenotype would allow a stronger immune response against a tumor.

In some embodiments, the invention provides a method of drug discovery for the treatment of a disease or condition involving an immune response involving Th17 T cell balance in a population of cells or tissue comprising: (a) providing a compound or plurality of compounds to be screened for their efficacy in the treatment of said disease or condition; (b) contacting said compound or plurality of compounds with said population of cells or tissue; (c) detecting a first level of expression of one or more of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) in Th17 cells, optionally calculating a ratio; (d) comparing the detected level to a control level of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA), optionally comparing the shift in ratio; and, (e) evaluating the difference between the detected level and the control level to determine the immune response elicited by said compound or plurality of compounds.

In some embodiments, a panel of lipids is detected. The panel may include saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) whose expression is changed at least 1.5 fold when comparing wild type Th17 cells to CD5L−/− Th17 cells after treatment with non-pathogenic inducing cytokines. The non-pathogenic inducing cytokines may be TGF-β1+IL-6. The panel may include lipids whose expression is changed upon differentiation into a pathogenic or non-pathogenic Th17 cell. In another embodiment single saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) representative of lipids whose expression is changed in response to CD5L loss or differentiation are detected. In a preferred embodiment, the SFA is a cholesterol ester or palmitic acid and the PUFA is a PUFA-containing triacylglyceride or arachidonic acid. In one embodiment only a single SFA or PUFA is detected.

In some embodiments, the treatment or therapy is a formulation comprising at least one lipid. The at least one lipid may be a synthetic lipid. Not being bound by a theory an autoimmune disease may be treated with polyunsaturated fatty acids (PUFA) and a disease requiring an enhanced immune response may be treated with saturated fatty acids (SFA).

Accordingly, it is an object of the invention to not encompass within the invention any previously known product, process of making the product, or method of using the product such that Applicants reserve the right and hereby disclose a disclaimer of any previously known product, process, or method. It is further noted that the invention does not intend to encompass within the scope of the invention any product, process, or making of the product or method of using the product, which does not meet the written description and enablement requirements of the USPTO (35 U.S.C. § 112, first paragraph) or the EPO (Article 83 of the EPC), such that Applicants reserve the right and hereby disclose a disclaimer of any such subject matter.

It is noted that in this disclosure and particularly in the claims and/or paragraphs, terms such as “comprises”, “comprised”, “comprising” and the like can have the meaning attributed to it in U.S. Patent law; e.g., they can mean “includes”, “included”, “including”, and the like; and that terms such as “consisting essentially of” and “consists essentially of” have the meaning ascribed to them in U.S. Patent law, e.g., they allow for elements not explicitly recited, but exclude elements that are found in the prior art or that affect a basic or novel characteristic of the invention. Nothing herein is to be construed as a promise.

These and other embodiments are disclosed or are obvious from and encompassed by, the following Detailed Description.

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIG. 1A-1G. Single-cell RNA-seq of Th17 cells in vivo and in vitro. (FIG. 1A) Experimental setup; left: Procedure to isolate Th17 cells from in vivo tissues. EAE was induced by MOG immunization of IL-17A reporter mice, and CD3+CD4+IL-17A/GFP+ cells were harvested at the peak of disease (inset cartoon graph: Y axis: disease score; X axis—days; Red arrow: the peak at clinical score 2.5-3) from the draining LNs and CNS and analyzed by single-cell RNA-Seq. Right: Procedure to differentiate Th17 cells in vitro. Naïve CD4+CD62L+CD44 T cells were isolated from the LN and the spleen of non-immunized mice and subsequently differentiated by CD3/CD28 activation and either TGF-β1+IL-6 to derive non-pathogenic Th17 cells, or IL-1β+IL-6+IL-23 to derive more pathogenic cells. Single-cell RNA-seq was performed at 48h into differentiation. (FIG. 1B-FIG. 1E) Quality of single-cell RNA-seq. Scatter plots (B-D) compare transcript expression (FPKM+1, log10) from the in vitro TGF-β1+IL-6 48 hr condition, between two bulk population replicates (FIG. 1B), the ‘average’ of single-cell profile and a matched bulk population control (FIG. 1C), or two single cells (FIG. 1D). Histograms (FIG. 1E) depict the distributions of Pearson correlation coefficients (X axis) between single cells and their matched population control (red) and between pairs of single cells (blue). The Pearson correlation coefficient between the two replicates or between the single cell average and the matched population profile are marked by a blue cross and red triangle, respectively. (FIG. 1F, FIG. 1G) Agreement between single-cell RNA-Seq and RNA Flow-FISH. (FIG. 1F) Comparison between expression distributions measured by RNA-seq (left) and transcript count distributions measured by RNA Flow-FISH (right) for the unimodally expressed gene Batf (top) and the bi-modally expressed Il17a (bottom). As a negative control, expression of the bacterial DapB gene was measured (light green). (FIG. 1G) Bright-field images of RNA Flow-FISH samples (n=5,000 cells) with the corresponding fluorescence channel for cells negative for Il17a transcripts (yellow) and positive for Il17a transcript (brown). Scale bar in the bright-field images is 7 μm. See also FIG. 6, Table S1, related FIG. 1.

FIG. 2A-2F. Th17 cells span a progressive trajectory of states from the LN to the CNS. (FIG. 2A) Principal component analysis (PCA) separates CNS-derived cells (purple diamonds) from LN-derived cells (orange crosses). Shown are 302 cells in the space of the first two PCs. Numbered circles are selected features (signatures) that significantly correlate with PC1 or PC2 (p<10−6, Table S2(Gaublomme 2015) positioned based on the values of their Pearson correlation coefficient with each PC (axis values; to facilitate this view, the plotted PC values were normalized to be in the range between −1 and 1). Features were identified by the analysis depicted in (FIG. 2B) as either significantly diverse within a condition (with GSEA; FDR<0.05); or between conditions (with a KS test comparing CNS and LN, FDR<101). (FIG. 2B) Functional annotation scheme. From top to bottom. Gene signatures are defined from literature (e.g., by comparing CD4+ memory and naïve T cells, top) distinguishing ‘plus’ and ‘minus’ genes (e.g., genes that are, respectively, high and low in CD4+ memory vs. naïve cells; bar plot). A signature score is calculated for each signature in each single cell, as the difference inweighted z scores between the ‘plus’ and ‘minus’ genes in the signature (Experimental Procedures). Finally (bottom), for each signature and PC Applicants compute the Pearson correlation coefficient between the signature score for each cell, and the loading on the PC for each cell. Applicants plot these Pearson correlation coefficients on the PCA plot (circled numbers in (FIG. 2A)). (FIG. 2C) Five progressive Th17-cell states from the LN to the CNS. Shown is the PCA plot as in A, but where Voronoi cells (defined by the signatures characterizing the cells populating the extremities of PCA space; Experimental Procedures (colored circles, Table S2 (Gaublomme 2015)) define five feature-specific subpopulations: Th17 self-renewing (green, defined by a LCMV-specific CD4 signature comparing naïve cells to cells isolated 8 days post acute LCMV infection, GSE30431), Th17/pre-Th1 effector (pink, defined by a signature using TRP1 CD4+ T cells comparing 5 day ex vivo Th17-polarized and stimulated cells to day 0 Th17 in vitro cells, GSE26030), Th17/Th1-like effector (yellow, LCMV-specific CD4 signature comparing cells isolated 8 days vs. 30 days post chronic LCMV infection, GSE30431), Th17/Th1-like memory (light blue, LCMV-specific CD4 signature comparing cells isolated 30 days post chronic infection to naïve cells, GSE30431), and Th17 dysfunctional/senescent (moss grey, inverse of a LCMV-specific CD4 signature comparing cells isolated 30 days post acute vs. chronic infection, GSE30431). The self-renewing state was observed in two technical replicates of one of the two in vivo biological replicates, potentially due to differences in disease induction or progression. (FIG. 2D) Example genes that distinguish each sub-population. For each of the five subpopulations in (C) (color coded rows) shown are cumulative distribution function (CDF) plots of expression for key selected genes. In each case, the gene's CDF is shown for cells from each sub-population. For the subpopulations that have a substantial mixture of LN and CNS cells, the dotted curve corresponds to cells from the CNS, and the solid line for cells from the LN of that subpopulation (FIG. 2E, FIG. 2F) Transcription factors (nodes) whose targets are significantly enriched in PC2 (E) or PC1 (F). Nodes are sized proportionally to fold enrichment (Table S3 Gaublomme 2015) and colored according to the loading of the encoding gene in the respective PC (red and green: high and low PC loading, respectively; loadings were normalized to have zero mean and standard deviation of 1). See also FIGS. 7 and 13-14, Table S2-5 (Gaublomme 2015), Table 2 and 6, related to FIG. 2.

FIG. 3A-3E. A spectrum of pathogenicity states in vitro (FIG. 3A) PCA plot of Th17 cells differentiated in vitro. PC1 separates cells from most (left) to least (right) pathogenic, as indicated both by the differentiation condition (color code), and by the correlated signatures (numbered circles). PC2 separates IL-17a+ sorted Th17 cells differentiated under pathogenic conditions (red triangles) from non-pathogenic cells (Light blue squares) and non-pathogenic cells not sorted to be IL-17A positive (Black circles) at 48h. Presented are features that correlate with PC1 or PC2 (p<0.05); and that were identified as significantly diverse within a condition (using GSEA; with an FDR cutoff of 0.05); or between conditions (using KS-test to compare CNS and LN, with an FDR cutoff of 1e-4).

(FIG. 3B-FIG. 3D) Key signatures related to pathogenicity. CDFs of the single-cell scores for key signatures for the three in vitro populations (colored as in A): (FIG. 3B) a signature distinguishing the in vivo Th17/Th1-like memory sub-population (blue in FIG. 2C); (FIG. 3C) a signature distinguishing the in vivo Th17 self-renewing sub-population (green in FIG. 2C); and (FIG. 3D) a signature of pathogenic Th17 cells (Lee et al., 2012). (FIG. 3E) CDFs of expression level (FPKM+1, log10) of Il10 for the three in vitro populations. See also Table S2 (Gaublomme 2015) related to FIG. 3.

FIG. 4A-4E. Modules of genes that co-vary with pro-inflammatory and regulatory genes across single cells. (FIG. 4A) Single-cell expression distribution of genes. The heat map shows for each gene (row) its expression distribution across single cells differentiated under the TGF-β1+IL-6 condition for 48h (without further IL-17A-based sorting). Color scale: proportion of cells expressing in each of the 17 expression bins (columns). Genes are sorted from more unimodal (top) to bimodal (bottom). (FIG. 4B) Modules co-varying with pro-inflammatory and regulatory genes. Heat map of the Spearman correlation coefficients between the single-cell expression levels of signature genes of pathogenic T cells (Lee et al., 2012) or of other CD4+ lineages (columns) and the single-cell expression of any other bimodally expressed gene (rows) in cells differentiated under the TGF-β1+IL-6 condition at 48h. Genes are clustered by similarity of these correlations, revealing two diametrically opposed modules of co-varying genes: a pro-inflammatory module (orange; e.g., Il17a, Il21, Ccl20) and a regulatory module (green, e.g., Il10, Il24, Il27ra). (FIG. 4C) The modules co-varying with pro-inflammatory and regulatory genes distinguish key variation. Each cell (TGF-β1+IL-6, 48h) is colored by a signature score comparing the two co-variation modules. Shown is a PCA plot (first two PCs) with the cells differentiated under the TGF-β1+IL-6 condition at 48h, where each cell is colored by a signature score (by the method of FIG. 2B) comparing the two modules from FIG. 4B (color code). Other signatures correlated to the PCs are marked by numbered circles. (FIG. 4D) Expression of key module genes. Each panel shows the PCA plot of (C) where cells are colored by an expression ranking score of a key gene, denoted on top. (from top left corner clockwise: Il10, Toso, Il17a, and Plzp. (FIG. 4E) A ranking of the top 100 candidate genes co-varying with pro-inflammatory or regulatory genes (out of 184; Table 2 herein), sorting from high (left) to lower (right) ranking scores (bar chart). Bar chart (top) indicates ranking score deduced from single-cell data (Experimental Procedures). Genes are ordered from high (left) to low (right) scores. Purple-white heat map (middle) shows ranking scores for (top to bottom row): pathogenicity, pro-inflammatory vs. regulatory co-variation module and in vitro and in vivo PC's. Bottom matrix indicates ‘known’ (black, top row) genes previously associated with Th17 function; ‘novel validated’ (black, middle) genes that were tested and validated by follow-up experiments, and assignment to the ‘pro-inflammatory/regulatory module’ (orange & green, bottom) determined in this study. See also FIGS. 10 and 15, Table S2 (Gaublomme 2015) & S8 related to FIG. 4.

FIG. 5A-5J. GPR65, TOSO and PLZP are validated as T-cell pathogenicity regulators. (FIG. 5A, FIG. 5B) Reduction in IL17A-producing cells in GPR65−/− T-cells differentiated in vitro. (FIG. 5A) Intracellular cytokine staining for IFN-γ (Y axis) and IL-17a (X axis) of CD4+ T cells from respective WT (top) or GPR65−/− (bottom) cells activated in vitro for 96h with anti-CD3 and anti-CD28, either without (Th0; left) or with Th17-polarizing cytokines (TGF-β1+IL-6, middle; or IL-1β+IL-6+IL-23, right). (FIG. 5B) Quantification of secreted IL-17A and Il-17F (Y axis) by cytometric bead assays (CBA) in corresponding samples (X axis). * p<0.05, ** p<0.01, *** p<0.001. (FIG. 5C) Reduced IL-17A and IFN-γ production by GPR65-memory (CD62LCD44+CD4+) T cells in a recall assay. Rag1−/− mice were reconstituted with 2×106 naïve CD4 T cells from WT or GPR65−/− mice, and, immunized with MOG35-55/CFA one week post transfer. Draining LN and spleen cells were isolated 8 days after immunization and cultured ex vivo for 4 days with MOG35-55 for recall assay (Experimental Procedures). These cells were subsequently analyzed for production of IFN-γ (Y axis) and IL-17A (X axis). (FIG. 5D) Loss of GPR65 reduces tissue inflammation and autoimmune disease in vivo. Rag-1−/− mice (n=10 per category) reconstituted with 2×106 naïve CD4 T-cells from WT or GPR65−/− mice, then induced with EAE one week post transfer. Shown is the mean clinical score (Y axis) at days post immunization (X axis) for WT (black circles) or GPR65−/− (open circles) mice. Error bars indicate the standard deviation of the mean clinical score. (FIG. 5E) Transcriptional impact of a loss of GPR65, TOSO and PLZP. Shown is the significance of enrichment (−log10 (P-value); hypergeometric test, Y axis) of genes that are dysregulated compared to WT during the TGF-β1+IL-6 differentiation of GPR65−/− (96h), PLZP (48h) and TOSO−/− (96h) cells. Red (blue) bars represent genes characterizing PC1 of FIG. 4C negatively (positively). Dashed red line: p=0.01. (FIG. 5F, FIG. 5G) Reduction in IL17A-producing cells in TOSO−/− T cells differentiated in vitro. (FIG. 5F) Intracellular cytokine staining as in (A) but for WT or TOSO−/− CD4+ T-cells, activated in vitro for 96h. (FIG. 5G) Quantification of secreted IL-17A and Il-17F for CD4+ T cells from respective WT (dark green) or TOSO−/− (light green) mice as in (B) but at 48h. * p<0.05, ** p<0.01, *** p<0.001. (FIG. 5H) Reduced IL-17A production by TOSO−/− LN memory T cells in a recall assay as in (C). (FIG. 5I) Hampered IL-17A production by PLZP−/− CD4+ T cells in an in vitro recall assay. PLZP−/− (bottom row) and littermate controls (top row) were immunized with 100 μg of MOG35-55/CFA. Cells were harvested from the draining LNs and spleen 8 days post immunization and cultured ex vivo for 4 days with progressive concentrations of MOG35-55 (left column: 0 μg, middle: 5 μg and right: 20 μg) and 20 ng/ml of IL-23. CD4+ T cells were analyzed for IFN-γ (Y axis) and IL-17A (X axis) production by intracellular cytokine staining. (FIG. 5J) Quantification of secreted IL-17A and IL-17F of a MOG35-55 recall assay for littermate controls (dark green) and PLZP−/− mice (light green) at 96h post ex vivo. All experiments are a representative of at least three independent experiments with at least three experimental replicates per group.

FIG. 6A-6I. related to FIG. 1. Single-cell RNA-seq quality control. (FIG. 6A, FIG. 6B) Correlation between the first three PCs (X axis), and different RNA-seq quality measures (colored bars). (FIG. 6A) Before filtering and normalization, the main PCs highly correlate with various library quality scores (Legend below panel A & B), indicating that the dominant signal in the pre-normalization data may reflect experimental artifacts. (FIG. 6B) Normalization strongly reduces these correlations. Applicants find that before filtering and normalization (panel A) the main PCs highly correlate with the various library quality scores, as opposed to post-normalization (panel B). These results indicate that the dominant signal in the pre-normalization data might reflect experimental artifacts. (FIG. 6C) An example of a cell-specific false-negative curve (FNC). The false-negative rate (Y axis, percentage of genes in an expression bin that are detected in this cell (non zero estimated abundance)) is depicted as a function of transcript abundance in the bulk population (X axis, average expression level of genes within each bin). Each blue circle corresponds to a set of housekeeping genes (stratified according to their bulk-population expression levels). The false-negative curve (black solid line) is derived using a logistic function fit. (FIG. 6D) Correlations between single-cell and bulk population profiles. Bar chart depicts the Spearman correlations coefficients (X axis) for each experimental batch (Y axis), where cells from each batch originate from a single mouse. A unique batch identifier is indicated in parentheses. Shown are Spearman correlations of gene expression profiles between pairs of single cells (blue bars, mean and standard deviation); between each single cell and a matched bulk population (orange bars, mean and standard deviation); between an average over all single cells and a matched bulk population (red bars); and between two bulk population replicates (green bars). (FIG. 6E) RNA Flow-Fish validation of expression distribution obtained by RNA-seq. Shown are the single-cell expression distributions for a set of select genes (rows) by RNA-seq (left column) and RNA Flow-Fish (right column). For RNA-seq distributions, the frequency of cells (Y axis) is shown as a function of expression (X axis, FPKM+1, log10), whereas RNA Flow-Fish is plotted as number of cells (Y axis) as a function of transcript (spot) count (X axis). Applicants find agreement for a variety of distributions, ranging from non-expressing (Csf2, Itgax, Sdc1) to unimodal distributions (Irf4, Batf, Actb) and bimodal distributions (Il7a, Il2). (FIG. 6F) Constitutively expressed genes are enriched for housekeeping functions. Shown is the fold enrichment of housekeeping genes among all the non-bimodally expressed genes (X axis) for each condition (Y axis) (FIG. 6G) As in (A), corresponding p-values (hypergeometric test). (FIG. 6H, FIG. 6I) Applicants find greater variation in expression levels for key immune genes. (H) Standard deviation (Y axis) of all the detectably expressed genes in the non-pathogenic (TGF-β1+IL-6) condition is plotted vs. their single-cell average expression (X axis). Shown are housekeeping genes (green crosses), immune-response-related genes (red crosses, based on Gene Ontology) and other genes (blue dots). Selected outliers are highlighted by black squares. (I) As in (G), but where the standard deviation (Y axis) and mean (X axis) of every gene are computed only for cells that express it (defined as those cells that are associated with the Gaussian distribution in our mixture model).

FIG. 7A-7E. Population controls compared to single cell profiles. (FIG. 7A) Gene expression levels of selected genes for in vivo derived cells projected on PCs. Cells (CNS cells: diamonds, LN cells: crosses) are shown in a PCA plot as in FIG. 2C and each cell is colored proportionally to the ranked expression of the denoted gene in this cell relative to the other cells (blue—low expression; red—high expression). Top: Gpr65 is predominantly expressed in the CNS, and particularly high in the Th17/Th1-like memory subpopulation (light blue). Bottom: Ccr8, previously associated with Th2 cells but not Th1/Th17 cells, is also highly expressed in most CNS derived cells. (FIG. 7B) Gene expression levels of selected genes for in vitro derived cells projected on PCs. Similar analysis as in (A) but for the different differentiation conditions in vitro and plotted on a PCA plot as in FIG. 3A; (Left column) regulatory genes (IL-9, IL-16, Podoplanin and Foxp1) show high expression in the non-pathogenic condition (TGF-β1+IL-6), whereas inflammatory genes such as IL-22, IL-23r, Cxcr3 and Gm-csf are more highly expressed in the pathogenic differentiation condition (IL-1β+IL-6+IL-23). FIG. 7 is sometimes also referred to as Supplementary FIG. 2. (FIG. 7C, FIG. 7D, FIG. 7E) Shown are PCA plots based on single cell profiles (small circles, triangles, squares and crosses) along with projected matching population controls (large circles) and single cell averages (large squares) for (FIG. 7C) In vitro Th17 single cells only from the non-pathogenic conditions (TGF-β1+IL-6); (FIG. 7D) In vivo Th17 cells (CNS: purple, LN: orange); and (FIG. 7E) In vitro Th17 cells from all conditions: pathogenic (IL-1β+IL-6+IL-23; red icons); and non-pathogenic conditions (TGF-β1+IL-6. Black icons: cells not sorted for IL-17A/GFP+; light blue icons: IL-17A/GFP+ cells).

FIG. 8A-8D. (FIG. 8A) GPR65−/− memory cells express less IL-17A upon IL-23 reactivation. Sorted memory (CD62LCD44+CD4+) T cells from wild type (WT, top row) and GPR65−/− (bottom row) mice were reactivated with IL-23 (20 ng/ml) for 96 h. Intracellular cytokine (ICC) analysis shows a reduction of ˜45% IL-17A-positive cells (X axis) for GPR65−/− cells when compared to WT (FIG. 8B) IL-17A and IFN-γ production is hampered in vivo for GPR65−/− cells. A reduced frequency of IL-17A (X axis) and IFN-γ (Y axis) positive cells from the draining LNs and spleen of MOG35-55/CFA-immunized RAG-1−/− mice reconstituted with WT (top row) or GPR65−/− (bottom row) naïve CD4+ T-cells 30 days post EAE induction (FIG. 8C) GPR65−/− CD4+ T-cells express less IL-17A and more IL-10. Quantification of secreted cytokines (Y axis) by cytometric bead assays (CBAs) for differentiation conditions (X axis) either without (Th0; left) or with Th17 polarizing cytokines (TGF-β1+IL-6, middle; or IL-1β+IL-6+IL-23, right) for GPR654 cells (light green) and littermate control cells (dark green). * p<0.05, ** p<0.01, *** p<0.001. All data presented here are a representative of three independent experiments, with at least 3 replicates per experiment. (FIG. 8D) Linear regression analysis of EAE disease progression for GPR65 KO vs. WT mice. Mean clinical score (Y axis) is shown as a function of days post immunization (X axis) for WT (solid line) and GPR651 mice (dotted line). *** p<0.001. Data presented here is a representative of at least three independent experiments.

FIG. 9A-9C. (FIG. 9A) TOSO−/− cells express less IL-17A but more IFN-γ upon IL-23 reactivation. Sorted memory (CD62LCD44+CD4+) T cells from WT and TOSO−/− mice were reactivated (anti-CD3/CD28) with IL-23 (20 ng/ml) for 96 h. The ICC analysis shows hardly any IL-17A (X axis) positive cells amongst TOSO−/− cells (bottom row) whereas WT does show a small IL-17A positive population (top row). On the other hand, IFN-γ (Y axis) gets induced to a larger extend in the TOSO−/− cells. (FIG. 9B) TOSO−/− cells exhibit lower FOXP3 levels during Treg differentiation. Naïve CD4+ T-cells from WT (top row) and TOSO−/− mice (bottom row) were differentiated in vitro with TGF-β1 (2 ng/ml) for 96h, and subsequently stained and analyzed by ICC for intracellular FOXP3 expression (Y axis) and CD4 expression (X axis). (FIG. 9C) TOSO−/− cells secrete less IL-17A, less IL-10, but more IFN-γ. Quantification of secreted cytokines (Y axis) by CBA for a 96h differentiation in conditions (X axis) without (Th0; left) or with Th17 polarizing cytokines (TGF-β1+IL-6, middle; or IL-1β+IL-6+IL-23, right) for TOSO−/− cells (light green) and WT cells (dark green). * p<0.05, ** p<0.01, *** p<0.001. All data presented here are a representative of three independent experiments, with at least three replicates per experiment.

FIG. 10A-10C. (FIG. 10A) PLZP. T cells show comparable IL-17A and IFN-γ production to littermate controls (PLZP HET). ICC staining for IFN-γ (Y axis) and IL-17A (X axis) of CD4+ T cells from respective littermate controls (top) or PLZP−/− (bottom) cells activated in vitro for 48h with anti-CD3 and anti-CD28 either without (Th0; left) or with Th17 polarizing cytokines (TGF-β1+IL-6, middle; or IL-1β+IL-6+IL-23, right). (FIG. 10B) PLZP−/− cells produce less IL-17A cells upon IL-23 stimulation. PLZP−/− mice and littermate controls were immunized with 100 μg of MOG35-55/CFA. Cells harvested 8 days after immunization from the draining LNs and spleen were cultured ex vivo for 4 days with (right column) or without (left) IL-23 (20 ng/ml). CD4+ T cells were analyzed for IFN-γ and IL-17A production by ICC staining. (FIG. 10C) PLZP−/− cells express significantly less pro-inflammatory cytokines in a MOG recall assay. Quantification of secreted cytokines (Y axis) by CBA in a MOG recall assay with different MOG35-55 concentrations (X axis) for PLZP. mice (light green) and littermate controls (dark green). * p<0.05, ** p<0.01, *** p<0.001, showing significant reduction of cytokine expression under MOG reactivation conditions. All data presented here are a representative of three independent experiments, with at least 3 replicates per experiment.

FIG. 11A-11M. CD5L shifts Th17 cell lipidome balance from saturated to unsaturated lipid, modulating Rorγt ligand availability and function. FIG. 11A, B show Lipidome analysis of Th17 cells. (FIG. 11A) WT and CD5L−/− naïve T cells were differentiated. Cells and supernatant were harvested at 96 hours and subjected to MS/LC. Three independent mouse experiments were performed. Data shown are median expression of each metabolite identified that have at least 1.5 fold differences between WT and CD5L−/− under the TGFβ1+IL-6 condition. (FIG. 11B, FIG. 1C) Expression of representative metabolites including cholesterol ester and a PUFA-containing TAG species. (FIG. 11D) Microscopy of wt and CD5L−/− cells stained for free cholesterol. (E,F) Rorγt ChIP from Th17 cells differentiated as described in A. under various conditions as indicated. (FIG. 11G-FIG. 11J) Dual luciferase reporter assays. (FIG. 11G, FIG. 11H) Dual luciferase reporter assays were performed in EL4 cells stably transfected with a control vector or Rorγt vector. CD5L retroviral vector was cotransfected in G. (FIG. 11H). CD5L retroviral vector was cotransfected at 0, 25, 50 and 100 ng/well. (FIG. 11I-FIG. 11J) 10 μM of either arachidonic acid (PUFA) or 20 μM of palmitic acid (SFA) were used whenever a single dose was indicated. All ChIP and luciferase assay are representative of at least 3 independent experiments. Representative metabolites were used, including a cholesterol ester and a PUFA-containing TAG species. (FIG. 11K) Lipids from the two clusters in (A) are partitioned based on the length and saturation of their fatty acyl (FA) side chains. Those carrying more than one FA are further grouped by their FAs with the least saturation or longest carbon chain (in that order). Complete FA profile is shown in (FIG. 11L) Ratio of specific lipids in WT vs. CD5L−/− Th17 cells carrying various PUFA side chains. Phospholipids included in this analysis: phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine and their respective lyso-metabolites. Neutral lipid included in this analysis: Triacylglyceride, diacylglyceride and monoacylglyceride. Asterisk (*) denotes to p<0.05 in Student's t-test. (FIG. 11M) Expression of cyp51 and sc4mol mRNA in WT or CD5L−/− Th17 cells (TGF-β1+IL-6, left panels) or WTTh17 cells (TGF-β1+IL-6 with control or IL-23, right panels). SFA (palmitic acid, 25 uM) or PUFA (arachidonic acid, 25 uM) was added at 48h and cells analyzed at 96h.

FIG. 12A-12F. Characterization of WT and CD5L−/− mice with EAE. Mice were immunized (FIG. 12A) 15 days post immunization, lymphocytes from CNS were isolated and directly stained and analyzed with flow cytometry for the expression of FoxP3. (FIG. 12B) Cells from CNS as in A were restimulated with PMA/ionomycin with Brefeldin A for 4 hours and profiled for cytokine production by flow cytometry. (FIG. 12C) Cells were isolated from Inguinal LN of mice 10 days after immunization. 3H Thymidine incorporation assays was used to determine T cell proliferation in response to MOG35-55 peptide; (FIG. 12D) Supernatant from C were harvested and the amount of IL-17 was determined by ELISA. (FIG. 12E, FIG. 12F) Summary data for FIG. 17 G, H respectively.

FIG. 13, related to FIG. 2. Differential gene expression of Th17 cells derived from LPL, LN and CNS. Shown are the expression levels of immune response related genes (rows; Z normalized per row) that are differentially expressed between bulk population samples from CNS, LN and LPL derived Th17 cells (columns).

FIG. 14A-14D, related to FIGS. 2 and 3. Temporal asynchrony between individual cells in vivo and in vitro. (FIG. 14A, FIG. 14B) Weighted Pearson correlation coefficient (red: positive; blue: negative) of each single cell's profile (row) with bulk profiles at each of 18 time points (columns) along a 72h time course of Th17 cell differentiation, previously collected with microarrays (Yosef et al., 2013). The weighted Pearson correlation weighs down the effect of false negatives, as done in the weighted PCA, and z-normalized per row. Cells collected in vitro (A) show more synchrony than those from in vivo samples (B). (FIG. 14C, FIG. 14D) Some of the cell-to-cell variation likely reflects time of differentiation. Shown are the PCA plots for in vitro cells (C, asin FIG. 3; IL-1β+IL-6+IL-23, triangles, TGF-β1+IL-6, squares and circles) and in vivo cells (D, as in FIG. 2; CNS cells: diamonds, LN cells: crosses). Each cell (point) is colored proportionally to the ranked associated time point of this cell's maximal correlation from the analysis in (A, B) (blue: early time points; red: late time points).

FIG. 15A-15B, related to FIG. 4. Population based studies do not prioritize genes that have top ranks for Th17 pathogenicity by single cell data Shown are the 184 genes from our co-variation matrix (rows, FIG. 4B), ordered according to population based ranking (X-axis) along with their rank (log 10 (#genes that are ranked equal to or better); Y-axis) based on either (FIG. 15A) a compendium of 41 studies of Th17 cells, or (FIG. 15B) a literature based ranking (Ciofani et al., 2012). Red crosses: our top ranking candidates that we followed up on. While the 184 genes from our covariation matrix are more highly ranked than the other 7,000 genes from the single cells in vitro (p<10−10 and ˜0.015 for A and B, respectively: Wilcoxon Rank Sum Test), they do not necessarily stand out.

FIG. 16A-16I. CD5L is a candidate regulator of Th17 cell functional states. (FIG. 16A-FIG. 16C) Single-cell RNA-seq analysis. (FIG. 16A) Cd5l expression of single-cells from in-vitro generated and in-vivo sorted Th17 cells (IL-17.GFP+) from mice at the peak of EAE. (FIG. 16B, FIG. 16C) Correlation of Cd5l expression in non-pathogenic Th17 cells (TGF-β1+IL-6) with (B) the cell pathogenicity score (based on the pathogenic signature of (Lee et al., 2012)). p=2.63×10-5 (Wilcoxon Rank Sum Test, comparing signature scores of Cd5l expressing vs. non-expressing cells); (FIG. 16C) the founding signature genes of the single-cell based proinflammatory (red) and regulatory (green) modules (Solid bars, significant correlation (p<0.05); striked bars, none significant correlation). (FIG. 16D-FIG. 16F) Validation of CD5L expression in vitro. Naïve T cells (CD4+CD62L+CD44−CD25−) were sorted and differentiated as indicated and analyzed by qPCR for CD5L expression at 48h (D) and 72h (E) and by flow cytometry at 48h (F); (E) IL-23 or control was added at 48h in fresh media. (FIG. 16G-FIG. 16I) Validation of Cd5l expression in vivo. (G,H) IL-17A.GFP reporter mice were immunized to induce EAE. Cells were sorted from spleen (G) and CNS (H) at the peak of disease. Cd5l and Il17a expression are measured by qPCR. Figure shown is representative data of three technical replicates from two independent experiments. (I) Cells were sorted from the gut of naïve mice and the number of RNA transcripts measured by nanostring nCounter platform.

FIG. 17A-17H. CD5L represses effector functions without affecting Th17 cell differentiation. (FIG. 17A) EAE was induced by MOG/CFA (40 μg) immunization. Left panel is pooled results from 3 independent experiments. Right panel: cytokine profile of CD4 T cells isolated from CNS at day 15 post immunization. (FIG. 17B-FIG. 17D) Naïve splenic T cells were sorted and differentiated with TGF-β1+IL-6 for 48h. Th17 cell signature genes were measured by flow cytometry (FIG. 17B), ELISA (FIG. 17C) and qPCR (FIG. 17D). (FIG. 17E-FIG. 17F) Effector Th17 cells were differentiated as in B and resuspended in fresh media with no cytokines for 72h followed by restimulation. Gene profile was measured by flow cytometry (FIG. 17E) and qPCR (FIG. 17F). (FIG. 17G-FIG. 17H) Effector memory T cells (CD4+CD62L−CD44+) (FIG. 17G) or Effector memory Th17 cells (CD4+CD62L−CD44+RorγtGFP+) (FIG. 17H) were sorted from spleen of naïve mice and activated with TCR stimulation.

FIG. 18A-18F. CD5L and PUFA/SFA profile regulate Rorγt function in a ligand-dependent manner. (FIG. 18A, FIG. 18B) Rorγt ChIP-PCR analyses in WT and CD5L−/− Th17 cells. WT, CD5L−/− and Rorγt−/− Th17 cells were differentiated with TGF-1+IL-6 for 96h. Enrichment of Rorγt binding to genomic regions of Il17 (FIG. 18A) and Il10 (FIG. 18B) is measured using qPCR. For fatty acid experiments, 10 μM of either SFA (palmitic acid) or PUFA (arachidonic acid or docosahexaenoic acid showed similar results) was added to WT Th17 cell culture at day 0. Three independent experiments were performed. (FIG. 18C, FIG. 18D) Rorγt transcriptional activity was measured by luciferase reporter of 1117 promoter in EL4 cells transfected with CD5L-RV at 0, 25, 50, 100 ng (FIG. 18C) or 100 ng with 7,27 dihydroxycholesterol (5, 0.5 or 0.05 uM) (FIG. 18D). (FIG. 18E) Naïve WT T cells were activated without polarizing cytokines (Th0) and infected with retrovirus expressing Rorγt in the presence of control-RV or CD5L-RV with or without FF-MAS (5 uM) as a source of Rorγt ligand. Each dot represents an independent infection. (FIG. 18F) WT or CD5L−/− naïve cells were differentiated with TGF-β1+IL-6. At 48h, cells were replated in fresh media with either control or FF-MAS (5 uM) as a source of Rorγt ligand. Cells were harvested for FACS analysis 72h later. 100961 FIG. 19A-19E. Single cell RNA-seq identifies Cd5l as a gene in covariance with the pathogenic module within non-pathogenic Th17 cells. (FIG. 19A) Histogram of Cd5l expression in single cell from unsorted in-vitro derived Th17 cells differentiated under the TGF-β1+IL-6 condition. (FIG. 19B) The expression of Cd5l within single cell is shown in covariance with the first PC of in-vitro derived cells as in (FIG. 19A) where it correlates with the pro-inflammatory module. (FIG. 19C) Within the same PC space as in (FIG. 19B), score of pathogenic signature is shown to also correlate with PC1 as defined in the text. (FIG. 19D, FIG. 19E) Regulation of CD5L expression. (FIG. 19D) Naïve CD4 T cells were sorted from WT, Stat3CD4Cre−/−, RorgtCD4Cre−/− and CD5L−/− and differentiated under Th0 or Th17 (TGFb1+IL-6) condition as in FIG. 17D. CD5L expression was measured intracellularly at 48 hour post differentiation. Upper panel: representative FACS plot; Lower panel: summary results from three independent experiments. (FIG. 19E) Naïve CD4 T cells were differentiated under Th0 condition and transfected with retrovirus carrying Stat3 construct to overexpress STAT3. CD5L expression was measured as in D.

FIG. 20A-20F. CD5L antagonizes pathogenicity of Th17 cells. (FIG. 20A, FIG. 20B) (FIG. 20A) Summary data for Cytokine profile of WT and CD5L−/− 2D2 cells isolated from CNS at day 27 post transfer. Cells were gated on Va3.2+CD4+. (FIG. 20B) Summary data for Cytokine profile of CD45.1 WT recipients that received 100,000 naïve WT or CD5L−/− 2D2 T cells and were immunized the following day with MOG/CFA without pertussis toxin. Cytokine profile of 2D2 T cells was examined on day 10 in draining LN (FIG. 20C-FIG. 20F) Passive EAE is induced. Briefly, naïve 2D2 cells were sorted from WT mice and differentiated under the pathogenic Th17 differentiation conditions with IL-1β+IL-6+IL-23. At 24h, either CD5L-RV or control-RV retrovirus was used to infect the activated cells. The expression of CD5L was analyzed at day 3 post-infection. 50% of cells expressed GFP in both groups. (FIG. 20C) Representative flow cytometry analysis of cytokine profile prior to transfer; (FIG. 20D) Weight loss curve after transfer; (FIG. 20E) EAE score; Dotted green and red lines are linear regression analysis performed as in FIG. 17A. (FIG. 20F) Representative flow cytometry data of cytokine profile of CD4+ T cells from CNS at day 30 post transfer.

FIG. 21A-21E. CD5L regulates lipid metabolism in Th17 cells and modulate Rorγt ligand. (FIG. 21A) Lipidomics analysis. Entire set of 39 lipids (rows) resolved from cell lysates (columns) that have significantly different levels among any Th17 cell conditions and are with a fold difference of at least 1.5. (FIG. 21B) The ratio of specific lipids (from all those resolved) between WT and CD5L−/− Th17 cells (both in TGF-β1+IL-6 conditions) (Y-axis) partitioned by their PUFA content (X axis). (FIG. 21C) Left panel: The ratio of a particular lipid with specific SFA or MUFA content in WT vs CD5L−/− Th17 cells (TGF-81+IL-6) is shown. Right panel, same data as left panel, segregating phospholipid from neutral lipids (FIG. 21D) MEVA analysis of all lipid species resolved (rows) comparing cell lysates or media in different Th17 cell conditions (1-6, legend). CE, cholesterol ester; LPC, lysophosphatidylcholine; PC, phosphatidylcholine; SM, sphingomyelin; TAG, triacylglyceride. B623: IL-1β+IL-6+IL-23 condition; T16: TGF-β+IL-6 condition. (FIG. 21E) Expression of free cholesterol in Th17 cells. WT and CD5L−/− Th17 cells were differentiated with TGF-β1+IL-6 for 48 hours and harvested for confocal microscopy. Cells were fixed using paraformaldehyde and stained with Filipin for 30 minutes, washed and sealed with DAPI-coated cover slides and analyzed by confocal microscopy.

This invention relates generally to compositions and methods for identifying the regulatory networks that control T cell balance, T cell differentiation, T cell maintenance and/or T cell function, as well compositions and methods for exploiting the regulatory networks that control T cell balance, T cell differentiation, T cell maintenance and/or T cell function in a variety of therapeutic and/or diagnostic indications.

The invention provides compositions and methods for modulating T cell balance. The invention provides T cell modulating agents that modulate T cell balance. For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level of and/or balance between T cell types, e.g., between Th17 and other T cell types, for example, regulatory T cells (Tregs). For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the level of and/or balance between Th17 activity and inflammatory potential. As used herein, terms such as “Th17 cell” and/or “Th17 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses one or more cytokines selected from the group the consisting of interleukin 17A (IL-17A), interleukin 17F (IL-17F), and interleukin 17A/F heterodimer (IL17-AF). As used herein, terms such as “Th1 cell” and/or “Th1 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses interferon gamma (IFNγ). As used herein, terms such as “Th2 cell” and/or “Th2 phenotype” and all grammatical variations thereof refer to a differentiated T helper cell that expresses one or more cytokines selected from the group the consisting of interleukin 4 (IL-4), interleukin 5 (IL-5) and interleukin 13 (IL-13). As used herein, terms such as “Treg cell” and/or “Treg phenotype” and all grammatical variations thereof refer to a differentiated T cell that expresses Foxp3.

These compositions and methods use T cell modulating agents to regulate, influence or otherwise impact the level and/or balance between T cell types, e.g., between Th17 and other T cell types, for example, regulatory T cells (Tregs).

The invention provides methods and compositions for modulating T cell differentiation, for example, helper T cell (Th cell) differentiation. The invention provides methods and compositions for modulating T cell maintenance, for example, helper T cell (Th cell) maintenance. The invention provides methods and compositions for modulating T cell function, for example, helper T cell (Th cell) function. These compositions and methods use T cell modulating agents to regulate, influence or otherwise impact the level and/or balance between Th17 cell types, e.g., between pathogenic and non-pathogenic Th17 cells. These compositions and methods use T cell modulating agents to influence or otherwise impact the differentiation of a population of T cells, for example toward the Th17 cell phenotype, with or without a specific pathogenic distinction, or away from the Th17 cell phenotype, with or without a specific pathogenic distinction. These compositions and methods use T cell modulating agents to influence or otherwise impact the maintenance of a population of T cells, for example toward the Th17 cell phenotype, with or without a specific pathogenic distinction, or away from the Th17 cell phenotype, with or without a specific pathogenic distinction. These compositions and methods use T cell modulating agents to influence or otherwise impact the differentiation of a population of Th17 cells, for example toward the pathogenic Th17 cell phenotype or away from the pathogenic Th17 cell phenotype, or toward the non-pathogenic Th17 cell phenotype or away from the non-pathogenic Th17 cell phenotype. These compositions and methods use T cell modulating agents to influence or otherwise impact the maintenance of a population of Th17 cells, for example toward the pathogenic Th17 cell phenotype or away from the pathogenic Th17 cell phenotype, or toward the non-pathogenic Th17 cell phenotype or away from the non-pathogenic Th17 cell phenotype. These compositions and methods use T cell modulating agents to influence or otherwise impact the differentiation of a population of T cells, for example toward a non-Th17 T cell subset or away from a non-Th17 cell subset. These compositions and methods use T cell modulating agents to influence or otherwise impact the maintenance of a population of T cells, for example toward a non-Th17 T cell subset or away from a non-Th17 cell subset.

As used herein, terms such as “pathogenic Th17 cell” and/or “pathogenic Th17 phenotype” and all grammatical variations thereof refer to Th17 cells that, when induced in the presence of TGF-β3, express an elevated level of one or more genes selected from Cxcl3, IL22, IL3, Ccl4, Gzmb, Lrmp, Ccl5, Casp1, Csf2, Ccl3, Tbx21, Icos, IL17r, Stat4, Lgals3 and Lag, as compared to the level of expression in a TGF-β3-induced Th17 cells. As used herein, terms such as “non-pathogenic Th17 cell” and/or “non-pathogenic Th17 phenotype” and all grammatical variations thereof refer to Th17 cells that, when induced in the presence of TGF-β3, express a decreased level of one or more genes selected from IL6st, IL1rn, Ikzf3, Maf, Ahr, IL9 and IL10, as compared to the level of expression in a TGF-β3-induced Th17 cells.

These compositions and methods use T cell modulating agents to influence or otherwise impact the function and/or biological activity of a T cell or T cell population. These compositions and methods use T cell modulating agents to influence or otherwise impact the function and/or biological activity of a helper T cell or helper T cell population. These compositions and methods use T cell modulating agents to influence or otherwise impact the function and/or biological activity of a Th17 cell or Th17 cell population. These compositions and methods use T cell modulating agents to influence or otherwise impact the function and/or biological activity of a non-Th17 T cell or non-Th17 T cell population, such as, for example, a Treg cell or Treg cell population, or another CD4+ T cell or CD4+ T cell population. These compositions and methods use T cell modulating agents to influence or otherwise impact the plasticity of a T cell or T cell population, e.g., by converting Th17 cells into a different subtype, or into a new state.

The methods provided herein combine transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing perturbations in primary T cells to systematically derive and experimentally validate a model of the dynamic regulatory network that controls Th17 differentiation. See e.g., Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi. 10.1038/naturel 1981, the contents of which are hereby incorporated by reference in their entirety. The network consists of two self-reinforcing, but mutually antagonistic, modules, with novel regulators, whose coupled action may be essential for maintaining the level and/or balance between Th17 and other CD4+ T cell subsets. Overall, 9,159 interactions between 71 regulators and 1,266 genes were active in at least one network; 46 of the 71 are novel. The examples provided herein identify and validate 39 regulatory factors, embedding them within a comprehensive temporal network and reveals its organizational principles, and highlights novel drug targets for controlling Th17 differentiation.

A “Th17-negative” module includes regulators such as SP4, ETS2, IKZF4, TSC22D3 and/or, IRF1. It was found that the transcription factor Tsc22d3, which acts as a negative regulator of a defined subtype of Th17 cells, co-localizes on the genome with key Th17 regulators. The “Th17 positive” module includes regulators such as MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, and/or FAS. Perturbation of the chromatin regulator Mina was found to up-regulate Foxp3 expression, perturbation of the co-activator Pou2af1 was found to up-regulate IFN-γ production in stimulated naïve cells, and perturbation of the TNF receptor Fas was found to up-regulate IL-2 production in stimulated naïve cells. All three factors also control IL-17 production in Th17 cells.

The immune system must strike a balance between mounting proper responses to pathogens and avoiding uncontrolled, autoimmune reaction. Pro-inflammatory IL-17-producing Th17 cells area prime case in point: as a part of the adaptive immune system, Th17 cells mediate clearance of fungal infections, but they are also strongly implicated in the pathogenesis of autoimmunity (Korn et al., 2009). In mice, although Th17 cells are present at sites of tissue inflammation and autoimmunity (Korn et al., 2009), they are also normally present at mucosal barrier sites, where they maintain barrier functions without inducing tissue inflammation (Blaschitz and Raffatellu, 2010). In humans, functionally distinct Th17 cells have been described; for instance, Th17 cells play a protective role in clearing different types of pathogens like Candida albicans (Hernandez-Santos and Gaffen, 2012) or Staphylococcus aureus (Lin et al., 2009), and promote barrier functions at the mucosal surfaces (Symons et al., 2012), despite their pro-inflammatory role in autoimmune diseases such as rheumatoid arthritis, multiple sclerosis, psoriasis systemic lupus erythematous and asthma (Waite and Skokos, 2012). Thus, there is considerable diversity in the biological function of Th17 cells and in their ability to induce tissue inflammation or provide tissue protection.

Mirroring this functional diversity, depending on the cytokines used for differentiation, in vitro polarized Th17 cells can either cause severe autoimmune responses upon adoptive transfer (‘pathogenic Th17 cells’) or have little or no effect in inducing autoimmune disease (‘non-pathogenic cells’)(Ghoreschi et al., 2010; Lee et al., 2012). In vitro differentiation of naïve CD4 T cells in the presence of TGF-β1+IL-6 induces an IL-17A and IL-10 producing population of Th17 cells, that are generally nonpathogenic, whereas activation of naïve T cells in the presence IL-1β+IL-6+IL-23 induces a T cell population that produces IL-17A and IFN-7, and are potent inducers of autoimmune disease induction (Ghoreschi et al., 2010).

Charting this functional heterogeneity of Th17 cells to understand the molecular circuits that control it is thus of both fundamental and clinical importance. Previous transcriptional profiling studies have identified sets of genes, dubbed ‘pathogenicity signatures’, that consist of genes differentially expressed between ‘pathogenic’ vs. ‘non-pathogenic’ in vitro differentiated Th17 cells (Ghoreschi et al., 2010; Lee et al., 2012). However, such studies relied either on genomic profiling of cell populations, which are limited in their ability to detect distinct cellular states within a cell mixture, or on tracking a handful of pre-selected markers by fluorescence-based flow cytometry (Perfetto et al., 2004), which cannot discover novel molecular factors that regulate Th17 cell function. Emerging technological and computational approaches for single-cell RNA-seq (Shalek et al., 2013; Shalek et al., 2014; Trapnell et al., 2014) have opened up the exciting possibility of a more unbiased and principled interrogation into the regulatory circuits underlying different cell states. Single-cell RNA-seq also facilitates the genomic study of samples with limited cell availability, such as in vivo derived Th17 cells from the sites of tissue inflammation during an autoimmune reaction.

Here, single-cell RNA-seq was performed of 806 mouse Th17 cells from in vivo and in vitro models and computationally analyzed the data to dissect the molecular basis of different functional Th17 cell states. It was found that Th17 cells isolated from the draining LNs and CNS at the peak of EAE span a spectrum of states ranging from self renewing cells in the LN to Th1-like effector/memory cells and a dysfunctional, senescent-like cell phenotype in the CNS. In vitro polarized Th17 cells also spanned a pathogenicity spectrum from potentially pathogenic to more regulatory cells. Genes associated with these opposing states include not only canonical regulators that were identified at a population level, but also novel candidates that have not been previously detected by population-level expression approaches (Ciofani et al., 2012; Yosef et al., 2013), which were prioritized for functional analysis. Testing four high-ranking candidates—Gpr6S, Plzp, Toso and Cd5l—with knockout mice, substantial effects were found both on in vitro Th17-cell differentiation and on the development of EAE in vivo. This work provides novel insights into Th17 cellular and functional states in vivo leading to the discovery of novel regulators for targeted manipulation of pathogenic functions of Th17 cells in autoimmune disease.

The T cell modulating agents are used to modulate the expression of one or more target genes or one or more products of one or more target genes that have been identified as genes responsive to Th17-related perturbations. These target genes are identified, for example, by contacting a T cell, e.g., naïve T cells, partially differentiated T cells, differentiated T cells and/or combinations thereof, with a T cell modulating agent and monitoring the effect, if any, on the expression of one or more signature genes or one or more products of one or more signature genes. In some embodiments, the one or more signature genes are selected from those listed in Table 1 or 2 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods).

In some embodiments, the target gene is one or more Th17-associated cytokine(s) or receptor molecule(s) selected from those listed in Table 3 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods). In some embodiments, the target gene is one or more Th17-associated transcription regulator(s) selected from those shown in Table S3 (Gaublomme 2015) or Table 4 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods).

In some embodiments, the target gene is one or more Th17-associated transcription regulator(s) selected from those shown in Table S3 (Gaublomme 2015) or Table 5 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods). In some embodiments, the target gene is one or more Th17-associated receptor molecule(s) selected from those listed in Table 6 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods). In some embodiments, the target gene is one or more Th17-associated kinase(s) selected from those listed in Table 7 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods). In some embodiments, the target gene is one or more Th17-associated signaling molecule(s) selected from those listed in Table 8 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods). In some embodiments, the target gene is one or more Th17-associated receptor molecule(s) selected from those listed in Table 9 of WO/2014/134351, incorporated herein by reference (alone or with those of other herein disclosed methods).

Automated Procedure for Selection of Signature Genes

The invention also provides methods of determining gene signatures that are useful in various therapeutic and/or diagnostic indications. The goal of these methods is to select a small signature of genes that will be informative with respect to a process of interest. The basic concept is that different types of information can entail different partitions of the “space” of the entire genome (>20 k genes) into subsets of associated genes. This strategy is designed to have the best coverage of these partitions, given the constraint on the signature size. For instance, in some embodiments of this strategy, there are two types of information: (i) temporal expression profiles; and (ii) functional annotations. The first information source partitions the genes into sets of co-expressed genes. The information source partitions the genes into sets of co-functional genes. A small set of genes is then selected such that there are a desired number of representatives from each set, for example, at least 10 representatives from each co-expression set and at least 10 representatives from each co-functional set. The problem of working with multiple sources of information (and thus aiming to “cover” multiple partitions) is known in the theory of computer science as Set-Cover. While this problem cannot be solved to optimality (due to its NP-hardness) it can be approximated to within a small factor. In some embodiments, the desired number of representatives from each set is one or more, at least 2, 5 or more, 10 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, or 100 or more.

An important feature of this approach is that it can be given either the size of the signature (and then find the best coverage it can under this constraint); or the desired level of coverage (and then select the minimal signature size that can satisfy the coverage demand).

An exemplary embodiment of this procedure is the selection of the 275-gene signature (Table 1 of WO/2014/134351, incorporated herein by reference), which combined several criteria to reflect as many aspect of the differentiation program as was possible. The following requirements were defined: (1) the signature must include all of the TFs that belong to a Th17 microarray signature (comparing to other CD4+ T cells, see e.g., Wei et al., in Immunity vol. 30 155-167 (2009)), see Methods in WO/2014/134351, incorporated herein by reference); that are included as regulators in the network and are at least slightly differentially expressed; or that are strongly differentially expressed; (2) it must include at least 10 representatives from each cluster of genes that have similar expression profiles; (3) it must contain at least 5 representatives from the predicted targets of each TF in the different networks; (4) it must include a minimal number of representatives from each enriched Gene Ontology (GO) category (computed over differentially expressed genes); and, (5) it must include a manually assembled list of −100 genes that are related to the differentiation process, including the differentially expressed cytokines, receptor molecules and other cell surface molecules. Since these different criteria might generate substantial overlaps, a set-cover algorithm was used to find the smallest subset of genes that satisfies all of five conditions. 18 genes whose expression showed no change (in time or between treatments) in the microarray data were added to this list.

Use of Signature Genes

The invention provides T cell related gene signatures for use in a variety of diagnostic and/or therapeutic indications. For example, the invention provides Th17 related signatures that are useful in a variety of diagnostic and/or therapeutic indications. “Signatures” in the context of the present invention encompasses, without limitation nucleic acids, together with their polymorphisms, mutations, variants, modifications, subunits, fragments, and other analytes or sample-derived measures.

Exemplary signatures are shown in Tables 1 and 2 of WO/2014/134351, incorporated herein by reference, and are collectively referred to herein as, inter alia, “Th17-associated genes,” “Th17-associated nucleic acids,” “signature genes,” or “signature nucleic acids.” These signatures are useful in methods of diagnosing, prognosing and/or staging an immune response in a subject by detecting a first level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes selected from those listed in Table 1 or 2 of WO/2014/134351, incorporated herein by reference, and comparing the detected level to a control of level of signature gene or gene product expression, activity and/or function, wherein a difference in the detected level and the control level indicates that the presence of an immune response in the subject.

These signatures are useful in methods of monitoring an immune response in a subject by detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes selected from those listed in Table 1 or 2 of WO/2014/134351, incorporated herein by reference, at a first time point, detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes selected from those listed in Table 1 or 2 of WO/2014/134351, incorporated herein by reference, at a second time point, and comparing the first detected level of expression, activity and/or function with the second detected level of expression, activity and/or function, wherein a change in the first and second detected levels indicates a change in the immune response in the subject.

These signatures are useful in methods of identifying patient populations at risk or suffering from an immune response based on a detected level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes selected from those listed in Table 1 or Table 2 of WO/2014/134351, incorporated herein by reference. These signatures are also useful in monitoring subjects undergoing treatments and therapies for aberrant immune response(s) to determine efficaciousness of the treatment or therapy. These signatures are also useful in monitoring subjects undergoing treatments and therapies for aberrant immune response(s) to determine whether the patient is responsive to the treatment or therapy. These signatures are also useful for selecting or modifying therapies and treatments that would be efficacious in treating, delaying the progression of or otherwise ameliorating a symptom of an aberrant immune response. The signatures provided herein are useful for selecting a group of patients at a specific state of a disease with accuracy that facilitates selection of treatments.

The present invention also comprises a kit with a detection reagent that binds to one or more signature nucleic acids. Also provided by the invention is an array of detection reagents, e.g., oligonucleotides that can bind to one or more signature nucleic acids. Suitable detection reagents include nucleic acids that specifically identify one or more signature nucleic acids by having hom*ologous nucleic acid sequences, such as oligonucleotide sequences, complementary to a portion of the signature nucleic acids packaged together in the form of a kit. The oligonucleotides can be fragments of the signature genes. For example the oligonucleotides can be 200, 150, 100, 50, 25, 10 or fewer nucleotides in length. The kit may contain in separate container or packaged separately with reagents for binding them to the matrix), control formulations (positive and/or negative), and/or a detectable label such as fluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase, radiolabels, among others. Instructions (e.g., written, tape, VCR, CD-ROM, etc.) for carrying out the assay may be included in the kit. The assay may for example be in the form of a Northern hybridization or DNA chips or a sandwich ELISA or any other method as known in the art. Alternatively, the kit contains a nucleic acid substrate array comprising one or more nucleic acid sequences.

Use of T Cell Modulating Agents

Suitable T cell modulating agent(s) for use in any of the compositions and methods provided herein include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent. By way of non-limiting example, suitable T cell modulating agents or agents for use in combination with one or more T cell modulating agents are shown in Table 10 of WO/2014/134351, incorporated herein by reference.

It will be appreciated that administration of therapeutic entities in accordance with the invention will be administered with suitable carriers, excipients, and other agents that are incorporated into formulations to provide improved transfer, delivery, tolerance, and the like. A multitude of appropriate formulations can be found in the formulary known to all pharmaceutical chemists: Remington's Pharmaceutical Sciences (15th ed., Mack Publishing Company, Easton, Pa. (1975)), particularly Chapter 87 by Blaug, Seymour, therein. These formulations include, for example, powders, pastes, ointments, jellies, waxes, oils, lipids, lipid (cationic or anionic) containing vesicles (such as Lipofectin™), DNA conjugates, anhydrous absorption pastes, oil-in-water and water-in-oil emulsions, emulsions carbowax (polyethylene glycols of various molecular weights), semi-solid gels, and semi-solid mixtures containing carbowax. Any of the foregoing mixtures may be appropriate in treatments and therapies in accordance with the present invention, provided that the active ingredient in the formulation is not inactivated by the formulation and the formulation is physiologically compatible and tolerable with the route of administration. See also Baldrick P. “Pharmaceutical excipient development: the need for preclinical guidance.” Regul. Toxicol Pharmacol. 32(2):210-8 (2000), Wang W. “Lyophilization and development of solid protein pharmaceuticals.” Int. J. Pharm. 203(1-2):1-60 (2000), Charman W N “Lipids, lipophilic drugs, and oral drug delivery-some emerging concepts.” J Pharm Sci. 89(8):967-78 (2000), Powell et al. “Compendium of excipients for parenteral formulations” PDA J Pharm Sci Technol. 52:238-311 (1998) and the citations therein for additional information related to formulations, excipients and carriers well known to pharmaceutical chemists.

Therapeutic formulations of the invention, which include a T cell modulating agent, are used to treat or alleviate a symptom associated with an immune-related disorder or an aberrant immune response. The present invention also provides methods of treating or alleviating a symptom associated with an immune-related disorder or an aberrant immune response. A therapeutic regimen is carried out by identifying a subject, e.g., a human patient suffering from (or at risk of developing) an immune-related disorder or aberrant immune response, using standard methods. For example, T cell modulating agents are useful therapeutic tools in the treatment of autoimmune diseases and/or inflammatory disorders. In certain embodiments, the use of T cell modulating agents that modulate, e.g., inhibit, neutralize, or interfere with, Th17 T cell differentiation is contemplated for treating autoimmune diseases and/or inflammatory disorders. In certain embodiments, the use of T cell modulating agents that modulate, e.g., enhance or promote, Th17 T cell differentiation is contemplated for augmenting Th17 responses, for example, against certain pathogens and other infectious diseases. The T cell modulating agents are also useful therapeutic tools in various transplant indications, for example, to prevent, delay or otherwise mitigate transplant rejection and/or prolong survival of a transplant, as it has also been shown that in some cases of transplant rejection, Th17 cells might also play an important role. (See e.g., Abadja F, Sarraj B, Ansari M J., “Significance of T helper 17 immunity in transplantation.” Curr Opin Organ Transplant. 2012 February; 17(1):8-14. doi: 10.1097/MOT.0b013e32834ef4e4). The T cell modulating agents are also useful therapeutic tools in cancers and/or anti-tumor immunity, as Th17/Treg balance has also been implicated in these indications. For example, some studies have suggested that IL-23 and Th17 cells play a role in some cancers, such as, by way of non-limiting example, colorectal cancers. (See e.g., Ye J, Livergood R S, Peng G. “The role and regulation of human Th17 cells in tumor immunity.” Am J Pathol. 2013 January;182(1):10-20. doi: 10.1016/j.ajpath.2012.08.041. Epub 2012 Nov. 14). The T cell modulating agents are also useful in patients who have genetic defects that exhibit aberrant Th17 cell production, for example, patients that do not produce Th17 cells naturally.

The T cell modulating agents are also useful in vaccines and/or as vaccine adjuvants against autoimmune disorders, inflammatory diseases, etc. The combination of adjuvants for treatment of these types of disorders are suitable for use in combination with a wide variety of antigens from targeted self-antigens, i.e., autoantigens, involved in autoimmunity, e.g., myelin basic protein; inflammatory self-antigens, e.g., amyloid peptide protein, or transplant antigens, e.g., alloantigens. The antigen may comprise peptides or polypeptides derived from proteins, as well as fragments of any of the following: saccharides, proteins, polynucleotides or oligonucleotides, autoantigens, amyloid peptide protein, transplant antigens, allergens, or other macromolecular components. In some instances, more than one antigen is included in the antigenic composition.

Autoimmune diseases include, for example, Acquired Immunodeficiency Syndrome (AIDS, which is a viral disease with an autoimmune component), alopecia areata, ankylosing spondylitis, antiphospholipid syndrome, autoimmune Addison's disease, autoimmune hemolytic anemia, autoimmune hepatitis, autoimmune inner ear disease (AIED), autoimmune lymphoproliferative syndrome (ALPS), autoimmune thrombocytopenic purpura (ATP), Behcet's disease, cardiomyopathy, celiac sprue-dermatitis herpetiformis; chronic fatigue immune dysfunction syndrome (CFIDS), chronic inflammatory demyelinating polyneuropathy (CIPD), cicatricial pemphigoid, cold agglutinin disease, crest syndrome, Crohn's disease, Degos' disease, dermatomyositis-juvenile, discoid lupus, essential mixed cryoglobulinemia, fibromyalgia-fibromyositis, Graves' disease, Guillain-Barre syndrome, Hashimoto's thyroiditis, idiopathic pulmonary fibrosis, idiopathic thrombocytopenia purpura (ITP), IgA nephropathy, insulin-dependent diabetes mellitus, juvenile chronic arthritis (Still's disease), juvenile rheumatoid arthritis, Meniere's disease, mixed connective tissue disease, multiple sclerosis, myasthenia gravis, pernicious anemia, polyarteritis nodosa, polychondritis, polyglandular syndromes, polymyalgia rheumatica, polymyositis and dermatomyositis, primary agammaglobulinemia, primary biliary cirrhosis, psoriasis, psoriatic arthritis, Raynaud's phenomena, Reiter's syndrome, rheumatic fever, rheumatoid arthritis, sarcoidosis, scleroderma (progressive systemic sclerosis (PSS), also known as systemic sclerosis (SS)), Sjögren's syndrome, stiff-man syndrome, systemic lupus erythematosus, Takayasu arteritis, temporal arteritis/giant cell arteritis, ulcerative colitis, uveitis, vitiligo and Wegener's granulomatosis.

In some embodiments, T cell modulating agents are useful in treating, delaying the progression of, or otherwise ameliorating a symptom of an autoimmune disease having an inflammatory component such as an aberrant inflammatory response in a subject. In some embodiments, T cell modulating agents are useful in treating an autoimmune disease that is known to be associated with an aberrant Th17 response, e.g., aberrant IL-17 production, such as, for example, multiple sclerosis (MS), psoriasis, inflammatory bowel disease, ulcerative colitis, Crohn's disease, uveitis, lupus, ankylosing spondylitis, and rheumatoid arthritis.

Inflammatory disorders include, for example, chronic and acute inflammatory disorders. Examples of inflammatory disorders include Alzheimer's disease, asthma, atopic allergy, allergy, atherosclerosis, bronchial asthma, eczema, glomerulonephritis, graft vs. host disease, hemolytic anemias, osteoarthritis, sepsis, stroke, transplantation of tissue and organs, vasculitis, diabetic retinopathy and ventilator induced lung injury.

Symptoms associated with these immune-related disorders include, for example, inflammation, fever, general malaise, fever, pain, often localized to the inflamed area, rapid pulse rate, joint pain or aches (arthralgia), rapid breathing or other abnormal breathing patterns, chills, confusion, disorientation, agitation, dizziness, cough, dyspnea, pulmonary infections, cardiac failure, respiratory failure, edema, weight gain, mucopurulent relapses, cachexia, wheezing, headache, and abdominal symptoms such as, for example, abdominal pain, diarrhea or constipation.

Efficaciousness of treatment is determined in association with any known method for diagnosing or treating the particular immune-related disorder. Alleviation of one or more symptoms of the immune-related disorder indicates that the T cell modulating agent confers a clinical benefit.

Administration of a T cell modulating agent to a patient suffering from an immune-related disorder or aberrant immune response is considered successful if any of a variety of laboratory or clinical objectives is achieved. For example, administration of a T cell modulating agent to a patient is considered successful if one or more of the symptoms associated with the immune-related disorder or aberrant immune response is alleviated, reduced, inhibited or does not progress to a further, i.e., worse, state. Administration of T cell modulating agent to a patient is considered successful if the immune-related disorder or aberrant immune response enters remission or does not progress to a further, i.e., worse, state.

A therapeutically effective amount of a T cell modulating agent relates generally to the amount needed to achieve a therapeutic objective. The amount required to be administered will furthermore depend on the specificity of the T cell modulating agent for its specific target, and will also depend on the rate at which an administered T cell modulating agent is depleted from the free volume other subject to which it is administered.

T cell modulating agents can be administered for the treatment of a variety of diseases and disorders in the form of pharmaceutical compositions. Principles and considerations involved in preparing such compositions, as well as guidance in the choice of components are provided, for example, in Remington: The Science And Practice Of Pharmacy 19th ed. (Alfonso R. Gennaro, et al., editors) Mack Pub. Co., Easton, Pa.: 1995; Drug Absorption Enhancement: Concepts, Possibilities, Limitations, And Trends, Harwood Academic Publishers, Langhorne, Pa., 1994; and Peptide And Protein Drug Delivery (Advances In Parenteral Sciences, Vol. 4), 1991, M. Dekker, New York.

Where polypeptide-based T cell modulating agents are used, the smallest fragment that specifically binds to the target and retains therapeutic function is preferred. Such fragments can be synthesized chemically and/or produced by recombinant DNA technology. (See, e.g., Marasco et al., Proc. Natl. Acad. Sci. USA, 90: 7889-7893 (1993)). The formulation can also contain more than one active compound as necessary for the particular indication being treated, preferably those with complementary activities that do not adversely affect each other. Alternatively, or in addition, the composition can comprise an agent that enhances its function, such as, for example, a cytotoxic agent, cytokine, chemotherapeutic agent, or growth-inhibitory agent. Such molecules are suitably present in combination in amounts that are effective for the purpose intended.

The invention comprehends a treatment method or Drug Discovery method or method of formulating or preparing a treatment comprising any one of the methods or uses herein discussed.

The present invention also relates to identifying molecules, advantageously small molecules or biologics, that may be involved in inhibiting one or more of the mutations in one or more genes selected from the group consisting of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l. The invention contemplates screening libraries of small molecules or biologics to identify compounds involved in suppressing or inhibiting expression of somatic mutations or alter the cells phenotypically so that the cells with mutations behave more normally in one or more of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l.

High-throughput screening (HTS) is contemplated for identifying small molecules or biologics involved in suppressing or inhibiting expression of somatic mutations in one or more of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l. The flexibility of the process has allowed numerous and disparate areas of biology to engage with an equally diverse palate of chemistry (see, e.g., Inglese et al., Nature Chemical Biology 3, 438-441 (2007)). Diverse sets of chemical libraries, containing more than 200,000 unique small molecules, as well as natural product libraries, can be screened. This includes, for example, the Prestwick library (1,120 chemicals) of off-patent compounds selected for structural diversity, collective coverage of multiple therapeutic areas, and known safety and bioavailability in humans, as well as the NINDS Custom Collection 2 consisting of a 1,040 compound-library of mostly FDA-approved drugs (see, e.g., U.S. Pat. No. 8,557,746) are also contemplated.

The NIH's Molecular Libraries Probe Production Centers Network (MLPCN) offers access to thousands of small molecules—chemical compounds that can be used as tools to probe basic biology and advance our understanding of disease. Small molecules can help researchers understand the intricacies of a biological pathway or be starting points for novel therapeutics. The Broad Institute's Probe Development Center (BIPDeC) is part of the MLPCN and offers access to a growing library of over 330,000 compounds for large scale screening and medicinal chemistry. Any of these compounds may be utilized for screening compounds involved in suppressing or inhibiting expression of somatic mutations in one or more of Toso, advantageously Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l.

The phrase “therapeutically effective amount” as used herein refers to a nontoxic but sufficient amount of a drug, agent, or compound to provide a desired therapeutic effect.

As used herein “patient” refers to any human being receiving or who may receive medical treatment.

A “polymorphic site” refers to a polynucleotide that differs from another polynucleotide by one or more single nucleotide changes.

A “somatic mutation” refers to a change in the genetic structure that is not inherited from a parent, and also not passed to offspring.

Therapy or treatment according to the invention may be performed alone or in conjunction with another therapy, and may be provided at home, the doctor's office, a clinic, a hospital's outpatient department, or a hospital. Treatment generally begins at a hospital so that the doctor can observe the therapy's effects closely and make any adjustments that are needed. The duration of the therapy depends on the age and condition of the patient, the stage of the cardiovascular disease, and how the patient responds to the treatment. Additionally, a person having a greater risk of developing a cardiovascular disease (e.g., a person who is genetically predisposed) may receive prophylactic treatment to inhibit or delay symptoms of the disease.

The medicaments of the invention are prepared in a manner known to those skilled in the art, for example, by means of conventional dissolving, lyophilizing, mixing, granulating or confectioning processes. Methods well known in the art for making formulations are found, for example, in Remington: The Science and Practice of Pharmacy, 20th ed., ed. A. R. Gennaro, 2000, Lippincott Williams & Wilkins, Philadelphia, and Encyclopedia of Pharmaceutical Technology, eds. J. Swarbrick and J. C. Boylan, 1988-1999, Marcel Dekker, New York.

Administration of medicaments of the invention may be by any suitable means that results in a compound concentration that is effective for treating or inhibiting (e.g., by delaying) the development of a cardiovascular disease. The compound is admixed with a suitable carrier substance, e.g., a pharmaceutically acceptable excipient that preserves the therapeutic properties of the compound with which it is administered. One exemplary pharmaceutically acceptable excipient is physiological saline. The suitable carrier substance is generally present in an amount of 1-95% by weight of the total weight of the medicament. The medicament may be provided in a dosage form that is suitable for oral, rectal, intravenous, intramuscular, subcutaneous, inhalation, nasal, topical or transdermal, vagin*l, or ophthalmic administration. Thus, the medicament may be in form of, e.g., tablets, capsules, pills, powders, granulates, suspensions, emulsions, solutions, gels including hydrogels, pastes, ointments, creams, plasters, drenches, delivery devices, suppositories, enemas, injectables, implants, sprays, or aerosols.

In order to determine the genotype of a patient according to the methods of the present invention, it may be necessary to obtain a sample of genomic DNA from that patient. That sample of genomic DNA may be obtained from a sample of tissue or cells taken from that patient.

The tissue sample may comprise but is not limited to hair (including roots), skin, buccal swabs, blood, or saliva. The tissue sample may be marked with an identifying number or other indicia that relates the sample to the individual patient from which the sample was taken. The identity of the sample advantageously remains constant throughout the methods of the invention thereby guaranteeing the integrity and continuity of the sample during extraction and analysis. Alternatively, the indicia may be changed in a regular fashion that ensures that the data, and any other associated data, can be related back to the patient from whom the data was obtained. The amount/size of sample required is known to those skilled in the art.

Generally, the tissue sample may be placed in a container that is labeled using a numbering system bearing a code corresponding to the patient. Accordingly, the genotype of a particular patient is easily traceable.

In one embodiment of the invention, a sampling device and/or container may be supplied to the physician. The sampling device advantageously takes a consistent and reproducible sample from individual patients while simultaneously avoiding any cross-contamination of tissue. Accordingly, the size and volume of sample tissues derived from individual patients would be consistent.

According to the present invention, a sample of DNA is obtained from the tissue sample of the patient of interest. Whatever source of cells or tissue is used, a sufficient amount of cells must be obtained to provide a sufficient amount of DNA for analysis. This amount will be known or readily determinable by those skilled in the art.

DNA is isolated from the tissue/cells by techniques known to those skilled in the art (see, e.g., U.S. Pat. Nos. 6,548,256 and 5,989,431, Hirota et al., Jinrui Idengaku Zasshi. September 1989; 34(3):217-23 and John et al., Nucleic Acids Res. Jan. 25, 1991; 19(2):408; the disclosures of which are incorporated by reference in their entireties). For example, high molecular weight DNA may be purified from cells or tissue using proteinase K extraction and ethanol precipitation. DNA may be extracted from a patient specimen using any other suitable methods known in the art.

It is an object of the present invention to determine the genotype of a given patient of interest by analyzing the DNA from the patent, in order to identify a patient carrying specific somatic mutations of the invention that are associated with developing a cardiovascular disease. In particular, the kit may have primers or other DNA markers for identifying particular mutations such as, but not limited to, one or more genes selected from the group consisting of Toso, advantageously Ctla2h, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l.

There are many methods known in the art for determining the genotype of a patient and for identifying or analyzing whether a given DNA sample contains a particular somatic mutation. Any method for determining genotype can be used for determining genotypes in the present invention. Such methods include, but are not limited to, amplimer sequencing, DNA sequencing, fluorescence spectroscopy, fluorescence resonance energy transfer (or “FRET”)-based hybridization analysis, high throughput screening, mass spectroscopy, nucleic acid hybridization, polymerase chain reaction (PCR), RFLP analysis and size chromatography (e.g., capillary or gel chromatography), all of which are well known to one of skill in the art.

The methods of the present invention, such as whole exome sequencing and targeted amplicon sequencing, have commercial applications in diagnostic kits for the detection of the somatic mutations in patients. A test kit according to the invention may comprise any of the materials necessary for whole exome sequencing and targeted amplicon sequencing, for example, according to the invention. In a particular advantageous embodiment, a diagnostic for the present invention may comprise testing for any of the genes in disclosed herein. The kit further comprises additional means, such as reagents, for detecting or measuring the sequences of the present invention, and also ideally a positive and negative control.

The present invention further encompasses probes according to the present invention that are immobilized on a solid or flexible support, such as paper, nylon or other type of membrane, filter, chip, glass slide, microchips, microbeads, or any other such matrix, all of which are within the scope of this invention. The probe of this form is now called a “DNA chip”. These DNA chips can be used for analyzing the somatic mutations of the present invention. The present invention further encompasses arrays or microarrays of nucleic acid molecules that are based on one or more of the sequences described herein. As used herein “arrays” or “microarrays” refers to an array of distinct polynucleotides or oligonucleotides synthesized on a solid or flexible support, such as paper, nylon or other type of membrane, filter, chip, glass slide, or any other suitable solid support. In one embodiment, the microarray is prepared and used according to the methods and devices described in U.S. Pat. Nos. 5,446,603; 5,545,531; 5,807,522; 5,837,832; 5,874,219; 6,114,122; 6,238,910; 6,365,418; 6,410,229; 6,420,114; 6,432,696; 6,475,808 and 6,489,159 and PCT Publication No. WO 01/45843 A2, the disclosures of which are incorporated by reference in their entireties.

The present invention further encompasses the analysis of lipids. Lipid profiling is a targeted metabolomics platform that provides a comprehensive analysis of lipid species within a cell or tissue. Profiling based on electrospray ionization tandem mass spectrometry (ESI-MS/MS) is capable of providing quantitative data and is adaptable to high throughput analyses. Additionally, Liquid chromatography-mass spectrometry (LC-MS, or alternatively HPLC-MS) may be used.

Examples & Technologies as to the Instant Invention

The following examples are given for the purpose of illustrating various embodiments of the invention and are not meant to limit the present invention in any fashion. The present examples, along with the methods described herein are presently representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the invention. Changes therein and other uses which are encompassed within the spirit of the invention as defined by the scope of the claims will occur to those skilled in the art.

In this regard, mention is made that mutations in cells and also mutated mice for use in or as to the invention can be by way of the CRISPR-Cas system or a Cas9-expressing eukaryotic cell or Cas-9 expressing eukaryote, such as a mouse. The Cas9-expressing eukaryotic cell or eukaryote, e.g., mouse, can have guide RNA delivered or administered thereto, whereby the RNA targets a loci and induces a desired mutation for use in or as to the invention. With respect to general information on CRISPR-Cas Systems, components thereof, and delivery of such components, including methods, materials, delivery vehicles, vectors, particles, and making and using thereof, including as to amounts and formulations, as well as Cas9-expressing eukaryotic cells, Cas-9 expressing eukaryotes, such as a mouse, all useful in or as to the instant invention, reference is made to: U.S. Pat. Nos. 8,697,359, 8,771,945, 8,795,965, 8,865,406, 8,871,445, 8,889,356, 8,889,418, 8,895,308, 8,932,814, 8,945,839, 8,906,616; US Patent Publications US 2014-0310830 (U.S. application Ser. No. 14/105,031), US 2014-0287938 A1 (U.S. application Ser. No. 14/213,991), US 2014-0273234 A1 (U.S. application Ser. No. 14/293,674), US 2014-0273232 A1 (U.S. application Ser. No. 14/290,575), US 2014-0273231 (U.S. application Ser. No. 14/259,420), US 2014-0256046 A1 (U.S. application Ser. No. 14/226,274), US 2014-0248702 A1 (U.S. application Ser. No. 14/258,458), US 2014-0242700 A1 (U.S. application Ser. No. 14/222,930), US 2014-0242699 A1 (U.S. application Ser. No. 14/183,512), US 2014-0242664 A1 (U.S. application Ser. No. 14/104,990), US 2014-0234972 A1 (U.S. application Ser. No. 14/183,471), US 2014-0227787 A1 (U.S. application Ser. No. 14/256,912), US 2014-0189896 A1 (U.S. application Ser. No. 14/105,035), US 2014-0186958 (U.S. application Ser. No. 14/105,017), US 2014-0186919 A1 (U.S. application Ser. No. 14/104,977), US 2014-0186843 A1 (U.S. application Ser. No. 14/104,900), US 2014-0179770 A1 (U.S. application Ser. No. 14/104,837) and US 2014-0179006 A1 (U.S. application Ser. No. 14/183,486), US 2014-0170753 (U.S. application Ser. No. 14/183,429); European Patents/Patent Applications: EP 2 771 468 (EP13818570.7), EP 2 764 103 (EP13824232.6), and EP 2 784 162 (EP14170383.5); and PCT Patent Publications WO 2014/093661 (PCT/US2013/074743), WO 2014/093694 (PCT/US2013/074790), WO 2014/093595 (PCT/US2013/074611), WO 2014/093718 (PCT/US2013/074825), WO 2014/093709 (PCT/US2013/074812), WO 2014/093622 (PCT/US2013/074667), WO 2014/093635 (PCT/US2013/074691), WO 2014/093655 (PCT/US2013/074736), WO 2014/093712 (PCT/US2013/074819), WO 2014/093701 (PCT/US2013/074800), WO 2014/018423 (PCT/US2013/051418), WO 2014/204723 (PCT/US2014/041790), WO 2014/204724 (PCT/US2014/041800), WO 2014/204725 (PCT/US2014/041803), WO 2014/204726 (PCT/US2014/041804), WO 2014/204727 (PCT/US2014/041806), WO 2014/204728 (PCT/US2014/041808), WO 2014/204729 (PCT/US2014/041809), and:

    • Multiplex genome engineering using CRISPR-Cas systems. Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., & Zhang, F. Science February 15; 339(6121):819-23 (2013);
    • RNA-guided editing of bacterial genomes using CRISPR-Cas systems. Jiang W., Bikard D., Cox D., Zhang F, Marraffini L A. Nat Biotechnol March; 31(3):233-9 (2013);
    • One-Step Generation of Mice Carrying Mutations in Multiple Genes by CRISPR/Cas-Mediated Genome Engineering. Wang H., Yang H., Shivalila C S., Dawlaty M M., Cheng A W., Zhang F., Jaenisch R. Cell May 9:153(4):910-8 (2013);
    • Optical control of mammalian endogenous transcription and epigenetic states. Konermann S, Brigham M D, Trevino A E, Hsu P D, Heidenreich M, Cong L, Platt R J, Scott D A, Church G M, Zhang F. Nature. 2013 Aug. 22; 500(7463):472-6. doi: 10.1038/Nature 12466. Epub 2013 Aug. 23;
    • Double Nicking by RNA-Guided CRISPR Cas9 for Enhanced Genome Editing Specificity. Ran, F A., Hsu, P D., Lin, C Y., Gootenberg, J S., Konermann, S., Trevino, A E., Scott, D A., Inoue, A., Matoba, S., Zhang, Y., & Zhang, F. Cell August 28. pii: S0092-8674(13)01015-5. (2013);
    • DNA targeting specificity of RNA-guided Cas9 nucleases. Hsu, P., Scott, D., Weinstein, J., Ran, F A., Konermann, S., Agarwala, V., Li, Y., Fine, E., Wu, X., Shalem, O., Cradick, T J., Marraffini, L A., Bao, G., & Zhang, F. Nat Biotechnol doi:10.1038/nbt.2647 (2013);
    • Genome engineering using the CRISPR-Cas9 system. Ran, F A., Hsu, P D., Wright, J., Agarwala, V., Scott, D A., Zhang, F. Nature Protocols November; 8(11):2281-308. (2013);
    • Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells. Shalem, O., Sanjana, N E., Hartenian, E., Shi, X., Scott, D A., Mikkelson, T., Heckl, D., Ebert, B L., Root, D E., Doench, J G., Zhang, F. Science December 12. (2013). [Epub ahead of print];
    • Crystal structure of cas9 in complex with guide RNA and target DNA. Nishimasu, H., Ran, F A., Hsu, P D., Konermann, S., Shehata, S I., Dohmae, N., Ish*tani, R., Zhang, F., Nureki, O. Cell February 27. (2014). 156(5):935-49;
    • Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells. Wu X., Scott D A., Kriz A J., Chiu A C., Hsu P D., Dadon D B., Cheng A W., Trevino A E., Konermann S., Chen S., Jaenisch R., Zhang F., Sharp P A. Nat Biotechnol. (2014) April 20. doi: 10.1038/nbt.2889,
    • CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling, Platt et al., Cell 159(2): 440-455 (2014) DOI: 10.1016/j.cell.2014.09.014,
    • Development and Applications of CRISPR-Cas9 for Genome Engineering, Hsu et al, Cell 157, 1262-1278 (Jun. 5, 2014) (Hsu 2014),
    • Genetic screens in human cells using the CRISPR:Cas9 system, Wang et al., Science. 2014 January 3; 343(6166): 80-84. doi:10.1126/science.1246981,
    • Rational design of highly active sgRNAs for CIRISPR-Cas9-mediated gene inactivation, Doench et al., Nature Biotechnology published online 3 Sep. 2014; doi:10.1038/nbt.3026, and
    • In vivo interrogation of gene function in the mammalian brain using CRISPR-Cas9, Swiech et al, Nature Biotechnology; published online 19 Oct. 2014; doi:10.1038/nbt.3055, each of which is incorporated herein by reference.

The invention involves a high-throughput single-cell RNA-Seq and/or targeted nucleic acid profiling (for example, sequencing, quantitative reverse transcription polymerase chain reaction, and the like) where the RNAs from different cells are tagged individually, allowing a single library to be created while retaining the cell identity of each read. In this regard, technology of U.S. provisional patent application Ser. No. 62/048,227 filed Sep. 9, 2014, the disclosure of which is incorporated by reference, may be used in or as to the invention.

A combination of molecular barcoding and emulsion-based microfluidics to isolate, lyse, barcode, and prepare nucleic acids from individual cells in high-throughput is used. Microfluidic devices (for example, fabricated in polydimethylsiloxane), sub-nanoliter reverse emulsion droplets. These droplets are used to co-encapsulate nucleic acids with a barcoded capture bead. Each bead, for example, is uniquely barcoded so that each drop and its contents are distinguishable. The nucleic acids may come from any source known in the art, such as for example, those which come from a single cell, a pair of cells, a cellular lysate, or a solution. The cell is lysed as it is encapsulated in the droplet. To load single cells and barcoded beads into these droplets with Poisson statistics, 100,000 to 10 million such beads are needed to barcode
˜10,000-100,000 cells. In this regard there can be a single-cell sequencing library which may comprise: merging one uniquely barcoded mRNA capture microbead with a single-cell in an emulsion droplet having a diameter of 75-125 μm; lysing the cell to make its RNA accessible for capturing by hybridization onto RNA capture microbead; performing a reverse transcription either inside or outside the emulsion droplet to convert the cell's mRNA to a first strand cDNA that is covalently linked to the mRNA capture microbead; pooling the cDNA-attached microbeads from all cells; and preparing and sequencing a single composite RNA-Seq library. Accordingly, it is envisioned as to or in the practice of the invention provides that there can be a method for preparing uniquely barcoded mRNA capture microbeads, which has a unique barcode and diameter suitable for microfluidic devices which may comprise: 1) performing reverse phosphoramidite synthesis on the surface of the bead in a pool-and-split fashion, such that in each cycle of synthesis the beads are split into four reactions with one of the four canonical nucleotides (T, C, G, or A) or unique oligonucleotides of length two or more bases; 2) repeating this process a large number of times, at least six, and optimally more than twelve, such that, in the latter, there are more than 16 million unique barcodes on the surface of each bead in the pool. (See www.ncbi.nlm.nih.gov/pmc/articles/PMC206447). Likewise, in or as to the instant invention there can be an apparatus for creating a single-cell sequencing library via a microfluidic system, which may comprise: an oil-surfactant inlet which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel further may comprise a resistor; an inlet for an analyte which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel may further comprise a resistor; an inlet for mRNA capture microbeads and lysis reagent which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel may further comprise a resistor; said carrier fluid channels have a carrier fluid flowing therein at an adjustable or predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a mixer, which contains an outlet for drops. Similarly, as to or in the practice of the instant invention there can be a method for creating a single-cell sequencing library which may comprise: merging one uniquely barcoded RNA capture microbead with a single-cell in an emulsion droplet having a diameter of 125 μm lysing the cell thereby capturing the RNA on the RNA capture microbead; performing a reverse transcription either after breakage of the droplets and collection of the microbeads; or inside the emulsion droplet to convert the cell's RNA to a first strand cDNA that is covalently linked to the RNA capture microbead; pooling the cDNA-attached microbeads from all cells; and preparing and sequencing a single composite RNA-Seq library; and, the emulsion droplet can be between 50-210 μm. In a further embodiment, the method wherein the diameter of the mRNA capture microbeads is from 10 μm to 95 μm. Thus, the practice of the instant invention comprehends preparing uniquely barcoded mRNA capture microbeads, which has a unique barcode and diameter suitable for microfluidic devices which may comprise: 1) performing reverse phosphoramidite synthesis on the surface of the bead in a pool-and-split fashion, such that in each cycle of synthesis the beads are split into four reactions with one of the four canonical nucleotides (T,C,G, or A); 2) repeating this process a large number of times, at least six, and optimally more than twelve, such that, in the latter, there are more than 16 million unique barcodes on the surface of each bead in the pool. The covalent bond can be polyethylene glycol. The diameter of the mRNA capture microbeads can be from 10 μm to 95 μm. Accordingly, it is also envisioned as to or in the practice of the invention that there can be a method for preparing uniquely barcoded mRNA capture microbeads, which has a unique barcode and diameter suitable for microfluidic devices which may comprise: 1) performing reverse phosphoramidite synthesis on the surface of the bead in a pool-and-split fashion, such that in each cycle of synthesis the beads are split into four reactions with one of the four canonical nucleotides (T, C, G, or A); 2) repeating this process a large number of times, at least six, and optimally more than twelve, such that, in the latter, there are more than 16 million unique barcodes on the surface of each bead in the pool. And, the diameter of the mRNA capture microbeads can be from 10 μm to 95 μm. Further, as to in the practice of the invention there can be an apparatus for creating a composite single-cell sequencing library via a microfluidic system, which may comprise: an oil—
surfactant inlet which may comprise a filter and two carrier fluid channels, wherein said carrier fluid channel further may comprise a resistor; an inlet for an analyte which may comprise a filter and two carrier fluid channels, wherein said carrier fluid channel further may comprise a resistor; an inlet for mRNA capture microbeads and lysis reagent which may comprise a carrier fluid channel; said carrier fluid channels have a carrier fluid flowing therein at an adjustable and predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a constriction for droplet pinch-off followed by a mixer, which connects to an outlet for drops. The analyte may comprise a chemical reagent, a genetically perturbed cell, a protein, a drug, an antibody, an enzyme, a nucleic acid, an organelle like the mitochondrion or nucleus, a cell or any combination thereof. In an embodiment of the apparatus the analyte is a cell. In a further embodiment the cell is a brain cell. In an embodiment of the apparatus the lysis reagent may comprise an anionic surfactant such as sodium lauroyl sarcosinate, or a chaotropic salt such as guanidinium thiocyanate. The filter can involve square PDMS posts; e.g., with the filter on the cell channel of such posts with sides ranging between 125-135 μm with a separation of 70-100 mm between the posts. The filter on the oil-surfactant inlet may comprise square posts of two sizes; one with sides ranging between 75-100 μm and a separation of 25-30 μm between them and the other with sides ranging between 40-50 μm and a separation of 10-15 μm. The apparatus can involve a resistor, e.g., a resistor that is serpentine having a length of 7000-9000 μm, width of 50-75 μm and depth of 100-150 mm. The apparatus can have channels having a length of 8000-12,000 μm for oil-surfactant inlet, 5000-7000 for analyte (cell) inlet, and 900-1200 μm for the inlet for microbead and lysis agent; and/or all channels having a width of 125-250 mm, and depth of 100-150 mm. The width of the cell channel can be 125-250 μm and the depth 100-150 μm. The apparatus can include a mixer having a length of 7000-9000 μm, and a width of 110-140 μm with 35-450 zig-zags every 150 μm. The width of the mixer can be about 125 μm. The oil-surfactant can be a PEG Block Polymer, such as BIORAD™ QX200 Droplet Generation Oil. The carrier fluid can be a water-glycerol mixture. In the practice of the invention or as to the invention, a mixture may comprise a plurality of microbeads adorned with combinations of the following elements: bead-specific oligonucleotide barcodes; additional oligonucleotide barcode sequences which vary among the oligonucleotides on an individual bead and can therefore be used to differentiate or help identify those individual oligonucleotide molecules; additional oligonucleotide sequences that create substrates for downstream molecular-biological reactions, such as oligo-dT (for reverse transcription of mature mRNAs), specific sequences (for capturing specific portions of the transcriptome, or priming for DNA polymerases and similar enzymes), or random sequences (for priming throughout the transcriptome or genome). The individual oligonucleotide molecules on the surface of any individual microbead may contain all three of these elements, and the third element may include both oligo-dT and a primer sequence. A mixture may comprise a plurality of microbeads, wherein said microbeads may comprise the following elements: at least one bead-specific oligonucleotide barcode; at least one additional identifier oligonucleotide barcode sequence, which varies among the oligonucleotides on an individual bead, and thereby assisting in the identification and of the bead specific oligonucleotide molecules; optionally at least one additional oligonucleotide sequences, which provide substrates for downstream molecular-biological reactions. A mixture may comprise at least one oligonucleotide sequence(s), which provide for substrates for downstream molecular-biological reactions. In a further embodiment the downstream molecular biological reactions are for reverse transcription of mature mRNAs; capturing specific portions of the transcriptome, priming for DNA polymerases and/or similar enzymes; or priming throughout the transcriptome or genome. The mixture may involve additional oligonucleotide sequence(s) which may comprise a oligo-dT sequence. The mixture further may comprise the additional oligonucleotide sequence which may comprise a primer sequence. The mixture may further comprise the additional oligonucleotide sequence which may comprise a oligo-dT sequence and a primer sequence. Examples of the labeling substance which may be employed include labeling substances known to those skilled in the art, such as fluorescent dyes, enzymes, coenzymes, chemiluminescent substances, and radioactive substances. Specific examples include radioisotopes (e.g., 32P, 14C, 125I, 3H, and 131I), fluorescein, rhodamine, dansyl chloride, umbelliferone, luciferase, peroxidase, alkaline phosphatase, β-galactosidase, β-glucosidase, horseradish peroxidase, glucoamylase, lysozyme, saccharide oxidase, microperoxidase, biotin, and ruthenium. In the case where biotin is employed as a labeling substance, preferably, after addition of a biotin-labeled antibody, streptavidin bound to an enzyme (e.g., peroxidase) is further added. Advantageously, the label is a fluorescent label. Examples of fluorescent labels include, but are not limited to, Atto dyes, 4-acetamido-4′-isothiocyanatostilbene-2,2′-disulfonic acid; acridine and derivatives: acridine, acridine isothiocyanate; 5-(2′-aminoethyl)aminonaphthalene-1-sulfonic acid (EDANS); 4-amino-N-[3-vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate; N-(4-anilino-1-naphthyl)maleimide: anthranilamide; BODIPY; Brilliant Yellow; coumarin and derivatives; coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4-(trifluoromethyl)coumarin (Coumarin 151); cyanine dyes; cyanosine; 4′,6-diamidino-2-2-phenylindole (DAPI); 5′5″-Dibromopyrogallolsulfonephthalein (Bromopyrogallol Red); 7-diethylamino-3-(4′-isothiocyanatophenyl)-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4′-diisothiocyanatodihydro-stilbene-2,2′-disulfonic acid; 4,4′-diisothiocyanatostilbene-2,2′-disulfonic acid: 5-[dimethylamino]naphthalene-1-sulfonyl chloride (DNS, dansyl chloride); 4-dimethylaminophenylazophenyl-4′-isothiocyanate (DABITC); eosin and derivatives; eosin, eosin isothiocyanate, erythrosin and derivatives; erythrosin B, erythrosin, isothiocyanate; ethidium; fluorescein and derivatives; 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazin-2-yl)aminofluorescein (DTAF), 2′,7′-dimethoxy-4′5′-dichloro-6-carboxyfluorescein, fluorescein, fluorescein isothiocyanate, QFITC, (XRITC); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4-methylumbelliferoneortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B-phycoerythrin; o-phthaldialdehyde; pyrene and derivatives: pyrene, pyrene butyrate, succinimidyl 1-pyrene; butyrate quantum dots; Reactive Red 4 (Cibacron™ Brilliant Red 3B-A) rhodamine and derivatives: 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulforhodamine B, sulforhodamine 101, sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N′,N′ tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid; terbium chelate derivatives; Cy3; Cy5; Cy5.5; Cy7; IRD 700; IRD 800; La Jolta Blue; phthalo cyanine; and naphthalo cyanine. A fluorescent label may be a fluorescent protein, such as blue fluorescent protein, cyan fluorescent protein, green fluorescent protein, red fluorescent protein, yellow fluorescent protein or any photoconvertible protein. Colorimetric labeling, bioluminescent labeling and/or chemiluminescent labeling may further accomplish labeling. Labeling further may include energy transfer between molecules in the hybridization complex by perturbation analysis, quenching, or electron transport between donor and acceptor molecules, the latter of which may be facilitated by double stranded match hybridization complexes. The fluorescent label may be a perylene or a terrylene. In the alternative, the fluorescent label may be a fluorescent bar code. Advantageously, the label may be light sensitive, wherein the label is light-activated and/or light cleaves the one or more linkers to release the molecular cargo. The light-activated molecular cargo may be a major light-harvesting complex (LHCII). In another embodiment, the fluorescent label may induce free radical formation. Advantageously, agents may be uniquely labeled in a dynamic manner (see, e.g., U.S. provisional patent application Ser. No. 61/703,884 filed Sep. 21, 2012). The unique labels are, at least in part, nucleic acid in nature, and may be generated by sequentially attaching two or more detectable oligonucleotide tags to each other and each unique label may be associated with a separate agent. A detectable oligonucleotide tag may be an oligonucleotide that may be detected by sequencing of its nucleotide sequence and/or by detecting non-nucleic acid detectable moieties to which it may be attached. Oligonucleotide tags may be detectable by virtue of their nucleotide sequence, or by virtue of a non-nucleic acid detectable moiety that is attached to the oligonucleotide such as but not limited to a fluorophore, or by virtue of a combination of their nucleotide sequence and the non-nucleic acid detectable moiety. A detectable oligonucleotide tag may comprise one or more nonoligonucleotide detectable moieties. Examples of detectable moieties may include, but are not limited to, fluorophores, microparticles including quantum dots (Empodocles, et al., Nature 399:126-130, 1999), gold nanoparticles (Reichert et al., Anal. Chem. 72:6025-6029, 2000), microbeads (Lacoste et al., Proc. Natl. Acad. Sci. USA 97(17):9461-9466, 2000), biotin, DNP (dinitrophenyl), fucose, digoxigenin, haptens, and other detectable moieties known to those skilled in the art. In some embodiments, the detectable moieties may be quantum dots. Methods for detecting such moieties are described herein and/or are known in the art. Thus, detectable oligonucleotide tags may be, but are not limited to, oligonucleotides which may comprise unique nucleotide sequences, oligonucleotides which may comprise detectable moieties, and oligonucleotides which may comprise both unique nucleotide sequences and detectable moieties. A unique label may be produced by sequentially attaching two or more detectable oligonucleotide tags to each other. The detectable tags may be present or provided in a plurality of detectable tags. The same or a different plurality of tags may be used as the source of each detectable tag may be part of a unique label. In other words, a plurality of tags may be subdivided into subsets and single subsets may be used as the source for each tag. One or more other species may be associated with the tags. In particular, nucleic acids released by a lysed cell may be ligated to one or more tags. These may include, for example, chromosomal DNA, RNA transcripts, tRNA, mRNA, mitochondrial DNA, or the like. Such nucleic acids may be sequenced, in addition to sequencing the tags themselves, which may yield information about the nucleic acid profile of the cells, which can be associated with the tags, or the conditions that the corresponding droplet or cell was exposed to.

The invention accordingly may involve or be practiced as to high throughput and high resolution delivery of reagents to individual emulsion droplets that may contain cells, organelles, nucleic acids, proteins, etc. through the use of monodisperse aqueous droplets that are generated by a microfluidic device as a water-in-oil emulsion. The droplets are carried in a flowing oil phase and stabilized by a surfactant. In one aspect single cells or single organelles or single molecules (proteins, RNA, DNA) are encapsulated into uniform droplets from an aqueous solution/dispersion. In a related aspect, multiple cells or multiple molecules may take the place of single cells or single molecules. The aqueous droplets of volume ranging from 1 pL to 10 nL work as individual reactors. 104 to 105 single cells in droplets may be processed and analyzed in a single run. To utilize microdroplets for rapid large-scale chemical screening or complex biological library identification, different species of microdroplets, each containing the specific chemical compounds or biological probes cells or molecular barcodes of interest, have to be generated and combined at the preferred conditions, e.g., mixing ratio, concentration, and order of combination. Each species of droplet is introduced at a confluence point in a main microfluidic channel from separate inlet microfluidic channels. Preferably, droplet volumes are chosen by design such that one species is larger than others and moves at a different speed, usually slower than the other species, in the carrier fluid, as disclosed in U.S. Publication No. US2007/0195127 and International Publication No. WO 2007/089541, each of which are incorporated herein by reference in their entirety. The channel width and length is selected such that faster species of droplets catch up to the slowest species. Size constraints of the channel prevent the faster moving droplets from passing the slower moving droplets resulting in a train of droplets entering a merge zone. Multi-step chemical reactions, biochemical reactions, or assay detection chemistries often require a fixed reaction time before species of different type are added to a reaction. Multi-step reactions are achieved by repeating the process multiple times with a second, third or more confluence points each with a separate merge point. Highly efficient and precise reactions and analysis of reactions are achieved when the frequencies of droplets from the inlet channels are matched to an optimized ratio and the volumes of the species are

matched to provide optimized reaction conditions in the combined droplets. Fluidic droplets may be screened or sorted within a fluidic system of the invention by altering the flow of the liquid containing the droplets. For instance, in one set of embodiments, a fluidic droplet may be steered or sorted by directing the liquid surrounding the fluidic droplet into a first channel, a second channel, etc. In another set of embodiments, pressure within a fluidic system, for example, within different channels or within different portions of a channel, can be controlled to direct the flow of fluidic droplets. For example, a droplet can be directed toward a channel junction including multiple options for further direction of flow (e.g., directed toward a branch, or fork, in a channel defining optional downstream flow channels). Pressure within one or more of the optional downstream flow channels can be controlled to direct the droplet selectively into one of the channels, and changes in pressure can be effected on the order of the time required for successive droplets to reach the junction, such that the downstream flow path of each successive droplet can be independently controlled. In one arrangement, the expansion and/or contraction of liquid reservoirs may be used to steer or sort a fluidic droplet into a channel, e.g., by causing directed movement of the liquid containing the fluidic droplet. In another, the expansion and/or contraction of the liquid reservoir may be combined with other flow-controlling devices and methods, e.g., as described herein. Non-limiting examples of devices able to cause the expansion and/or contraction of a liquid reservoir include pistons. Key elements for using microfluidic channels to process droplets include: (1) producing droplet of the correct volume,
(2) producing droplets at the correct frequency and (3) bringing together a first stream of sample droplets with a second stream of sample droplets in such a way that the frequency of the first stream of sample droplets matches the frequency of the second stream of sample droplets. Preferably, bringing together a stream of sample droplets with a stream of premade library droplets in such a way that the frequency of the library droplets matches the frequency of the sample droplets. Methods for producing droplets of a uniform volume at a regular frequency are well known in the art. One method is to generate droplets using hydrodynamic focusing of a dispersed phase fluid and immiscible carrier fluid, such as disclosed in U.S. Publication No. US 2005/0172476 and International Publication No. WO 2004/002627. It is desirable for one of the species introduced at the confluence to be a pre-made library of droplets where the library contains a plurality of reaction conditions, e.g., a library may contain plurality of different compounds at a range of concentrations encapsulated as separate library elements for screening their effect on cells or enzymes, alternatively a library could be composed of a plurality of different primer pairs encapsulated as different library elements for targeted amplification of a collection of loci, alternatively a library could contain a plurality of different antibody species encapsulated as different library elements to perform a plurality of binding assays. The introduction of a library of reaction conditions onto a substrate is achieved by pushing a premade collection of library droplets out of a vial with a drive fluid. The drive fluid is a continuous fluid. The drive fluid may comprise the same substance as the carrier fluid (e.g., a fluorocarbon oil). For example, if a library consists of ten pico-liter droplets is driven into an inlet channel on a microfluidic substrate with a drive fluid at a rate of 10,000 pico-liters per second, then nominally the frequency at which the droplets are expected to enter the confluence point is 1000 per second. However, in practice droplets pack with oil between them that slowly drains. Over time the carrier fluid drains from the library droplets and the number density of the droplets (number/mL) increases. Hence, a simple fixed rate of infusion for the drive fluid does not provide a uniform rate of introduction of the droplets into the microfluidic channel in the substrate. Moreover, library-to-library variations in the mean library droplet volume result in a shift in the frequency of droplet introduction at the confluence point. Thus, the lack of uniformity of droplets that results from sample variation and oil drainage provides another problem to be solved. For example if the nominal droplet volume is expected to be 10 pico-liters in the library, but varies from 9 to 11 pico-liters from library-to-library then a 10,000 pico-liter/second infusion rate will nominally produce a range in frequencies from 900 to 1,100 droplet per second. In short, sample to sample variation in the composition of dispersed phase for droplets made on chip, a tendency for the number density of library droplets to increase over time and library-to-library variations in mean droplet volume severely limit the extent to which frequencies of droplets may be reliably matched at a confluence by simply using fixed infusion rates. In addition, these limitations also have an impact on the extent to which volumes may be reproducibly combined. Combined with typical variations in pump flow rate precision and variations in channel dimensions, systems are severely limited without a means to compensate on a run-to-run basis. The foregoing facts not only illustrate a problem to be solved, but also demonstrate a need for a method of instantaneous regulation of microfluidic control over microdroplets within a microfluidic channel. Combinations of surfactant(s) and oils must be developed to facilitate generation, storage, and manipulation of droplets to maintain the unique chemical/biochemical/biological environment within each droplet of a diverse library. Therefore, the surfactant and oil combination must (1) stabilize droplets against uncontrolled coalescence during the drop forming process and subsequent collection and storage, (2) minimize transport of any droplet contents to the oil phase and/or between droplets, and (3) maintain chemical and biological inertness with contents of each droplet (e.g., no adsorption or reaction of encapsulated contents at the oil-water interface, and no adverse effects on biological or chemical constituents in the droplets). In addition to the requirements on the droplet library function and stability, the surfactant-in-oil solution must be coupled with the fluid physics and materials associated with the platform. Specifically, the oil solution must not swell, dissolve, or degrade the materials used to construct the microfluidic chip, and the physical properties of the oil (e.g., viscosity, boiling point, etc.) must be suited for the flow and operating conditions of the platform. Droplets formed in oil without surfactant are not stable to permit coalescence, so surfactants must be dissolved in the oil that is used as the continuous phase for the emulsion library. Surfactant molecules are amphiphilic—part of the molecule is oil soluble, and part of the molecule is water soluble. When a water-oil interface is formed at the nozzle of a microfluidic chip for example in the inlet module described herein, surfactant molecules that are dissolved in the oil phase adsorb to the interface. The hydrophilic portion of the molecule resides inside the droplet and the fluorophilic portion of the molecule decorates the exterior of the droplet. The surface tension of a droplet is reduced when the interface is populated with surfactant, so the stability of an emulsion is improved. In addition to stabilizing the droplets against coalescence, the surfactant should be inert to the contents of each droplet and the surfactant should not promote transport of encapsulated components to the oil or other droplets. A droplet library may be made up of a number of library elements that are pooled together in a single collection (see, e.g., US Patent Publication No. 2010002241). Libraries may vary in complexity from a single library element to 1015 library elements or more. Each library element may be one or more given components at a fixed concentration. The element may be, but is not limited to, cells, organelles, virus, bacteria, yeast, beads, amino acids, proteins, polypeptides, nucleic acids, polynucleotides or small molecule chemical compounds. The element may contain an identifier such as a label. The terms “droplet library” or “droplet libraries” are also referred to herein as an “emulsion library” or “emulsion libraries.” These terms are used interchangeably throughout the specification. A cell library element may include, but is not limited to, hybridomas, B-cells, primary cells, cultured cell lines, cancer cells, stem cells, cells obtained from tissue, or any other cell type. Cellular library elements are prepared by encapsulating a number of cells from one to hundreds of thousands in individual droplets. The number of cells encapsulated is usually given by Poisson statistics from the number density of cells and volume of the droplet. However, in some cases the number deviates from Poisson statistics as described in Edd et al., “Controlled encapsulation of single-cells into monodisperse picolitre drops.” Lab Chip, 8(8): 1262-1264, 2008. The discrete nature of cells allows for libraries to be prepared in mass with a plurality of cellular variants all present in a single starting media and then that media is broken up into individual droplet capsules that contain at most one cell. These individual droplets capsules are then combined or pooled to form a library consisting of unique library elements. Cell division subsequent to, or in some embodiments following, encapsulation produces a clonal library element. A bead based library element may contain one or more beads, of a given type and may also contain other reagents, such as antibodies, enzymes or other proteins. In the case where all library elements contain different types of beads, but the same surrounding media, the library elements may all be prepared from a single starting fluid or have a variety of starting fluids. In the case of cellular libraries prepared in mass from a collection of variants, such as genomically modified, yeast or bacteria cells, the library elements will be prepared from a variety of starting fluids. Often it is desirable to have exactly one cell per droplet with only a few droplets containing more than one cell when starting with a plurality of cells or yeast or bacteria, engineered to produce variants on a protein. In some cases, variations from Poisson statistics may be achieved to provide an enhanced loading of droplets such that there are more droplets with exactly one cell per droplet and few exceptions of empty droplets or droplets containing more than one cell. Examples of droplet libraries are collections of droplets that have different contents, ranging from beads, cells, small molecules, DNA, primers, antibodies. Smaller droplets may be in the order of femtoliter (fL) volume drops, which are especially contemplated with the droplet dispensors. The volume may range from about 5 to about 600 fL. The larger droplets range in size from roughly 0.5 micron to 500 micron in diameter, which corresponds to about 1 pico liter to 1 nano liter. However, droplets may be as small as 5 microns and as large as 500 microns. Preferably, the droplets are at less than 100 microns, about 1 micron to about 100 microns in diameter. The most preferred size is about 20 to 40 microns in diameter (10 to 100 picoliters). The preferred properties examined of droplet libraries include osmotic pressure balance, uniform size, and size ranges. The droplets within the emulsion libraries of the present invention may be contained within an immiscible oil which may comprise at least one fluorosurfactant. In some embodiments, the fluorosurfactant within the immiscible fluorocarbon oil may be a block copolymer consisting of one or more perfluorinated polyether (PFPE) blocks and one or more polyethylene glycol (PEG) blocks. In other embodiments, the fluorosurfactant is a triblock copolymer consisting of a PEG center block covalently bound to two PFPE blocks by amide linking groups. The presence of the fluorosurfactant (similar to uniform size of the droplets in the library) is critical to maintain the stability and integrity of the droplets and is also essential for the subsequent use of the droplets within the library for the various biological and chemical assays described herein. Fluids (e.g., aqueous fluids, immiscible oils, etc.) and other surfactants that may be utilized in the droplet libraries of the present invention are described in greater detail herein. The present invention can accordingly involve an emulsion library which may comprise a plurality of aqueous droplets within an immiscible oil (e.g., fluorocarbon oil) which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise the same aqueous fluid and may comprise a different library element. The present invention also provides a method for forming the emulsion library which may comprise providing a single aqueous fluid which may comprise different library elements, encapsulating each library element into an aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise the same aqueous fluid and may comprise a different library element, and pooling the aqueous droplets within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, thereby forming an emulsion library. For example, in one type of emulsion library, all different types of elements (e.g., cells or beads), may be pooled in a single source contained in the same medium. After the initial pooling, the cells or beads are then encapsulated in droplets to generate a library of droplets wherein each droplet with a different type of bead or cell is a different library element. The dilution of the initial solution enables the encapsulation process. In some embodiments, the droplets formed will either contain a single cell or bead or will not contain anything, i.e., be empty. In other embodiments, the droplets formed will contain multiple copies of a library element. The cells or beads being encapsulated are generally variants on the same type of cell or bead. In another example, the emulsion library may comprise a plurality of aqueous droplets within an immiscible fluorocarbon oil, wherein a single molecule may be encapsulated, such that there is a single molecule contained within a droplet for every 20-60 droplets produced (e.g., 20, 25, 30, 35, 40, 45, 50, 55, 60 droplets, or any integer in between). Single molecules may be encapsulated by diluting the solution containing the molecules to such a low concentration that the encapsulation of single molecules is enabled. In one specific example, a LacZ plasmid DNA was encapsulated at a concentration of 20 fM after two hours of incubation such that there was about one gene in 40 droplets, where 10 μm droplets were made at 10 kHz per second. Formation of these libraries rely on limiting dilutions.

The present invention also provides an emulsion library which may comprise at least a first aqueous droplet and at least a second aqueous droplet within a fluorocarbon oil which may comprise at least one fluorosurfactant, wherein the at least first and the at least second droplets are uniform in size and comprise a different aqueous fluid and a different library element. The present invention also provides a method for forming the emulsion library which may comprise providing at least a first aqueous fluid which may comprise at least a first library of elements, providing at least a second aqueous fluid which may comprise at least a second library of elements, encapsulating each element of said at least first library into at least a first aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, encapsulating each element of said at least second library into at least a second aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein the at least first and the at least second droplets are uniform in size and may comprise a different aqueous fluid and a different library element, and pooling the at least first aqueous droplet and the at least second aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant thereby forming an emulsion library. One of skill in the art will recognize that methods and systems of the invention are not preferably practiced as to cells, mutations, etc. as herein disclosed, but that the invention need not be limited to any particular type of sample, and methods and systems of the invention may be used with any type of organic, inorganic, or biological molecule (see, e.g., US Patent Publication No. 20120122714). In particular embodiments the sample may include nucleic acid target molecules. Nucleic acid molecules may be synthetic or derived from naturally occurring sources. In one embodiment, nucleic acid molecules may be isolated from a biological sample containing a variety of other components, such as proteins, lipids and non-template nucleic acids. Nucleic acid target molecules may be obtained from any cellular material, obtained from an animal, plant, bacterium, fungus, or any other cellular organism. In certain embodiments, the nucleic acid target molecules may be obtained from a single cell. Biological samples for use in the present invention may include viral particles or preparations. Nucleic acid target molecules may be obtained directly from an organism or from a biological sample obtained from an organism, e.g., from blood, urine, cerebrospinal fluid, seminal fluid, saliva, sputum, stool and tissue. Any tissue or body fluid specimen may be used as a source for nucleic acid for use in the invention. Nucleic acid target molecules may also be isolated from cultured cells, such as a primary cell culture or a cell line. The cells or tissues from which target nucleic acids are obtained may be infected with a virus or other intracellular pathogen. A sample may also be total RNA extracted from a biological specimen, a cDNA library, viral, or genomic DNA. Generally, nucleic acid may be extracted from a biological sample by a variety of techniques such as those described by Maniatis, et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor, N.Y., pp. 280-281 (1982). Nucleic acid molecules may be single-stranded, double-stranded, or double-stranded with single-stranded regions (for example, stem- and loop-structures). Nucleic acid obtained from biological samples typically may be fragmented to produce suitable fragments for analysis. Target nucleic acids may be fragmented or sheared to desired length, using a variety of mechanical, chemical and/or enzymatic methods. DNA may be randomly sheared via sonication,

e.g. Covaris method, brief exposure to a DNase, or using a mixture of one or more restriction enzymes, or a transposase or nicking enzyme. RNA may be fragmented by brief exposure to an RNase, heat plus magnesium, or by shearing. The RNA may be converted to cDNA. If fragmentation is employed, the RNA may be converted to cDNA before or after fragmentation. In one embodiment, nucleic acid from a biological sample is fragmented by sonication. In another embodiment, nucleic acid is fragmented by a hydro shear instrument. Generally, individual nucleic acid target molecules may be from about 40 bases to about 40 kb. Nucleic acid molecules may be single-stranded, double-stranded, or double-stranded with single-stranded regions (for example, stem- and loop-structures). A biological sample as described herein may be hom*ogenized or fractionated in the presence of a detergent or surfactant. The concentration of the detergent in the buffer may be about 0.05% to about 10.0%. The concentration of the detergent may be up to an amount where the detergent remains soluble in the solution. In one embodiment, the concentration of the detergent is between 0.1% to about 2%. The detergent, particularly a mild one that is nondenaturing, may act to solubilize the sample. Detergents may be ionic or nonionic. Examples of nonionic detergents include triton, such as the Triton™ X series (Triton™ X-100 t-Oct-C6H4-(OCH2-CH2)xOH, x=9-10, Triton™ X-100R, Triton™ X-114 x=7-8), octyl glucoside, polyoxyethylene(9)dodecyl ether, digitonin, IGEPAL™ CA630 octylphenyl polyethylene glycol, n-octyl-beta-D-glucopyranoside (betaOG), n-dodecyl-beta, Tween™. 20 polyethylene glycol sorbitan monolaurate, Tween™ 80 polyethylene glycol sorbitan monooleate, polidocanol, n-dodecyl beta-D-maltoside (DDM), NP-40 nonylphenyl polyethylene glycol, C12E8 (octaethylene glycol n-dodecyl monoether), hexaethyleneglycol mono-n-tetradecyl ether (C14E06), octyl-beta-thioglucopyranoside (octyl thioglucoside, OTG), Emulgen, and polyoxyethylene 10 lauryl ether (C12E10). Examples of ionic detergents (anionic or cationic) include deoxycholate, sodium dodecyl sulfate (SDS), N-lauroylsarcosine, and cetyltrimethylammoniumbromide (CTAB). A zwitterionic reagent may also be used in the purification schemes of the present invention, such as Chaps, zwitterion 3-14, and 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate. It is contemplated also that urea may be added with or without another detergent or surfactant. Lysis or hom*ogenization solutions may further contain other agents, such as reducing agents. Examples of such reducing agents include dithiothreitol (DTT), β-mercaptoethanol, DTE, GSH, cysteine, cysteamine, tricarboxyethyl phosphine (TCEP), or salts of sulfurous acid. Size selection of the nucleic acids may be performed to remove very short fragments or very long fragments. The nucleic acid fragments may be partitioned into fractions which may comprise a desired number of fragments using any suitable method known in the art. Suitable methods to limit the fragment size in each fragment are known in the art. In various embodiments of the invention, the fragment size is limited to between about 10 and about 100 Kb or longer. A sample in or as to the instant invention may include individual target proteins, protein complexes, proteins with translational modifications, and protein/nucleic acid complexes. Protein targets include peptides, and also include enzymes, hormones, structural components such as viral capsid proteins, and antibodies. Protein targets may be synthetic or derived from naturally-occurring sources. The invention protein targets may be isolated from biological samples containing a variety of other components including lipids, non-template nucleic acids, and nucleic acids. Protein targets may be obtained from an animal, bacterium, fungus, cellular organism, and single cells. Protein targets may be obtained directly from an organism or from a biological sample obtained from the organism, including bodily fluids such as blood, urine, cerebrospinal fluid, seminal fluid, saliva, sputum, stool and tissue.
Protein targets may also be obtained from cell and tissue lysates and biochemical fractions. An individual protein is an isolated polypeptide chain. A protein complex includes two or polypeptide chains. Samples may include proteins with post translational modifications including but not limited to phosphorylation, methionine oxidation, deamidation, glycosylation, ubiquitination, carbamylation, s-carboxymethylation, acetylation, and methylation. Protein/nucleic acid complexes include cross-linked or stable protein-nucleic acid complexes. Extraction or isolation of individual proteins, protein complexes, proteins with translational modifications, and protein/nucleic acid complexes is performed using methods known in the art.

The invention can thus involve forming sample droplets. The droplets are aqueous droplets that are surrounded by an immiscible carrier fluid. Methods of forming such droplets are shown for example in Link et al. (U.S. patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163), Stone et al. (U.S. Pat. No. 7,708,949 and U.S. patent application number 2010/0172803), Anderson et al. (U.S. Pat. No. 7,041,481 and which reissued as U.S. Pat. No. RE41,780) and European publication number EP2047910 to Raindance Technologies Inc. The content of each of which is incorporated by reference herein in its entirety. The present invention may relates to systems and methods for manipulating droplets within a high throughput microfluidic system. A microfluid droplet encapsulates a differentiated cell. The cell is lysed and its mRNA is hybridized onto a capture bead containing barcoded oligo dT primers on the surface, all inside the droplet. The barcode is covalently attached to the capture bead via a flexible multi-atom linker like PEG. In a preferred embodiment, the droplets are broken by addition of a fluorosurfactant (like perfluorooctanol), washed, and collected. A reverse transcription (RT) reaction is then performed to convert each cell's mRNA into a first strand cDNA that is both uniquely barcoded and covalently linked to the mRNA capture bead. Subsequently, a universal primer via a template switching reaction is amended using conventional library preparation protocols to prepare an RNA-Seq library. Since all of the mRNA from any given cell is uniquely barcoded, a single library is sequenced and then computationally resolved to determine which mRNAs came from which cells. In this way, through a single sequencing run, tens of thousands (or more) of distinguishable transcriptomes can be simultaneously obtained. The oligonucleotide sequence may be generated on the bead surface. During these cycles, beads were removed from the synthesis column, pooled, and aliquoted into four equal portions by mass; these bead aliquots were then placed in a separate synthesis column and reacted with either dG, dC, dT, or dA phosphoramidite. In other instances, dinucleotide, trinucleotides, or oligonucleotides that are greater in length are used, in other instances, the oligo-dT tail is replaced by gene specific oligonucleotides to prime specific targets (singular or plural), random sequences of any length for the capture of all or specific RNAs. This process was repeated 12 times for a total of 412=16,777,216 unique barcode sequences. Upon completion of these cycles, 8 cycles of degenerate oligonucleotide synthesis were performed on all the beads, followed by 30 cycles of dT addition. In other embodiments, the degenerate synthesis is omitted, shortened (less than 8 cycles), or extended (more than 8 cycles); in others, the 30 cycles of dT addition are replaced with gene specific primers (single target or many targets) or a degenerate sequence. The aforementioned microfluidic system is regarded as the reagent delivery system microfluidic library printer or droplet library printing system of the present invention. Droplets are formed as sample fluid flows from droplet generator which contains lysis reagent and barcodes through microfluidic outlet channel which contains oil, towards junction. Defined volumes of loaded reagent emulsion, corresponding to defined numbers of droplets, are dispensed on-demand into the flow stream of carrier fluid. The sample fluid may typically comprise an aqueous buffer solution, such as ultrapure water (e.g., 18 mega-ohm resistivity, obtained, for example by column chromatography), 10 mM Tris HCl and 1 mM EDTA (TE) buffer, phosphate buffer saline (PBS) or acetate buffer. Any liquid or buffer that is physiologically compatible with nucleic acid molecules can be used. The carrier fluid may include one that is immiscible with the sample fluid. The carrier fluid can be a non-polar solvent, decane (e.g., tetradecane or hexadecane), fluorocarbon oil, silicone oil, an inert oil such as hydrocarbon, or another oil (for example, mineral oil). The carrier fluid may contain one or more additives, such as agents which reduce surface tensions (surfactants). Surfactants can include Tween, Span, fluorosurfactants, and other agents that are soluble in oil relative to water. In some applications, performance is improved by adding a second surfactant to the sample fluid. Surfactants can aid in controlling or optimizing droplet size, flow and uniformity, for example by reducing the shear force needed to extrude or inject droplets into an intersecting channel. This can affect droplet volume and periodicity, or the rate or frequency at which droplets break off into an intersecting channel. Furthermore, the surfactant can serve to stabilize aqueous emulsions in fluorinated oils from coalescing. Droplets may be surrounded by a surfactant which stabilizes the droplets by reducing the surface tension at the aqueous oil interface. Preferred surfactants that may be added to the carrier fluid include, but are not limited to, surfactants such as sorbitan-based carboxylic acid esters (e.g., the “Span” surfactants, Fluka Chemika), including sorbitan monolaurate (Span 20), sorbitan monopalmitate (Span 40), sorbitan monostearate (Span 60) and sorbitan monooleate (Span 80), and perfluorinated polyethers (e.g., DuPont Krytox 157 FSL, FSM, and/or FSH). Other non-limiting examples of non-ionic surfactants which may be used include polyoxyethylenated alkylphenols (for example, nonyl-, p-dodecyl-, and dinonylphenols), polyoxyethylenated straight chain alcohols, polyoxyethylenated polyoxypropylene glycols, polyoxyethylenated mercaptans, long chain carboxylic acid esters (for example, glyceryl and polyglyceryl esters of natural fatty acids, propylene glycol, sorbitol, polyoxyethylenated sorbitol esters, polyoxyethylene glycol esters, etc.) and alkanolamines (e.g., diethanolamine-fatty acid condensates and isopropanolamine-fatty acid condensates). In some cases, an apparatus for creating a single-cell sequencing library via a microfluidic system provides for volume-driven flow, wherein constant volumes are injected over time. The pressure in fluidic channels is a function of injection rate and channel dimensions. In one embodiment, the device provides an oil/surfactant inlet; an inlet for an analyte; a filter, an inlet for for mRNA capture microbeads and lysis reagent; a carrier fluid channel which connects the inlets, a resistor; a constriction for droplet pinch-off; a mixer; and an outlet for drops. In an embodiment the invention provides apparatus for creating a single-cell sequencing library via a microfluidic system, which may comprise: an oil-surfactant inlet which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel may further comprise a resistor; an inlet for an analyte which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel may further comprise a resistor; an inlet for mRNA capture microbeads and lysis reagent which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel further may comprise a resistor; said carrier fluid channels have a carrier fluid flowing therein at an adjustable or predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a mixer, which contains an outlet for drops. Accordingly, an apparatus for creating a single-cell sequencing library via a microfluidic system microfluidic flow scheme for single-cell RNA-seq is envisioned. Two channels, one carrying cell suspensions, and the other carrying uniquely barcoded mRNA capture bead, lysis buffer and library preparation reagents meet at a junction and is immediately co-encapsulated in an inert carrier oil, at the rate of one cell and one bead per drop. In each drop, using the bead's barcode tagged oligonucleotides as cDNA template, each mRNA is tagged with a unique, cell-specific identifier. The invention also encompasses use of a Drop-Seq library of a mixture of mouse and human cells. The carrier fluid may be caused to flow through the outlet channel so that the surfactant in the carrier fluid coats the channel walls. The fluorosurfactant can be prepared by reacting the perfluorinated polyether DuPont Krytox 157 FSL, FSM, or FSH with aqueous ammonium hydroxide in a volatile fluorinated solvent. The solvent and residual water and ammonia can be removed with a rotary evaporator. The surfactant can then be dissolved (e.g., 2.5 wt %) in a fluorinated oil (e.g., Fluorinert (3M)), which then serves as the carrier fluid. Activation of sample fluid reservoirs to produce regent droplets is based on the concept of dynamic reagent delivery (e.g., combinatorial barcoding) via an on demand capability. The on demand feature may be provided by one of a variety of technical capabilities for releasing delivery droplets to a primary droplet, as described herein. From this disclosure and herein cited documents and knowledge in the art, it is within the ambit of the skilled person to develop flow rates, channel lengths, and channel geometries; and establish droplets containing random or specified reagent combinations can be generated on demand and merged with the “reaction chamber” droplets containing the samples/cells/substrates of interest. By incorporating a plurality of unique tags into the additional droplets and joining the tags to a solid support designed to be specific to the primary droplet, the conditions that the primary droplet is exposed to may be encoded and recorded. For example, nucleic acid tags can be sequentially ligated to create a sequence reflecting conditions and order of same. Alternatively, the tags can be added independently appended to solid support. Non-limiting examples of a dynamic labeling system that may be used to bioinformatically record information can be found at US Provisional Patent Application entitled “Compositions and Methods for Unique Labeling of Agents” filed Sep. 21, 2012 and Nov. 29, 2012. In this way, two or more droplets may be exposed to a variety of different conditions, where each time a droplet is exposed to a condition, a nucleic acid encoding the condition is added to the droplet each ligated together or to a unique solid support associated with the droplet such that, even if the droplets with different histories are later combined, the conditions of each of the droplets are remain available through the different nucleic acids. Non-limiting examples of methods to evaluate response to exposure to a plurality of conditions can be found at US Provisional Patent Application entitled “Systems and Methods for Droplet Tagging” filed Sep. 21, 2012. Accordingly, in or as to the invention it is envisioned that there can be the dynamic generation of molecular barcodes (e.g., DNA oligonucleotides, fluorophores, etc.) either independent from or in concert with the controlled delivery of various compounds of interest (drugs, small molecules, siRNA, CRISPR guide RNAs, reagents, etc.). For example, unique molecular barcodes can be created in one array of nozzles while individual compounds or combinations of compounds can be generated by another nozzle array. Barcodes/compounds of interest can then be merged with cell-containing droplets. An electronic record in the form of a computer log file is kept to associate the barcode delivered with the downstream reagent(s) delivered. This methodology makes it possible to efficiently screen a large population of cells for applications such as single-cell drug screening, controlled perturbation of regulatory pathways, etc. The device and techniques of the disclosed invention facilitate efforts to perform studies that require data resolution at the single cell (or single molecule) level and in a cost effective manner. The invention envisions a high throughput and high resolution delivery of reagents to individual emulsion droplets that may contain cells, nucleic acids, proteins, etc. through the use of monodisperse aqueous droplets that are generated one by one in a microfluidic chip as a water-in-oil emulsion. Being able to dynamically track individual cells and droplet treatments/combinations during life cycle experiments, and having an ability to create a library of emulsion droplets on demand with the further capability of manipulating the droplets through the disclosed process(es) are advantageous. In the practice of the invention there can be dynamic tracking of the droplets and create a history of droplet deployment and application in a single cell based environment. Droplet generation and deployment is produced via a dynamic indexing strategy and in a controlled fashion in accordance with disclosed embodiments of the present invention. Microdroplets can be processed, analyzed and sorted at a highly efficient rate of several thousand droplets per second, providing a powerful platform which allows rapid screening of millions of distinct compounds, biological probes, proteins or cells either in cellular models of biological mechanisms of disease, or in biochemical, or pharmacological assays. A plurality of biological assays as well as biological synthesis are contemplated. Polymerase chain reactions (PCR) are contemplated (see, e.g., US Patent Publication No. 20120219947). Methods of the invention may be used for merging sample fluids for conducting any type of chemical reaction or any type of biological assay. There may be merging sample fluids for conducting an amplification reaction in a droplet. Amplification refers to production of additional copies of a nucleic acid sequence and is generally carried out using polymerase chain reaction or other technologies well known in the art (e.g., Dieffenbach and Dveksler, PCR Primer, a Laboratory Manual, Cold Spring Harbor Press, Plainview, N.Y. [1995]). The amplification reaction may be any amplification reaction known in the art that amplifies nucleic acid molecules, such as polymerase chain reaction, nested polymerase chain reaction, polymerase chain reaction-single strand conformation polymorphism, ligase chain reaction (Barany F. (1991) PNAS 88:189-193; Barany F. (1991) PCR Methods and Applications 1:5-16), ligase detection reaction (Barany F. (1991) PNAS 88:189-193), strand displacement amplification and restriction fragments length polymorphism, transcription based amplification system, nucleic acid sequence-based amplification, rolling circle amplification, and hyper-branched rolling circle amplification. In certain embodiments, the amplification reaction is the polymerase chain reaction. Polymerase chain reaction (PCR) refers to methods by K. B. Mullis (U.S. Pat. Nos. 4,683,195 and 4,683,202, hereby incorporated by reference) for increasing concentration of a segment of a target sequence in a mixture of genomic DNA without cloning or purification. The process for amplifying the target sequence includes introducing an excess of oligonucleotide primers to a DNA mixture containing a desired target sequence, followed by a precise sequence of thermal cycling in the presence of a DNA polymerase. The primers are complementary to their respective strands of the double stranded target sequence. To effect amplification, primers are annealed to their complementary sequence within the target molecule. Following annealing, the primers are extended with a polymerase so as to form a new pair of complementary strands. The steps of denaturation, primer annealing and polymerase extension may be repeated many times (i.e., denaturation, annealing and extension constitute one cycle; there may be numerous cycles) to obtain a high concentration of an amplified segment of a desired target sequence. The length of the amplified segment of the desired target sequence is determined by relative positions of the primers with respect to each other, and therefore, this length is a controllable parameter. Methods for performing PCR in droplets are shown for example in Link et al. (U.S. Patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163), Anderson et al. (U.S. Pat. No. 7,041,481 and which reissued as U.S. Pat. No. RE41,780) and European publication number EP2047910 to Raindance Technologies Inc. The content of each of which is incorporated by reference herein in its entirety. The first sample fluid contains nucleic acid templates. Droplets of the first sample fluid are formed as described above. Those droplets will include the nucleic acid templates. In certain embodiments, the droplets will include only a single nucleic acid template, and thus digital PCR may be conducted. The second sample fluid contains reagents for the PCR reaction. Such reagents generally include Taq polymerase, deoxynucleotides of type A, C, G and T, magnesium chloride, and forward and reverse primers, all suspended within an aqueous buffer. The second fluid also includes detectably labeled probes for detection of the amplified target nucleic acid, the details of which are discussed below. This type of partitioning of the reagents between the two sample fluids is not the only possibility. In some instances, the first sample fluid will include some or all of the reagents necessary for the PCR whereas the second sample fluid will contain the balance of the reagents necessary for the PCR together with the detection probes. Primers may be prepared by a variety of methods including but not limited to cloning of appropriate sequences and direct chemical synthesis using methods well known in the art (Narang et al., Methods Enzymol., 68:90 (1979); Brown et al., Methods Enzymol., 68:109 (1979)). Primers may also be obtained from commercial sources such as Operon Technologies, Amersham Pharmacia Biotech, Sigma, and Life Technologies. The primers may have an identical melting temperature. The lengths of the primers may be extended or shortened at the 5′ end or the 3′ end to produce primers with desired melting temperatures. Also, the annealing position of each primer pair may be designed such that the sequence and, length of the primer pairs yield the desired melting temperature. The simplest equation for determining the melting temperature of primers smaller than 25 base pairs is the Wallace Rule (Td=2(A+T)+4(G+C)). Computer programs may also be used to design primers, including but not limited to Array Designer Software (Arrayit Inc.), Oligonucleotide Probe Sequence Design Software for Genetic Analysis (Olympus Optical Co.), NetPrimer, and DNAs is from Hitachi Software Engineering. The TM (melting or annealing temperature) of each primer is calculated using software programs such as Oligo Design, available from Invitrogen Corp.

A droplet containing the nucleic acid is then caused to merge with the PCR reagents in the second fluid according to methods of the invention described above, producing a droplet that includes Taq polymerase, deoxynucleotides of type A, C, G and T, magnesium chloride, forward and reverse primers, detectably labeled probes, and the target nucleic acid. Once mixed droplets have been produced, the droplets are thermal cycled, resulting in amplification of the target nucleic acid in each droplet. Droplets may be flowed through a channel in a serpentine path between heating and cooling lines to amplify the nucleic acid in the droplet. The width and depth of the channel may be adjusted to set the residence time at each temperature, which may be controlled to anywhere between less than a second and minutes. The three temperature zones may be used for the amplification reaction. The three temperature zones are controlled to result in denaturation of double stranded nucleic acid (high temperature zone), annealing of primers (low temperature zones), and amplification of single stranded nucleic acid to produce double stranded nucleic acids (intermediate temperature zones). The temperatures within these zones fall within ranges well known in the art for conducting PCR reactions. See for example, Sambrook et al. (Molecular Cloning, A Laboratory Manual, 3rd edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 2001). The three temperature zones can be controlled to have temperatures as follows: 95° C. (TH), 55° C. (TL), 72° C. (TM). The prepared sample droplets flow through the channel at a controlled rate. The sample droplets first pass the initial denaturation zone (TH) before thermal cycling. The initial preheat is an extended zone to ensure that nucleic acids within the sample droplet have denatured successfully before thermal cycling. The requirement for a preheat zone and the length of denaturation time required is dependent on the chemistry being used in the reaction. The samples pass into the high temperature zone, of approximately 95° C., where the sample is first separated into single stranded DNA in a process called denaturation. The sample then flows to the low temperature, of approximately 55° C., where the hybridization process takes place, during which the primers anneal to the complementary sequences of the sample. Finally, as the sample flows through the third medium temperature, of approximately 72° C., the polymerase process occurs when the primers are extended along the single strand of DNA with a thermostable enzyme. The nucleic acids undergo the same thermal cycling and chemical reaction as the droplets pass through each thermal cycle as they flow through the channel. The total number of cycles in the device is easily altered by an extension of thermal zones. The sample undergoes the same thermal cycling and chemical reaction as it passes through N amplification cycles of the complete thermal device. In other aspects, the temperature zones are controlled to achieve two individual temperature zones for a PCR reaction. In certain embodiments, the two temperature zones are controlled to have temperatures as follows: 95° C. (TH) and 60° C. (TL). The sample droplet optionally flows through an initial preheat zone before entering thermal cycling. The preheat zone may be important for some chemistry for activation and also to ensure that double stranded nucleic acid in the droplets is fully denatured before the thermal cycling reaction begins. In an exemplary embodiment, the preheat dwell length results in approximately 10 minutes preheat of the droplets at the higher temperature. The sample droplet continues into the high temperature zone, of approximately 95° C., where the sample is first separated into single stranded DNA in a process called denaturation. The sample then flows through the device to the low temperature zone, of approximately 60° C., where the hybridization process takes place, during which the primers anneal to the complementary sequences of the sample. Finally the polymerase process occurs when the primers are extended along the single strand of DNA with a thermostable enzyme. The sample undergoes the same thermal cycling and chemical reaction as it passes through each thermal cycle of the complete device. The total number of cycles in the device is easily altered by an extension of block length and tubing. After amplification, droplets may be flowed to a detection module for detection of amplification products. The droplets may be individually analyzed and detected using any methods known in the art, such as detecting for the presence or amount of a reporter. Generally, a detection module is in communication with one or more detection apparatuses. Detection apparatuses may be optical or electrical detectors or combinations thereof. Examples of suitable detection apparatuses include optical waveguides, microscopes, diodes, light stimulating devices, (e.g., lasers), photo multiplier tubes, and processors (e.g., computers and software), and combinations thereof, which cooperate to detect a signal representative of a characteristic, marker, or reporter, and to determine and direct the measurement or the sorting action at a sorting module. Further description of detection modules and methods of detecting amplification products in droplets are shown in Link et al. (U.S. patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163) and European publication number EP2047910 to Raindance Technologies Inc.

Examples of assays are also ELISA assays (see, e.g., US Patent Publication No. 20100022414). The present invention provides another emulsion library which may comprise a plurality of aqueous droplets within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise at least a first antibody, and a single element linked to at least a second antibody, wherein said first and second antibodies are different. In one example, each library element may comprise a different bead, wherein each bead is attached to a number of antibodies and the bead is encapsulated within a droplet that contains a different antibody in solution. These antibodies may then be allowed to form “ELISA sandwiches,” which may be washed and prepared for a ELISA assay. Further, these contents of the droplets may be altered to be specific for the antibody contained therein to maximize the results of the assay. Single-cell assays are also contemplated as part of the present invention (see, e.g., Ryan et al., Biomicrofluidics 5, 021501 (2011) for an overview of applications of microfluidics to assay individual cells). A single-cell assay may be contemplated as an experiment that quantifies a function or property of an individual cell when the interactions of that cell with its environment may be controlled precisely or may be isolated from the function or property under examination. The research and development of single-cell assays is largely predicated on the notion that genetic variation causes disease and that small subpopulations of cells represent the origin of the disease. Methods of assaying compounds secreted from cells, subcellular components, cell-cell or cell-drug interactions as well as methods of patterning individual cells are also contemplated within the present invention.

Another aspect of the invention is the combination of the technologies described herein. For example, the use of a high-throughput single-cell RNA-Seq and/or targeted nucleic acid profiling (for example, sequencing, quantitative reverse transcription polymerase chain reaction, and the like) where the RNAs from different cells are tagged individually, allowing a single library to be created while retaining the cell identity of each read, as explained above. RNA-Seq profiling of single cells (e.g. single Th17 cells) may be performed on cells isolated in vivo (e.g. isolated directly from a subject/patient, preferably without further culture steps). RNA-Seq profiling of single cells may be performed on any number of cells, including tumor cells, associated infiltrating cells into a tumor, immune derived cells, microglia, astrocytes, CD4 cells, CD8 cells, most preferably Th17 cells. Computational analysis of the high-throughput single-cell RNA-Seq data. This allows, for example, to dissect the molecular basis of different functional cellular states. This also allows for selection of signature genes as described herein. Once selection of signature genes is performed, an optional further step is the validation of the signature genes using any number of technologies for knock-out or knock-in models. For example, as explained herein, mutations in cells and also mutated mice for use in or as to the invention can be by way of the CRISPR-Cas system or a Cas9-expressing eukaryotic cell or Cas-9 expressing eukaryote, such as a mouse.

Such a combination of technologies, e.g. in particular with direct isolation from the subject/patient, provides for more robust and more accurate data as compared to in vitro scenarious which cannot take into account the full in vivo system and networking. This combination, in several instances is thus more efficient, more specific, and faster. This combination provides for, for example, methods for identification of signature genes and validation methods of the same. Equally, screening platforms are provided for identification of effective therapeutics or diagnostics.

These and other technologies may be employed in or as to the practice of the instant invention.

Upon immunological challenge, diverse immune cells collectively orchestrate an appropriate response. Extensive cellular heterogeneity exists even within specific immune cell subtypes classified as a single lineage, but its function and molecular underpinnings are rarely characterized at a genomic scale. Here, single-cell RNA-seq was use to investigate the molecular mechanisms governing heterogeneity and pathogenicity of murine Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions. Computational analysis reveals that Th17 cells span a spectrum of cellular states in vivo, including a self-renewal state in the LN, and Th1-like effector/memory states and a dysfunctional/senescent state in the CNS. Relating these states to in vitro differentiated Th17 cells, novel genes governing pathogenicity and disease susceptibility were discovered. Using knockout mice, the crucial role in Th17 cell pathogenicity of four novel genes was tested: Gpr65, Plzp, Toso and Cd5l. Th17 cellular heterogeneity thus plays an important role in defining the function of Th17 cells in autoimmunity and can be leveraged to identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones.

RNA-Seq profiling of single Th17 cells isolated in vivo and in vitro. The transcriptome of 1,029 Th17 cells (subsequently retaining a final set of 806 cells, below), either harvested in vivo or differentiated in vitro (FIG. 1A and Table S1) was profiled. For in vivo experiments, EAE was induced by myelin oligodendrocyte glycoprotein (MOG) immunization, CD3+CD4+IL-17A/GFP+ cells were harvested from the draining LNs at the peak of disease and profiled immediately. For in vitro experiments, cells were collected during differentiation of CD4+ naïve T cells under two polarizing conditions: TGF-β1+IL-6 and IL-1β+IL-6+IL-23; while both lead to IL-17A-producing cells, only the latter induces EAE upon adoptive transfer of cell ensembles into wild type or RAG-1 −/− mice (Chung et al., 2009; Ghoreschi et al., 2010). At least two independent biological replicates were used for each in vivo and in vitro condition, and two technical replicates for two in vivo conditions. Single-cell mRNA SMART-Seq libraries were prepared using microfluidic chips (Fluidigm C1) for single-cell capture, lysis, reverse transcription, and PCR amplification, followed by transposon-based library construction. Corresponding population controls (>50,000 cells for in vitro samples; ˜2,000-20,000 cells for in vivo samples, as available) were also profiled, with at least two replicates for each condition.

The libraries were filtered by a set of quality metrics, removing 223 (˜21%) of the 1,029 profiled cells, and controlled for quantitative confounding factors and batch effects (FIGS. S1A, B), ˜7,000 appreciably expressed genes (fragments per kilobase of exon per million (FPKM)>10) in at least 20% of each sample's cells) were retained for in vitro experiments and ˜4,000 for in vivo ones. To account for expressed transcripts that are not detected (false negatives) due to the limitations of single-cell RNA-Seq (Deng et al., 2014; Shalek et al., 2014), subsequent analysis down-weighted the contribution of less reliably measured transcripts (Shalek et al., 2014) (FIG. S1C. Following these filters, expression profiles were tightly correlated between population replicates (FIG. 1C), and the average expression across all single cells correlated well with the matching bulk population profile (r˜0.76-0.89; FIG. 1C, FIG. S1D, red bars, and Table S1). While the average expression of single cells correlated well with the bulk population, substantial differences were found in expression between individual cells in the same condition (r˜0.3-0.8; FIG. 1D and Figure S1D, blue bars) comparable to previous observations in other immune cells (Shalek et al., 2014). High-throughput, high-resolution, flow RNA-fluorescence was applied in situ hybridization (RNA Flow-Fish), an amplification-free imaging technique (Lalmansingh et al., 2013) to validate the observed patterns of gene expression heterogeneity for nine representative genes (FIG. 1F, FIG. 6E), chosen to span a wide range of expression and variation levels at 48h under the TGF-β1+IL-6 in vitro polarization condition. These experiments reveal that although canonical Th17 transcripts (e.g., Rorc, Irf4, Baf) are expressed unimodally, other key immune transcripts (e.g., Il-17a, 11-2) can vary in their expression across Th17 cells and exhibit a bimodal distribution. The analysis of this variation can provide clues on the functional states of the Th17 cells that have been associated with different disease states or specificity to various pathogens.

A functional annotation of single cell heterogeneity shows that Th17 cells span a spectrum of states in vivo. To study the main sources of cellular variation in vivo and their functional ramifications, a principal component analysis (PCA, FIG. 2A) was used followed by a novel analysis for functional annotation of the PC space based on the single cell expression of gene signatures of previously characterized T cell states (FIG. 2B). Specifically, drawing from previous studies feature-specific gene signatures were assembled for various T-cell types and perturbation states, each consisting of a set of ‘plus’ and ‘minus’ genes that are highly and lowly expressed in each signature, respectively (FIG. 2B). For every cell-signature pair, a score reflecting the difference in the average expression of ‘plus’ is. ‘minus’ genes in that cell was computed, and then estimated whether each signature score significantly varied: either (1) across cells of the same source (either LN or CNS; using a one vs. all Gene Set Enrichment Analysis (GSEA); FDR<0.05 in at least 10% of cells); or (2) between the LN and the CNS cells (KS-test, FDR<104). For the signatures with significant variation in at least one test, the correlations of the respective single cell signature scores with the projection of cells to each of the first two principal components (PCs; FIG. 2B and Table S2 (Gaublomme 2015)) were computed, and selected correlations were plotted on a normalized PCA map (FIG. 2A, numbered open circles). To identify transcription factors that may orchestrate this heterogeneity, the single-cell RNA-seq data were combined with transcription factor target enrichment analysis (Yosef et al., 2013) to find factors whose targets are strongly enriched (Fisher exact test, p<10−5) in genes that correlated with each PC (Pearson correlation, FDR<0.05; FIG. 2E, F, Table S3 (Gaublomme 2015)).

Based on the functional annotation, the first PC (PC1) positively correlates with a recently defined effector vs. memory signature following viral infection (Crawford et al., 2014), and negatively correlates with an independent molecular signature characterizing memory T cells (Wherry et al., 2007) (FIG. 2A, number 4 and 7, respectively; Table S2 (Gaublomme 2015)). This suggests that cells with high positive PC1 scores adopt an effector phenotype, and those with negative PC1 scores obtain a memory profile, and at the extreme—a dysfunctional/senescent profile. The second PC (PC2) separates cells by their source of origin (CNS and LN, FIG. 2A) and correlates with a transition from a naïve-like self-renewal state (negatively correlated with PC2; p<10−33, FIG. 2A, number 5: Table S2 (Gaublomme 2015)) with low cell cycle activity (negatively correlated with PC2, FDR<5%) to a Th1-like effector or memory effector state (positively correlated with PC2, FIG. 2, number 2 and 3, p<10−19 and p<10−23, respectively). Consistently, an MsigDB analysis of genes that highly correlate with the PCs (Pearson correlation, FDR<5%) shows strong association with immune response (PC1; p<1.2×10−27 and PC2; p<1.2×10−28, hypergeometric test) and cell cycle stage (PC1; p<10−30).

A trajectory of progressing cell states from the LN to the CNS. To further explore the diversity of LN and CNS cells, five of the key signatures discovered by functional annotation were used to divide the PCA space into distinct subsets of cells (FIG. 2C, Table S2 (Gaublomme 2015)). To this end, a Voronoi diagram was computed that delineates regions that are most strongly associated with each of the five signatures. The resulting putative subpopulations exhibit a gradual progression from a self-renewing state to a pre-Th1 effector phenotype in the LN and CNS, to a Th1-like effector state and a Th1-like memory state in the CNS, and finally a dysfunctional/senescent state in the CNS, as detailed below.

First, self-renewing Th17 cells in the LN (FIG. 2C, green) are characterized by: (1) a signature of Wnt signaling (p<10−7, KS, FIG. 2A, number 6, Table S4 (Gaublomme 2015)), Table 6, a known feature critical for self-renewal of hematopoietic stem cells and survival of thymocytes (Ioannidis et al., 2001; Reya et al., 2003), and supported by high expression of Tcf7 (p<10−2, FIG. 2D, Table S4 (Gaublomme 2015)) Table 6, a key target of the Wnt pathway. Tcf7 is a key transcription factor regulating the stem cell-like state of Th17 cells (Muranski et al., 2011), whose expression is lost when T-cells acquire an effector phenotype (Gattinoni et al., 2009; Willinger et al., 2006); (2) high expression (p<10−10, KS-test, see Table S4 (Gaublomme 2015), Table 6) of the known naïve state marker Cd62l (De Rosa et al., 2001) (FIG. 2D); and (3) up-regulation (p<10−9) of Cd27, a pro-survival gene lacking in short-lived T cells (Dolfi et al., 2008; Hendriks et al., 2000: Hendriks et al., 2003: Snyder et al., 2008) (FIG. 2D). Transcription factors analysis (negative PC2, FIG. 2E, green) suggests that Etv6, Med12 and Zfx specifically drive this self-renewing population. While neither of them has been linked to Th17 self-renewal, each is associated with such functions in other cells: Med12 is essential for Wnt signaling and early mouse development (Rocha et al., 2010); Etv6, a known positive regulator of Th17 cell differentiation (Ciofani et al., 2012; Yosef et al., 2013), functions as an essential regulator of hematopoietic stem cell survival (Hock et al., 2004) and an initiator of self-renewal in pro-B cells (Tsuzuki and Seto, 2013); and Zfx is required for self renewal in embryonic and hematopoietic stem cells (Galan-Caridad et al., 2007; Harel et al., 2012), and of the tumorigenic, non-differentiated state in glioblastoma stem cells (Fang et al., 2014) and acute T-lymphoblastic and myeloid leukemia (Weisberg et al., 2014).

Second, cells from the LN and CNS adopt similar (overlapping) cell states only in the central state of PCA plot (FIG. 2C, pink), reflecting effector Th17 cells with a pre-Th1 phenotype. Compared to the self-renewing subpopulation, these effector Th17 cells (1) begin to express receptors for IFN (IFNAR-1, p<10−3, KS, Table S4 (Gaublomme 2015), Table 6) and IL-18 (IL-18R1, p<10−3, FIG. 2D), both of which mediate differentiation of Th1 cells (Esfandiari et al., 2001; Shinohara et al., 2008); and (2) induce the Th1 associated chemokine receptor Cxcr6 (p<10−3, KS, FIG. 2D) (Aust et al., 2005; Latta et al., 2007), and Ccr2 (p≤10−6, KS, FIG. 2D), associated with recruitment to the CNS in EAE/MS (Mahad and Ransohoff, 2003). Since these cells begin to express receptors that make them responsive to both IFN-γ and IL-18 and poised for recruitment to the CNS, they may therefore be the precursors that lead to the generation of Th17/Th1-like effector T cells observed in the CNS.

IL-17a/GFP+ sorted cells acquire a Th17/Th1-like effector phenotype in the CNS (FIG. 2C, yellow), as indicated by up-regulation (p<10−3, KS, Table S4 (Gaublomme 2015), Table 6) of: (1) Ifn-γ, consistent with a Th1 phenotype (FIG. 2D); (2) Rankl (FIG. 2D), a marker of Th1 and IL-23 induced Th17 cells (Nakae et al., 2007), especially pathogenic Th17 cells in arthritis (Komatsu et al., 2014); and (3) cell cycle genes (e.g., Geminin (Codarri et al., 2011), FIG. 2D). Surprisingly, the Th1-like cells in the CNS (except dysfunctional/senescent state; FIG. 2C,D grey) also induce Ccr8 (FIG. 7A, bottom), previously described as a cell marker of Th2 cells (Zingoni et al., 1998), but not of Th17/Th1 cells (Annunziato et al., 2007). Mice deficient for Ccr8 exhibit later onset and milder signs of EAE (Ghosh et al., 2006; Hamann et al., 2008). Transcription factor analysis shows that these effector cells are associated with both canonical Th17 factors (Stat3, Irf4 and Hif1a) and Th1-associated factors, including Rel and Stat4 (Kaplan et al., 1996; Nishikomori et al., 2002; Thierfelder et al., 1996) (FIG. 2E, red), which are associated with EAE (Hilliard et al., 2002; Mo et al., 2008) or with autoimmune disease in humans (Gilmore and Gerondakis, 2011). These sorted IL-17A/GFP+ cells could either be a stable population of double producers or reflect Th17 plasticity into the Th1 lineage, as Th17 cells transition into a Th1 state.

Next, Th1-like memory cells detected in the CNS (FIG. 2C, light blue) correlate highly with both a memory phenotype (negative PC1) and a Th1-like phenotype (positive PC2). These cells are associated with an effector memory signature (p<10−5, KS-test compared with all other sub-populations, see Table S4 (Gaublomme 2015), Table 6), and up-regulate (p<10−5, KS) memory signature genes (e.g., Nur77; FIG. 2D, Samsn1, Il2ra, Il2rb, Tigit, Ifingr1 and 2), and inflammatory genes (Gm-csf and Gpr65; FIG. 2D). Il-Ir2 is a decoy receptor in the IL-1 pathway involved in Th17 pathogenicity (Sutton et al., 2006), the cytokine Gm-csf (FIG. 2D) is essential for Th17 encephalitogenicity (El-Behi et al., 2011) and neuroinflammation (Codarri et al., 2011). Nur77 (Nr4a1) (FIG. 2D), a transcriptional repressor of IL-2 (Harant and Lindley, 2004), is strongly up-regulated, to maintain cells in a Th17 state despite acquiring a Th1 factor (Sester et al., 2008). Note that while IL-2 is a growth factor for Th1 cells, IL-2 affects Th17 differentiation and stability. Transcription factor analysis (FIG. 2F) suggests that this cell state is in part driven by Egr1, a regulator of Tbet expression (Shin et al., 2009) that may help route Th1-like cells into the memory pool; Bcl6, a repressor of lymphocyte differentiation, inflammation, and cell cycle genes, essential for CD4 T-cell memory generation (Ichii et al., 2007); and Hif1a, crucial for controlling human Th17 cells to become long-lived effector memory cells (Kryczek et al., 2011) and particularly associated with cells that correlate highly with the memory and Th1 signatures (negative PC1, positive PC2).

Finally, Th17 cells acquire a dysfunctional, senescent-like state in the CNS (negative PC1 and PC2 scores; FIG. 2C, moss grey), with (1) down-regulation (p<10−3) of genes critical to T-cell activation, including Cd3 (FIG. 2D) (Chai and Lechler, 1997; Lamb et al., 1987; Trimble et al., 2000), Cd28 (Trimble et al., 2000; Wells et al., 2001), Lat (FIG. 2D) (Hundt et al., 2006), Lck (Isakov and Biesinger, 2000; Nika et al., 2010), and Cd2 (Bachmann et al., 1999; Lamb et al., 1987) (Table S4 (Gaublomme 2015), Table 6); (2) up-regulation of genes associated with senescence, such as Ccrl2 (up regulated in exhausted CD8+ T-cells (Wherry et al., 2007)), Marcks (FIG. 2D) (inducer of senescence (Jarboe et al., 2012)), and Cd74 (a receptor to Mif in the Hif-Mif senescence pathway (Maity and Koumenis, 2006; Salminen and Kaarniranta, 2011; Welford et al., 2006)); and (3) association with signatures for CD28 costimulation (p<10−11, GSEA, Table S2 (Gaublomme 2015)) and PD-1 signaling (p<10−10, GSEA, Table S2 (Gaublomme 2015)). Among the possible regulators of this cell state is mir-144, an inhibitor of TNF-α and IFN-γ production and of T-cell proliferation (Liu et al., 2011), whose targets are enriched (p<104, hypergeometric test) in these cells.

In vitro derived cells span a broad spectrum of pathogenicity states with key similarities and distinctions from in vivo isolated cells. The analysis of in vivo Th17 cells harvested from mice undergoing EAE identified a progressive trajectory of at least five states, from self-renewing cells in the LN, through effector LN cells, effector Th1-like CNS cells, memory cells, and senescent ones. Given the limited number of cells available from in vivo samples, obtained as a mixed “snapshot” of an asynchronous process, it is difficult to determine their distinct pathogenic potential and underlying regulatory mechanisms. A complementary strategy is offered by profiling in vitro differentiated cells, where one can assess the heterogeneity of Th17 cells at the same condition (time point and cytokine stimulation). Furthermore, comparing in vivo and in vitro profiles can help uncover to what extent the in vitro differentiation conditions faithfully mirror in vivo states.

Single-cell RNA-seq profiles of 414 individual Th17 cells derived under non-pathogenic conditions (TGF-β1+IL-6, unsorted: 136 cells from 2 biological replicates, TGF-β1+IL-6, sorted for IL-17A/GFP+: 159 cells from 3 biological replicates) and pathogenic conditions (Il-1β+IL-6+IL-23, sorted for IL-17a/GFP+: 147 cells from 2 biological replicates) (FIG. 3A) were then analyzed.

Using the functional annotation approach (FIG. 2B) to annotate the cells with immune cell signatures, it was found that in vitro differentiated Th17 cells vary strongly in a key signature of pathogenicity and tolerance (Lee et al., 2012), reflecting the conditions in which they were derived (FIG. 3A, number 1, and 3D). High pathogenicity scores were associated with IL-17A/GFP+ sorted cells polarized under a pathogenic condition (FIG. 3A,D red, number 1, PC1), whereas IL-17A/GFP+ sorted cells from non-pathogenic conditions correlate highly with the expression of regulatory cytokines, such as IL-10, and their targets, which are barely detected in the pathogenic cells (FIG. 3E). Finally, a signature obtained from the T-cells harvested from IL23R knockout mice and differentiated under the IL-1β+IL-6+IL23 condition correlates highly with the cells that adopt a more regulatory profile, further confirming a crucial role of the IL-23 pathway in inducing a pathogenic phenotype in Th17 cells (FIG. 3A, number 4, positive PC1).

Importantly, there is a clear zone of overlap in cell states between the pathogenic and non-pathogenic conditions, with pathogenic-like cells present (in a small proportion) in populations differentiated in non-pathogenic conditions (FIG. 3A, red oval shading). In particular, cells polarized under the non-pathogenic (TGF-β1+IL-6) condition that were not specifically sorted to be IL-17A/GFP+ span the broadest pathogenicity spectrum: from cells resembling the least pathogenic cells in the IL-17A/GFP+ TGF-β1+LL-6 condition to those resembling more pathogenic cells in the IL-17A/GFP+IL-1β+IL-6+IL23 condition (FIG. 3D, open black circles). At one end of this spectrum Th17 cells were observed with high expression of regulatory transcripts such as IL-9, IL-16, Foxp1 and Podoplanin Peters et al. 2014) (FIG. 7B, left), and at the other end, Th17 cells were observed that express high levels of pro-inflammatory transcripts such as IL-22, IL23r, Cxcr3 and Gm-csf (FIG. 7B, right).

To relate the in vitro differentiated cells to the in vivo observed behavior the in vitro cells (FIG. 2B) were scored for immune related genes that characterize the in vivo identified subpopulations (FIG. 2C) (FIG. 3B,C). Cells derived in the non-pathogenic conditions scored more highly for the self-renewing signature (p<1e-9 KS test; Table S2 (Gaublomme 2015) and FIG. 3A, number 6, and 3C), whereas those derived in pathogenic conditions resembled more the Th-17/Th-1 like memory phenotype identified in the CNS (p<1e-7 KS test; Table S2 (Gaublomme 2015) and FIG. 3B).

(Co-variation with pro-inflammatory and regulatory modules in Th17 cells highlights novel candidate regulators. The cellular heterogeneity within a single population of in vitro differentiated cells was then leveraged to identify regulators that might selectively influence pathogenic vs. nonpathogenic states of Th17 cells. Focusing on the (unsorted) cells from the TGF-β1+IL-6 in vitro differentiation condition, in which the broadest spectrum of cells spanning from pathogenic to nonpathogenic-like profiles was observed, first transcriptome-wide gene expression distributions across the population were analyzed. About 35% (2,252) of the detected genes are expressed in >90% of the cells (FIG. 4A) with a unimodal distribution: these include housekeeping genes (p<10−10, hypergeometric test, FIGS. 6F & 6G), the Th17 signature cytokine IL-17f and transcription factors (e.g., Batf Stat3 and Hif1a) that are essential for Th17 differentiation. On the other hand, bimodally expressed genes (FIG. 4A, bottom)—with high expression in at least 20% of the cells and much lower (often undetectable) levels in the rest—include cytokines like Il-17a and Il-10 and other pro-inflammatory (e.g., Il-21, Ccl20) and regulatory cytokines or their receptors (Il-24, Il-27ra, FIG. 4A). This suggests that variation in expression across Th17 cells may be related more to their (varying) pathogenicity state than to their (more uniform) differentiation state. Furthermore, while almost all cells express transcripts encoding the pioneer and master transcription factors for the Th17 lineage (Rorc, Irf4, Bat), a minority (<30%) also express transcripts encoding one or more of the transcription factors and cytokines that characterize other T-cell lineages (e.g., Stat4 for Th1 cells, and Ccr4 for Th2 cells). This may suggest the presence of “hybrid” double-positive cells, consistent with reports on plasticity in T-cell differentiation (Antebi et al., 2013), and/or reflect the previous model of duality in the Th17 transcriptional network (Yosef et al., 2013). Finally, the expression of many key immune genes varies more than the rest of the genome, even with the same mean expression level (FIG. 6H), or when only considering the expressing cells (FIG. 6I), implying a greater degree of diversity in immune gene regulation. While such patterns may be biologically important, they must be interpreted with caution. First, some (e.g., Il-17a, Il-24 and Ccl20), but not all (e.g., Il-9), of the transcripts with bi-modal patterns are also lowly expressed (on average) and thus may not be detected as reliably (Shalek et al., 2014). Second, transcription bursts coupled with instability of transcripts may lead to ‘random’ fluctuations in gene expression levels at any given cell.

To overcome these challenges and to identify candidate regulators of pathogenicity, co-variation between transcripts across cells (FIG. 4B) was analyzed. It was reasoned that if transcript variation reflects distinct physiological cell states, entire gene modules should robustly co-vary across the cells. Furthermore, transcription factors and signaling molecules that are members of such modules may highlight new putative regulators of these modules and functional states. Focusing on significant co-variation (Spearman correlation; FDR<0.05) between each bimodally expressed transcript (expressed by less than 90% of the cells; FIG. 4B, rows) and a curated set of bimodally expressed immune response genes (cytokines, cytokine receptors, T helper cell specific signatures, FIG. 4B, columns), two key transcript modules were found: a pro-inflammatory module (FIG. 4B, orange) of transcripts that co-vary with known Th17 cytokines, such as Il-17a and Ccl-20, and a regulatory module (FIG. 4B, green) of transcripts that co-vary with known regulatory genes, such as Il-10, Il-24, and Il-9. Using these modules as signatures to annotate the original in vitro cell states (FIGS. 3A and 4C), the pro-inflammatory module (FIG. 4C, number 1) and key inflammatory genes (FIG. 4D, bottom) are correlated with the most pathogenic cells (PC1, negative correlation) and the regulatory module (FIG. 4C), and key members (FIG. 4D top), are correlated with the least pathogenic (PC1, positive correlation).

Co-variation of genes with each module highlights many novel putative regulators, many not detected by previous, population-level, approaches (Ciofani et al., 2012; Yosef et al., 2013). To select the most compelling candidate genes in the two modules (FIG. 4b, rows) for follow-up functional studies, a computational ranking scheme was developed that considers each gene's correlation with the pro-inflammatory or regulatory modules, their loading on the first in vitro PC marking for pathogenic potential, and their role in the EAE context in vivo (FIG. 4E, Table 2 herein). While the genes from our co-variation matrix (rows, FIG. 4B) tend to be highly ranked compared to all genes also in bulk-population data (p<10−10, Wilcoxon Rank Sum test) or rankings (Ciofani et al., 2012), they do not necessarily stand out in bulk population rankings (FIG. 15), highlighting the distinct signal from single-cell profiles. Based on this ranking and availability of knockout mice, three genes were chosen for functional follow up: Plzp, Cd5l and Gpr65 that are co-expressed with the pro-inflammatory module, and Toso, co-expressed with the regulatory module. None of these genes was previously implicated in differentiation or effector function of Th17 cells.

GPR65 promotes Th17 cell pathogenicity and is essential for EAE. GPR65, a glycosphingolipid receptor, is co-expressed with the pro-inflammatory module (FIG. 4B), suggesting that it might have a role in promoting pathogenicity. GPR65 is also highly expressed in the in vivo Th17 cells harvested from the CNS that attain a Th1-like effector/memory phenotype (FIG. 2D). Importantly, genetic variants in the GPR65 locus are associated with multiple sclerosis (International Multiple Sclerosis Genetics et al., 2011), ankylosing spondylitis (International Genetics of Ankylosing Spondylitis et al., 2013), inflammatory bowel disease (Jostins et al., 2012), and Crohn's disease (Franke et al., 2010).

The role of GPR65 was tested in Th17 differentiation in vitro and in the development of autoimmunity in vivo. Naïve T-cells isolated from Gpr65−/− mice in vitro were differentiated with TGF-β1+IL-6 (non-pathogenic condition) or with IL-10+IL-6+IL-23 (pathogenic condition) for 96 hours. In both cases, there was a ˜40/a reduction of IL-17a positive cells in Gpr65−/− cells compared to their wild type (WT) controls as measured by intracellular cytokine staining (ICC) (FIG. 5A). Memory cells from Gpr65−/− mice that were reactivated with IL-23 also showed a ˜45% reduction in IL-17a-positive cells when compared to wild type controls (FIG. S3A). Consistently, an enzyme-linked immunosorbent assay (ELISA) of the supernatant obtained from the activated Th17 culture showed a reduced secretion of IL-17a (p<0.01) and IL-17f (p<10−4) (FIG. 5B) and increased IL-10 secretion (p<0.01, FIG. S3A) under pathogenic (IL-1β+IL-6+L-23) Th17 differentiation conditions in the knockout mice.

To further validate the effect of GPR65 on Th17 function, RNA-seq profiles were measured of a bulk population of Gpr65−/− Th17 cells, differentiated in vitro under both non-pathogenic (TGF-β1+IL-6) and pathogenic (IL-1β+IL-6+IL-23) conditions for 96 hours. Supporting a role for GPR65 as a driver of pathogenicity of Th17 cells, it was found that genes up-regulated in Gpr65−/− cells (compared to WT) are most strongly enriched (P<10−28, hypergeometric test, FIG. 5E) for the genes characterizing the more regulatory cells under TGF-β1+IL-6 (positive PC1, FIG. 4C, Table S6 (Gaublomme 2015), Table 7).

To determine the effect of loss of GPR65 on tissue inflammation and autoimmune disease in vivo, RAG-1−/− mice were reconstituted with naïve CD4+ T-cells from wild type or Gpr65−/−, then induced EAE with myelin oligodendrocytes glycoprotein peptide emulsified with complete Freund's adjuvant (MOG35-55/CFA). It was found that in the absence of GPR65-expressing T cells, mice are protected from EAE (FIG. 5D) and far fewer IL-17A and IFN-γ positive cells are recovered from the LN and spleen compared to wild-type controls transferred with wild-type cells (FIG. S3B). Furthermore, in vitro restimulation with MOG35-55 of the spleen and LN cells from the immunized mice showed that loss of GPR65 resulted in dramatic reduction of MOG-specific IL-17A or IFN-7 positive cells compared to their wild-type controls (FIG. 5C), suggesting that GPR65 regulates the generation of encephalitogenic T cells in vivo. Taken together, the data strongly validates that GPR65 is a positive regulator of the pathogenic Th17 phenotype, and its loss results in protection from EAE.

TOSO is implicated in Th17-mediated induction of EAE TOSO (FAIM3) is an immune cell specific surface molecule, is known to negatively regulate Fas-mediated apoptosis (Hitoshi et al., 1998; Nguyen et al., 2011; Song and Jacob, 2005), and is co-expressed with the regulatory module in Th17 cells. Although its covariance with the regulatory module (FIG. 4B) may naïvely suggest that it positively regulates the regulatory module. Toso knockout mice were recently reported to be resistant to EAE (Lang et al., 2013). This may be consistent with a hypothesis that Toso is a negative regulatore of the non-pathogenic state, co-expressed with the regulatory module, as has been often observed for negative regulators and their targets in other systems (Amit et al., 2007; Segal et al., 2003) To test this hypothesis, in vitro differentiation and MOG recall assays on TOSO−/− cells were performed. Differentiation of TOSO−/− cells showed a defect in the production of pro-inflammatory cytokine IL-17A for both differentiation conditions (FIG. 5F), which was confirmed by ELISA (FIG. 5G). Moreover, memory cells stimulated with IL-23 show a lack of IL-17A production (FIG. S4A). Consistently, in a MOG recall assay, CD3+CD4+ Toso−/− T cells showed no production of IL-17a across a range of MOG35-55 concentrations (FIG. 5H). This supports a role for TOSO as a promoter of pathogenicity.

To further explore this, RNA-seq analysis of Toso−/− Th17 cell populations, differentiated in vitro under non-pathogenic conditions for 96 hours was performed. Loss of TOSO results in suppression of the key regulatory genes (e.g., IL-24 (FC=0.08), IL-9 (FC=0.33) and Procr (FC=0.41)(Table S6 (Gaublomme 2015), Table 7), consistent with the reduction of IL-10 production as measured by ELISA (FIG. S4C), and a reduced number of FOXP3+ cells under Treg differentiation conditions (FIG. S4B). On the other hand, in pathogenic conditions, IL-17a (FC=0.21) is down regulated in the absence of TOSO. Enrichment analysis with respect to PC1 of the non-pathogenic differentiation condition suggests that TOSO knockout cells, rather than up-regulating regulatory genes, down-regulate genes associated with a more pro-inflammatory cell phenotype (FIG. 5E). Taken together, the data suggest that TOSO plays a critical role as a positive regulator of Th17-cell mediated pathogenicity.

MOG-stimulated Plzp−/− cells have a defect in generating pathogenic Th17 cells. PLZP (ROG), a transcription factor, is a known repressor of (the Th2 master regulator) GATA3 (Miaw et al., 2000), and regulates cytokine expression (Miaw et al., 2000) in T-helper cells. Since Plzp is co-expressed with the pro-inflammatory module, it was hypothesized that it may regulate pathogenicity in Th17 cells.

While in vitro differentiated Plzp−/− cells produced IL-17A at comparable levels to wild-type (FIG. S5A), a MOG-driven recall assay revealed that Plzp−/− cells do have a defect in IL-17A production that becomes apparent with increasing MOG concentration during restimulation (FIG. 5I). Furthermore, Plzp−/− cells also produced less IL-17A than wild-type cells when reactivated in the presence of IL-23, which acts to expand previously in vivo generated Th17 cells (FIG. S5B). Finally, Plzp−/− T cells secreted less IL-17A, IL-17F (FIG. 5J), IFN-γ, IL-13 and GM-CSF (FIG. S5C). These observations suggest that PLZP regulates the expression of a wider range of inflammatory cytokines. Based on RNA-Seq profiles, at 48 hours into the non-pathogenic differentiation of Plzp−/− cells, Irf1 (FC=5.2), Il-9 (FC=1.8) and other transcripts of the regulatory module are up regulated compared to WT (Table S6 (Gaublomme 2015), Table 7), whereas transcripts from the pro-inflammatory module, such as Ccl-20 (FC=0.38), if (FC=0.10) and Il-17a (FC=0.42), are repressed. A similar pattern is observed with respect to PC1, where genes characterizing the more pro-inflammatory cells are strongly enriched among the down-regulated genes in Plzp−/− T cells (FIG. 5E).

DISCUSSION: Genome-wide analysis of single-cell RNA expression profiles opens up a new vista for characterizing cellular heterogeneity in ensembles of cells, previously studied as a population. By profiling individual Th17 cells from the LN and CNS at the peak of EAE, it was found that Th17 cells adopt a spectrum of cellular states, ranging from cells with a self-renewing gene signature, to pro-inflammatory Th1-like effector or memory-like cells, to a dysfunctional/senescent phenotype. These findings shed light on the controversy in the field on whether Th17 cells are short-lived, terminally differentiated, effector cells (Pepper et al., 2010) or long-lived self-renewing T cells (Muranski et al., 2011). The analysis also shows that Th17 cells present in the lymph node and CNS generally appear to have different transcriptional profiles and that the only group of Th17 cells that transcriptionally overlap are those that attain a pre-Th1-like state with acquisition of cytokine receptors (like IL-18R) that push Th17 cells into a Th1 phenotype. This fits well with the data that most Th17 cells begin to co-express Th1 genes in the CNS and become highly pathogenic.

The Th1-like phenotype of Th17 cells observed in the CNS might facilitate memory cell formation, as the entry of Th1 cells into the memory pool is well established (Harrington et al., 2008; Sallusto et al., 1999). It is unclear if cells that adopt a Th1 phenotype are stable ‘double producers’ or if they show plasticity towards a Th1 fate. IL-23, which induces a pathogenic phenotype in Th17 cells has been shown to induce IFN-g in Th17 cells. Consistent with this data, IL-23R-deficient mice have lower frequencies of double producers (McGeachy et al., 2009) and chronic exposure of Th17 cells to IL-23 induces IFN-g production from Th17 cells. Additionally, a conversion from a Th17 to a Th1-like phenotype is also documented in other disease models and these are considered to be the most pathogenic T cells (Bending et al., 2009; Lee et al., 2009; Muranski et al., 2011; Palmer and Weaver, 2010; Wei et al., 2009b).

Despite being differentiated under the same culture conditions, in vitro differentiated Th17 cells also exhibit great cellular diversity, with a pathogenic, pro-inflammatory state on the one end of the spectrum and an immunosuppressive, regulatory state on the other end. A comparative analysis of in vivo and in vitro derived cells with respect to immune-related genes reveals that in vitro polarization towards a pathogenic Th17 phenotype (with IL-1β+IL-6+IL-23) produces cells that resemble more the Th17/Th1 memory cells in the CNS found during EAE (FIG. 3A).

Single cell RNA-seq further showed that pro-inflammatory genes that render Th17 cells pathogenic and regulatory genes that render Th17 cell nonpathogenic are expressed as modules in groups of Th17 cells. This allowed for dissection of factors that relate to this specific facet of Th17 cell functionality, rather than their general differentiation. Strong correlation (either positive or negative) between two genes suggests that their biological function may be linked. In this study, strong co-variation with key Th17 genes allowed us to recover many known regulators, but also to identify many promising novel candidates that were coexpressed with either a proinflammatory or a regulatory module in Th17 cells. For example, Gpr65 positively correlated with the in vitro derived pro-inflammatory gene module. Consistently, Gpr65−/− CD4 T cells reconstituted to Rag1 mice were incapable of inducing EAE and had compromised IL-17A production. There are many genes similarly highlighted by this analysis, including Gem, Cst7, and Rgs2, all of which significantly correlate with the in vitro derived pro-inflammatory gene module and are highly expressed in the in vivo Th17/Th1-like memory subpopulation the are present in the CNS during peak inflammation. Foxp1, on the other hand, one of the genes negatively correlated with the pro-inflammatory module, was lowly expressed in the inflammatory Th17/Th1-like subpopulations in vivo, but was highly expressed in the LN-derived Th17 self-renewing subpopulation (p<10−7, KS test; Table S4 (Gaublomme 2015), Table 6). In line with this finding, in T follicular helper cells, Foxp1 has very recently been shown to directly and negatively regulate IL-21 (Wang et al., 2014), a driver of Th17 generation (Korn et al., 2007; Nurieva et al., 2007; Zhou et al., 2007), and to dampen the expression of the co-stimulatory molecule ICOS and its downstream signaling at the early stages of T-cell activation (Wang et al., 2014). Further functional studies with Foxp1 knockout mice in the context of EAE could elucidate its potential role in regulating Th17 cell differentiation and development of autoimmune tissue inflammation.

Importantly, it should be noted that the co-variation of a gene with the pro-inflammatory or regulatory module does not necessarily indicate a pro-inflammatory or regulatory function to this gene. For example, one of the follow-up genes, Toso, co-varies with the regulatory module, but its absence protects mice from EAE (Brenner et al., 2014) and compromises IL-17A production, suggesting Toso does not serve as a regulatory factor. This is consistent with previous studies—from yeast (Segal et al 2003) to human (Amit et al 2007), showing how regulators with opposite, antagonistic functions, are co-regulated.

Examining the single-cell RNA-seq data together with ChIP data reveals transcription factors that regulate various cellular states observed in the study. For example, Zfx was identified as a strong candidate regulator of the self-renewing state of Th17 cells in the LN, because its targets are strongly enriched in this subpopulation, it is a known regulator of self-renewal in stem cells (Cellot and Sauvageau, 2007; Galan-Caridad et al., 2007; Harel et al., 2012), and it prevents differentiation in leukemias (Weisberg et al., 2014). In contrast, for the pathogenic effector and memory cells observed in the CNS during EAE, a prominent role is assigned to known Th17/Th1 transcription factors such as Hif1a, Fosl2, Stat14 and Rel, and it is specified in which subpopulations their regulatory mechanisms contribute to disease. As such, this study elaborates on Th17 pathogenicity beyond differentiation and development. This data suggests that processes such as self-renewal, observed in the lymph node, may provide a pool of cells that are precursors for differentiating Th17 cells to effector/memory formation in the CNS that may contribute to Th17 pathogenicity in EAE. These cellular functional states enable us to map the contribution of novel and known genes to each of these processes during Th17 differentiation and function. Whereas population-based expression profiling has enabled identification of cytokines and transcription factors that set the differentiation states of Th17 cells, using single cell RNA-seq new granularity is provided in the transcriptome of a rather hom*ogenous population of T cells. Many of the novel regulators that identified by single cell RNA-seq are regulating pathogenic vs. nonpathogenic functional states in Th17 cells. These novel regulators will allow the manipulation of pathogenic Th17 cells without affecting nonpathogenic Th17 cells that may be critical for tissue homeostasis and for maintaining barrier functions.

Single-cell RNA-seq identifies CD5L as a candidate regulator of pathogenicity. Cd5l is one of the high-ranking genes by single-cell analysis of potential regulators, exhibiting two surprising features: although Cd5l is expressed in Th17 cells derived under non-pathogenic conditions (FIG. 16A), in these non-pathogenic cells, Cd5l positively correlates with the first PC of in-vitro derived cells and co-varies with other genes in the pro-inflammatory module (FIG. 19A, B, C). In addition, Cd5l positively correlates with the cell pathogenicity score (FIG. 16B, C). Comparing Cd5l expression at the single-cell level in Th17 cells (sorted IL-17.GFP+) derived in vitro showed ˜80% of Th17 cells derived with IL-1β+IL-6+IL-23 lacked Cd5l expression, whereas Th17 cells differentiated with TGF-β1+IL-6 predominantly expressed Cd5l (FIG. 16A). Neither Th17 cells differentiated under an alternative pathogenic condition (TGF-β3+IL-6) nor encephalitogenic Th17 cells sorted from the CNS of mice undergoing active EAE expressed Cd5l at the single-cell level (FIG. 16A). However, Cd5l expressed in nonpathogenic Th17 cells (unsorted single-cell analysis, FIG. 19A) correlates with the first PC and co-varies with the pro-inflammatory module (FIG. 19B) that is indicative of the pathogenic signature (FIG. 19C) as previously defined (Lee et al., 2012). Furthermore, Cd5l correlates with the defining signature of the pro-inflammatory module, and negatively correlates with that of the regulatory module (FIG. 16C). Finally, it is among the top 8 genes in the single cell based pro-inflammatory module whose expression most strongly correlates with the previously defined pathogenic gene signature (FIG. 16B, p=2.63 10{circumflex over ( )}-5). CD5L is a member of the scavenger receptor cysteine rich superfamily (Sarrias et al., 2004). It is expressed in macrophages and can bind cytosolic fatty acid synthase in adipocytes following endocytosis (Miyazaki et al., 1999). CD5L is also a receptor for pathogen associated molecular patterns (PAMPs), and may regulate innate immune responses (Martinez et al., 2014). However, its expression has not been reported in T cells, and its role in T-cell function has not been identified.

CD5L expression is associated with non-pathogenic Th17 cells in vitro and in vivo. Applicants determined that the preferential expression of CD5L in non-pathogenic Th17 cells, but in association with the pro-inflammatory module, may reflect a unique role for CD5L in regulating the transition between a non-pathogenic and pathogenic state. While co-expression with the proinflammatory module (FIG. 16C) and correlation with a pathogenicity signature (FIG. 16B) per se could suggest a function as a positive regulator of pathogenicity, the apparent absence of CD5L from Th17 cells differentiated in vitro under the pathogenic conditions or isolated from lesions in the CNS (FIG. 16A) suggested a more nuanced role. Applicants hypothesized that CD5L is a negative regulator of pathogenicity, explaining its absence from truly pathogenic cells. In fact, mRNAs encoding negative regulators of cell states are often positively co-regulated with the modules they suppress in eukaryotes from yeast (Pe'er et al., 2002; Segal et al., 2003) to human (Amit et al., 2007).

Applicants first validated and extended the initial finding that CD5L is uniquely expressed in nonpathogenic Th17 cells by analyzing naïve CD4 T cells cultured under various differentiation conditions using qPCR and flow cytometry (FIG. 16D, E, F). At the mRNA level, Applicants found little Cd5l expression in Th0, Th1 or Th2 helper T cells, high expression in Th17 cells differentiated with TGF-β1+IL-6, but low expression in Th17 cells differentiated with IL-1β+IL-6+IL-23 or in iTregs (FIG. 16D). Protein measurements confirmed the presence of CD5L in a large proportion of non-pathogenic Th17 cells (FIG. 16F).

Next, Applicants explored whether CD5L expression is associated with less pathogenic Th17 cells in vivo. Applicants analyzed Th17 cells isolated from mice induced with EAE. Th17 cells (CD3+CD4+IL-17.GFP+) sorted from the spleen expressed Cd5l but IL-17-T cells did not (FIG. 16G). In contrast, Cd5l was not expressed in Th17 cells from the CNS despite significant expression of Il17(FIG. 16H), consistent with the single-cell RNA-seq data (FIG. 16A). Next, Applicants analyzed Th17 cells from mesenteric lymph nodes (mLN) and lamina propria (LP) of naïve mice, where Th17 cells contribute to tissue homeostasis and mucosal barrier function. IL-17+ but not IL-17− T cells harvested from mLN and LP expressed high levels of Cd5l (FIG. 16I and data not shown). Thus, CD5L is a gene expressed in non-pathogenic but not pathogenic Th17 cells in vivo. Applicants asked if IL-23, known to make Th17 cells more pathogenic, can regulate Cd5l expression. Applicants hypothesized that if CD5L is a positive regulator of IL-23-dependent pathogenicity, its expression will be increased by IL-23, whereas if it is a negative regulator, its expression will be suppressed. As IL-23R is induced after T-cell activation, Applicants differentiated naïve T cells with TGF-β1+IL-6 for 48h and expanded them in IL-23 in fresh media. IL-23 suppressed Cd5l (FIG. 16E), consistent with these cells acquiring a pro-inflammatory module and becoming pathogenic Th17 cells, and with our hypothetical assignment of CD5L as a negative regulator of pathogenicity. CD5L expression can be promoted by STAT3 but not RORγt (FIG. 19D, E), as IL-23 can enhance STAT3 function further studies are required to elucidate the pathways involved in regulating CD5L expression.

CD5L represses effector functions without affecting Th17 differentiation. To analyze the functional role of CD5L in vivo, Applicants immunized mice with MOG35-55/CFA to induce EAE. CD5L−/− mice exhibited more severe clinical EAE that persisted for at least 28 days, whereas wildtype (WT) mice began recovering 12 days post immunization (FIG. 17A). Similar frequencies of FoxP3+CD4+ Treg cells were found in WT and CD5L−/− mice, suggesting that the increased severity of the disease was not due to changes in the number of Tregs in CD5L−/− mice (FIG. 12A). In contrast, more CD4 T cells produced IL-17 and fewer cells produced IFNγ in the CNS of CD5L−/− mice (FIGS. 17A, 12B). In response to MOG reactivation in vitro, cells from the draining lymph nodes of CD5L−/− mice showed higher proliferative responses and produced more IL-17 (FIG. 12C, 12D). These observations are consistent with either a direct or indirect role for CD5L in defining Th17 cell function. Applicants studied the impact of CD5L on Th17 cells differentiated from naïve WT and CD5L−/− T cells by analyzing signature gene expression. CD5L deficiency did not affect Th17 differentiation as measured by 11-17 expression (FIG. 17B, C), nor did it affect other Th17 signature genes including Il7f, Il21, Il23r, Rorc or Rorα (FIG. 17D). Of note, under the non-pathogenic differentiation condition, CD5L−/− Th17 cells made less IL-10 (FIG. 17C, D). These observations suggest that changes in differentiation alone cannot explain the increased susceptibility to EAE in CD5L−/− mice, but that CD5L may indeed affect the internal state of differentiated Th17 cells. Applicants determined if CD5L regulates effector/memory Th17 cells by differentiation of nonpathogenic Th17 cells from naïve cells. Upon restimulation, more CD5L−/− Th17 cells produced IL-17 and expressed IL-23R without affecting viability (FIG. 17E and data not shown), suggesting that CD5L deficiency leads to more stable expansion of Th17 cells. Consistently, CD5L−/− Th17 cells expressed more Il17 and Il23r, less Il10 and similar levels of Rorc or Rorα (FIG. 17F). Thus, CD5L does not regulate Th17 cell differentiation, but affects Th17 cell expansion and/or effector functions over time. Similarly, effector memory cells (CD4+CD62LCD44+) isolated ex vivo from CD5L−/− mice have higher frequencies of IL-17+ and lower frequencies of IL-10+ cells (FIGS. 17G, 12E), possibly reflecting the greater stability of Th17 cells that persist in the repertoire of CD5L−/− mice. To address if Th17 cells isolated in vivo also produced more IL-17 per-cell, Applicants sorted RORγt+(GFP+) effector/memory T cells from WT and CD5L−/− mice and found more IL-17+ and fewer IL-10+ cells in CD5L−/− cells, suggesting RORγt+ cells are better IL-17 producers in the absence of CD5L (FIGS. 17H, 12F).

CD5L is a major switch that regulates Th17 cells pathogenicity. To determine if loss of CD5L can convert non-pathogenic Th17 cells into disease-inducing Th17 cells, Applicants crossed CD5L−/− mice to 2D2 transgenic mice expressing a T-cell receptor specific for MOG35-55/IAb (Bettelli et al., 2003). Naïve CD5L−/− 2D2 T cells were differentiated with the nonpathogenic (TGF-β1+IL-6) Th17 condition and transferred into WT recipients. Applicants analyzed the phenotype of T cells from the CNS of mice undergoing EAE. The 2D2 CD5L−/− Th17 cells retained more IL-17+ and fewer IL-10+ cells (FIG. 20A). A considerable proportion of endogenous T cells produced IL-10 compared to transferred 2D2 T cells (FIG. 20A), suggesting that extracellular IL-10 is not sufficient to restrain the pathogenicity of CD5L−/− Th17 cells. WT 2D2 T cells also acquired IFNγ expression in vivo, whereas CD5L−/− 2D2 T cells produced little IFNγ, suggesting CD5L may also regulate Th17 cell stability. Consistently, naïve CD5L−/− 2D2 T cells transferred into WT hosts immunized with MOG35-55/CFA without inducing EAE made more IL-17 and little IL-10 in contrast to WT 2D2 T cells (FIG. 20B). As IL-23 suppresses CD5L (FIG. 16E) and CD5L restrains Th17 cell pathogenicity, Applicants reasoned that sustained CD5L expression should antagonize IL-23-driven pathogenicity. To test this hypothesis, Applicants generated a retroviral vector for ectopic expression of CD5L. Naïve 2D2 T cells were differentiated with IL-1β+IL-6+IL-23, transduced with CD5L, transferred into WT recipients, and followed for weight loss and the development of clinical EAE (Experimental Procedures). 2D2 T cells transduced with CD5L (CD5L-RV 2D2) had a small reduction in IL-17 and higher IL-10 levels (FIG. 20C). Ectopic expression of CD5L in pathogenic Th17 cells reduced their pathogenicity as CD5L-RV 2D2 recipients had reduced weight loss and a significant decrease in the incidence and peak severity of EAE (FIG. 20D, E). Furthermore, CD5L-RV 2D2 Th17 cells transferred in vivo lost IL-17 production and began producing IFNγ (FIG. 20F). Therefore, sustained expression of Cd5l in pathogenic Th17 cells converts them to a less pathogenic and less stable phenotype in that these cells lose the expression of IL-17 and acquire an IFNγ-producing phenotype in vivo. This observation, combined with the observation that the loss of CD5L converts non-pathogenic Th17 cells into pathogenic Th17 cells in vivo, unequivocally supports the role of CD5L as a negative regulator of the functional pathogenic state of Th17 cells.

CD5L shifts the Th17 cell lipidome balance from saturated to unsaturated lipids, modulating Rorγt ligand availability and function: Since CD5L is known to regulate lipid metabolism, by binding to fatty acid synthase in the cytoplasm of adipocytes (Kurokawa, Arai et al. 2010), it was speculated that CD5L may also regulate Th17-cell function by specifically regulating lipid metabolites in T cells. To test this hypothesis, it was analyzed whether lipid metabolism is regulated by CD5L and is associated with the increased pathogenicity observed in Th17 cells from CD5L, mice. The lipidome of WT and CD5L−/− Th17 cells differentiated under the non-pathogenic (TGFβ1+IL-6) and pathogenic (TGFβ1+IL-6+IL-23) conditions was profiled. It was possible to resolve and identify around 200 lipid metabolites intracellularly or in the supernatant of differentiating Th17 cells using mass spectrometry and liquid chromatography (Table 3 herein). Of those metabolites that were differentially expressed between WT and CD5L, a striking similarity between the lipidome of CD5L−/− Th17 cells differentiated under the non-pathogenic condition and WT Th17 cells differentiated under the pathogenic condition (FIG. 11A) was observed. Among other metabolic changes, CD5L deficiency significantly increased the levels of saturated lipids (SFA), including metabolites that carry saturated fatty acyl and cholesterol ester (CE) as measured by liquid chromatography and mass spectrometry (FIG. 11B), and free cholesterol as shown by microscopy (FIG. 11D). Moreover, the absence of CD5L resulted in a significant reduction in metabolites carrying poly-unsaturated fatty acyls (PUFA) (FIG. 11B). Similar increase in CE and reduction in PUFA is observed in the lipidome of Th17 cells differentiated under either of two pathogenic conditions (IL-1β+IL-6+IL-23 and TGFβ3+IL-6+IL-23) compared to non-pathogenic WT cells (FIG. 11C). Thus, Th17 cell pathogenicity is associated with a shift in the balance of lipidome saturation as reflected in the increase in saturated lipids and decrease in PUFA metabolites.

Cholesterol metabolites, such as oxysterols, have been previously reported to function as agonistic ligands of Rorγt (Jin, Martynowski et al. 2010, Soroosh, Wu et al. 2014). Previous ChIP-Seq analysis (Xiao, Yosef et al. 2014) suggests that Rorγt binds at several sites in the promoter and intronic regions of Il23r and Il17 (FIG. 11D) and near CNS-9 of Il10, where other transcription factors, such as cMaf, which regulates Il10 expression, also binds. As showed above, CD5L restrains the expression of IL-23R and IL-17 and promotes IL-10 production in Rorγt+ Th17 cells, and because CD5L-deficient Th17 cells contain higher cholesterol metabolite and lower PUFA (FIG. 11A, B). Putting these data together, it was hypothesized that CD5L regulates the expression of IL-23R, IL-17 and IL-10 by affecting the binding of Rorγt to these targets, through affecting the SFA-PUFA balance.

Applicants hypothesized that CD5L could regulate Th17-cell function by regulating fatty acid (FA) profiles in T cells. Applicants asked if lipid metabolites are regulated by CD5L and if any such changes are associated with the increased pathogenicity of CD5L−/− Th17 cells. Applicants profiled the lipidome of WT and CD5L−/− Th17 cells differentiated under the non-pathogenic (TGF-β1+IL-6) and pathogenic (TGF-β1+IL-6+IL-23) conditions using a non-targeted approach. Applicants detected 178 lipid metabolites from Th17 cells, 39 of which showed differences among various Th17 polarizing conditions (FIG. 11A, p<0.05, fold change >1.5; Table 4). Strikingly, non-pathogenic WT Th17 cells had a unique lipidome profile that was distinct from those of CD5L−/− Th17 cells and WT Th17 cells differentiated with TGF-β1+IL-6+IL-23 (FIG. 11A). Applicants analyzed the FA profile and lipid class in the Th17 cell lipidome. As Applicants did not detect free FA except myristic acid, Applicants analyzed the FA content (side-chain) of the lipids in FIG. 11A. WT non-pathogenic Th17 cells (compared to CD5L−/− Th17 cells of the same conditions) have increased polyunsaturated fatty acid (PUFA), accompanied by a decrease in lipids containing saturated (SFA) and monounsaturated fatty acids (MUFA) (FIG. 11K). Applicants then extended this analysis to the 178 lipids detected. Not all PUFA are different in WT vs. CD5L−/− Th17 cells: linoleic acid (C18:2) and linolenic acid (C18:3) are equally distributed in the lipidome, whereas downstream PUFA, in particular arachidonic acid (C20:4), are elevated in WT non-pathogenic Th17 cells (FIG. 21B). In contrast, MUFA is equivalently distributed and the corresponding SFA is decreased in WT non-pathogenic Th17 cells (FIG. 21C). The PUFA increase in WT non-pathogenic Th17 is equivalently distributed among the phospholipid and neutral lipid compartments (FIG. 11L), whereas the relative decrease of SFA is only significant in phospholipid (FIG. 11L). Finally, comparing the difference in specific lipid species (FIG. 21D), Applicants found a higher level of cholesterol ester (CE), lysophosphatidylcholine (LPC) and phosphatidylcholine (PC), as well as decreased triacylglyceride (TAG) in both the CD5L−/− and more pathogenic cells (FIG. 21D). Taken together, these findings suggest CD5L predominantly regulates FA composition in Th17 cells, resulting in elevation of PUFA and changes in specific lipid species, including cholesterol metabolites. Similar changes are also observed in WT Th17 cells differentiated under the pathogenic condition. Cholesterol metabolites, such as oxysterols, can function as agonists of Rorγt (Jin et al., 2010; Soroosh et al., 2014), and the cholesterol synthesis pathway has been linked to the production of endogenous Rorγt ligand. While Applicants did not detect any oxysterols or intermediates of cholesterol synthesis, the higher level of cholesterol esters (FIG. 21D) prompted us to further investigate the cholesterol pathway. Applicants confirmed the higher intensity of free cholesterol in CD5L−/− Th17 cells using microscopy (FIG. 21E). Next, Applicants analyzed the expression of cyp51 and sc4 mol, two enzymes of the cholesterol synthesis pathway responsible for generating endogenous Rorγt ligands (Santori et al., 2015), and found both increased in CD5L−/− Th17 cells or in pathogenic WT Th17 cells (FIG. 11M), suggesting this may be a common mechanism by which Th17 cells regulate their function. Applicants asked if the change in FA profile in CD5L−/− Th17 cells is responsible for the regulation of cyp51 and sc4 mol. Indeed, while SFA had a modest effect, PUFA abolished the increased expression of the enzymes in CD5L−/− Th17 cells (FIG. 11M). Thus CD5L can regulate fatty acid composition in Th17 cells and alter the cholesterol synthesis pathway, a source of Rorγt ligand.

CD5L and PUFA/SFA profile regulate Rorγt function in a ligand-dependent manner. Applicants analyzed if CD5L and the PUFA/SFA profile can alter Rorγt binding and function. Our previous chromatin immunoprecipitation (ChIP)-Seq analysis (Xiao et al., 2014) suggested Rorγt binds at several sites in the promoter and intronic regions of Il23r and Il17 and near CNS-9of I110 (FIG. 54 WO2015130968) where other Il10-regulating transcription factors, such as cMaf, also bind (Xiao et al., 2014). As CD5L restrains IL-17 and promotes IL-10 in Rorγt+Th17 cells (FIG. 46 WO2015130968) and CD5L−/− Th17 cells have more cholesterol metabolites and lower PUFA (FIGS. 11A, 11K, 11M, 21E), Applicants hypothesized that CD5L regulates the expression of IL-23R, IL-17, IL-10 and, in turn, pathogenicity by affecting the binding of Rorγt to these targets by changing the SFA/PUFA profile and cholesterol biosynthesis. Applicants assessed if CD5L regulates Rorγt binding and transcription using ChIP-PCR and luciferase reporter assays. ChIP of Rorγt showed higher binding in the Il17 and Il23r region and reduced binding to the Il10 region in CD5L−/− Th17 cells despite similar Rorγt expression compared to WT (FIG. 18A, B, FIG. 54 WO2015130968). Further, CD5L overexpression was sufficient to suppress Rorγt dependent transcription of Il17 and Il23r luciferase reporters (FIG. 18C, FIG. 54 WO2015130968) and to enhance the transcription of the Il10 reporter (FIG. 54 WO2015130968). This effect of CD5L is not observed with PPARγ, another regulator of Il10, further supporting the hypothesis that the effect of CD5L depends on Rorγt (FIG. 54 WO2015130968). Applicants then examined whether changing the lipidome of WT Th17 cells with exogenous SFA or PUFA can regulate Rorγt binding to genomic regions (FIG. 18A, B and FIG. 54 WO2015130968). SFA enriched binding of Rorγt at Il17 and Il23r loci and PUFA decreased such binding (FIG. 15A, FIG. 54 WO2015130968). Instead, PUFA increased Rorγt binding to the Il10 CNS-9 locus (FIG. 18B), suggesting that manipulation of the lipid content of Th17 cells can indeed modulate Rorγt binding to DNA. Applicants reasoned that if CD5L regulates Rorγt transcriptional activity by limiting Rorγt ligand, adding exogenous agonists of Rorγt would rescue CD5L-induced suppression. Indeed, 7β, 27-dihydroxycholesterol, previously shown as an endogenous ligand of Rorγt (Soroosh et al., 2014), rescued the CD5L-driven suppression of Il17 reporter transcription, suggesting ligand availability partly contributes to the regulation of Rorγt function by CD5L (FIG. 18D). Consistently, CD5L inhibited IL-17 expression in unpolarized Th0 cells with ectopic Rorγt expression and this inhibition could be partially rescued by the addition of a Rorγt ligand (FIG. 18E). Addition of Rorγt ligand also increased IL-17 production from non-pathogenic Th17 cells (FIG. 18F), suggesting that ligand restriction may be one of the mechanisms by which CD5L regulates Th17 cell pathogenicity. Applicants then determined if SFA/PUFA regulate Rorγt activity through Rorγt ligand. While Rorγt strongly transactivates the Il23r enhancer in the presence of an agonistic ligand, the addition of PUFA to the agonist ligand inhibited Rorγt-mediated Il23r transactivation and enhanced Il10 transactivation (FIG. 48 WO2015130968). Similarly, adding SFA alone had little impact on Rorγt-dependent transcription, but it modified the transcriptional effect of oxysterol (FIG. 48 WO2015130968). Thus, PUFA/SFA can modulate Rorγt transcriptional activity via a Rorγt-ligand dependent mechanism, although the precise mechanism of exogenous PUFA and SFA require further studies. Taken together, these observations suggest that CD5L shifts the FA composition in the lipidome, changes Rorγt ligand availability and Rorγt genomic binding, and regulates Il23r and Il10, members of the proinflammatory vs. regulatory modules.

PUFA/SFA regulate Th17 cell and contribute to CD5L function. As CD5L−/− Th17 cells have an altered balance in lipid saturation, and PUFA/SFA modulate Rorγt binding and function, Applicants analyzed the relevance of FA moieties to Th17 cell function and their contribution to CD5L-driven Th17 cell pathogenicity. Applicants first tested the effect of PUFA/SFA on the generation of Th17 cells. WT Th17 cells were differentiated with TGF-β1+IL-6 and expanded using IL-23 in fresh media with either PUFA or SFA. PUFA suppressed IL-17 and IL-23R expression consistent with reduced transactivation in WT but not in Rorγt−/− Th17 cells, suggesting PUFA can limit pathogenic Th17 cell function in a Rorγt dependent manner (FIG. 50 WO2015130968). CD5L−/− Th17 cells differentiated with TGF-β1+IL-6 were also sensitive to PUFA treatment, resulting in reduced percentage of IL-17+CD4+ T cells (FIG. 50 WO2015130968). In contrast, addition of SFA only slightly increased the expression of both IL-17 and IL-23R expression, and this effect was not significant, possibly because pathogenic Th17 cells had already very high levels of SFA. Applicants studied the contribution of lipid saturation to Th17 cell pathogenicity. Applicants speculated that if the balance of lipid saturation distinguishes non-pathogenic WT Th17 cells and pathogenic CD5L−/− Th17 cells, the addition of SFA to WT and PUFA to CD5L−/− Th17 cells can result in reciprocal changes in the transcriptional signature relevant to Th17 cell pathogenicity. Applicants analyzed the expression of a 312 gene signature of Th17 cell differentiation and function (Yosef et al., 2013) in SFA- or control-treated WT Th17 cells and in PUFA- or control-treated CD5L−/− Th17 cells differentiated with TGF-81+IL-6. Of those genes that are differentially expressed (Table 5, >1.5 fold), PUFA-treated CD5L−/− Th17 cells resemble WT non-pathogenic Th17 cells, and SFA-treated WT non-pathogenic Th17 cells are more similar to CD5L−/− Th17 cells (FIG. 50 WO2015130968, Table 5). qPCR analysis confirmed that PUFA and SFA reciprocally regulated effector molecule expression of the pathogenicity signature (Lee et al., 2012), including Il10, Il23r, Ccl5, Csf2 and Lag3 (FIG. 50 WO2015130968). Notably, in some cases PUFA and SFA have the same effects; for example, Il22 expression is increased following either FA treatment. Taken together, these observations suggest that the balance of lipid saturation contributes to CD5L-dependent regulation of Th17 cells by regulating the Th17-cell transcriptome.

DISCUSSION. Th17 cells are a helper cell lineage capable of diverse functions ranging from maintaining gut homeostasis, mounting host defense against pathogens, to inducing autoimmune diseases. How Th17 cells can mediate such diverse and opposing functions remains a critical open question. Addressing this is especially important since anti-IL-17 and Th17-based therapies have been highly efficacious in some autoimmune diseases, but had no impact on others (Baeten and Kuchroo, 2013; Genovese et al., 2010; Hueber et al., 2012; Leonardi et al., 2012; Papp et al., 2012; Patel et al., 2013), even when Th17 cells have been genetically linked to the disease process (Cho, 2008; Lees et al., 2011). Using single-cell genomics Applicants have addressed this issue and have identified novel functional regulators of pathogenicity in Th17 cells. Here, Applicants highlight and investigate CD5L as one of the novel regulators that affect the pathogenicity of Th17 cells. Applicants show that: (1) Among CD4 T cells, CD5L is highly expressed only in non-pathogenic Th17 cells, but in them positively co-varies with a pro-inflammatory module, a pattern consistent with being a negative regulator of pathogenicity; (2) CD5L does not affect Th17 differentiation but affects their long-term expansion and function; (3) CD5L deficiency converts non-pathogenic Th17 cells into pathogenic Th17 cells; (4) CD5L regulates lipid metabolism in Th17 cells and alters their fatty acid composition; and (5) change in the lipidome in CD5L−/− Th17 cells affects the ligand availability and binding of Rorγt to its target genes.

In a seemingly paradoxical way, CD5L is expressed only in non-pathogenic Th17 cells, but in co-variance with the pro-inflammatory module. This observation led us to hypothesize that CD5L is a negative regulator of a non-pathogenic to pathogenic transition, since negative regulators are often known to co-vary in regulatory networks with the targets they repress in organisms from yeast (Segal et al., 2003) to mammals (Amit et al., 2007; Amit et al., 2009). Our functional analysis bears out this hypothesis, suggesting that CD5L might indeed be expressed to restrain the pro-inflammatory module in the non-pathogenic Th17 cells. Similarly, other genes with this specific pattern, i.e. exclusive expression in non-pathogenic cells but in co-variance with the pro-inflammatory module, may also be repressors that quench pro-inflammatory effector functions and make Th17 cells non-pathogenic. Thus, depending on the environmental context or trigger, non-pathogenic Th17 cells can be readily converted into pathogenic Th17 cells by inhibiting a single gene like CD5L. This is supported by our data showing IL-23R signalling can suppress CD5L and persistent CD5L expression inhibits the pro-inflammatory function of Th17 cells. In addition to suppressing the pro-inflammatory module, CD5L also promotes the regulatory module, acting as a switch to allow rapid responses to environmental triggers such that Th17 cells can change their functional phenotype without intermediary pathways.

Both pathogenic and non-pathogenic Th17 cells are present in peripheral lymphoid organs, but pathogenic Th17 cells appear at sites of tissue inflammation (CNS) and non-pathogenic Th17 cells appear in the gut or other mucosal surfaces. This is mirrored in the expression of CD5L. IL-23, which is present in the CNS during EAE, can suppress CD5L and convert non-pathogenic Th17 cells into pathogenic Th17 cells. At steady state, it is unknown what promotes CD5L expression and non-pathogenicity in the gut. TGF-3 could be a candidate given its abundance in the intestine and its role in both differentiation of IL-10-producing CD4 T cells in vivo (Konkel and Chen, 2011; Maynard et al., 2007) and Th17 cell differentiation (Bettelli et al., 2006; Veldhoen et al., 2006). Specific commensal bacteria (Ivanov et al., 2009; Yang et al., 2014) and metabolites from microbiota (Arpaia et al., 2013) can also regulate T cell differentiation. Notably, CD5L is reported as a secreted protein and can recognize PAMPs (Martinez et al., 2014). It is possible CD5L expressed by non-pathogenic Th17 cells in the gut can interact with the immune cells interacting with gut microbiota and maintain gut tolerance and a non-pathogenic Th17 phenotype. Other CD5L-expressing cells in the intestine may also contribute to such a function. Therefore, the two functional states of Th17 cells may be highly plastic, in that either pathogenic or non-pathogenic Th17 cells can be generated by sensing changes in the tissue microenvironment. CD5L is critical for maintaining the non-pathogenic functional state of Th17 cells, and IL-23 rapidly suppresses CD5L rendering the cells pathogenic. This hypothesis also predicts that non-pathogenic Th17 cells can be easily converted into pathogenic Th17 cells by production of IL-23 locally in the gut during inflammatory bowel disease. How does CD5L regulate Th17 cell pathogenicity? Applicants provide evidence CD5L can regulate Th17 cell function by regulating intracellular lipid metabolism and limiting Rorγt ligand. CD5L inhibits the de novo synthesis of fatty acid through direct binding to fatty acid synthase. Applicants discovered that in Th17 cells CD5L is more than a general inhibitor, as it regulates the fatty acid composition of PUFA vs. SFA and MUFA. Applicants showed CD5L suppresses the cholesterol synthesis pathway by regulating critical enzymes sc4 mol and cyp51 and the addition of PUFA could reverse this phenotype. Importantly, exogenous Rorγt ligand can rescue the suppressive effect of CD5L on IL-17 expression. PUFA metabolites can function as ligands of several transcription factors and the exact mode of function for PUFA requires further investigation. Applicants showed that PUFA limits ligand-dependent function for Rorγt, such that in the presence of CD5L or PUFA, Rorγt binding to the Il17a and Il23r loci is decreased, along with reduced transactivation of both genes, whereas binding at and expression from the Il10 locus is enhanced. Notably, Rorγt's ability to regulate Il10 expression was not reported previously. As CD5L does not impact overall Th17 cell differentiation, this suggests a nuanced effect of CD5L and lipid balance on Rorγt function, enhancing its binding to and transactivation at some loci, while reducing it in others. In Th17 cells, Stat3 and c-Maf can promote Il10 (Stumhofer et al., 2007; Xu et al., 2009). As Stat3, C-Maf and Rorγt can all bind to the same Il10 enhancer element, it is possible that, depending on the quality and quantity of the available ligands, Rorγt may interact with other transcription factors and regulate Il10 transcription. This supports a hypothesis in which the spectrum of Rorγt ligands depends, at least in part, on the CD5L-regulated PUFA vs. SFA lipid balance in the cell, and these resulting ligands can impact the specificity of Rorγt, allowing it to assume a spectrum of functional states. Several metabolic pathways are associated with Th17 cell differentiation. HIF1α regulates Th17 cells through direct transactivation of Rorγt (Dang et al., 2011; Shi et al., 2011) and acetyl-coA carboxylase influences the Th17/Treg balance through the glycolytic and lipogenic pathways (Berod et al., 2014). Mice harbouring mutations in genes that regulate Th17 cell differentiation and function acquire an obese phenotype, associating Th17 cell development with obesity (Ahmed and Gaffen, 2010; Jhun et al., 2012; Mathews et al., 2014; Winer et al., 2009). A hallmark of obesity is the accumulation of saturated fat and cholesterol and mice fed with a diet rich in PUFA were reported to have reduced severity of chronic colitis and Th17 cell polarization (Monk et al., 2013; Monk et al., 2012). In this study, Applicants provided evidence that at the cellular level, lipidome saturation can promote Th17 cell function by regulating Rorγt function.

In conclusion, by using single-cell genomics and computational analysis, Applicants identified CD5L as a novel repressor of Th17 cell pathogenicity, highlighting the power of single-cell genomics to identify molecular switches that are otherwise obscured by population-level genomic profiles. CD5L appears to be a molecular switch that does not affect Th17 differentiation per se but one that impacts the function (pathogenic vs. non-pathogenic phenotype) of Th17 cells, potentially by regulating the quality and/or quantity of available Rorγt ligands, allowing a single master regulator to possibly assume multiple functional states. Our results connect the lipidome to essential functions of immune cells, opening new avenues for sensitive and specific therapeutic intervention.

EXPERIMENTAL PROCEDURES. Mice: C57BL/6 wild-type and CD4−/− (2663) mice were obtained from Jackson Laboratory. IL-17A-GFP mice were from Biocytogen. All animals were housed and maintained in a conventional pathogen-free facility at the Harvard Institute of Medicine in Boston (IUCAC protocols: 0311-031-14 (V.K.K.) and 0609-058015 (A.R.)). All experiments were performed in accordance to the guidelines outlined by the Harvard Medical Area Standing Committee on Animals at the Harvard Medical School. In addition, spleens and lymph nodes from GPR65−/− mice were generously provided by Yang Li (IACUC protocol: 453). PLZP−/− mice and TOSO−/− mice were provided by Pier Paolo Pandolfi from Beth Israel Deaconess medical center and John Coligan from National institute of Allergy and Infectious Diseases respectively.

Cell sorting and in vitro T-cell differentiation: CD4+ T cells were purified from spleen and lymph nodes using anti-CD4 microbeads (Miltenyi Biotech) then stained in PBS with 1% FCS for 20 min at room temperature with anti-CD4-PerCP, anti-CD62l-APC and anti-CD44-PE antibodies (all Biolegend). Naïve CD4+CD62lhighCD44low T cells were sorted using the BD FACSAria cell sorter. Sorted cells were activated with plate-bound anti-CD3 (2 μg ml-1) and anti-CD28 (2 μg ml-1) in the presence of cytokines. For Th17 differentiation, the following reagents were used: 2 ng/ml recombinant human TGF-β1 and recombinant human TGF-β3 (Miltenyi Biotec), 25 ng/ml recombinant mouse IL-6 (Miltenyi Biotec), 20 ng/ml recombinant mouse IL-23 (R&D Biosystems) and 20 ng/ml recombinant mouse IL-1β (Miltenyi Biotec). Cells were cultured for 48h and collected for RNA, intracellular cytokine staining, flow-fish, and flow cytometry.

Active induction of EAE and disease analysis: For active induction of EAE, mice were immunized by subcutaneous injection of 100 μg MOG(35-55) (MEVGWYRSPFSRVVHLYRNGK) in CFA, then received 200 ng pertussis toxin intraperitoneally (List Biological Laboratory) on days 0 and 2. Mice were monitored and were assigned scores daily for development of classical and atypical signs of EAE according to the following criteria (Jager et al., 2009): 0, no disease; 1, decreased tail tone or mild balance defects; 2, hind limb weakness, partial paralysis or severe balance defects that cause spontaneous falling over; 3, complete hind limb paralysis or very severe balance defects that prevent walking; 4, front and hind limb paralysis or inability to move body weight into a different position; 5, moribund state.

Isolation of T-cells from EAE mice at the peak of disease: At the peak of disease, T cells were collected from the draining lymph nodes and the CNS. For isolation from the CNS, mice were perfused through the left ventricle of the heart with cold PBS. The brain and the spinal cord were flushed out with PBS by hydrostatic pressure. CNS tissue was minced with a sharp razor blade and digested for 20 min at 37 C with collagenase D (2.5 mg/ml; Roche Diagnostics) and DNaseI (1 mg/ml; Sigma). Mononuclear cells were isolated by passage of the tissue through a cell strainer (70 μm), followed by centrifugation through a Percoll gradient (37% and 70%). After removal of mononuclear cells, the lymphocytes were washed, stained and sorted for CD3 (Biolegend), CD4 (Biolegend), 7AAD and IL-17a-GFP or FOXP3-GFP.

Memory cell isolation reactivation: Spleen and lymph nodes were isolated from indicated mice and CD4+ T cells were purified using Automacs using the manufacturers protocol (Miltenyi Biotec, CA). Cells were stained with CD44−PE, CD62L-APC and CD4-Percp antibodies prior to being sorted on the Aria FACS sorter for CD4+CD44+CD62L− cells. Cells were plated on anti-CD3/anti-CD28 (2 ug/ml each) coated flat-bottomed 96 well plate at 2×10{circumflex over ( )}5 cells/well with or without IL-23 (20 ng/ml) for reactivation. Cells were cultured in vitro for 96 hours and then live cells (7AAD-) were analyzed for intracellular cytokine staining or sorted for harvesting prior to RNA purification.

Recall experiments: Naïve CD4 T cells (CD4+CD62L+CD44−) were sorted from indicated KO and WT (or littermate) controls then adoptively transferred at 1×10{circumflex over ( )}6 cells into Rag-1 KO mice for reconstitution. Two weeks post adoptive transfer; mice were immunized with 100 ug of MOG35-55/CFA. Cells were harvested from draining LNs and spleen 8 days post immunization and restimulated with MOG35-55 with or without IL-23 (20 ng/ml) for 4 days. Cells were harvested for intracellular cytokine analysis.

Isolation of T cells from lamina propria: Cells were isolated from the lamina propria of the large intestine from 3-6 month old IL-17GFP KI mice using Miltenyi Biotec Lamina Propria Dissociation kit following the manufacturer's protocol (Miltenyi Biotec, Calif.). GFP+CD4+ TCRb+7AAD− T cells were sorted using a MoFlow Astrios into RLT lysis buffer (Qiagen RNeasy micro kit) and subsequently taken through the ‘RNA-seq of population controls’ protocol described below.

Whole transcriptome amplification: Cell lysis and SMART-Seq (Ramskold et al., 2012) whole transcriptome amplification (WTA) was performed on the C1 chip using the C1 Single-Cell Auto Prep System (C1 System) using the SMARTer Ultra Low RNA Kit for Illumina Sequencing (Clontech) with the following modifications: Cell Lysis Mix:

CompositionStock Conc.Volume
C1 Loading Reagent20×0.60 ul
SMARTer Kit RNase Inhibitor40×0.30 ul
SMARTer Kit 3′ SMART CDS 12 μM4.20 ul
Primer II A
SMARTer Kit Dilution Buffer 1×6.90 ul

Cycling Conditions I:
a) 72° C., 3 min
b) 4° C., 10 min
c) 25° C., 1 min
Reverse Transcription (RT) Reaction Mix:

CompositionStock Conc.Volume
C1 Loading Reagent 20.0×0.45 ul
SMARTer Kit 5× First-Strand Buffer  5.0×4.20 ul
(RNase-Free)
SMARTer Kit Dithiothreitol100 mM0.53 ul
SMARTer Kit dNTP Mix (dATP, dCTP, 10 mM2.10 ul
dGTP, and dTTP, each at 10 mM)
SMARTer Kit SMARTer II A 12 uM2.10 ul
Oligonucleotide
SMARTer Kit RNase Inhibitor  40×0.53 ul
SMARTer Kit SMARTScribe ™ 100.0×2.10 ul
Reverse Transcriptase

Cycling Conditions II:
a) 42° C., 90 min
b) 70° C., 10 min
PCR Mix:

CompositionStock Conc.Volume
PCR Water35.2 ul
10× Advantage 2 PCR Buffer10.0× 5.6 ul
50× dNTP Mix10 mM 2.2 ul
IS PCR primer12 uM 2.2 ul
50× Advantage 2 Polymerase Mix50.0× 2.2 ul
C1 Loading Reagent20.0× 2.5 ul

Cycling Conditions III:
a) 95° C., 1 min
b) 5 cycles of:
i) 95° C., 20s
ii) 58° C., 4 min
ii) 68° C., 6 min
c) 9 cycles of:
i) 95° C., 20s
ii) 64° C., 30s
ii) 68° C., 6 min
d) 7 cycles of:
i) 95° C., 30s
ii) 64° C., 30s
ii) 68° C., 7 min
e) 72° C., 10 min

Single cell RNA-Seq. WTA products were harvested from the C1 chip and cDNA libraries were prepared using Nextera XT DNA Sample preparation reagents (Illumina) as per the manufacturer's recommendations, with minor modifications. Specifically, reactions were run at % the recommended volume, the tagmentation step was extended to 10 minutes, and the extension time during the PCR step was increased from 30s to 60s. After the PCR step, all 96 samples were pooled without library normalization, cleaned twice with 0.9× AMPure XPSPR1 beads (Beckman Coulter), and eluted in buffer TE. The pooled libraries were quantified using Quant-IT DNA High-Sensitivity Assay Kit (Invitrogen) and examined using a high sensitivity DNA chip (Agilent). Finally, samples were sequenced deeply using either a HiSeq 2000 or a HiSeq 2500 sequencer.

Single-cell RNAseq data acquisition and analysis. Applicants profiled the transcriptome of 806 Th17 cells, either harvested in vivo or differentiated in vitro. For in vivo experiments, CD3+CD4+IL-17A.GFP+ cells were isolated from draining LNs and CNS of mice at peak of EAE. For in vitro experiments, cells were sorted at 48h post induction of differentiation of naïve CD4+ T cells under different conditions. Applicants had at least two independent biological replicates for each in vivo and in vitro condition (except for TGF-β3+IL-6 for which Applicants only had one replicate), as well as two technical replicates for two in vivo conditions.

Applicants prepared single-cell mRNA SMART-Seq libraries using microfluidic chips (Fluidigm C1) for single-cell capture, lysis, reverse transcription, and PCR amplification, followed by transposon-based library construction. For quality assurance, Applicants also profiled corresponding population controls (>50,000 cells for in vitro samples; ˜2,000-20,000 cells for in vivo samples, as available), with at least two replicates for each condition. RNA-seq reads were aligned to the NCBI Build 37 (UCSC mm9) of the mouse genome using TopHat (Trapnell et al., 2009). The resulting alignments were processed by Cufflinks to evaluate the abundance (using FPKM) of transcripts from RefSeq (Pruitt et al., 2007). Applicants used log transform and quantile normalization to further normalize the expression values (FPKM) within each batch of samples (i.e., all single-cells in a given run). To account for low (or zero) expression values Applicants added a value of 1 prior to log transform. Applicants filtered the set of analyzed cells by a set of quality metrics (such as sequencing depth), and added an additional normalization step specifically controlling for these quantitative confounding factors as well as batch effects. Our analysis is based on

˜7,000 appreciably expressed genes (fragments per kilobase of exon per million (FPKM)>10 in at least 20% of cells in each sample) for in vitro experiments and ˜4,000 for in vivo ones. Applicants also developed a strategy to account for expressed transcripts that are not detected (false negatives) due to the limitations of single-cell RNA-seq (Deng et al., 2014; Shalek et al., 2014). Our analysis (e.g., computing signature scores, and principle components) down-weighted the contribution of less reliably measured transcripts. The ranking of regulators shown in FIG. 16 is based on having a strong correlation to at least one of the founding signature genes, and in addition, the significance of the overall pattern relative to the proinflammatory vs. regulatory signature by comparing the aggregates pattern across the individual correlations to shuffled data.

Mice. C57BL/6 wildtype (WT) was obtained from Jackson laboratory (Bar Harbor, Me.). For EAE experiment, littermate control WT was used in comparison to CD5L−/− mice in one experiment which produced similar results compared to WT from Jackson. CD5L−/− mice were provided by Dr. Toru Miyazaki from the University of Tokyo (Miyazaki et al., 1999). CD5L−/− 2D2 mice were generated by crossing CD5L−/− mice with WT 2D2 transgenic mice. IL-23R GFP reporter mice were generated as previously published (Awasthi et al., 2009). Rorγt. GFP reporter mice were provided by Dr. Dan Littman and bred at the Harvard Institute of Medicine animal facility. All experiments were performed in accordance to the guidelines outlined by the Harvard Medical Area Standing Committee on Animals at the Harvard Medical School (Boston, Mass.).

Experimental Autoimmune Encephalomyelitis (E4E). For active EAE immunization, MOG35-55 peptide was emulsified in complete freund adjuvant (CFA). Equivalent of 40 μg MOGpeptide was injected per mouse subcutaneously followed by pertussis toxin injection intravenously on day 0 and day 2 of immunization. For adoptive transfer EAE, naïve 2D2 transgenic T cells were sorted as described in T cell culture and co-cultured with irradiated APC in the presence of soluble anti-CD3 and anti-CD28 antibodies (2.5 μg/ml) and cytokines for five days. Cells were then harvested and restimulated with plate-bound anti-CD3 and anti-CD28 (2 μg/ml) for 2 days prior to transfer. For overexpression of CD5L, retroviruses, MSCV, carrying either GFP empty vector control or GFP.CD5L vector was used to infect T cell culture as outlined above one day after T cell activation. Five million cells were transferred per mouse intravenously. EAE is scored as previously published (Jager et al., 2009).

T cell differentiation culture. Naïve CD4+CD44−CD62L+CD25− T cells or Effector memory CD4+CD44+CD62L− were sorted using BD FACSAria sorter and activated with plate-bound anti-CD3 and anti-CD28 antibodies (both at 2 μg/ml) in the presence of cytokines at a concentration of 2.5×105 cells/ml. For Th17 differentiation: 2 ng/ml of rhTGFβ1, 2 ng/ml of rhTGFβ3, 25 ng/ml rmIL-6, 20 ng/ml rmIL-18 (all from Miltenyi Biotec) and 20 ng/ml rmIL-23 (R & D systems) were used at various combinations as specified in figures. For Th1 differentiation, 20 ng/ml rmIL-12 (R & D systems); for Th2 differentiation 20 ng/ml rmIL-4 (Miltenyi Biotec); for iTreg differentiation, 2.5 ng/ml of rhTGFβ1 were used (Miltenyi Biotec). For differentiation experiments, cells were harvested at 48 hours. For restimulation experiments, cells were differentiated for 48 hours and resuspended in fresh media with no additional cytokines for 48-72 hours. Cells were re-stimulated with PMA/ionomycin for four hours before analysis for cytokines by intracellular cytokine staining. For experiments with exogenous fatty acid, fatty acids were purchased and resuspended first with serum-free media containing BSA prior being added to culture.

Lipidomics. Th17 cells were differentiated from naïve WT and CD5L−/− T cells. Culture media were snap frozen. Cells were harvested at 96h. 10×106 cells per sample were snap frozen and extracted in either 80% methanol (for fatty acids and oxylipids) or isopropanol (for polar and nonpolar lipids). Two liquid chromatography tandem mass spectrometry (LC-MS) methods were used to measure fatty acids and lipids in cell extracts.

Fatty acid extracts (10 pL) were injected onto a 150×2 mm ACQUITY T3 column (Waters; Milford, Mass.). The column was eluted isocratically at a flow rate of 400 IL/min with 25% mobile phase A (0.1% formic acid in water) for 1 minute followed by a linear gradient to 100% mobile phase B (acetonitrile with 0.1% formic acid) over 11 minutes. MS analyses were carried out using electrospray ionization in the negative ion mode using full scan analysis over m/z 200-550 at 70,000 resolution and 3 Hz data acquisition rate. Additional MS settings were: ion spray voltage, −3.5 kV; capillary temperature, 320° C.; probe heater temperature, 300° C.; sheath gas, 45; auxiliary gas, 10; and S-lens RF level 60. Lipids extracts (2 μL) were injected directly onto a 100×2.1 mm ACQUITY BEH C8 column (1.7 μm; Waters; Milford, Mass.). The column was eluted at a flow rate of 450 μL/min isocratically for 1 minute at 80% mobile phase A (95:5:0.1 vol/vol/vol 10 mM ammonium acetate/methanol/acetic acid), followed by a linear gradient to 80% mobile-phase B (99.9:0.1 vol/vol methanol/acetic acid) over 2 minutes, a linear gradient to 100% mobile phase B over 7 minutes, and then 3 minutes at 100% mobile-phase B. MS analyses were carried out using electrospray ionization in the positive ion mode using full scan analysis over m., 200-1100 at 70,000 resolution and 3 Hz data acquisition rate. Additional MS settings were: ion spray voltage, 3.0 kV; capillary temperature, 300° C.; probe heater temperature, 300° C.; sheath gas, 50; auxiliary gas, 15; and S-lens RF level 60. Raw data from methods 1-3 were processed using Progenesis CoMet and QI software (Nonlinear Dynamics Ltd.; Newcastle upon Tyne, UK) for feature alignment, nontargeted signal detection, and signal integration. Targeted processing of a subset of known metabolites was conducted using TraceFinder software (Thermo Fisher Scientific; Waltham, Mass.).

ChIP-qPCR Chromatin ImmunoPrecipitation (ChIP) for Rorγt was performed as previously published (Xiao et al., 2014) using anti-Rorγt antibody (AFKJS-9) and RatIgG2a isotype control antibody (eBioscience, CA). qPCR was performed using the following primers: Il17a CNS2: Fwd: 5′-TGG AAA GTT TTC TGA CCC ACT T; Rv: 5′-GGA AGC TGA GTA CGA GAA GGA A; l17a Inl: Fwd: 5′-ACC AAA GGA ACA AGT GGA AAG A; Rv:5′-TTT GAG AAC CAG TCA TGT CAC C; Il17ap5: Fwd: 5′-GGG GTA GGG TCA ATC TAA AAG C; Rv: 5′-GTG TGC TGA CTA ATT CCA TCC A; Il10 CNS-9: Fwd: 5′ TTA CAG AAT GGC ACT TCC AGA G; Rv: 5′ CGA TGT ATT AGT TCC GGT GTG T; Il23r in3: Fwd 5′-CTT GGC ATC ACA AAG CTT ACA G: Rv: 5′-ACT GCC AGG CAA GAA TTT ACT C; Il23r in6: Fwd 5′-TAC CTG AAA GCT GTG CAG AGA G; Rv: 5′-AAG TCC AAG CCT GTGAAA CAA T.

Nanostring nCounter. Nanostring nCounter platform (NanoString Technologies) is used to measure the number of RNA transcripts in RNA samples (FIG. 16I, FIG. 18D). A codeset containing 312 signature genes of Th17 cell differentiation and function as well as 4 additional house-keeping genes were custom-made (Yosef et al., 2013) and used in these experiments. Experimental procedures as detailed by the manufacturer is strictly followed.

Antibodies. Biotinylated anti-CD5L antibody used for flow cytometry analysis was purchased from R & D systems. All other flow cytometry antibodies were purchased from Biolegend. ELISA coating and capturing antibodies for IL-10 were from BD Biosciences and anti-IL-17 were purchased from Biolegend.

Statistical Analysis. Unless otherwise specified, all statistical analyses were performed using the two-tail student t test using GraphPad Prism software. P value less than 0.05 is considered significant (P<0.05=*; P<0.01=**; P<0.001=***).

RNA-Seq of population controls. Population controls were generated by extracting total RNA using RNeasy plus Micro RNA kit (Qiagen) according to the manufacturer's recommendations. Subsequently, 1 μL of RNA in water was added to 2 μL of lysis reaction mix, thermocycled using cycling conditions I (as above). Next, 4 μL of the RT Reaction Mix were added and the mixture was thermocycled using cycling conditions II (as above). Finally, 1 μL of the total RT reaction was added to 9 μL of PCR mix and that mixture was thermocycled using cycling conditions III (as above). Products were quantified, diluted to 0.125 ng/μL and libraries were prepared, cleaned, and tested as above.

RNA-Seq preprocessing. RNA-Seq preprocessing was performed using the following. RNA-seq reads are aligned to the NCBI Build 37 (UCSC mm9) of the mouse genome using TopHat (Trapnell et al., 2009). The resulting alignments are processed by Cufflinks to evaluate the abundance (using FPKM) of transcripts from RefSeq (Pruitt et al., 2007). Log transform and quantile normalization is used to further normalize the expression values (FPKM) within each batch of samples (i.e., all single cells in a given run). To account for low (or zero) expression values a value of 1 prior to log transform was added.

Sample filtering and normalization. For each library quality scores were computated using Fastqc, Picard tools, and in-house scripts. Computed scores included: (1) Number of reads, (2) Number of aligned reads, (3) Percentage of aligned reads, (4) Percentage of transcripts identified (compared with the overall number of transcripts identified by at least one cell in the respective run), (5) Percentage of duplicate reads, (6) primer sequence contamination, (7) insert size (mean), (8) insert size (std), (9) Complexity, (10) Percentage of Ribosomal reads, (11) Percentage of Coding reads, (12) Percentage of UTR reads, (13) Percentage of Intronic reads, (14) Percentage of Intergenic reads, (15) Percentage of mRNA reads, (16) Coefficient of variation of coverage, (17) mean 5′ Bias, (18) mean 3′ Bias, (19) mean 5′ to 3′ Bias.

Libraries are excluded from further analysis with poor values in either the number of aligned reads, the percentage of aligned reads, or the percentage of identified transcripts. To this end, for a given performance measure x, a minimum cutoff value cx was set by taking the maximum over: {AVG(x)−1.645*STD(x), MED(x)−1.645*MAD(x)} (MED stands for median and MAD is the median absolute deviation). For the latter two performance measures, a Gaussian mixture model is fit to x; if x fits a multi-modal distribution rather than a single Gaussian (using Bayesian Information Criteria to determine the best model), then an additional cutoff z determined as the boundary between the right-most distribution and the other distributions is used. Finally, hard lower bounds (h/h) are introduced for the cutoff values (#aligned reads >25k; percentage of aligned reads>20%; percentage of identified transcripts>20%). Then the cutoff is re-set to be max{cx, z, hlb}. Only cells are retained that scored above the cutoff in all three cases.

As an additional pre-processing step a normalization technique (Risso et al., 2011) is employed to reduce the effects of the quality scores. To this end, a principal component analysis (PCA) is computed over the quality score matrix (a matrix with columns corresponding to cells and rows corresponding to quality scores). Then a global-scaling normalization approach (previously used for GC content normalization in RNA-Seq data (Risso et al., 2011) is used to remove the effects of the top principal components (PCs), until >90% of the variance in the quality matrix is covered (Notably, the quality scores are correlated, and usually the top one or two principal components are sufficient). For a given PC, the cells are divided into 10 equally-sized bins based on their projected values. The normalized expression measures are defined as:
E′(i,j)=E(i,j)−Median({E(i,j′),s.t. j′∈k(j)})+Median({E(i,:)})
where E(i,j) is the original expression value of gene i in cell j; k(j) denotes the PC-value bin to which cell j belongs; and E(i,:) denotes the median value of gene i across all cells.

This approach was validated by computing PCA on the expression data (before filtering, after filtering, but before normalization, and after filtering and normalization) and calculating the correlation between the quality scores and the top PCs. It was found that before filtering and normalization the main PCs highly correlate with the various library quality scores; indicating that the dominant signal in the pre-normalization data might reflect experimental artifacts. These correlations are reduced after normalization, indicating that the remaining signal is less affected by artifacts (FIG. 6).

Batch correction. Two or more replicates for the majority of the analyzed conditions were obtained. Since the replicates were divided into batches, a procedure to eliminate the pertaining batch effects was applied. Due to substantial differences in the number of detected genes between in vivo and in vitro samples, this analysis is performed separately for the in vivo and the in vitro samples. For a given sample, its filtered gene set is defined as the genes that have an expression level exceeding 10 FPKM in at least 20% of the cells. For a given set of samples (in vivo or in vitro), only the genes that appear in the filtered set of at least two of the samples are retained. This results in ˜4,000 genes for the in vivo data and ˜7,000 genes for the in vitro. Batch correction is then performed on the resulting matrices (generated by combining all the samples and filtering for the selected genes) using the COMBAT software (Johnson et al., 2007; Novershtern et al.). To eliminate the effects of quality scores on the resulting matrix (i.e., systematic differences in the quality of different samples, rather than cells within a sample), the correction procedure described in the previous section was re-applied.

Taking into account false negatives using weighted analysis. The estimation of transcript abundance as zero can be attributed to false-negatives in the RNA-Seq data. Different individual cells within a sample can have different rates of false-negatives, depending on the quality of the library, and cell integrity. To account for this, for every cell a false-negative curve (FNC) was constructed using the following. The cell-specific FNC represents the false-negative rate as a function of transcript abundance in the bulk population. The FNC is built by taking all the housekeeping genes that are detectable (non zero estimated abundance) in the bulk population and in at least one cell, and arranging them into 30 bins. Then for every bin, the ratio of housekeeping genes that are detectable is computed. Finally, a sigmoid function is fitted to the estimated values (See, e.g., FIG. 6C). These values are used to weigh down possible false-negatives in the subsequent analysis: (1) For correlation-based analysis weighted correlations are used where a zero-value of a gene i in cell j is weighted by the value associated in the FNC of j with the expression of i in the bulk population. For lowly expressed genes the weight will be lower, indicating a higher chance for them to be false-negatives. Notably, the PCA analysis is done by computing the eigenvectors of the weighted covariance matrices. (2) For signature-based scores a weighted version of the gene set enrichment analysis algorithm is used, described next.

RNA Flow-Fish analysis of RNA-expression. Cells prepared under the same conditions as the RNA-seq samples were prepared with the QuantiGene® ViewRNA ISH Cell Assay kit from Affymetrix following the manufacturers protocol. High throughput image acquisition at 60× magnification with an ImageStream X MkII allows for analysis of high-resolution images, including brightfield, of single cells. Genes of interest were targeted by type 1 probes, housekeeping genes by type 4 probes, and nuclei were stained with DAPI. Single cells were selected based on cell properties like area, aspect ratio (brightfield images) and nuclear staining. As a negative control, Bacterial DapB gene (Type 1 probe) were used. Spot counting was performed with the amnis IDEAS software to obtain the expression distributions.

Weighted gene signature scores and gene set enrichment analysis. To interpret the functional implications of the variation between cells, a set of gene signatures was assembled that are indicative of various cell states, using the following. A typical signature is comprised of a “plus” subset and a “minus” subset. A strong match will have extreme, and opposite values for the expression of genes in the two sets (e.g., high values for the “plus” genes and low values for the “minus” genes). The signatures from the following sources are assembled: (1) The immunological signature (ImmSig) collection from MSigDB ((Liberzon et al., 2011); denoted as collection C7): ˜2,000 gene sets (each divided into “plus” subset and a “minus” subset) found by comparing immune cells under different conditions (e.g., knockout vs. WT, different stimulations, time post infection etc.). (2) Cell cycle gene sets from M SigDB (Liberzon et al., 2011) and based on the gene ontology database (Huntley et al., 2009); (3) TheNetPath database (Kandasamy et al., 2010): a collection of gene sets (each divided into “plus” subset and a “minus” subset) that are downstream of various immune signaling and are either positively or negatively regulated. (4) Signatures of T helper cell subsets, based on previous work (Wu et al., 2013)(Xiao et al., 2014). (5) Signatures of exhausted and memory T cells (Crawford et al., 2014); (6) Microarray data from Sarkar et al (Sarkar et al., 2008), comparing memory vs. effector CD8+CT cells; (7) Microarray data from Muranski et al (Muranski et al., 2011), tracking the development of Th17 and Th1 cell in an adoptive transfer model. (8) Microarray data from Kurachi et al (Kurachi et al., 2014), tracking the development of CD4+ and CD8+ T cells in acute and chronic infection models. (9) Microarray data comparing IL-23R knockout mice CD4+ T cells differentiated in IL-1β+IL-6+IL-23 to WT (Y. L. and V. K. K, unpublished data). Notably, while sources 1-5 already provide processed gene sets, analysis of the remaining sources is based on the raw data (microarrays). This data was analyzed to infer differentially expressed genes. To this end, all genes with a fold change over 1.5 are reported; if there are at least two replicates, consistent (up or down) and >1.5 fold change in all pairwise comparisons is required (all replicates of condition “A” vs. all replicates of condition “B” must show fold change above the cutoff). To avoid spurious fold levels due to low expression values a small constant is added to the expression values (c=50) prior to the analysis. To search for signatures that are significantly expressed in a subset of cells the following test was performed: First, standardizing the rows of the expression matrix (i.e., every cell is normalized w.r.t. the other cells) and weighing down zero entries as above (multiplying the respective entries in the Z-normalized matrix by (1−probability for false negative)). Given a signature S={S+,S}, a gene set enrichment analysis (GSEA) for every cell independently is performed, using the values in the standardized, weighted matrix. To account for the direction, the values in the rows that correspond to the genes in S are negated. The standard GSEA formulation with 250 randomizations is used, where in each randomized run a random selection of S is considered, and 50 randomly selected cells. The reported p-values are computed empirically by comparing to the resulting 12,500 random scores. A 5% FDR cutoff is computed using the Benjamini-Hochberg scheme (Benjamini and Hochberg (1995) and only signatures that had a p-value below the cutoff in at least 10% of the cells is reported. To associate gene signatures with cell's location along the principle components, for every cell a signature score is computed. For every cell-signature pair, Applicants estimated whether the expression of genes in the signature significantly varied either: (1) across cells of the same source or (2) between conditions (e.g., LN vs. CNS). A subset of the results for this analysis are presented in FIGS. 2 and 4. The complete result set is provided in Table S2 (Gaublomme 2015). To identify signatures that significantly vary between conditions, Applicants then compute for every cell a signature score. Given a signature S={S+,S}, Applicants define the score as the weighted mean of the genes in S+ minus the weighted mean of the genes in S. Applicants use the gene expression values under the same normalization and weighting scheme as in the weighted PCA analysis above. Signatures that significantly vary between two given conditions (“A”, “B”) were identified by comparing the distributions of signature scores of cells from condition “A” vs. cells of condition “B” (Kolmogorov-Smirnov (KS) test, FDR<104). For the signatures with significant variation in at least one of the two tests above, Applicants next investigated whether they are significantly associated with the main PCs. To this end, Applicants computed a Pearson correlation coefficient between the signature score and each of the first two PCs (i.e., comparing two vectors whose length equals the number of cells: one vector is the signature scores, the other vector is the projection value (i.e., x- or y-coordinate) of that cell in the PC space; FIGS. 2-4 and Table S2 (Gaublomme 2015)). Applicants plotted selected correlations on a normalized PCA map (for example: FIG. 2A, numbered open circles).

TF binding enrichment analysis. TFs were looked for with a significant overlap between their previously annotated target genes and the genes that correlated with each principal component using the following. TF-target interaction data is obtained from public databases (Chen et al., 2011; Ciofani et al., 2012a; Lachmann et al., 2010; Liberzon et al., 2011; Linhart et al., 2008). To select the set of genes for a given PC (PC1 or PC2), for every gene the Pearson correlation between its log expression value in every cell (adding a value of 1 to avoid effects of low expression levels) and the projection of this cell to that PC (i.e., the X [for PC1] or Y [for PC2] coordinate in the PC plot) is computed. Only genes with a p-value lower than a 5% FDR cutoff are retained. For every TF in the database, the statistical significance of the overlap between its putative targets and each of the groups defined above using a Fisher's exact test is computed. Cases where p<5*10−5 and the fold enrichment >1.5 are included. Finally, in FIG. 2, only cases in which the TF was expressed above a minimal level (5 FPKM) in at least one of the respective bulk population conditions are reported.

Relating the in vitro differentiated cells to their in vivo counterparts. To perform the analysis presented in FIG. 3B, C genes are identified that are significantly up- or down-regulated in each sub-population of in-vivo cells (FDR<0.05; one-vs-all KS test; Table S4 (Gaublomme 2015), Table 6). A signature is then defined by retaining only genes that are annotate with immune response function based on the gene ontology database (Huntley et al., 2009). Finally, the signature analysis above is repeated to score the in-vitro derived cells.

Voronoi diagrams. Voronoi diagrams were used in order to delineate areas (in the space of the first two principle components (PC)) that are most strongly associated with given signatures. Specifically, given a set of signature S={s_1, . . . , s_k} is computed for every cell k signature scores (one for each signature). For each signature i the top 5 high-scoring cells are selected, and point c_i is computed as the centroid of these points in the PC map (be averaging over their x and y coordinates). Given a set of centroid points {c_1, . . . , c_k}, the Voronoi diagram divides the space into respective regions r_1, . . . , r_k such that for every 1≤i ≤k, c_i is the closest centroid to all the points in r i. Given a set of signatures that were significantly associated with the PC map in FIG. 2a, the above procedure was followed to compute the Voronoi diagram in FIG. 2b.

Defining biomodal genes. To quantify the shape of heterogeneity in the expression levels of genes across cells, the following scheme was devised: First, a number of statistical tests are applied in order to identify genes that exhibit a bimodal distribution: (1) Hartigans Dip Test (with a p-value cutoff of 5%); (2) Gaussian mixture model—comparing a 2- or 3-Gaussian model to a 1-Gaussian model using the Bayesian Information Criteria; (3) More than 10% of cells deviate from the mean by more than 2.32 times the standard deviation (corresponding to a p-value of 1%), (4) More than 10% of cells deviate from the median by more than 2.32 times the median absolute deviation. For genes identified by at least one of the tests, two mixture models are fit using expectation maximization: (1) Exponential (for “non-expressing” cells) and normal (for “expressing” cells); and (2) Uniform (for “non-expressing” cells) and normal (for “expressing” cells). The model with the best fit us retained. Using this model a cutoff x is determined for each gene such that cells with expression higher than x are considered “expressing cells”. x is determined as the maximum between {0, the boundary between the Gaussian distribution and the alternative distribution (for bi-modal genes)}. Finally, to define the set of bimodal genes, it is required (in addition to the aforementioned tests) that the percentage of “expressing cells” is smaller than 90%.

Gene ranking. An unbiased approach was used to select potential regulator of Th17 pathogenicity. The ranking is based on: (1) Correlation with the first principle component in the in-vitro derived Th17 cells (using Tgfb1+IL6; FIG. 4c). To this end, the correlation between the expression of a given gene in each cell and the PC1 projection value of each cell (X coordinate in FIG. 4b) is computed. A 5% FDR cutoff is computed using the Benjamini-Hochberg scheme and only correlations below that cutoff are reported. (2), (3) A similar analysis is performed for correlations with the first and second principle components in the in-vivo derived Th17 cells (FIG. 2a). (4) Correlation with immune-related genes in the anti-correlated modules in FIG. 4b (a “single cell pathogenicity signature” consisting of a pro-inflammatory module: Ccr6, Il18r1, Ccl4, Ccl20, Ctla4, Il17a, Il2, Cd40lg, Tnf, Il21, Cxcr3, Tnfsf9, Ebi3, and Stat4; and a regulatory module: Ccr4, Il10, Il24, IL9, Il16, Irf4, Sigirr, Il21r, and Il4ra). (5) A similar analysis using a curated pathogenicity signature (genes that are positively or negatively associated with pathogenic Th17). In the following the analysis done to evaluate selection criteria (4) and (5) is explained. For a given gene, and a signature (consisting of two opposing subsets; e.g., pro-inflammatory genes and regulatory genes) it is desirable to evaluate the statistical relationship between them. To this end, the values x1 and x2 are computed as its average correlation with the two opposing subsets respectively. Then for cases where sign(x1)!=sign(x2) its score is designated as sign(x1)*min{abs(x1), abs(x2)}. To estimate the significance of this score the original expression matrix is shuffled, and the test is repeated for 50 times. The shuffling is done independently for each row (gene), but it retains the original values of the gens in the signature. This way it conserves the expression distribution of each gene, as well as correlations between the member genes of the signature. Only genes that “failed” at most twice are reported, when compared against the shuffled data (empirical p-value<=0.04). Finally, the genes are ranked based on their scores (correlation values for criteria (1)-(3) and an aggregate score for criteria (4)-(5)). Here genes are stratified into groups of 5 (first five genes are ranked 1st; next five genes are ranked 2nd, etc.). The final score is set as the second best rank among criteria (1-5), thus requiring a gene to perform well in at least two tests. This score is amended to prioritize (ranking 1st) genes that come up both in the in-vitro analysis (criteria 1, 4, 5; top 95%) and the in-vivo analysis (criteria 2, 3; top 75%). To break the ties between equally ranked genes, the following features are used, which are based on bulk-population studies: (a) whether the gene is significantly induced during Th17 differentiation (using previous analysis (Yosef et al., 2013), which considers only cases where the induction happened after 4 hours to exclude non-specific hits); (b) whether the gene was differentially expressed in response to Th17-related perturbations in previous studies, using the same collection of knockouts used for ranking in previous work (Yosef et al., 2013). (c) Whether the gene is bound by key Th17 transcription factors, and is affected by their perturbation during Th17 differentiation. To this end, the combined score computed by Ciofani et al. (Ciofani et al., 2012b) is used.

Population based studies used to compare top ranking genes found by bulk population vs. single-cell analysis: Population based data was based on either a compendium of 41 studies of Th17 cells from our labs, (Table S7 (Gaublomme 2015)), or a literature based ranking (Ciofani et al., 2012). Each study from our labs is a comparison of two treatments (e.g., Th17 cells with or without sodium) for which Applicants identified differentially expressed genes (as described in the Methods section “Signature scores and gene set enrichment analysis”). Applicants then ranked each gene according to the number of studies (0-41) in which it was identified as differentially expressed. The literature based study (Ciofani et al., 2012) considers a combination of RNA-seq and ChIP-seq data, prioritizing genes that are differentially expressed, and bound by key Th17 transcription factors, such as Rorc.

Flow cytometry and intracellular cytokine staining. Sorted naïve T cells were stimulated with phorbol 12-myristate 13-acetate (PMA) (50 ng/ml, Sigma-aldrich), ionomycin (1 μg/ml, Sigma-aldrich) and a protein transport inhibitor containing monensin (Golgistop) (BD Biosciences) for 4 h before detection by staining with antibodies. Surface markers were stained in PBS with 1% FCS for 20 min at room temperature, then subsequently the cells were fixed in Cytoperm/Cytofix (BD Biosciences), permeabilized with Perm/Wash Buffer (BD Biosciences) and stained with Biolegend conjugated antibodies, that is, Brilliant violet 650 anti-mouse IFN-γ (XMG1.2) and allophycocyanin-anti-IL-17A (TC11-18H10.1), diluted in Perm/Wash buffer as described (Bettelli et al., 2006). Foxp3 staining was performed with the Foxp3 staining kit by eBioscience (00-5523-00) in accordance with their ‘One-step protocol for intracellular (nuclear) proteins’. Data were collected using either a FACS Calibur or LSRII (Both BD Biosciences), then analysed using Flow Jo software (Treestar).

Analysis of RNA-Seq data from knockout cells. RNA-Seq was used to identify genes that are differentially expressed in knockout T cells, (compared with WT). To this end, replicate data was used to empirically infer a decision cutoff, above which the genes are reported. The decision cutoff is defined as a function of the magnitude of gene expression—genes that are lowly expressed are associated with a higher decision cutoff. To infer the cutoffs, first a set of replicate RNA-Seq experiments is collected. For each pair of replicates, the fold difference across all genes is calculated. The genes are then stratified into 10 bins (taking 10 quartiles), and then for each bin i the standard deviation d_i of fold changes between all pairs of replicates is computed. The fold change cutoff is then determined in each bin i to be mar {1.5, d_i}. As an additional stringent step, the obtained fold change cutoffs is smoothed, such that if the cutoff for a bin i is lower than bin i+1 (which includes genes with higher expression levels) then the cutoff of bin i+1 is set to that of bin i. For given knockout experiments with n “cases” and m “controls”, differentially expressed only cases are expressed in which more than (n×m)/2 comparisons are above the cutoff, and all comparisons are consistent (i.e., up- or down-regulation). As above, to avoid spurious fold levels due to low expression values a small constant to the expression values (5 FPKM) prior to the analysis is added. For the analysis in FIG. 5E Applicants define the sets of all genes that either positively or negatively correlate with the first PC in cells differentiated with TGF-β1+IL-6 (FIG. 4C; Pearson correlation, FDR<5%). Applicants then evaluate the significance of overlaps between these sets and the knockout-affected genes using a hypergeometric test. Applicants use the same approach to identify genes that are differentially expressed in the gut vs. the LN or CNS.

RNA Flow-Fish. RNA-fish using QuantiGene® FlowRNA Assay was performed in accordance with manufacturers guidelines for suspension cells, with minor modifications such as pipetting instead of vortexing, cells were stained with dapi and type 1 gene probes only. Cells were imaged using an ImageStream X MkII with a 60× objective. As a negative control, the expression of the bacterial DapB gene, in addition to Csf2, Itgax and Scd1, which are not expressed on Th17 cells in the TGF-β1/IL-6 condition at 48h was checked.

Quantification of cytokine secretion using ELISA. Naïve T cells from knockout mice and their wild-type controls were cultured as described above, their supernatants were collected after 48h and 96h, and cytokine concentrations were determined by ELISA (antibodies for IL-17 and IL-10 from BD Bioscience) or by cytometric bead array for the indicated cytokines (BD Bioscience), according to the manufacturers' instructions.

TABLES

The following Tables form a part of this disclosure:

TABLE 1
Sample information:
Columns Name; indicates sample origin,
Batch; samples with the same batch number originated from the same animal
(in addition, batch 1&2 also come from the same animal and serve as technical replicates),
#Cells before filtering; the number of captured, viable single cells on the Fluidigm C1 chip,
#Cells after filtering; number of cells that survived filtering criteria (Experimental Procedures),
#Sequencing Reads; Number of reads sequenced on Illumina HiSeq (average across all cells),
% Aligned reads: percentage of reads that align to the NCBI Build 37 (UCSC mm9)
of the mouse genome using TopHat (average across all cells)
#Cells#CellsAverageAverage
afterbefore#Sequencing% Aligned
NameBatchfilteringfilteringReadsreads
EAE-CNS-IL-17A/GFP+148862890292 41,134266
EAE-CNS-IL-17A/GFP+261752728575 45,292021
EAE-CNS-IL-17A/GFP+45768280028548,0711565
EAE-LN-IL-17A/GFP+13933299060935,9401461
EAE-LN-IL-17A/GFP+24038259352945,8093984
EAE-LN-IL-17A/GFP+35770302520974,5995014
TGFB1_IL6-48h55680546897569,3823763
TGFB1_IL6-48h67493222985667,1002098
TGFB1_IL6-48h-IL-17A/GFP+76786194521263,6447485
TGFB1_IL6-48h-IL-17A/GFP+86794346093561,2721476
TGFB1_IL6-48h-L-17A/GFP+91777631692956,1096561
IL1B_IL6_IL23-48h-IL-17A/GFP+86990320814861,2153719
IL1B_IL6_IL23-48h-IL-17A/GFP+77086193642565,2173455
TABLE 2
Ranking of potential regulators of Th17 pathogenicity. Table 2: Potential
regulators of Th17 pathogenicity (rows in FIG. 4B) are ranked based on: (1) Correlation with
the first principle component in the in vitro derived Th17 cells (using TGF-β1 + IL-6; FIG. 4C).
(2, 3) Correlations with the first and second principle components in the in vivo derived Th17
cells (FIG. 2A). (4) Correlation with immune-related genes in the columns of FIG. 4B. (5) A
Correlation with a curated pathogenicity signature (genes that are positively or negatively
associated with pathogenic TH17 cells, (Lee et al., 2012)). The values in these respective
columns indicate the rank (percentile) of the gene in the respective test, relative to all other
candidate genes. Highly scoring genes are the ones that are bound by key Th17 transcription
factors, and affected by perturbation of these factors during Th17 differentiation. The values in
the respective column indicate the rank (percentile) of the gene, relative top all other candidate
genes. Negative values indicate a negative correlation.
Sources for single-cell score
GeneDescriptionRankAttributesKnownProfiledScoreIn-vivo PC1 rank
In-vivo PC2 rankIn-vitro (Tgfb1 + IL6) PC1 rank Rank by correlation with single-cell
pathogenicity signature (FIG. 4c) Rank by correlation with curate pathogenic signature (Lee
et al. 2012)
CTLA4cytotoxic T-lympocyte-associated protein 41“ImmuneResponse, Known, CellSurface”
10100.994565217−0.8043478260.7771739130.913043478
GPR65G-protein coupled receptor 65101100.967391304
−0.3695652170.5869565220.777173913
RELreticuloendotheliosis oncogene1“TF, Known, PathogenicSignature (pos)”1
0100.967391304−0.8043478260.7228260870.451086957
TMEM109transmembrane protein 1091001−0.9673913040
−0.6141304350.6684782610.804347826
CD2261CellSurface00100.967391304−0.423913043
0.695652174
BCL2A1B100100.994565217−0.9945652170.75
0.913043478
GBP2guanylate binding protein 21PathogenicSignature (pos)001
0.9673913040.8315217390.77717391300
ECE1endothelin converting enzyme 110010
0.967391304−0.478260870.6684782610.885869565
RAMP1receptor (calcitonin) activity modifying protein 11PathogenicSignature (pos)
00100.967391304−0.6956521740.5597826090.695652174
BCL2A1D1ImmuneResponse00100.994565217−0.994565217
0.7228260870.994565217
PLEKpleckskin1TF0010.9945652170.885869565
0.8586956520.7228260870.940217391
BCL2A1A10010.8586956520.994565217−0.994565217
0.7771739130.967391304
ABCG1“ATP-binding cassette, sub-family G (WHITE), member 1”100
10.967391304−0.913043780.940217391−0.858695652−0.614130435
IL2interleukin 22“ImmuneResponse, Known, CytokineChemokine”10
0.99456521700−0.9945652170.9130434780.885869565
FAIM33010.967391304000.885869565
0.994565217−0.994565217
1600014C10RIKRIKEN cDNA 1600014C10 gene3000.9673913040
00.967391304−0.940217391−0.940217391
PDCD1programmed cell death 13“PathogenicSignature (neg), CellSurface”0
00.96739130400.777173913−0.9673913040.8586956520.722826087
ID3inhibitor of DNA binding 33“ImmuneResponse, TF, Known, PathogenicSignature (pos)”
100.96739130400−0.9673913040.8586956520.994565217
SLFN2schlafen 23000.96739130400−0.967391304
0.7771739130.641304348
ZBTB32zinc finger and BTB domain containing 323TF010.967391304
00−0.9673913040.9673913040.994565217
NFKBID4000.94021739100.75−0.994565217
0.8315217390.586956522
IL16interleukin 164“Known, CytokineChemokine”100.9402173910
−0.9402173910.9130434780.4510869570.505434783
SLAsrc-like adaptor4PathogenicSignature (pos)000.940217391
00.804347826−0.750.8315217390.994565217
GM27924000.94021739100.831521739−0.885869565
0.9673913040.967391304
MS4A4B“membrane-spanning 4-domains, subfamily A, member 4B”5
PathogenicSignature (pos)000.91304347800−0.586956522
0.8858695650.913043478
TGTP25PathogenicSignature (pos)000.9130434780.913043478
0.6956521740.9130434780.3695652170.39673913
TGTP15000.9130434780.9402179310.750.913043478
−0.559782609−0.586956522
IL17Ainterleukin 17A6
“ImmuneResponse, PathogenicSignature (neg), Known, CytokineChemokine”10
0.88586956500−0.9130434780.4510869570.804347826
ACSL4acyl-CoA synthetase long-chain family member46PathogenicSignature (neg)
000.8858695650.8858695650.885669565−0.6413043480
0.260869565
SOCS2suppressor of cytokine signaling 26
“PathogenicSignature (neg), Known, CytokineChemokine”100.8858695650
00.885869565−0.967391304−0.885869565
FOXP1forkhead box P16“ImmuneResponse, TF”000.885869565
0.885869565−0.9945652170.2065217390.2880434780
SYTL3synaptotagmin-like 36000.885869565−0.858695652
0.9402173910.8586956520.9130434780.858695652
MAPKAPK36“Kinase, PathogenicSignature (neg)”000.885869565
00.940217391−0.88586956500.179347826
PRKCSHprotein kinase C substrate 80K-H6000.885869565
0.8858695650−0.3152173910.9945652170.940217391
GNG10“guanine nucleotide binding protein (G protein), gamma 10”600
0.8858695650.9402173910.885869565000.423913043
GM28336000.88586956500.885869565−0.804347826
0.8858695650.831521739
PPID6000.885869565−0.8858695650.75−0.940217391
0.6684782610.233695652
CD5LCD5 antigen-like6CellSurface010.85869565200
−0.8586956520.9402173910.967391304
TNFtumor necrosis factor6“ImmuneResponse, Known, CytokineChemokine”10
0.8586956520.8586956520.858695652−0.5597826090.396739130.423913043
IFI47interferon gamma inductible protein 476000.8586956520
00.940217391−0.804347826−0.668478261
CD44CD44 antigen6CellSurface000.85869565200.858695652
−0.8043478260.8586956520.858695652
GADD45Bgrowth arrest and DNA-damage-inductible 45 beta600
0.8586956520.8586956520.913043478−0.5054347830.6413043480.559782609
SH2D1A6“ImmuneResponse, PathogenicSignature (pos)”000.858695652
00−0.36395652170.9945652170.858695652
GATMglycine amidinotransferase (L-arginine: glycine amidinotransferase)70
00.831521739000.831521739−0.831521739−0.858695652
N4BP1NEDD4 binding protein 17000.83152173900
0.722826087−0.913043478−0.913043478
NEK6NIMA (never in mitrosis gene a)-related expressed kinase 67
“Kinase, PathogenicSignature (neg)”000.83152173900
0.8315217390.6413043480.75
SUSD37000.831521739−0.8315217390.8586956520.75
00
MOV10Moloney leukemia virus 107000.83152173900
0.75−0.967391304−0.831521739
DUSP47000.83152173900−0.831521739
0.8315217390.641304348
IER3immediate early response 38PathogenicSignature (neg)00
0.8043478260.9945652170.804347826000.75
EEA1early endosome antigen 18000.80434782600
−0.9402179310.8043478260.315217391
BCAT1“branched chain aminotransferase 1, cytosolic”800
0.80434782600−0.9130434780.396739130.423913043
MAPKAPK2MAP kinsae-activate protein kinase 28“Kinase, PathogenicSignature (neg)”
000.8043478260.9130434780.8043478260−0.668478261
0.804347826
SASH3SAM and SH3 doman containing38ImmuneResponse000.804347826
−0.913043478−0.8043478260.5869565220.4239130430.451086957
STAT4signal transducer and activator of transcription 48
“ImmuneResponse, TF, Known, PathogenicSignature (pos)”100.8043478260
0.858695652−0.3152173910.8858695650.804347826
CTLA2Bcytotoxic T lymphocyte-associated protein 2 beta900
0.777173913000.831521739−0.885869565−0.777173913
CCL20chemokine (C—C motif) ligand 209“ImmuneResponse, Known, CytokineChemokine”
100.77717391300−0.7228260870.5326086960.777173913
PDGFB“platelet derived growth factor, B polypeptide”9PathogenicSignature (neg)
000.77717391300−0.7771739130.3152173910.342391304
TNFSF9“tumor necrosis factor (ligand) superfamily, member 9”9
“ImmuneResponse, Known, CellSurface, CytokineChemokine”100.7771739130
0.777173913−0.478260870.750.940217391
IFI35interferon-induced protein 359TF000.7771739130
0.8043478260.695652174−0.695652174−0.777173913
1810029B16RIKRIKEN cDNA 180029B16 gene9000.7771739130
0−0.5326086960.6141304350.913043478
GEMGTP binding protein (gene overexpressed in skeletal muscle)10
PathogenicSignature (pos)000.7500.831521739−0.423913043
0.3695652170.75
IL4RA“interleukin 4 receptor, alpha”10
“ImmuneResponse, SurfaceReceptor, Known, CellSurface, CytokineChemokine”10
0.750.8858695650.7228260870.451086957−0.505434783−0.641304348
INPP5Binositol polyphosphate-5-phosphatase B10000.750
0.722826087−0.5326086960.7228260870.75
RHOF10000.7500.75−0.750.695652174
0.559782609
PPANpeter pan hom*olog (Drosophila)10000.7500
−0.9402173910.750.179347826
MAGOHBmago-nashi hom*olog B (Drosophila)10000.75−0.913043478
0−0.750.5326086960.532608696
TYW310000.7500−0.7771739130.777173913
0.668478261
IRF1interferon regulatory factor 111
“ImmuneResponse, TF, Known, PathogenicSignature (pos)”100.7228260870
00.8858695650.3695652170
CD40LGCD40 ligand11“ImmuneResponse, Known, CellSurface, CytokineChemokine”10
0.72282608700.75−0.7228260870.6413043480.614130435
BCL2L1BCL2-like 111PathogenicSignature (neg)000.7228260870
0.722826087−0.8043478260.3423913040.369565217
SLC35A111000.72282608700−0.777173913
0.6141304350.315217391
RPF211000.722826087−0.8586956520−0.722826087
0.2065217390
TM2D3TM2 domain containing 311000.72282608700
−0.7228260870.7771739130.722826087
IRF4interferon regulatory factor 412
“ImmuneResponse, TF, PathogenicSignature (neg), Known”100.6956521740
00.559782609−0.75−0.722826087
IL18R1interleukin 18 receptor 112
“ImmuneResponse, SurfaceReceptor, Known, CellSurface, CytokineChemokine”10
0.69565217400.94021739100.7228260870.695652174
ZFP36zinc finger protein 3612000.6956521740.9945652170
0.695652174−0.5869565220.423913043
ASRGL1asparaginase like 112000.69565217400
CBWD1COBW domain containing 112000.69565217400
−0.7771739130.5054347830.668478261
GTPBP4GTP binding protein 412000.6956521740−0.885869565
−0.6956521740.2065217390.206521739
IRF2interferon regulatory factor 212TF000.695652174
0.913043478−0.6956521740.2336956520.1793478260.668478261
HIVEP3human immunodeficiency virus type I enhancer binding protein 313TF0
00.66847826100−0.6684782610.2336956520.206521739
MS4A6B“membrane-spanning 4-domains, subfamily A, member 6B”13
“PathogenicSignature (pos), CellSurface”000.6684782610
0.66847826100.9673913040.722826087
OLFM213000.66847826100−0.6684782610
0.152173913
CCR6chemokine (C—C motif) receptor 613
“SurfaceReceptor, PathogenicSignature (neg), Known, CellSurface, CytokineChemokine”1
00.6413043480−0.913043478−0.64130434800.369565217
COG6component of oligomeric golgi complex 613“ImmuneResponse, Known”10
0.641304348000−0.559782609−0.641304348
PIK3R1“phosphatidylinostol 3-kinase, regulatory subunit, polypeptide 1 (p85 alpha)”13
000.6413043480.83152173900.451086957−0.614130435
0.315217391
IL21Rinterleukin 21 receptor13
“ImmuneResponse, SurfaceReceptor, Known, CellSurface, CytokineChemokine”10
0.641304348000.668478261−0.8043478260.206521739
IMP2inositol (myo)-1 (or 4)-monophosphatase 213000.641304348
000.6413043480.179347826−0.668478261
RSPH3A13000.641304348000.532608696
0.940217391−0.722826087
CDS2CDP-diacylgycerol synthase (phosphatidate cytidylyltransferase) 213
PathogenicSignature (neg)000.641304348000.641304348
−0.695621740.39673913
CD42ACD24a antigen13“ImmuneResponse, PathogenicSignature (pos), CellSurface”0
00.61413043500−0.6141304350.5597826090.233695652
IL24interleukin 2413“PathogenicSignature (neg), Known, CytokineChemokine”10
0.614130435000.614130435−0.994565217−0.940217391
SLC15A3“solute carrier family 15, member 3”13PathogenicSignature (neg)00
0.61413043500.913043478−0.478260870.2336956520.614130435
IKZF313TF000.614130435000.559782609
0.940217391−0.858695652
HIST1H4D13000.614130435−0.9945652170
0.5054347830.152173913−0.614130435
ITGAVintegrin alpha V13CellSurface000.6141304350
0.831521739−0.58695652200.342391304
PROCR“protein C receptor, endothelial”13“ImmuneResponse, SurfaceRecpetor, CellSurface”
000.614130435000.288043478−0.913043478−0.831521739
TPRtranslocated promoter region13000.614130435−0.967391304
000.6141304350.614130435
IL9interleukin 914“ImmuneResponse, PathogenicSignature (neg), Known, CytokineChemokine”
100.586956522000.559782609−0.994565217−0.967391304
CD84CD84 antigen14CellSurface000.58695652200
0.5869565220.3152173910.342391304
TREML214000.586956522000.532608696
0.668478261−0.804347826
POLB“polymerase (DNA directed), beta”14000.5869565220
0−0.6684782610.8586956520.233695652
SMAP1stromal membrane-associated protein 114000.5597826090
00.260869565−0.641304348−0.641304348
INSL6insulin-like 614000.55978260900−0.451086957
0.4510869570.559782609
CYLD14000.5597826090−0.8586956520
0.478260870.559782609
MAPRE2“microtubule-associated protein, RP/EB family, member 2”1500
0.53260869600.940217391−0.4239130430.5597826090.532608696
STK38L15Kinase000.53260869600−0.858695652
0.4239130430.260869565
DOT1L15000.53260869600.777173913−0.532608696
0.2608695650.260869565
BDH215000.53260869600−0.451086957
0.3152173910.831521739
ACAT315000.32608696000.233695652
0.586956522−0.586956522
BTBD1916000.50543478300−0.505434783
0.3695652170.39673913
BC031181cDNA sequence BC03118116000.5054347830
0.777173913−0.3967391300.505434783
SP3trans-acting transcription factor 316TF000.505434783
0.96739130400−0.6141304350.505434783
IRAK1interleukin-1 receptor-associated kinase 116
“ImmuneResponse, Kinase, Known, CytokineChemokine”100.505434783
0.9402173910−0.2065217390.5054347830.505434783
EXOSC1exosome component 116000.50543478300
0.5054347830.3152173910.586956522
EBI3Epstein-Barr virus induced gene 317“Known, CytokineChemokine”10
0.4782608700−0.3152173910.8315217390.967391304
ACIN1apoptotic chromatin condensation inducer 117TF000.47826087
000.315217391−0.505434783−0.75
FASTKD2FAST kinase domains 217000.4782608700
0.85869565200
PPP1R8“protein phosphatase 1, regulatory (inhibitor) subunit 8”1700
0.47826087−0.94021739100−0.5869565220.47826087
MAF1MAF1 hom*olog (S. cerevisiae)17TF000.4782608700
0.478260870.3423913040.586956522
TRMU17000.4782608700−0.451086957
0.8043478260.695652174
STAT5Bsignal transducer and activator of transcription 5B18“ImmuneResponse, TF, Known”
100.451086957000.2880434780.4239130430.451086957
LTAlymphotoxin A18“ImmuneResponse, Known, CytokineChemokine”10
0.45108695700.722826087−0.2065217390.4239130430.451086957
EGR2early growth response 218“TF, PathogenicSignature (neg)”00
0.45108695700.695652174−0.3695652170.3695652170.39673913
SIRT618TF000.451086957000.5597826090
0.233695652
EXT1exostoses (multiple) 119000.423913043000
0.396739130.423913043
NHEJ1nonhom*ologous end-joining factor 119000.4239130430
00.423913043−0.47826087−0.695652174
SERPINF1“serine (or cysteine) peptidase inhibitor, clade F, member 1”20
000.3967391300−0.396739130.6956521740.831521739
TGM2“transglutaminase 2, C polypeptide”20000.396739130
00.39673913−0.885869565−0.885869565
ADI1acireductone dioxygenase 120000.3967391300
0−0.47826087−0.532608696
RNF181ring finger protein 18120000.3967391300
−0.396739130.1793478260.179347826
METT10D20000.3967391300−0.342391304
0.5869565220.532608696
NIP7nuclear import 7 hom*olog (S. cerevisiae)20000.39673913
00−0.8315217390.396739130
PSRC1proline/serine-rich coiled-coil 120000.3695652170
00.3695652170.2880434780.288043478
TBL2transducin (beta)-like 220000.36956521700
0.3695652170.2880434780.342391304
PQLC3PQ loop repeat containing20000.36956521700
0.641304348−0.478260870.233695652
NIF3L1Ngg1 interacting factor 3-like 1 (S. pombe)20000.369565217
00−0.5869565220.3423913040.369565217
CYSLTR1cysteinyl leukotrine receptor 121PathogenicSignature (neg)00
0.342391304000.342391304−0.8043478260.179347826
PDLIM5PDZ and LIM domain 521PathogenicSignature (neg)000.342391304
00−0.61413043500
LAG3lymphocyte-activation gene 321
“ImmuneResponse, PathogenicSignature (pos), CellSurface”000.342391304
00.777173913−0.2608695650.9402173910.315217391
SLC25A13“solute carrier family 25 (mitochondrial carrier, adenine nucleotide
translocator), member 13”21000.34239130400
0.34239130400.451086957
GTF2E121TF000.34239130400−0.6141304350
0.342391304
TSPAN6tetraspanin 622PathogenicSignature (neg)000.3152173910
0−0.50543478300
CHD222“TF, PathogenicSignature (pos)”000.31521739100
0.6684782610.1793478260.152173913
ASB3ankyrin repeat and SOCS box-containing 322000.315217391
000.3152173910.2336956520.260869565
DAPL123000.2880434780000.913043478
0.885869565
UBA3ubiquitin-like modifier activating enzyme 323000.288043478
00−0.2880434780.2336956520.559782609
ZUFSPzinc finger with UFM1-specific peptidase domain23TF00
0.28804347800−0.2880434780.6413043480.315217391
MED21mediator complex subunit 2123000.2880434780
0.83152173900.2608695650.288043478
NGDN“neuroguidin, EIF4E binding protein”23000.2880434780
−0.913043478000.288043478
PIN423000.288043478000.2880434780
0.2608695650
BCDIN3DBCDIN3 domain containing23000.28804347800
−0.3423913040.5326086960.152173913
RIPK3receptor-interacting serine-threonine kinase 324Kinase00
0.260869565−0.9402173910000.260869565
CENPMcentromere protein M24000.260869565000
00.260869565
TACC3“transforming, acidic coiled-coil containing protein 3”2400
0.260869565−0.99456521700.2608695650.2336956520
STAG124TF000.2608695650000.451086957
0.505434783
PDSS1“prenyl (solanesyl) diphosphate synthase, subunit 1”2400
0.26086956500−0.2608695650−0.532608696
CEP5724000.260869565000.39673913
0.3152173910
MRPS22mitochondrial ribosomal protein S2224000.2608695650
0−0.2608695650.478260870
KIF5Bkinesin family member 5B25PathogenicSignature (neg)00
0.2336956520−0.695652174−0.2336956520.4239130430
BC05532425000.2336956520000.75
0.695652174
CAMTA125TF000.233695652000.233695652
0.5326086960.47826087
C2CD3C2 calcium-dependent domain containing 326000.206521739
000.3423913040.2065217390.206521739
NGLY1N-glycanase 127000.179347826000.47826087
00
DEGS1degenerative spermatocyte hom*olog 1 (Drosophila)2700
0.17934782600−0.4239130430.396739130
GALK1galactokinase 128Kinase000.152173913000
00.39673913
SPSB3splA/ryanodine receptor domain and SOCS box containing 32800
0.15217391300000.152173913
CSNK1E“casein kinase 1, epsilon”29Kinase000.125000
0.3423913040.369565217
TTC27tetratricopeptide repeat domain 2729000.12500
−0.2336956520.2880434780
LINS29000.12500−0.20652173900
INO80CINO80 complex subunit C30PathogenicSignature (neg)00
0.0978260870000.2880434780.288043478
FDX1ferredoxin 130000.097826087000
0.2608695650.288043478
ITM2Aintegral membrane protein 2A31000.07065217400
00.2065217390.206521739
MTPAP31000.07065217400.6956521740
0.5054347830
DHX9DEAH (Asp-Glu-Ala-His) box polypeptide 932000.043478261
0000.1521739130.152173913
CEP55centrosomal protein 5533000000
0.4510869570.47826087
FAM118A“family with sequence similarity 118, member A”33000
00000
2500003M10RIKRIKEN cDNA 2500003M10 gene3300000
000
ICAM1intercellular adhesion molecule 133“ImmuneResponse, CellSurface”00
000000.369565217
GNPDA2glucosamine-6-phosphate deaminase 23300000
00.3423913040
MTA3metastasis associated 333TF00000.722826087
00.2608695650
CCDC9coiled-coil domain containing 93300000
00.2065217390.179347826
2210016L21RIKRIKE cDNA 2210016L21 gene3300000
00.1793478260
TABLE 3
Normalized data of lipidome analysis. WT and CD5L-/- näive T cells
were differentiated. Cells and supernatant were harvested at 96 hours and subjected to
MS/LC. Three independent mouse experiments were performed.
TGFb1 + IL6_WTTGFb1 + IL6_CD5LKO
TGFb1 + IL6 + IL23_WTTGFb1 + IL6 + IL23_CD5LKO
TGFb1 + IL6_no cellsTGFb1 + IL6 + IL23_no cells
TGFb1 + IL6_WTTGFb1 + IL6_CD5LKOTGFb1 + IL6 + IL23_WT
TGFb1 + IL6 + IL23_CD5LKO
MethodCompoundm/zRTHMDB ID Metabolite l_media1a_media1b_media
 2_media2a_media2b_media 3_media3a_media3b_media4_media
4a_media4b_media5_media5a_media5b_media6_media6a_media
6b_media1_cells1a_cells1b_cells2_cells2a_cells2b_cells
3_cells3a_cells3b_cells 4_cells 4a_cells4b_cells
C18-NEG TF1355.241710.85Internal StandardPGE2-d4 26815668 26815668
2681566826815668 268156682681566826815668 26815668
2681566826815668 26815668 26815668 2681566826815668
26815668 26815668 26815668 26815668 3219023232190232
321902323219023232190232321902323219023232190232
32190232321902323219023232190232
C18-NEGTF16227.200616.5HMDB00806Myristic acid
39044592545447346041223624171
C18-NEGTF18255.231917.6HMDB00220Palmitic acid51206669
55956114
16628393742884660457356885506
C18-NEGTF22283.263218.45HMDB00827Stearic acid
5586
254934119357451925674
C18-NEGTF6303.231916.95HMDB01043Arachidonic acid
181344214866264314172799
212733190235
4403
C18-NEGTF9327.231916.7HMDB02183Docosahexaenoic acid7338
4140388565391793411429458935
3921934153254137
C18-NEGTF3295.227914.3HMDB0466713-S-HODE5135
97565592482151422437632547287763826936384
178169894270065063657308170341739585814411461798563454
20151
C18-NEGTF5319.226815HMDB111345-HETE
568613028274301712623621
C18-NEGTF2319.226814.85HMDB0611112-HETE
605651534051 571461616076556886619527
C18-NEG TF4319.226814.6HMDB0387615-HETE
253617666295464913848717
C18-NEG TF20351.216610.85HMDB01220PGE29450216562775971144941137472
119832128956156919133633105390116751100092113499 922441059539787696506
111171166567145815151644150862128839143503116128 138440150043153804132273
160265
C18-NEG TF21378.240412.9HMDB00277Sphingosine 1-Phosphate
4855247264961289659479
17886241581267886777199953506756683142635871225595958936
321572631441178
C18-NEG TF8391.284313.7HMDB00626Deoxycholic acid/Chenodeoxycholic acid.
33842129235729312491780429575163065156847450527138828804
2246966746706046745665810873011469755174615102822272116
1431461141926116130107583279792
C18-NEGTF7407.279212HMDB00619Cholic acid 84131282998918958541060513
945565 95986187852291477496359587498191545793703110449799738791012834 1083731
1055547 1066135279570 14865459232287384326291472859274745249696
4172158405
C18-NEGTF13432.310913.65HMDB00698Glycolithocholic acid358320307449804941
446981464141452405420928417936453376399497450928417387494639474051511277
464895434862599613306566191381221224228104218413348429 112692233360
245261143848236992
C18-NEGTF10448.305812.05HMDB00637Glycochenodeoxycholic acid7723861 7584903
167307449175994892590785664718185757 8624925 8045296 1872327837336687074389173126
8790607913016797156219130630 9973976 23711485145533014463192993424752720397
2879944162822624579298221653840
C18-NEGTF12448.305812.4HMDB00631Glycodeoxychtilic acid1294337 12184072895264
15262751473921 14322691381864 1434129 134417212355041371185 1399989145533815305551537222
1682132 1596115 1620654732878147574 10613696251105425870148947411119858
81342670296214418
C18-NEGTF14448.305811HMDB00708Glycoursodeoxycholic acid2750725021
779792668814758613697012166214785111849364251343800822933
289172808830834
C18-NEGTF11464.300711HMD00138/Glycocholic acid2936939 30418406885427
35550563419546 3325521318579432935103182289308472032660523319168 33570903482238 3297525
3631694339935138972724434151133513201166765503766274685156958617
54538475607127209
C18-NEGTF27482.293512.85HMDB00722Taurolithocholic acid2843408 27258026610292
3637940343678631870523098266313757932685032985852 322623431537833478013 3330871 3486687
356922633900693871627678802178585589613015369549488585582443382
9531835588086020
C18-NEGTF23498.288411.35HMDB00951Taurochenodesoxycholic acid97113339187433
217654181081414711432901105001381038792810774996
10569920985704410085203101357011058833910195689
1061526010721686106010111110621830052061274941 983454508378
8670349211013540079833310946736 34699768132961033422
C18-NEGTF25498.288411.65HMDB00896Taurodeoxycholic acid129371212993442209563
1097889120480011867021291165 113839610629441244038 10814701190853111417610282421053480
15200991145610 11641201108272 8026646738354958227181235219821028133 564107564084
1012843535227539591
C18-NEGTF26498.288410.3HMDB00874Taurohyodeoxycholic
acid/Tauroursodeoxycholic acid60760353016974950365618564781460525409550364218
347874 292734365255317061314646277955320850281048216878298726914503837410
858965683257700081687038778814613338 617107530759531342465380
C18-NEGTF24514.283310.35HMDB00036Taurocholic acid368321537881208155464
42890304360551407820639399553860197375038137308893862014 40767094195779140736663961542
4543412 420904745846831022009 6094455304723390784870875045781079071478051474946
945912412479395216
C8-pos2646622.44446.88Internal StandardC24:0 PC10125391158015996417
11617711043447 10870521030377103770310235661019157 1013596103211692177110885901006026
10594051018984998964
C8-pos1266468.30884.58HMDB10379C14:0 LPC3332267951415977
7970790237136841685054167519666110608963885479207
8432592988338161635312478986572757776896256148336
92386158
C8-pos1392494.32434.75 HBDB10383C16:1 LPC70145646828213214
14635124877561106241196711642130261110542413432534605939641
421622597770629879855613284103008166101181213379689734
109608711
C8-pos1685496.34005.12HMDB10382C16:0 LPC205378185047284161287427
351653316290164849308496316058293955316343280511615570602681657403558708
602325451796408232274878300060310202378407255703303547344659258328314892
371463287901
C8-pos1536520.34124.95HMDB10386C18:2 LPC2120165316062623
31361558161895146721151490199129086318062326027216
293002084112541762282712301764692177114279752009
1520778
C8-pos1817522.35595.31HMDB02815C18:1 LPC37313332354706354536
6599054998374845304453484588516062051574336070323141340550305699
3319602123357472595810121857138419130839100262116800120798109338114039
122626109496
C8-pos2049524.37165.73HMDB10384C18:0 LPC8918693212150175106945
15632013928278631143240140446 115978144991131814513627484891539919452124
541318380513515430389585382528305556425178306964290336483268 341639377311
496284398060
C8-pos1565544.34084.98HMDB10395C20:4 LPC3155462629
10789573417133861538172036347160485703394843474
4510325078
C8-pos1686518.32225.12HMDB10393C20:3 LPC9853679703133563127294
16162014031073814147798143482135002147118135323283389278500302446252437
273364205344172119123723131367139487169528118266132188154304116932147822
175532138138
C8-pos1543568.34094.96HMBD10404C22:6 LPC494584
20736339919416424160851303613927
135418409
C8-pos1716434.29165.15HMDB11503C16:0 LPE476111111799
3652548114310357693422131750
222906251317825162983312321742481416217903408
32991874
C8-pos1843480.30935.33HMDB11506C18:1 LPE2218146613941398
120921234672170419181684226310832374188518701811
23021519109501031112382140751240988421263996701107113057
107429984
C8-pos2057482.32435.75HMDB11130C18:0 LPE
354163023724614205952639119130196113135221418240543024325746
C8-pos1516502.29294.89HMDB11517C20:4 LPE
1312997428024720374446125865987056801760290127862
C8-pos3036704.52198.17HMDB07870C30:1 PC14998251612231525216
19814213835701815177133291809217893166631456247924801441
1314219331794972637808 211522339348532521297 2762885 29917822105914 17886413155606
2511736 2342464
C8-pos3174706.53818.50HMDB07869C30:0 PC95742139255118195139529
104080121173263005100045977221031521030969917318413226251859215786
19556902423932912136805979555005 1149587211288552
114649868510608110033308981976128832351214287211754712
C8-pos3094730.53768.32HMDB07874C32:2 PC294369714181476
6192607966932840
41160201419077261312863 2753385 192711219116191924029159289012742482097057 1740467
1617004
C8-pos3294732.35398.66HMDB07873C32:1 PC205285286601262106282180
231946 24207149853721027223147422254920714720338897691869879054271470
83548511045519496733848332222468033510721226193771
2778416327670368 25706127226023063242467529776663
26605312
C8-pos 3502734.56938.96HMDB07871C32:0 PC309174 390337393097385227
321514354180391822311399324195329510336354318227287073 278414320104264113
2851511902973328796925702073162362541646460716426187
167315271217917419370270164316001789046221505865
18037146
C8-pos3164756.55308.49HMDB08006C34:3 PC
702323
8751719888566734831023525867319827735701311723025644826810044783009740564
C8-pos 3370758.56908.80HMDB07973C34:2 PC169769212274214180212897
212089187074402369163864177569164380162867167024188001150595175966139347
1521711046002616673326958332200678303368613526641163
255605172340534722095533204366882548503723869850
22262117
C8-pos3631760.58499.11HMDB07972C34:1 PC1437013178113519337221716678
15585301672468216224714937131715862147171314777601479214161996915622521751912 1444734
15231001105535178901591143335374109157114104342448104833865
111146354117279402122152546109711913123307892123029810
119131624
C8-pos3853762.60049.40HMDB07970C34:0 PC93512121057127626122565
1028421095231089691028701170471060101070859905812639410685012276297715
10270567949896882579197295888575472605560627465415346472782872131266142124 6076837
76122065900373
C8-pos3481784.58468.93HMDB08105C36:3 PC147616187415202520172746
173027186187191142161841161995153635170015158580199672189187215520175659
19453012520471602459618295 77147138676343779504583141286651223761048777210786945637
81640577771753
C8-pos3732786.60059.24HMDB08039C36:2 PC463073 626529642678540064
5012055476091132092467091522166430865451021454155502200 468531516696427261
45437832794680860255945077288918369710403420290569180
921685859163237386054248862700478064309388651540
86428668
C8-pos 3995788.61639.53HMDB08038C36:1 PC888964 112046611538881063681
9624941020635 12791059341101093301 9150489162389034301180935 10526071154069 935346
1039898 71435210049485877489842617387374715782155637534
600542595595468071002509601734205902838068769288
63205618
C8-pos3355806.56878.77HMDB07991C38:6 PC102208137870139801127593
12462612737884574121859139186107861115065114944186101151302164681144477
153478101856731004908514411716737076484366515978497744443482419329556145
496703475949
C8-pos 3619810.60029.11HMDB08048C38:4 PC116733150165163751136678
13734115163295753129561153570119951128201117325162699152936176914139421
1616371090671119481172842213035151786146148069315168611485202 137092613317651448781
13577281458638
C8-pos 3856812.61449.40HMDB08047C38:3 PC338892 418953471728 409861
385841425796299347376452428895383143386432367059523006 455579527243426327
47524732442132393623647621 2833978 27283322922200 3106003 2670371284147626841902651238
30382072840122
C8-pos4068814.63199.62HMDB08270C38:2 PC49919730279493971994
5541374436154969584187639057351520995774675316646497286748600
6436436744929384095208561001084480823748421060950622783121018578760 8643930
757802686003478689160
C8-pos 3148826.53568.45HMDB08511C40:10 PC
1763426748502432216920381208531734817961841439676035
C8-pos 3350828.55118.77HMDB08731C40:9 PC42395525785827950320
522775933535165526765670049452475624760880225646317462159199
6655940590194698287249 130844220042150639144369158304133617123826183219
152956141857
C8-pos3540834.59999.01HMDB08057C40:6 PC28677403154584142134
293543520922163295204148725526273852741243491391094698435182
3589328580396980487550307629580470379342413709 364802296967313224412487
347813331729
C8-pos3518740.55578.97HMDB11212C34:4 PCplasmalogen479798485518
1139825152578590011064122130522890466
975243469131244965803 80484035528922 589554864812305421549 46870226946355
5858720 5478828
C8-pos3632744.58949.11HMDB11210C34:2 PCplasmalogen124542245018110
180866210105667087779708550794560706583720735216094
5643610731721168698511672448802582211282617865744692213773
10856382850363277280709822941 86894988418221
C8-pos3851746.60589.40HMDB11208C34:1 PCplasmalogen-A182689244679236142
233131157412181046693064142337154211145569147135140333951378431494238
706298050354294113475188750812875890642759320720
575901486074426065669647606488085394619863244389
6208654357585328
C8-pos3653768.58899.13HMDB11310C36:4 PCplasmalogen57201517713211
127447914 78833042876266416672640424683264552873711
2480362195346371227535900 39632021920937 390856345393794602133 3752765 3707617
4063852 37748163949412
C8-pos3756770.60519.27HMDB11244C36:3 PCplasmalogen308979636406
490963893488834827321648325729563199269622952585
39122784209231625513313974 24964833416792266585227721513002241 2361016 2289667
24770372534783 2423949
C8-pos3978772.62029.52HMDB11243C36:2 PCplasmalogen168433052133682
28970134532279310123313874190071300599359837836282535283
620458424795129499171179173010168997124085359858472
1021694611686965102658519667752 981060198909939199747
C8-pos4219774.6368 9.82HMDB11241C36:1 PCplasmalogen179023085933665
2499297851186912149864398920657778688346282539815139
24223980286729606412157260471492339410393625
113108711117046613061965130560461173435011681531
1228598011488631
C8-pos3654790.5759.13HMDB11229C38:7 PCplasmalogen178362523928831
246371622121351200492098126327192501783221398154721929424064
1596022703160472077724 317029217732912377221190795121136042111468 17146171634288
1900370 1741635 1798776
C8-pos3752792.5868 9.26HMDB11319C38:6 PCplasmalogen852578067202
78606572984732224317970480827415195311168755614587
778662684227902722 11424879121511196680 9818809841571041252822009812605
898017870846884659
C8-pos3909796.62029.43HMDB11252C38:4 PCplasmalogen
9872831078238776557928342837424905446843780 727237736031 760104798661
768371
C8-pos3912818.60249.44HMDB11294C40:7 PCplasmalogen
295190373860270756338174294254315094290149247372244937 273112266929
262422
C8-pos3313690.50648.67HMDB08924C32:1 PE
547852447388 270517356301320563333391291585317531265621360641344883322313
C8-pos3061692.52238.25HMDB08923C32:0 PE
1982161880001085151101761017641222809194510059684567125078116680112959
C8-pos3342720.55388.74HMDB08925C34:0 PE7869113521248111974
6597927185567874976773408328 868013062867375467099
586155499554291117347458844457968495784535348383557494401421738501847
558652509220
C8-pos3415740.52228.83HMDB08937C36:4 PE
146129218649118460142636124663131621118010129102104233149847143295130559
C8-pos3484742.53768.95HMDB09060C36:3 PE
340121509273450084506655416406447064416794385030424752417774438176414984
C8-pos3733744.53329.24HMDB08994C36:2 PE12784146711316310983
5651568152821260254552245494540818665418731086
18131933959624208916 4122531505593639239684165180444850935697143960404 3807167
36802463786859
C8-pos3999746.56849.33HMDB08993C36:1 PE24108354403114141722
239693428747783208022652219924202701932118675130181851111254
190851674220058242363433 178109915038951642338 17543451592292194717316887591726200
18551811744152
C8-pos3357764.52228.77HMBD09102C38:6 PE
511494805625196349922382128201195723047821866279102614321786
C8-pos3506766.53568.96HMDB09067C38:5 PE
256395514281 489954539670420889472317 397375396650472420403384414368435699
C8-pos3765768.55309.28HMDB09003C38:4 PE
666081903253709029539081572674633779568470699881 651459591925705578672680
C8-pos4080772.58439.63HMDB08942C38:2 PE
6143671717 689906502953722653475095854137 62557567085183556264
C8-pos3700792.55229.21HMDB09012C40:6 PE
729949653444341261551739847544298854181145483473904815842202
C8-pos3637700.52699.11HMDB11343C34:3 PEplasmalogen3268 45293581
3508100134351043694892799420131205315529
31315121521676374 141675415048131473490 160149116639591502088 1403889 1554480
1577519 1566274
C8-pos3854702.5428 9.40HMDB08952C34:2 PEplasmalogen741288765781951
67685446785975.51297835835755681472805317541392148171841515351
145652019016468 84863857744044 739827265317267174122 77037237111560 70303627049910
71749807402199 7251904
C8-pos3640724.52679.11HMDB11410C36:5 PEplasmalogen407525430552833
485633777140184737603499334243303553488926915107371354613544
1317216303156773922581 5501537490844549658774842835262135512234648432664855766
4634771 5015778 5044780
C8-pos3748726.54199.26HMDB11442C36:4 PEplasmalogen
1424357 1566291126238313821561335926144030414655971265627 120424312575161382164
1317246
C8-pos3985728.55819.53HMDB11441C36:3 PEplasmalogen
1925675 2110833200547923510682067346219776622081211776859196933720195562059995
2039071
C8-pos4193730.57439.81HMDB09082C36:2 PEplasmalogen
4179185 31901903372145 2458572 27325912758344 28001562969895294739027172662933454
2973411
C8-pos4416732.589010.09HMDB09016C36:1 PEplasmalogen
15518110399973301849346683289010220883413318348
8091
C8-pos3575748.52709.05HMDB11420C38:7 PEplasmalogen499673874712
5394373054371683744056788310154213898727904514
3752111235218717022995521919386206819620060112193189197974519939731841759
1987230 21492322042178
C8-pos3673750.54249.17HMDB11387C38:6 PEplasmalogen280960624439
5818538143121502157713285240036893794258531862475
349824013124258465332648033002272325098130559683338845305021428187212867826
27756953103546 3128792
C8-pos3895752.55809.42HMDB11386C38:5 PEplasmalogen151123651670
91314731931104212241391123793414875647981074
86811191878728946900779808932845912841931958588821804762124779000
802626863949855063
C8-pos4271756.59009.89HMDB11384C38:3 PEplasmalogen
173311150214188897121355136541163291113677134051161849109697151354
143403
C8-pos3669796.52319.16n/aC42:11 PE plasmalogen
52892986234910473593614736271284312542916635217633528640358
C8-pos2944772.54627.88n/aC36:3 PSplasmalogen
571413951528147247832871920741196263304723444287813242630383
C8-pos1809300.28965.30HMDB00252sphingosine6279626257175830
561059863683578564375857578261865987659559586481
800970133544508172778406756969617978548759827425
58226297
C8-pos3317538.51938.68 HMDB04949C16:0 Ceramide(d18:1)2312113174392
538128572784641221482180164013772053172202
3869352292245895514680649030719654667782553474704804545020
777682826322679639
C8-pos4349622.613110.02HMDB04952C22:0 Ceramide(d18:1)
278913453439152636145634164607139831162430182508143835167696179750
141571
C8-pos4556650.644410.42HMDB04956C24:0 Ceramide(d18:1)209896985789
7208389221171429604175825793121278586611501290
53746111631809880239665191548810662989850358670551032546 1083149871600
10970761074958872705
C8-pos4396648.628710.07HMDB04953C24:1 Ceramide(d18:1)
7633341162889576791515576551942603086578682548454518092554878595973
567193
C8-pos3000673.54348.04HMDB12097C14:0 SM79599559922210776
91421159112733908792177689777088831218111585939112347
110874828 350938253737254918195241214284227967228444238444220909230001
253549237241
C8-pos3051701.55918.20n/aC16:1 SM5468668168671776425863072
639435705757853623626392657874553037031677401780696320769809
39520611116522262517495583006489869541404520994481286444595509098535642
499032
C8-pos3204703.57508.54HMDB10169C16:0 SM620807740512748856733877
667158695108627640662029730328664682625295632621795200868058821109673210
729767510351168964858378801 6983095724278671148197189596651863375484286816746
819501277909217269105
C8-pos3328 729.59098.70HMDB12101C18:1 SM36412455944754944730
419784423326625400954662038147403473736161111481865241943633
4947831248119011784647008054835575176371959973626516576961558
6523356986
C8-pos3555731.60619.02HMDB01348C18:0 SM117458142563158800141233
13614914612596927119938142660132775125791121110132121157719173907142351
153438111808855269386046297581254760241201248992256835323721305762297472
310293268467
C8-pos 3948759.63749.47HMDB12102C20:0 SM22951275033402326421
2408328583134442497030886237702237623359391543823338767 30489
3560020834201123863916138759219481403985068887670200342758245
5821549057
C8-pos4071785.65319.62HMDB12104C22:1 SM734849987810630894758
92451995586065883489107560889598554989952127628 10690111702795753
11468968821185200976699333454181512576190272809799178664658221
6395362029
C8-pos 4287787.66859.90HMDB12103C22:0 SM164665202116213210196619
182556203372 120739173962 224541175775165709178380 254378226522242826198254
248143140499702174289858252521230953174751178482 279662242316214210253557
226269177955
C8-pos4305813.68459.94HMDB12107C24:1 SM319121385323410943382997
358027368273 255992343329404465347245320583344153474535415063462816370610
43917828556945026871892962 2121997 1786867 1458200 1625235207021017838271848098 1981652
1673533 1680676
C8-pos 4505815.700110.32HMDB11697C21:0 SM97691117738 128094112782
109528 1058097306110004310832310488599062101794 147764136788140707111109
14332975575265981910303879158541131635 730826742938 11536858770327311891077622
788961723401
C8-pos 5101614.588511.99HMDB06725C14:0 CE20792280983558532731
244542680912157254993212621322255652064629736267213478423149
38303136617215645113560333198283261852552330343572176704716887
1923638826
C8-pos 5147640.603012.07 HMDB00658C16:1 CE572917702793720891675160
637519705343 443695623115759708633693608399599442774345826925913589674051
9034385162476818615239618652310696815377517245312940413161720088691436
103872140093
C8-pos 5331642.618812.41HMDB00885C16:0 CE456623544473562196436316
455614 583547310548488377 616083443440457486 429573509366582357662568423166
611127361132389211003189482951778728658873869459837479620454128
6830966424
C8-pos5059664.603011.88HMDB10370C18:3 CE103755106548128366118564
11551112713878785106565131011109779111078111157152048148947160984122979
164341917431748521619892797344518957354
21832988
C8-pos 5207666.618312.17HMDB00610C18:2 CE13610631579498 17113701542494
14753801592270105946715751181732351 14882331429105 14533881752063173734821611111526199
2046971120984035675131075867336302915052086322554821029868759843174
8755072046
C8-pos 5396668.634112.49HMDB00918C18:1 CE213156725662782741344 2514407
2443677 2524289 166205224576912963070232221822867332264956 28160632947078 37348192490701
3302551 1885560 27355465972010322335779487458751004224 698951662546946763461988
563798817379
C8-pos 5559670.649612.89HMDB10368 C18:0 CE38324463355137038640
434565042622610431855992340826400303844462024585318133351476
7341742521464847819710664916117551169306361185735249099529667
4576472789
C8-pos4997688.602811.74HMDB06731C20:5 CE812819446110073097839
98552101133 6560990393106798948658736287704123068 12234112907099677
13212675271
C8-pos 5107690.618412.01HMDB06726C20:4 CE18310982095091 2347139 2110729
2073841 2087516 14165131930944 2369481 205024318788301967259 24122432362367 2666967 2126271
2679860 1516435 124348835316176335191253641623219814719712265420978
6889210559
C8-pos 5256692.634312.28 HMDB06736C20:3 CE189943217622244177220165
207090221011134211224788244533210119190141200855263401246441316787224163
291995184042
C8-pos5050714.618411.87HMDB06733C22:6 CE200437226618254381228037
227977230159150814217444248072224341211038220477275331284378288663235494
301789166812447598469338302475346
20701674
C8-pos5169716.633712.11HMDB10375C22:5 CE23888319493604336006
300103527914882279784124821732259222783242449447965275733758
4748816790
C8-pos1468318.26414.80HMDB11562C14:1 MAG4800500042385011
474852654819418746314910500448664967486250804698
592251922293338244565693528847325838389942715093
42844770
C8-pos1856346.29525.33HMDB11565C16:1 MAG61100640936717364524
646806434663207640106458464508650036306460145673706313161735
74608630493533152234696218248276327746678544757468 6054876804
6481767822
C8-pos2302376.34226.19HMDB11131C18:0 MAG1155974887122818196894474
8467548847648635078325658756288692878415348195868727848332369065291174404
8433798862384004406902128894591003586982395989857997477767027767494975103
303618842776
C8-pos2744430.38937.29HMDB11582C22:1 MAG60148649876959160714
598096859361639595196057262221586576219157795612196423857780
646605831029805529646407174037702437168873082581275679369763
5946661118
C8-pos3571558.50939.04HMDB07011C30:0 DAG34891397774368542156
350393935036322322173628635543363333458831794355293438832658
2919133609315638239799152405243928217691165348195972205369157756264527
234491177298
C8-pos3436582.50938.86HMDB07128C32:2 DAG195
72266379064542543021102106181119214884391713268154587174
C8-pos3683584.52469.18HMDB07099C32:1 DAG6253804965389031
5628 83827167602465215339542349364288634049535884
44255182334952475015255061569607421645299463413401352685265995438176
372323265994
C8-pos3958591.49609.48HMDB07098C32:0 DAG230834309105232115244291
240784231703224520230244233669241661244424222337215569229119230259237316
222946234768 1026254118269087712114172851335693897664117351113895989963041608300
14874831030933
C8-pos3796610.54049.31HMDB07103C34:2 DAG
144.576 394267156413371997294663187924241902244659172468 249012254769166382
C8-pos4054612.55609.61HMDB07102C34:1 DAG7545125881073711297
7436749416464601279274042781468167559527047532951
3612747515069551703845 111943515209091346234 100724114402891389832 10976301433900
13343611023250
C8-pos4292614.57209.91HMDB07100C34:0 DAG292394280602287972279073
257524265547260852242016275783263176249314255239274650250588260634305939
272960253428107500797168879456088713792158470218090799310512458268121019799
1110093775022
C8-pos3921636.55569.44HMDB07219C36:3 DAG
1896111729529309641875016634538354674177836017432524315329550
C8-pos4154638.57169.73HMDB07218C36:2 DAG1320362920472802
83396160831463134837177991588273798336258
746382395937563598305986304810333545140695589656042574819708836689206
514261
C8-pos4369640.587210.03HMDB07216C36:1 DAG2121644326133865
3661354243258482312355918439362264550849617
5141164816507968136567275608300643713482239 629550701081556750682088
696032533043
C8-pos4503642.603010.31HMDB07158C36:0 DAG508832372153387710379161
343892342740375734323635369944368085333929328988 403680355330398580566272
370959338549217481303614366236406492408882414941426119340417332082410756
366333353108
C8-pos4826740.676311.25n/aC42:0 TAG3736943923487184855440169
496323945934869396253587438684356303704243723441053970648263
3678212072213097298713105263109815117394972069839411279710782698858
112682
C8-pos4806764.675911.16n/aC44:2 TAG55458885939060055122
456752544893590955233516532436595314521537544250
58512011656884194712210816403207811318213510370541213716879
17821
C8-pos4859766.691811.36n/aC44:1 TAG5514764327700356165254715
582275577555086525025930255884577325194557483571475399164503
56365353201388320265627225393240562289308227513257786322758239002254194
289403
C8-pos4936768.707511.59HMDB42063C44:0 TAG178738194756227900226558
211935209117192809193037199574198534189270182887186303212158186566170578
217189180753825264744969647869588998667889677918556458690896703949610664
634995673130
C8-pos4913792.707311.51HMDB10419C46:2 TAG8365110158895120102028
3667094319918638137010311594476867138985880248992118684788469
10281790730349382529890351965353238332568 372316318760316671450181315919
315800363068
C8-pos4976794.723111.69HMDB10412C46:1 TAG224904260251278319284146
249655273855 246132237058270928 2402932415345228513 227298259268 248006236908
2775462258233022706260917920488131552798174501519642341670872197653623354151717551
19492702164580
C8-pos5070796.738811.94HMDB10411C46:0 TAG461228 566849578068583191
488767560364477913483196583093489897475003467858 479370518846502407458390
5806704564263143311275987724034041843221227765723973121716677252159126010751931286
24151922296616
C8-pos4923818.7228 11.54HMDB05432C48:3 TAG149415341958
4891360523540105414191253209594868476
9711147142780225768 160536136250139403160147 104767130187175511110337
133041153543
C8-pos5017820.738711.79HMDB05376C48:2 TAG226866277617291382296356
246870284240243054240393286231241095239610235775230134250176239586220812
2879172278093092120299012724205281767866200736722458601768998222524226746191883797
21370502257235
C8-pos5127822.754712.03HMDB05359C48:1 TAG442318567443570644561138
472862532438449152472466565226475726 459583447573476449531643540101450695
588147421709146909461158695398095515813923800061485686805922305
1020212211195516676166795393639053389
C8-pos5268 824.769712.30HMDB05356C48:0 TAG515393608742634496637841
518976597758 524206571423606066544968 519445511523 512039543511630404498694
62504453795053963475428185 474808130214294267458422716330121025686474 52105823386390
50110274259963
C8-pos4954844.738611.65HMDB05435C50:4 TAG
133168218410126176811329436610032876652102834 15257569267100758104266
C8-pos5054846.754211.88HMDB05433C50:3 TAG87259110639115424108401
90772111913782728361611797176652842038593975601896809278977011
11944970843165749721861541786164 1262113148031216670401167374156807820459591172454
14301661592102
C8-pos5181848.769912.13 HMDB05377C50:2 TAG274652367013389715370911
289828 327021291285332783354176287345274108275109284678308581371907275917
370283291410180858251636960213821058743154910423726
10706423792256013292884149196408428689 1142755311754062
C8-pos5302850.785312.38HMDB05360C50:1 TAG325014448769 454143415848
344924408051301505393750445769338992317759341316352894410302 451401324123
4305293140182435443720387591195904631013568013695408
162106931124347222401227221046861254856418665895
17287267
C8-pos5461852.802312.66HMDB05357C50:0 TAG163122222828 233162230123
195689204302188776191481212823192575165080156692201973208703208102160029
213253170824461321247299594760887259119542497293622921 26685016074739 5181511 3170897
50638394078311
C8-pos5038870.753111.84 HMDB05380C52:5 TAG675187317542272
40829552501213168811846227899155
1401429122400264430117707439835993380698450089078712583838998
7629582395
C8-pos5088872.769311.98 HMDB05363C52:4 TAG4663148371525711073
51671072931544424153245887418135765854936087034635
2240144059332251264170 9841756101787561568222315722918685651129926546278
749902804516
C8-pos5239874.785612.23HMDB05384C32:3 TAG7603511158812372998141
87433998407525694645101753836646268174607860908042910304581090
10989590060514789470742626815650409214553691505024709393429260107329 7482990 3764748
5293073 5677924
C8-pos5368876.800612.48 HMDB05369C52:2 TAG216419309244331857 290766
251983266518217777261403307597227753217432220341232763259660310781214528
2805961868733202057926039568299118701430632319022015
220745411564421228574897315157041654897823527421
24598791
C8-pos5500878.816712.75HMDB05367C52:1 TAG93308143165145841121055
1039451085911109889729012227510081410640610651810121698972105375102183
120885104117159341691549733516709333720216413365588
12426254887461419194773185490779938402 1508475913784123
C8-pos5614880.832713.06HMDB05365C52:0 TAG77655101915100396108753
8487598707823409262710448886197780997553589258944139916282382
10411190553216980820867242209777 1073673169196416727561480353315789925297861446319
23703741835281
C8-pos5013894.756211.76 HMDB05447C54:7 TAG1712161218351508
198815321964194315811614130412581922127911631361
56556661911107826 5149828755233724117123504376265039520679
3031230120
C8-pos5102896.768211.99HMDB05391C54:6 TAG24726117041920812277
290721449030686267571812417973335842593830347149792454424956
8941271572683444653982263611031931261921758917742518707727237173058
150105158620
C8-pos5168 898.785112.10HMDB05385C54:5 TAG45839476374143650813
417794611135255405024731540548523403850057529560385233051168
46060337458528251402450 902521539037696995746485 5436807858291014925 496383
710805753468
C8-pos5284900.807812.32 HMDB05370C54:4 TAG3186895293110329370122912884
34078222822252 33770803209017302477033485973329543 3340020 3163787310889132077403323080
2975426307761534525804694005 5560806 46233565176966 52038335224874523853058818434977277
50442625404865
C8-pos5437902.816012.58 HMDB05405C54:3 TAG1015086916038957776914912
109553988782610912291049941 100424911145261079602 1120857 1061604106092310849451050798
9941911072922 126862111201445915064281788205211100007
115462238639554 14222512161890698142347 1112255212073765
C8-pos5539904.832012.85 HMDB05403C54:2 TAG609741581598603193539757
617872607071648497602083570480635317655183 658020623058627414588417662444
576931593008147850271244900915060181640515411402769
117533558030046162306121629139986453451242828512451251
C8-pos5645906.848013.14HMDB05395C54:1 TAG388587328353358229356214
399533366077421354384306360391413574431978428456384376374731406145426703
3871994207714527640358159539413101984355277063631095552668140489288243517902645219
35987713336165
C8-pos5045920.769611.85HMDB05392C56:8 TAG
29238819333403917018210292523662511903637027132121340624675
C8-pos5165922.784612.09HMDB05462C56:7 TAG
427333639806391359159598223479246039177444304798396523161746252993287899
C8-pos5292924.801012.34HMDB05456C56:6 TAG
624616973633720460385051520191582461372169607606769519366005526222555357
C8-pos5362926.815512.45HMDB05406C56:5 TAG
1207940 1599057152830168165692202611623327384011295519 16177196928061041186 1160337
C8-pos5478928.831812.69HMDB05398C56:4 TAG
147221413963821611175620597107353811546847065981513989 168418175294611476821249745
C8-pos5575930.847812.94HMDB05410C56:3 TAG
246067119363802567613989546157062718435401292129 248427326743111211022 1728121 2008602
C8-pos5674932.863613.24HMDB05404C56:2 TAG
2439948 1739238 2119900905634145844116494931327623239221022590691385794 16161841710332
C8-pos5784934.879213.60HMDB05396C56:1 TAG
11495468628368511454738417203306889166224761122804954629673128 921504754286
C8-pos5219948.801112.18HMDB05413C58:8 TAG
166519 308720263297175703208380222760138605196049287264128092164740187556
C8-pos5274930.816012.30HMBD05471C58:7 TAG
874421444351153075809969912863874227994168 131536461985966479383
C8-pos 5424952.831612.56HMDB05458C58:6 TAG
305241335779295146109192196952 207320124707261118315760116415209967221993
C8-pos887.55978.94HMDB09813C38:4 PI
478504264239130272243273 236428197145274277272264213828360193283494207122
C8-pos706.46543.8HMDB12333C30:1 PS39816472194269248479
414515446843314399934744248893419214098243167386904551042707
380123669417902362044420746292508735155159917328694158546242
4100045056
C8-pos764.54318.5HMDB12356C34:0 PS
182478218389103411220445162547154758151109139892111202175856167202144916
C8-pos808.50928.50HMDB12362C38:6 PS
9464910012167324125913797028415910270373505604711101778296481884
C8-pos3134808.50718.41HMDB10167C40:6 PS11597171601513412448
9208106031454813971110431276481921142119682142961658414854
170508731743019646047515868 572721559644557435614265577272496869663441
630754590984
TABLE 4
Lipidomics data showing all lipids detected except those shown
in FIG. 21A. Data shown are normalized to WT (TGFb1 + IL-6)
condition showing average of 3 independent biological experiments.
Lipids that are not
significantly different WTCDSL−/− WT CDSL−/−
or have a fold changeMin (TGFb1 + (TGFb1 + (TGFb1 + (TGFb1 +
less thatn 1.5P valueIL-6)IL-6)IL-6 + IL-23)IL-6 + IL-23)
12-HETEN/AundetectedN/AN/AN/A
13-S-HODE0.21210.8491.6491.161
15-HETEN/AundetectedN/AN/AN/A
5-HETEN/AundetectedN/AN/AN/A
Arachidonic acidN/AundetectedN/AN/AN/A
C14:0 CE0.09610.7891.0210.563
C14:0 LPC0.12411.2690.9571.016
C14:0 SM0.03910.7420.8000.839
C16:0 CE0.04310.9121.0660.807
C16:0 LPC0.27710.9600.9220.991
C16:0 LPE0.18110.6930.7060.718
C16:0 SM0.05210.6680.6470.721
C16:1 CE0.10711.0641.1350.836
C16:1 LPC0.14011.2451.1851.153
C16:1 SM0.07210.9780.8760.935
C18:0 CE0.08310.7100.9760.641
C18:0 LPC0.11110.8060.8660.988
C18:0 LPE0.05210.7320.8020.887
C18:1 CE0.16311.1841.1740.938
C18:1 LPC0.11311.2641.1871.184
C18:1 LPE0.36611.0500.9921.004
C18:1 SM0.05910.6580.7040.687
C18:2 CE0.16511.1830.9710.800
C18:2 LPC0.13310.6310.7140.737
C18:3 CE0.20411.0461.5500.866
C20:3 CEN/AundetectedN/AN/AN/A
C20:3 LPC0.14111.0000.9441.080
C20:4 CE0.27611.4950.9770.857
C20:4 LPCN/AundetectedN/AN/AN/A
C20:4 LPE0.04810.6720.7820.792
C20:5 CEN/AundetectedN/AN/AN/A
C22:0 Coramide (d18:1)0.08610.5090.5520.553
C22:0 SM0.06310.4690.5920.529
C22:5 CEN/AundetectedN/AN/AN/A
C22:6 CEN/A11.6130.5940.788
C22:6 LPCN/AundetectedN/AN/AN/A
C24:0 Coramide (d18:1)0.08310.5700.5830.594
C24:0 SM0.15310.5660.6000.562
C24:1 Coramide (d18:1)0.08810.6670.6570.686
C30:0 DAG0.12810.8860.7900.955
C30:0 PC0.01510.7260.6040.780
C30:1 PC0.12111.1620.8681.010
C32:0 DAG0.07611.1831.1531.337
C32:0 PE0.00610.6760.5600.717
C32:1 DAG0.19411.2120.9691.011
C32:1 PC0.06410.8000.6830.798
C32:1 PE0.02610.7980.6910.812
C32:2 DAG0.08610.7380.3870.489
C32:2 PC0.07211.3680.9931.131
C34:0 DAG0.17010.8840.9811.022
C34:0 PC0.04510.7110.7940.860
C34:0 PS0.06511.0660.7980.968
C34:1 DAG0.22210.8950.9070.876
C34:1 PC0.00210.7430.8090.847
C34:1 PC plasmalogen-A0.11210.7180.7280.739
C34:2 DAG0.16311.2290.9480.964
C34:2 PC plasmalogen0.15710.9290.8630.858
C34:2 PE plasmalogen0.02010.9060.8970.924
C34:3 PC0.01411.0710.8150.920
C34:3 PE plasmalogen0.30311.0070.9921.020
C34:4 PC plasmalogen0.16010.9000.7670.845
C36:1 DAG0.09010.7370.8020.813
C36:1 PC0.04910.6790.7810.797
C36:1 PE0.04310.7970.8500.856
C36:2 DAG0.17811.2211.0040.997
C36:2 PC0.05011.0840.9980.967
C36:2 PC plasmalogen0.03710.9300.9060.828
C36:2 PE0.07311.1211.0210.961
C36:2 PE plasmalogen0.02210.7400.8120.803
C36:3 DAG0.12410.8990.6840.700
C36:3 PC0.04611.0120.8980.934
C36:3 PC plasmalogen0.05610.9870.8530.829
C36:3 PE0.08811.0540.9440.978
C36:3 PE plasmalogen0.05811.0951.0191.013
C36:4 PC plasmalogen0.08110.8290.7480.731
C36:4 PE0.02910.8260.7270.877
C36:4 PE plasmalogen0.11310.9780.9250.930
C36:5 PE plasmalogen0.28011.0511.0341.029
C38:2 PC0.00510.9020.8860.863
C38:2 PE0.01110.9110.8290.815
C38:3 PC0.05110.9010.8430.877
C38:3 PE plasmalogen0.05110.8220.7990.789
C38:4 PC0.06811.1521.0091.027
C38:4 PC plasmalogen0.00910.9400.8120.819
C38:4 PE0.04310.7660.8430.865
C38:4 PI0.14010.7750.8710.975
C38:5 PE0.08411.1371.0050.994
C38:5 PE plasmalogen0.04411.0850.9691.034
C38:6 PC0.08410.8470.6630.745
C38:6 PC plasmalogen0.03811.0690.9050.897
C38:6 PE0.05910.6990.5780.610
C38:6 PE plasmalogen0.02511.0900.9871.018
C38:6 PG0.20611.1060.9031.049
C38:7 PC plasmalogen0.04510.9110.7780.775
C38:7 PE plasmalogen0.05510.9780.9080.964
C40:10 PC0.09311.0030.3580.575
C40:6 PC0.05611.1520.8180.916
C40:6 PS0.01910.8870.8860.990
C40:7 PC plasmalogen0.01011.0080.8330.854
C40:9 PC0.11610.8400.6780.780
C42:0 TAG0.13010.9490.8800.911
C44:0 TAG0.09110.8720.8800.888
C44:1 TAG0.05610.7500.8020.777
C44:2 TAG0.08410.6150.6610.486
C46:0 TAG0.04710.7850.8230.800
C46:1 TAG0.02910.6850.7790.759
C46:2 TAG0.13610.8590.8820.808
C48:0 TAG0.02210.7400.8930.813
C48:2 TAG0.01510.7080.7840.738
C48:3 TAG0.09610.8240.7760.750
C50:0 TAG0.03310.7420.9870.873
C50:3 TAG0.03810.7830.8490.745
C52:0 TAG0.01510.6861.1090.874
C52:1 TAG0.03010.6850.9680.806
C52:3 TAG0.02010.7530.8750.735
C52:4 TAG0.02510.6880.8080.660
C54:2 TAG0.04610.6990.9590.793
C54:3 TAG0.05310.7680.9820.788
C54:4 TAG0.09811.0951.1921.125
C54:5 TAG0.05210.6280.7420.621
C56:6 TAG0.07210.4360.4290.353
C58:6 TAG0.06510.8220.8420.650
Cholic acid0.12610.3151.1011.211
Deoxycholic acid/0.20410.4700.8461.010
Chenodeoxycholic acid
Docosahexaenoic acidN/AundetectedN/AN/AN/A
Glycochenodeoxycholic acid0.12810.2081.0761.122
Glycocholic acid0.11710.1991.2531.271
Glycodeoxycholic acid0.13210.2041.0991.113
Glycolithocholic acid0.11410.5510.9660.871
Glycoursodeoxycholic acidN/AundetectedN/AN/AN/A
Palmitic acid0.05810.3720.4500.000
PGE20.08310.9120.8720.962
sphingosine0.05711.4421.2231.229
Stearic acid0.20810.4530.2040.223
Taurochenodesoxycholic 0.10010.4361.0111.010
acid
Taurocholic acid0.08010.6160.9400.811
Taurodeoxycholic acid0.06310.6720.8340.808
Taurohyodeoxycholic acid/0.00010.7930.7700.585
Tauroursodeoxycholic acid
Taurolithocholic acid0.12510.0581.2141.269
TABLE 5
PUFA/SFA treatment recapitulates the transcriptome (restricted) of WT
versus CD5L−/− Th17 cells. Data used to generate heatmap shown in
WO2015130968 FIG. 50. Nanostring data are shown using a Th17 cell
codeset Applicants previously generated containing 312 genes.
3 independent experiments were performed and the median values are
normalized to WT. Only genes that show differential expression
(1.5 fold) among any of the four groups are included.
CD5LKO.PUFAWTCD5LKOWT.SFA
Ccr41.691.000.330.61
Lgals3bp1.341.000.340.58
Il12rb10.801.000.350.39
Vav31.201.000.410.56
Ifng0.931.000.430.55
Il101.011.000.440.12
IL-330.661.000.441.33
Klrd10.631.000.460.92
Elk31.041.000.470.58
Itga30.761.000.470.50
nrp10.901.000.470.74
Sult2b10.611.000.480.38
Tmem229b1.521.000.510.69
Cxcr31.441.000.520.48
Klf90.751.000.550.68
Peli20.831.000.550.88
Acvr2a1.321.000.550.66
Ccl200.841.000.550.31
Gusb0.941.000.561.02
Spp10.661.000.561.10
Maf0.841.000.560.79
Tcf41.291.000.590.72
Rasgrp11.211.000.600.75
Cxcr51.421.000.601.17
Rela0.961.000.600.70
Stat61.131.000.600.73
Hip1r0.891.000.600.70
Tgfb10.681.000.620.83
Grn1.161.000.620.78
Ubiad11.161.000.620.94
Bcl11b1.031.000.620.82
Irf40.651.000.620.68
Ccr80.711.000.630.74
Trat10.851.000.630.61
Ifih11.251.000.630.87
Map3k51.491.000.640.80
Foxo11.031.000.640.79
Bcl2l110.711.000.640.82
Il6st0.891.000.640.87
Ski0.861.000.640.88
Il7r1.371.000.640.85
Il2ra0.991.000.650.71
Serpinb1a0.771.000.650.56
Il10ra0.951.000.650.71
Litaf0.611.000.651.48
Rfk1.071.000.660.79
Slc6a61.031.000.660.79
Socs31.381.000.660.78
c
Smad31.031.000.660.81
Lad11.181.000.660.91
Tnip20.781.000.660.90
Tgfbr30.941.000.680.58
Ahr1.081.000.680.83
Mina1.081.000.680.72
Stat41.211.000.680.77
Il27ra1.551.000.680.70
Mbnl31.301.000.690.71
Jak31.271.000.690.91
Tal21.521.000.691.15
Gmfg0.761.000.700.62
Irf71.171.000.700.54
Abcg21.201.000.700.77
Il4ra1.131.000.720.75
Notch21.201.000.720.78
Clcf11.251.000.720.74
Foxp11.251.000.720.77
Stat5b1.191.000.730.82
Bcl31.131.000.730.85
Ikzf31.061.000.740.82
Il12rb21.601.000.740.88
Tgfb31.671.000.750.88
Irf81.291.000.750.99
Nfkbie1.521.000.760.69
Trps11.441.000.770.84
Trim251.171.000.770.89
Tgm21.511.000.780.78
Ercc50.661.000.790.90
Etv61.701.000.790.94
Xrcc51.271.000.800.93
Il1r11.361.000.820.61
Csf21.201.000.830.97
Fli11.351.000.830.84
Klf101.301.000.830.91
Arl5a1.331.000.840.93
Jun0.641.000.841.11
Flna1.101.000.840.65
Foxp31.221.000.850.71
Inhba0.811.000.860.60
Cd2471.321.000.880.81
Faim31.311.000.890.61
Pstpip11.241.000.901.16
Kat2b1.221.000.900.69
Gja10.661.000.930.94
Cd861.731.000.940.99
Lpxn1.391.000.940.85
Ccl10.671.000.950.58
Plagl11.071.000.952.19
Ctla41.631.000.960.81
Cd91.271.000.970.84
Pou2af10.861.001.001.30
Pmepa11.191.001.000.74
Prkd31.511.001.000.73
Il17f0.711.001.040.90
EBF11.641.001.110.51
Gimap51.581.001.181.05
Tsc22d30.661.001.181.06
Gem0.731.001.181.00
Gap430.681.001.211.30
Maff0.771.001.220.99
pou2f10.661.001.231.34
Atf40.731.001.231.11
Rel0.731.001.231.20
Frmd4b1.281.001.261.05
Nkg71.401.001.310.62
Casp41.521.001.320.95
Mt20.841.001.331.33
BC0216141.041.001.340.96
ATF20.891.001.381.18
Cxcr40.871.001.391.00
Bhlhe400.701.001.431.22
Il17a1.111.001.440.96
Casp30.711.001.451.21
Sap300.811.001.471.23
Tnfrsf41.051.001.511.28
Plac80.851.001.511.04
Il23r1.111.001.511.12
Rab33a1.501.001.551.23
Sema7a1.041.001.601.44
Il210.971.001.651.64
Oas20.821.001.661.28
Fxd70.721.001.711.07
Rorc1.521.001.801.25
Mt10.791.001.851.56
Spry11.021.002.041.57
Egr21.641.002.211.53
Il31.451.002.242.11
Cd830.881.002.331.23
Cd700.771.002.510.89
Cxcl101.641.003.053.83
TABLE 6
Shown are genes that are significantly up or down regulated in different
sections of the Voronoi diagram (subpopulations) (corresponding to FIG. 2C).
Differentially expressed genes in in-vivo sub-populations
Th17/Th1-
Th17/Th1-like effector-Th17/Th1-likeTh17/Th1-like
like memoryLNeffector-CNSeffector
STMN1OSTF1STMN1PSPHSTMN1RAB1STMN1
RRM2BCL2A1BRRM2CCDC21RRM2TNFSF11RRM2
2810417H13RIKAA4671972810417H13RIKPRDX42810417H13RIKPAPOLA2810417H13RIK
HMGN2UBE2FHMGN2XPO1HMGN2CNOT6HMGN2
TOP2ATMEM128TOP2ANOL12TOP2AHIST2H2AA2TOP2A
SMC2GIT2SMC2SNRNP25SMC2DHRS3SMC2
GM7125GM10247GM7125CAB39LGM7125HIST2H2AA1GM7125
SSNA1IFITM3SSNA1MRPL15NUTF2-AC131675.1NUTF2-
PS1PS1
BIRC5RGS1HIST1H4DCLDND1SSNA1VAMP4SSNA1
PCNABHLHE40SNRPA1ILF2HIST1H4DNUBP1HIST1H4D
H2AFVGOT1UBE2CH2-SNRPA1USP1SNRPA1
KE2
NDUFA5RAB11ABIRC5UCHL5UBE2CSTK39UBE2C
ASF1B5430421N21RIKCKS1BPPP1R8BIRC5AP3S1BIRC5
NME1SELLCDCA3UCHL3CKS1BRAB4BCKS1B
BCAP31PTPRSMRPS16POLE4MRPL42GPS1MRPL42
2700094K13RIKGGHH2AFVHSP90B1AC161456.1RIOK1AC131456.1
TYMSPGAM1ASF1BSNRPB2ANP32ECASP3ANP32E
TACC3GM2574CCNB2NUP214PCNAPPME1PCNA
SNRPBGPR171TIPINPDLIM1CDCA3PDLIM2CDCA3
GM11276RAMP12700094K13RIKMRPL53MRPS16IPO7MRPS16
HIST1H2AOITKTIMM17ARPAINH2AFVACTR1AH2AFV
NUF2H13TYMSBZW2NDUFA5HMOX2NDUFA5
HIST1H2AEGM5138TACC3WDR12ASF1BNEDD1ASF1B
HMGB2P2RX7GMNNVRK1RANBP1NUDCRANBP1
MRPS14RPL31GM11276PHPT1CCNB2CSDACCNB2
BANF1HIF1AHIST1H2AOUFC1NME1LARP7NME1
CDCA8SMARCC1NUF2C330027C09RIKCDK1COPB2CDK1
MRPL18PDHA1HIST1H2AENFU1BCAP31GM9396BCAP31
DDX39HIGD2AHMGB2DPH3PSMD14TSPAN32PSMD14
NDUFA4RPL30-CDCA8MRPL11TIPINSEPW1TIPIN
PS8
MDH2ARHGAP4DDX39ATP6V1H2700094K13RIKGM100362700094K13RIK
SNRPD2RGS16NDUFA4NUP93CDC123GM10071CDC123
SDHBNDUFS1RRM1GABARAPL2GM10349PPP2R4GM10349
TK1GM3272MAD2L1MKKSTIMM17ARPS23TIMM17A
SPC25LGALS3SPC25GNG5TYMSARMC1TYMS
CDK4ANXA5PSMB7DHX15TACC3GM9000TACC3
PMF1STK38DCTPP1PRKAG1EXOSC8GM7808EXOSC8
KIF23ITGB1BP1FBXO5TRAT1GMNNRSRC1GMNN
AURKB2510002D24RIKPMF1NGDNDBINDFIP1DBI
HIST1H2AGSERPINE2KIF23CCNCSNRPBRPS27ASNRPB
PSAT1ECE1HIST1H2AGHMGN1GM11276UBAP2GM11276
ERHGM2792NDUFB7PTCD2HIST1H2AOGM7536HIST1H2AO
TAGLN2MED13PSAT1CCDC69SNRPD1HIST1H1CSNRPD1
BUB3MAPKAPK3CDKN3FAM111ANUF2SMC6NUF2
NUSAP1GIMAP3ERHCCR6HIST1H2AECD2BP2HIST1H2AE
NDC80GPR65MRPL544930453N24RIKLSM6RPL10ALSM6
EMG1RPS13H2AFZBADHMGB2SF1HMGB2
SEC13MAP3K8BUB3ELP2TUBA1BRPL19TUBA1B
TPX2EIF4EBP1NUSAP1PPP2R5AMRPS14MAP2K3MRPS14
CCNB1RCSD1RFC3PMPCBBANF1SETD8BANF1
HMGB3RPL15-TPX2RNASEKRANUQCRFS1RAN
PS2
HINT1OSBPL9CCNB1MAPKSP1CDCA8ELK3CDCA8
RBBP7BPTFHMGB3IL16DPY30RPL27MRPL18
TUBB5PBRM1HINT1DEDDPSMB6NOL72900010M23RIK
CLSPNMGST2TUBB5TNFRSF25DDX39HAVCR2DPY30
DTYMKGM9858MRPL51CMAHKIF22GM9846PSMB6
BAT1ARARSCL5PNGPATCH8NDUFA4TUBB6DDX39
ETFATRPC4APDTYMKPSMG4NSMCE2NCBP1KIF22
TUBB2CFTH1UHRF1NAA15MDH2DGAT1NDUFA4
CASC5ARHGAP10610007P14RIKNUDT3LSMD1AC119211.2NSMCE2
SNRPEUBE2G1CASC5DLDREXO2GM10237MDH2
PSMC1COTL1D2ERTD750EPRPF4FAM36AFAM65BLSMD1
CDCA2UBE2J1ERGIC2DDRGK1RRM1ATAD2REXO2
170029F09RIKGM4609CDCA2PIN1MAD2L1RPL10PSMC2
RPP21CMC1LBRE2F4TK1MED21FAM36A
WBP5PDE4BSLBPTNFRSF9CCT5EIF4A1RPS27L
LBRTNFRSF9MCM7CKBSPC25OSBPL3RRM1
TUBG1TOXPOLD3GM3150CDK42010002N04RIKSDHB
SLBPFAM110AMNS1ARF6DCTPP1RPS12MAD2L1
TNFRSF4HNRPLTUBA4APIM1FBXO5STX11TK1
MCM7D16ERTD472EMCM3ZFP488RFC4TSPOCCT5
HMMRCSF2FH1RGS10MRPS18CSMARCA4SPC25
ANP32ARFC1KPNA2NR4A1PMF1SFPQPSMB7
ORC6TMEM87ARPA1GM3550HPRTAA467197DCTPP1
LGALS1BSCL2KIF2CPAN3DUTAC134548.2FBXO5
GTF2A2AGXT2L2AAASJUNDYWHAHTMEM128RFC4
CD3GH2-K1MRPS33TNFRSF1BPSMA1GM16477MRPS1BC
TMEM49LARSANAPC5IFI27L2BLSM5ACADLPMF1
PLP2REEP5ACTL6AATN1KIF23GM8730HPRT
MCM3LZTR1HMGB1KIF24AURKBGM10247DUT
KPNA2DHX40PTMARABGAP1LHIST1H2AGIFITM3SEC11C
ATP5G3GM7665GM6104GM10313NHP2TMED9YWHAH
NDUFV3HNRNPA3SPC24BTG2COMMD1SCAND3PSMA1
RPA1STK24MRPL4IG42RFKBP3SELLLSM5
ACOT7DDX42ACO87117.1SKILPSAT1PGAM1KIF23
WDR61ZNHIT1ATPSKRAB10CDKN3CCDC59AURKB
GM10108PRKCHIMMTRPL21-STRA13EIF2S2HIST1H2AG
PS7
CKS2ELF2RFC2RPL21-ERHGTPBP1NDUFB7
PS11
RBBP4OBFC2ACITSRRM2COMMD3STAG1NHP2
KIF2CSS18ZWINTRPL29-MRPL54RPL31COMMD1
PS2
COX17RBPSUH-CCDC34GM10291H2AFZBIRC2FKBP3
RS3
ANAPC5EHD1MKI67GM10327TAGLN2RPS27PSAT1
HP1BP3SAMSN1NUDT1GM5507BUB3RPL30-CDKN3
PS8
HMGB1XRN2EXOSC9GM6316NUSAP1PFDN5STRA13
PTMAHNRPDLPHF5AALKBH5NDC80RGS16ERH
BC021614GM10155TIMM22MLL2RFC3CNOT2COMMD3
SNRPGZFP148NAA38INSIG12310028O11RIKMRP63MRPL54
GM6104CYB5BHELLSGM89093200002M19RIKFAUH2AFZ
NT5CRUNX2NGFRAP1GN11127PSMA4RPL27-TAGLN2
PS1
RPS17NFKBIARNASEH2BH2-Q2TPX2RPL17BUB3
MEAF6ITM2BCDKN1B1810027O10RIKORC5NUSAP1
GNG10BNIP3NOTCH2CCNB1TSHZ1NDC80
EEF1B2GM5518SGIP1HMGB3RPL5NDUFB2
BRD8GM10358NR4A3HINT1AC127419.1MED10
SPC24IFITM2GVIN1TAF9VAMP3NDUFV2
DRG1NEDD9RBBP7ING1RFC3
ANAPC13SF3B3CDC45SHISA52310028O11RIK
AC087117.1CHSY10610010K14RIKRAP2C3200002M19RIK
FIGNL1CDK7TUBB5GPR65PSMA4
NKG7TCOF1MRPL51TAP1TPX2
S100A4FOXN2CISD3RPS131810027O10RIK
SRPK1TAGAPCLSPNRPL15-CCNB1
PS2
CITCCPG1NDUFC2GM9858HMGB3
ZWINTMGACENPAGM5148HINT1
CXCR6MAST4NDUFB6HSPH1TAF9
GM6169GM5220RP23-FTL1RBBP7
378I13.5
MRPL41RPS19-BAT1AAPOL7BCDC45
PS2
CCDC34POLR2AETFATOXEIF4A3
GM6984GPSM3LIG1FAM110ACHCHD1
MKI67CREMMPHOSPH6RFC1THOC7
2610029G23RIKPOLR3CUHRF1RAPGEF6TUBB5
RPL22L1TCF7TUBB2CGM7665MRPL51
BZW1EPS15NRMRALBP1TMEM14C
FAM60ACCDC50CASC5SLC24A5PA2G4
EXOSC9ATP2B4SNRPEEHD1CLSPN
CD2P4HA1D2ERTD750ERPS8-NDUFC2
PS1
ECH1FBXO46ATP5BAC120410.1DTYMK
CBX3IKBKBERGIC2XRN2CENPA
HNRNPA2B1CCR2CBX5GM10155NDUFB6
CDCA7PLIN1SUMO2SEC61GRP23-
378I13.5
ANXA2ISG20CDCA2CYB5BBAT1A
NAA38ZYXRBM3RUNX2ETFA
PRC1UBASH3BWBP5GM5518SRP19
DNAJC9RORATCP1GM12666POLR2G
TNFRSF18GEMLBRGM10358LIG1
DKC1SLC15A3TUBG1NKAPMPHOSPH6
DNAJC8PSD4NAP1L1CHSY1UHRF1
HNRNPF1110007A13RIKMRPS17ZRANB2TUBB2C
TPI1SFT2D1TNFRSF4GM52200610007P14RIK
ENO1ZC3HC1MCM7DYNLT1CNRM
CCDC21YTHDC1HMMRDDX21PCMT1
DDX47IFNGR1MRPL23-RPS19-NDUFS8
PS1PS2
NSMCE1GOLGA7POLD3POM121CASCS
TIGITIL18R1PHGDHGABARAPSNRPE
TMEM50ALITAFNUDT21HNRNPLDZERTD750E
GNG2ATF6ORC6TCF7ATP5B
CORO1ADOT1LMNS1CCND2ERGIC2
CAB39LTAB2LGALS1UAP1CBX5
DNAJC15USP4HIST1H1EA830010M20RIKUQCR10
GM5506AC151275.1LCKRPL7A-AURKAIP1
PS5
EZH2INPP5FSSBSERBP1NDUFB9
APOBEC3CD44LATLAMC1VDAC3
ISY1KLF6CISD1GM10136SUMO2
DLGAP5PTP4A1TMEM49WDR9HAT1
CENPEZFP295PLP2U2AF1FXC1
BCAS2GM5561MCM3RPL27ACDCA7
H2-RASGRP1FH1LITAF1700029F09RIK
KE2
SLC25A5ATXN1KPNA2MDN1RBM3
PSMD6CD27ATPSG3YY1WBP5
COX6CSLC2A3RPA1TACC1DEK
PPP1R8ZFMLACOT7AC151275.1TCP1
UCHL3TNFAIP3TXN1GM5561LBR
UBL4CORO2ANDUFAB1GM6139TUBG1
CCL1RPRD2MCM6CORO2ANAP1L1
XRCC6NRIP1GTF2H5PRPF39SLBP
CTLA4CCR1ASNSSLAMF6MRPS17
2900073G15RIKVPS54GM10108GM10054MCM7
NDE1PRPF39CKS2SONHMMR
GLRXSPIN1GM10053RPL13-POLD3
PS3
HNRNPRSLAMF6KIF2CZGPATCCT2
LPXNEIF2C2HN1GM5805PSMA6
SDF4UBL3AAASGM3940PHGDH
CAPGCD200R12310061C15RIKGM7589NUDT21
NUP214INPP4ACOX17WDR70ORC6
PRKAR1ASONANAPC5RPL12-MNS1
PS1
CST7RPL13-CKAP5QSOX1LGALS1
PS3
PDLIM1ADAM19MCM4HSD3B2HIST1H1E
SERPINB1AGM5805RANGAP1AC156282.1PSMC3
CDC26GRINATUBA1CGM10481SSB
DERL2ARAP2HMGB1TNRC6BLAT
YARSCKBPTMARPS2-CISD1
PS6
GCLMSQSTM1SSBP12310016C08RIKTUBA4A
IFNGGM7589EIF3LGM5619PLP2
SNRNP70WDR70BC021614GM3150MCM3
PPIL2BCL2A1ASNRPGRELFH1
FAM33AVGLL4TFF1GM8910KPNA2
FAM162ARPL12-GM6104GM6180ATP5G3
PS1
PSMD2ARIH1PPP1CAUTRNSRSF7
4933434E20RIKZFAND5GM3090CCRN4LCWC15
CAPZA2HSD3B2ELOF1BC005537RPA1
SUPT16HTEX10MEAF6TRIM12AACOT7
OGDHCTSDMTHFD2RPL21-TXN1
PS3
RPS20GM10481ANP32BNSA2NDUFAB1
BZW22310016C08RIKGNG10ACSL4CD48
SFXN1KPNA1SPC24AL844854.1TXNDC17
RPSA-RUNX1C79407CDKN1AMCM6
PS10
ATP5LRNF132700029M09RIK4921517L17RIKGTF2H5
VRK1DENND4ACHCHD3GM6807ASNS
CD226DCTN4COPS6FURINWDR61
SF3A3HK2RQCD1COQ10BGM108
NASPRELAC087117.1KLF13CKS2
SYTL3GM8910FIGNL1UPF1TRP53
HERPUD1CD81NKG7RAB8BGM10053
TXNDC9HSF2CD6ARF5KIF2C
RPL8WDFY1RFC2PRKACAGLO1
CSNK2A1TRIM12APRDX1CT033780.1HN1
MRPL10TOB1GOLT1BWTAPMRPL33
PRR13ZBP1LSM2CAPS2AAAS
RPLP1FAM102APFDN1GM8815MRP533
DDTACSL4CUTATSC22D32310063C15RIK
LUC7L3CDKN1ATMPOBAT2L2COX17
ZCCHC17SRSF2IPSMC4ARF61810009A15RIK
BC031181GM68076-SepGM10012ANAPC5
2310036O22RIKFURINSSRP1GM10154ACTL6A
HJURPIL4RAZC3H15AC117259.1CKAP5
MTIF2COQ10BSETTGOLN1MCM4
ALDOATNFMCM5193.412F15RIKRANGAP1
TSG101PDE4DCLIC1GM5453TUBA1C
PFKPD14ABB1ECITGM10063HMGB1
TAF6GM8815PDZD11GM5908PSMD7
LXNARF6FKBP2AC155816.1DNAJC19
CD40LGPHC3SMC1ALRRC58PTMA
CDK5RAP2RALGPS2GM10123RPL36-SSBP1
PS3
SKP1AANXA1GM6169AMD1EIF3L
S100A6JMJD1CPSMA5CFLARBC021614
GABARAPL2GABARAPL1SUMO3MACF1SNRPG
RPLP2SMPDL3AAIPFOXP1YWHAQ
TBC1D10CRPL36-FDPSPPP1R12AUQCRC2
PS3
HNRNPMSEC62CCDC34MLL5TFF1
PSMD5FAM177AGM6984SP110GM6104
RPS15FOXP1FABP5CDC42SE2PPP1CA
NCLCAMK2DMKI67ZMYND8GM3090
CISHZMYND82610029G23RIKKRR1NAA10
GM200ANKRD12MRPL34ANKRD12SRPR
RPS29NFKBIZSLC29A1GM8054ELOF1
RPL28GM8054POP4AMD2PPIG
TMSB4XLARP4TFDP1GSK3BMRPL28
CCR8MAPKAPK2TTC1AC108412.1MEAF6
RPS25SLFN1ECH1SPOPLMTHFD2
LLPHRBPJCBX3GM7592ANP32B
HCSTRNF19AMYG1MNDALGNG10
WBSCR221500012F01RIKGM4737ZFP488COX7A2
NUCKS1CTNNA1UBE2AAC142450.1PPIB
RIF1RGS10CALM2AC117184.1SPC24
IL1R2CHD7RDM1JARID2DRG1
CD69SEMA4BHELLSTMEM71C79407
RPL35NR4A1PRC1SEMA4B2700029M09RIK
BC016495KLRD1DNAJC9GM2026ICT1
GM11353GM3839NUTF2GM7609CHCHD3
CBX1RBM47PPIAAMD-COPS6
PS3
POLR2EZFP187PCIF1GM14305C5
GM10073GM9104RPP30GM14434MRPL4
SSU72RABGEF1HSPA14EMDRQCD1
PGK1ASAH1CNIHLY6C1AC087117.1
POLR2BGPR132MRPS11GM3839ATP5K
BCLAF1CTSBHDGFGM10916DERA
AC124742.1ECM1STIP1A230046K03RIKPDCD5
GM5559CSRNP1NSMCE1GM9104FIGNL1
5-SepZEB2AHSA1LARS2CD6
RPS15AGM10293GARSB4GALT1RFC2
GM12033ANKRD17TIGITHNRNPUL1PRDX1
TRAT1AI848100XPO1KHSRPGOLT1B
NGDNSMAD7CHMP2AGM14391LSM2
ELOVL1CCL4CD160GM11167NDUFA9
TPRKBGP49APTGES3RAD9PFDN1
IL2RAPELI1SNRNP2SH2-PSMB3
GS10
PSMD4XRN1IL18RAP0610031J06RIKCUTA
KPNB1PLAC8TMEM109SPNA2TMPO
RPL21NRP1MCM2GADL1SMC4
TTC39BRPL21-NUP210FAM113BPPIE
PS10
HMGN1RAB11FIP1RPA3GPBP1L1SSRP1
CCDC55GADD45BEZH2PTENBC056474
PPP1R16BBTG1D17WSU104EGM10S66ZC3H15
TNFSF11CHD1NXT1GM10293SET
PAPOLALGALS3BPISY1ACOT2MCM5
GM10250JUNDDLGAP5AC159008.1ICOS
GAPDHTNFRSF1BPSMB1GM3550CLIC1
CCR6PLD3CENPERPL7A-CIT
PS3
ANAPC11CTSCSLC25A5RALBPDZD11
DHRS3TTF1CYC1NIPBLZWINT
MIER1ANKFY1COX6CUBXN11FKBP2
FXYD5ANXA4UCHL5LNPEPCOPS3
S100A11EFHD2ACTBRRM2BSMC1A
CD4HEXBHMGN5PSMB10GM10123
SRGNATN1LSM4PRPF4BCCDC101
GM10359GM3222ADH5PPIP5K1P5MD1
ORC3SLAMF7DPYSL2HEXDCUSMG5
IL2RBGBP72900073G15RIKKDM5BP5MA5
SRSF1H2-RNPS1PAN3CCDC56
DMA
ABLIM1C330021F23RIKNFYBRPL21-SUMO3
PS10
KLRC1SP3MRPS25RPS6-AIP
PS1
ID2NFIL3EFTUD2GM5921FDPS
GM4963DUSP5GTL3RPGRIP1CCDC34
PPP2R5AGM10313AI314976BTG1GM6984
SNX5BTG2SNX3CLASP2FABP5
BCL2A1DPPMIKPDHBLRRC8DNDUFB3
GM10263IGF2RSNRPB2CAP1PRPS1
AC129078.1TGTP1NDUFAF2RSBN1LMKI67
RPL15SKILSNAPC5RPL7A-2610029G23RIK
PS10
UNC13DRAB10HNRNPABJUNDIMPA1
CENPQGBP2PRKAR1AAP2A2MRPL34
RECQLRPL21-AHCYDYNC1H1ZFP207
PS7
0910001L09RIKC1QAVDAC14632428N05RIKSLC29A1
EIF3AIER3AP1S1IFI27L2B10-Sep
GM2606IFRD1PPIDGAS2L3MRPL40
FOLR4RPL21-BLMHTTF1CACYBP
PS11
HSPA5SIK1TPD52L2ANKFY1POP4
GM6636TET22810428I15RIKDNAJB14EXOSC9
LONP2GBP6FAM125AGM10362TFDP1
FTSJ3SPTY2D1HAUS3TLCD1PSMB4
ZFRRPL21-ACTG1RAP2BTTC1
PS6
EXOSC2NR4A2CALM1DDX6CD5
GM9396MED13LYARSPYHIN1TIMM22
TSPAN32RRBP10610037P05RIKCAPN12ECH1
GM10036RASSF2CRBNSYNRGCBX3
SYPLLILRB4GM10076ATN1SMU1
SUCLG2AHNAKIFNGTRPM2HNRNPA2B1
LTARGS2UBASH3AGM3222CDCA7
IL16PLEKTSNMTMR2MYG1
TRA2BFOSL2FAM33AKIF24GM4737
VPS35DUSP1POLA1C330021F23RIKPPP6C
GM2833PER1PSIP1IL7RUBE2A
GM10240GM10327OGDHMS4A4CBLVRA
ERMNIRF2BP2ARL1MLL3SRP9
DENND2DGM5507ABCF22810422J05RIKCNIH4
FAM165BTOB2KIF15PBXIP1CALM2
RPS28GM6316TIPRLGM2058TIMM8B
CTSWKDM6BACTN4JHDM1DNAA38
AQRGM6109CORO1CKTN1RDM1
TMEM147JUNSYTL3ELMOD2HELL5
NDFIP1ALKBH5OXCT1ABHD2PRC1
RPS27AJUNBC330027C09RIKGM10313NGFRAP1
SAP30BPCD63WDR33DTX3LDNAJC9
UTP14AMNDASNRPAPPM1KITPRIPL1
SIN3AINSIG1HIST1H4IGM8225NUTF2
BSGRNF213AC5L5RUNX3ATP5J2
SUZ12FOSBFAM96BIGF2RPPIA
0610011F06RIKPSAP9130401M01RIKMYCBP2VIM
RPL10A9930111J21RIK2BAZ1BSKILPCIF1
MAP4K1CCL5ZCCHC17SMG1RNASEH2B
GATA3GM11127THOC4ABCG1RPP30
PTPN22EGR12310036O22RIKAL732476.1PSPH
YIF1AAPOEHJURPRPL21-AK2
PS7
MUM1NOTCH2IFT27NPC2HSPA14
CMAHCCRL2SLC35D1RPL21-NUDT5
PS11
YTHDF2NR4A3EXOSC5SIK1CNIH
GBAGM4070SLC1A5AC163269.1HNRNPF
LIMD2GM7030NUP93TNRC6CMRPS11
MAP2K3GVIN1PPP1R114930470H14RIKHDGF
SETD8CD86L7RN6SRRM2MRPS18A
DDX5PRDX5RNASEH2CGM10718STIP1
TAPBPAIF1TAF6IFI203CCDC21
LAPTM5H2-CDK5RAP2RPL21-H3F3A
AB1PS6
DGKAH2-SH3BGRL3HMHA11500032L24RIK
EB1
HAUS2LYZ2CDKN2AIPNLMED13LNSMCE1
CST3GRNDNMT1RPL29-AHSA1
PS2
ITGAVC1QBRAD21RASSF2MRPL46
HAVCR2LY86MEMO1NCOA3PRDX4
SLC3A2FCER1GMRPS24STAT1NUDCDZ
MAPRE2TYROBPCISHFMO1GARS
CLK3PRPF38AGM10291XPO1
GPATCH8CCDC124RPL17-MRPL45
PS3
ATP6V1G1MRPS6SLC39A1CD160
SH2D2AUBBGM10327PTGES3
DGAT1GTF2A1BIRC6NOL12
EIF1ADMKKSIRF2BP2GDI2
MRPS26TRIM28GM5507SNRNP25
AW112010CCR8MAP3K1TUBA1A
FAM65BPUF60GM108005930416I19RIK
NUMA1TMED2TOB2IL18RAP
EMBNEBLGM6316SIVA1
2010111I01RIKHCFC1KDM6BTMEM109
MED21D930014E17RIKGM6109MCM2
2310004N24RIKEIF4HLY6C2NUP210
ARPC5LNUCKS1ZFP36L1RPA3
AC114648.1LNPJUNEZH2
SDF2SCAMP2ALKBH5TAF12
THEMISGALMMLL2CLDND1
S1PR12810407C02RIKPDCD4GLTP
IL12RB2CENPLINSIG1NT5C3L
GM9234UFSP2RNF213NXT1
B2MDCTN3GM8909ILF2
ZFP825DKKL1C030046E11RIKDLGAP5
GM5160RPS21PSAPPSMB1
MIIPHIST1H2BC9930111J21RIK2CENPE
NSD1UBE2SARID1BH5D17B12
SATB1RPL12GM11127H2-HE2
AP2S1H2-Q2SLC25A5
CDKN1BHAUS1
NOTCH2FGFR1OP2
SLFN5CYC1
SGIP1COX6C
GM4070UCHL5
BMP2KPPP1R8
GM7030UCHL3
GVIN1UBL4
ZFP36XRCC6
LYZ2YWHAE
H2-AAHMGN5
CTSSCIAPIN1
CD74LSM4
PFDN4
PQLE4
ADH5
DPYSL2
2900073G15RIK
DCP5
M6PR
RNPS1
NFYB
MRPS25
EFTUD2
HSPE1
ESD
MFF
GTL3
AI314976
SNX3
ATP5C1
PDHB
H47
SNRPB2
NDUFAF2
NUP214
SNAPC5
HNRNPAB
AHCY
LSM10
PAK1IP1
GM10736
MRPL53
VADC1
AP1S1
MAD2L2
PPID
UBA1
BLMH
TPDS2L2
MAGOHB
2810428I15RIK
RUVBL2
FAMI25A
HAUS3
CALM1
YARS
VBP1
0610037P05RIK
CRBN
GM10076
UBASH3A
TSN
FAM33A
PQLA1
SBDS
PSIP1
QGDH
ARL1
PPIH
ABCF2
KIF15
CNPY4
TIPRL
ACTN4
POLR3K
CORO1C
SSSCA1
SF3A3
SYTL3
OXCT1
C330027C09RIK
WDR33
SNRPA
ORC4
HI5T1H4I
ACSL5
NRF1
9130401M01RIK
MRPL11
CINP
BAZ1B
LUC7L3
ZCCHC17
PPIL1
MRPS36
GABPB2
THQC4
2310036O22RIK
HJURP
IFT27
NOP58
SLC9A3R1
SLC35D1
SLA2
EXOSC5
SLC1A5
NUP93
PPP1R11
IMPDH2
L7RN6
PSMD13
RNASEH2C
CRMP1
UTP3
LJXT
CDKSRAP2
CDKN2AIPNL
DNMT1
RAD21
ADPRH
MEMO1
ITPA
RNF7
EXOSC3
PRPF38A
CCDC124
MRPS6
MKKS
TRIM28
CCR8
POLR2H
PLIF60
LTA4H
TMED2
NEBL
HCFC1
Th17/pre-
Th1-likeT17
Th17/Th1-likeeffectorTh17 self-Dysfunctional/
effectorRPS8-renewingsenescent
ATOX1CKS2PS1TOP2AEZRSTMN1AC127419.1
LYARFIGNL1XAF1UBE2CGM102372810417H13RIK4833420G17RIK
GNG5CITTXLNGBIRC5LEF1HMGN2SNRNP200
RWDD1MRPL27NCK2NDUFA5FAM65BTOP2AFTH1
D930014E17RIKDOK2CDK7CCNB2HK1SMC2SYT11
EIF4HPPP1R8MGANME1EMBGM7125GM5148
NUCKS1HSPE1ISCA1TIPIN2010111I01RIKNUTF2-5830405N20RIK
PS1
APIPCDC26POM121SNRPBMED21SSNA1MYSM1
RIF1IL22LARP4BNDUFA4COMT1HIST1H4DWAS
EIF2S3XYARSPOLR3CNSMCE22310004N24RIKSNRPA1RNF5
LNPIFNGTNFRSF26FAM36ATHADACKS1BAGXT2L2
DHX15TBL3TCF7SNRPD2SDF2MRPL42IRGM1
EXOSC10ALDOAMRPS7DUTFARSBANP32ERP23-
71J17.1
2610039C10RIKCD7RASSF1SEC11CH2-Q7PCNAAC120410.1
CD3ECCR8CPNE8KIF23THEMISMRPS16HNRPDL
2400001E08RIKMICAL1TTC5COMMD1NUCB1H2AFVGM10155
SYNCRIPSDHCGPR68STRA13S1PR1NDUFA5FXR1
HIST1H2BGRPS15AGRIPAP1H2AFZBRP44LASF1BGM10358
POLR2BTNFSF11SFI1TAGLN2OSBPL3RANBP1SF3B3
HSPA4CCR6LITAFEMG1B2MNME1USP50
MRPS36-ASRGL1AC151275.11810027O10RIKTTC39CBCAP31GM5220
PS1
AKR1A4DHRS3BRAPCISD32010002N04RIKPSMD14RPS19-
PS2
WDYHV1MDP1GM5561SRP19NDFIP2GM10349GPSM3
2810407C02RIKGGPS1ADOLIG1RPS12TIMM17AATP2B4
CENPLPOT1AWBP11MPHOSPH6APOL7EEXOSC8CNOT3
UFSP2ORC3EZH1UHRF1DDX18GMNNEXOC1
LGTNODF2GM10054ERGIC2NSD1SNRPBSERBP1
KPNB1TMEM154GM3940TXN2BCL2L1NUF2ZC3HC1
DCTN3LSG1GM7589MRPS17SATB1TUBA1BYTHDC1
DKKL1UTP23RPL12-TNFRSF4SFPQMRPS14RPL27A
PS1
HIST1H2BCPMPCAEXOC4HMMRCAR5BMRPL18GM11273
CCNCSYPLHSD3B2MANFOSTF1DPY30AC151275.1
FAIMCDKAL1SOCS2LGALS1BCL2A1BPSMB6GM5561
UBE25ERMN2310016C08RIKCISD1UBAP2LPSMC22810474O19RIK
CTCFTRAF2KPNA1TMEM49AA467197FAM36AGM6139
RPL12CTSWIL1ORBPLP2AC134548.2CCTSFRMD4B
AP2S1AGTPBP1HK2EMP3UBE2FCDK4GM100S4
FAM111ADEGS1RELSRSF7LY6G5BDCTPP1RPL13-
PS3
RAB1SIKE1GM6180ACOT7ACADLMRPS18CGM5805
ACP1PFKLRPL21-NOP56GM10247HPRTSMG7
PS3
PAPOLAPIGUCDKN1ATXN1IFITM3YWHAHGM7589
CNOT6MUM1IL4RACD48RGS1H2AFZQSOX1
SNX4TAF1ZBTB20TXNDC17BHLHE40NDC80SAMHD1
ANAPC1MAP2K3D14ABB1ECKS2HDLBPNDUFB2RPS2-
PS6
ANAPC11DNPEPGM8815RBBP4PFDN2EMG1GM6180
TRNT1RINT1GM10012SEC61BFAM129AMED10NSA2
HIST2H2AA2SLC3A2HERC2COX17WDR43SEC13AL844854.1
AGPAT3NSFGM10154KRTCAP2SELLNDUFV24921517L17RIK
BADFAM65BIFNGR2HP1BP3GGHHMGB3SRP54A
HIST2H2AA1WIBG4930412F15RIKTMEM208PGAM1TAF9GM6807
AC131675.1UBR1GM10063TFF1RAMP10610010K14RIKWTAP
VAMP4UPF3BGABARAPL1GM3090ITKEIF4A3GM10695
NUBP1ARPC5LKDM6ACCT8MTA3THOC7GCNT2
USP1IL27RALRRC58RPS17BAXTUBB5GM8815
STK39AHCYL2RPL36-GNG10EIF4G1MRPL51MEX3C
PS3
AP351ZFP825AP1B1EFF1B2PRKACBPA2G4GM10012
RAB4BGM5160KRR1SPC24RPL31NDUFB6ZZEF1
SRSF1MIIP1500012F01RIK31-HIF1ASSR2GM10154
Aug
GPS1CLEC2NR4A1NDUFA1KHDRB51POLR2GAC117259.1
ELP2AA467197CREBL2IMMTALKBH4UHRF1GM8991
THOC6POGLUT1H2-NKG7DNAJB6PCMT14930412F15RIK
GS10
RIOK1HAUS8CSRNP1HSD17B10PD55ACBX5GM5453
CASP3IFITM3GADL1S100A4RPS27HAT1GM10063
ZCRB12410002O22RIKISCUGM10120RPL30-MRPS21GM5908
PS8
PPP2R5AMTPNUBXN11CRIP1MAGT11810006K21RIKAC155816.1
PDLIM2COX10PLAC8SRPK1GOLM1ORC6RPL36-
PS3
IPO7SSBP2RPL21-SETGTPBP4NDUFB11MT1
PS10
PMPCBPHKG2RPS6-S100A10DHX9LGALS1ZMYND8
PS1
SMC3TEX261MS4A6CCITRGS16LATGM8054
DDOSTBCAT2TPDS2ZWINTDDRGK1ANXA6MAPKAPK2
MRPL35PLDNTTF1FKBP2MRP63POLR2FAC142450.1
UBE2BPDHA1ATN1NAP1L4LGALS3MRPL21AC117184.1
ACTR1AMAGT1LY6ICXCR6LMAN2TRAPPC1GM3839
SNRPCRGS16C330021F23RIKGM6169ANXA5CWC15GM10916
DDB1TAF13NK1RAS1MRPL41WBP2MCM6A230046K03RIK
CENPQ2510002D24RIKABHD2AIPSTK38GTF2H5GM9104
RECQLGM4759BAZ2BUQCR11RPL17GLO1LARS2
HMOX2MAPKAPK3RPL21-FABP5RBM38ANAPC5HNRNPUL1
PS11
RPL9-GPR65RPL21-RPL22L1ACTN2HMGB1KHSRP
PS4PS6
RNASEKEIF4EBP1RPL29-10-SepORC5PSMD7IRAK1
PS2
DDX27ARHGAP1LILRB4ZAP70RPL5PTMAGM11167
STARD3NLCOTL1KLHL24POP4FAM49BVPS25RAD9
NEDD14732418C07RIKFOSL2FIF5AAC127419.1EIF3LH2-
GS10
SSR4TOXGM6316PTPRCAP4833420G17RIKTMEM208RC3H1
PDCL3MYD88GM6109HNRNPA2B1EIF4A2GM6104GADL1
FTSJ3DDHD2LY6C2ANXA2ECE1PPP1CAGM10566
SMSARL5CGM8909TNFRSF18GM2792ARHGDIAGM10293
NUDCCTSHPSMG2ATP6V08SRPRAC159008.1
CSDAGM11127DKC1MAPKAPK32700029M09RIKGM3550
GOT2EGR1VIMPIK3CDMRPL4ISCU
RPL37NR4A3CCT7GPR65PHBRPL7A-
PS3
LARP7GM7030CNIHTAP1GM10120UBXN11
CCDC41SDC4HNRNPFRPS13PPIEPICALM
COPB2H2-TPI1MAP3K8VDAC2MYO1E
AB1
SEPW1C1QBENO1STK4NAP1L4PPIP5K1
GM10071DDX47RPL15-SMC1AHEXDC
PS2
PPP2R41500032L24RIKHBS1LGM10123CLINT1
KCNAB2PARK7IL1R1SUMO3PAN3
APISHSP90AA1PRDM1CCDC34MFSD11
SUGT1TIGITGM9858XLR4CRPL21-
PS10
PRPF18GNG2APPL1MRPL34RPS6-
PS1
TARSCAMK4FTH1PTTG1GM5921
RPS23CORO1ARBM5PPP6CRNF149
ARMC1IL18RAPLIN7CDCTN6LRRC8D
GM9000DNAJC15ARHGAP1TIMM8BRPL7A-
PS10
RSRC1TCEB2CNPPQBP1JUND
UBAP2SUSD3GM5148HELLSTMEM123
GM7536GM5506UBE2J1SARNPMXD1
HIST1H1CISY1SERINC3NUTF2GAS2L3
SIN3AEEF1GRNGTTOLA1TTF1
SUZ12HSD17B12LRRFIP1PCIF1TGFBR2
SMC6BCAS25830405N20RIKNSMCE1ANKFY1
MAP4K1CTLA4CMC1EIF3CDNAJB14
SF1PKP3TULP4PTPN2CAMK2G
RPL192900073G15RIKPDE48UBE2V2GM10362
SETD8ADSLTNFRSF9GIMAP4TLCD1
UQCRFS1PSMC4MYSM1ANAPC16RAP2B
GM83941810037I17RIKAPOL7BADSLDDX6
IKLPXNTEX2TRMT112PYHIN1
RPL27SDF4YME1L1CST1TRAFD1
ITGAVCAPGTOXBRIX1CAPN12
NOL7SNX3FAM110ATPD52L2SYNRG
GPX1ADK1700123O20RIKUFD1LBAT2L
GM9846PRKAR1ARBMS1DCAF1ATN1
MSL3RPLP0D16ERTD472EMRPS18BTRPM2
DNAJC2PDLIM1CSF2ESF1GM3222
NCBP1CSNK2BRFC1EIF3DMAP3K14
GPATCH82810428I15RIKTMEM874PSIP1C330021F23RIK
EIF1ADACTG1SNX2BZW2MLL3
ARGLU1CALM1DNAJB1WDR12ELMOD2
CCDC107YARSH2-K1KIF15ABHD2
AC119211.2EIF3KFAM98BUCF1ADIPOR1
GM10237IFNGTMEM149C330027C09RIKGM10313
ATAD2S100A13REEP5WDR33DTX3L
TPT1TMEM176BGM76652410001C21RIKPPM1K
OSBPL3GSTP2MPHOSPH10MRPL48GM8225
UCP2FAM162A2610101N10RIKZCCHC17MYCB92
2010002N04RIKGTF2E2STK24GABPB2SMG1
A430093F15RIKPSMD2ZNHIT1NOP58RAB10
RPS12CPSF3LCNOT1PNRC2AL732476.1
TSPOCDC42F2R2610030H06RIKRPL21-
PS7
SMARCA4RPS20SS18ACADVLNPC2
SFPQBZW2RBPSUH-TMED2RPL21-
RS3PS11
GATAD1SLAMF1EHD1CLP1SIK1
AC134548.2RPSA-GNL3RIF1AC163269.1
PS10
NAA15ATP5LRP58-SDHCTNRC6C
PS1
GM16477HAX1NSG2SCAMP44930470H14RIK
ACADLCD226SAMSN1BIN2GM10718
GM8730HSP90AB1AC120410.1CPMIFI203
SF3A1PSMB8XRN2GM10250RPL21-
PS6
TMED9NASPGLULARAFMED13L
SCAND3SYTL3GM10155CCR6RPL29-
PS2
MTPNOXCT1FASLGIMAP6RASSF2
KIF2ARPL36AXAF14930453N24RIKSTAT1
PUM2RPL8RASA3RPL18AHNAK
GTPBP1GM8759RUNX2AC131675.1ARID5B
STAG1TBCBNFKBIARRP1BFMO1
MED29RPS8HOPXLONP2GM10291
SMN1RPSAITM2BEEF1E1RPL17-
PS3
SREK1RPL7GM5518ENY2SLC39A1
RPL312410001C21RIKPLEKHB2GM10257TAX1BP3
HMGA1PRR13GM10358PRPF18GM10327
KHDRBS1RPLP1PUM1GM7808BIRC6
BIRC2YWHAZNXF1PPP1R7IRF2BP2
RPS27DDTELK4YIF1AGM5507
FMR1PPP1CCARHGEF3INTS7GM10800
RPL30-ZCCHCIFITM2TPT1GM6316
PS8
PFDN5MRP536NEDD9PSME2B-KDM6B
PS
RGS16CALM3CHSY1GM9234GM6109
2810008M24RIKSLC35D1CDK7ATP6V1FPNRC1
MRP63SLA2ZRANB2TSPOZFP36L1
PIN1PIH1D1CHD4RAB27AALKBH5
GNL3LALDOAPPP1CBGM2574MLL2
FAUTSG101TCOF1GM5138JUNB
RPL27-NDUFA13NOL8SREK1PDCD4
PS1
RPL17L7RN6MDM4ING5INSIG1
ORC5LXNTRPS1PFDN5GM8909
TSHZ1CRMP1AL732569.1GTPBP4C030046E11RIK
RPL5SH3BGRL3ZFP91DHX9PSAP
FAM49BS100A6AKNAGM3272ARID1B
AC127419.1GABARAPL2MGARPL27-GM11127
PS1
VAMP3RPLP2GM5220RPL5H2-Q2
ING1RAD21RPS19-CDKN1B
PS2
KRCC1TBC1D10CZFP106NOTCH2
SHISA5GM4294BCL2A1CSLFN5
GPR65SEC22BDYRK1ASGIP1
TAP1RPS15CREMNR4A3
RPS13NCLTNFSF10GM4070
RPL15-G3BP1SEMA4ABMP2K
PS2
GM9858MRPS24SRSF5GM7030
GM5148ATPSG2CASP8GVIN1
GM4609NPTNTSC22D4ZFP36
HSPH1CISHGLTSCR2LY22
FTL1PRPF38ATCF7GRN
WBP4GM20001600014C10RIK
RFC1RPL3ATP2B4
GM6736RPS29CDK11B
GM10116RPL28PSMD9
REEP5TMSB4XCCND2
D19BWG1357ERPL7ALAG3
GM7665RPL38PTPN18
RALBP1CCR8CCR2
DDX42TIMM17BTMEM66
RP23-RP53APLIN2
71117.1
RPS8-SLAEROIL
PS1
AC120410.1ATOX1UAP1
XRN2RPS25COX16
HNRPDLRPS18GPR68
GM10155LLPHNVL
PCBP1RP53ARHGAP26
BRD9RWDD1ZYX
SEC61GEIF4HGIMAP7
CYB5B1700012B07RIKPMAIP1
RUNX2TMSB10UBASH3B
ITM2BRIF1RORA
GM5518IL1R2APAF1
GM10358RPL35PIAS1
USP50SNRNP27RNF20
NFATC2BC016495SLC15A3
GM5220RPL22PSD4
DYNLT1CLASS21110007A13RIK
RPS19-GM11353WDR4SL
PS2
GABARAPRPL14YTHDC1
LARP4BPOLR2ERHOH
HNRNPLNACAGM10136
TCF7RPS19IFNGR1
GRCC10RPL39DNAIC1
CCND2GM10073IL18R1
TLN1POLR2BRPL27A
A830010M20RIKBCLAF1USP7
GIMAP7AC124742.1C330019G07RIK
RPL7A-MRP536-DNAJB4
P55PS1
SERBP1COMMD6LITAF
WDR45L5P1GNGT2
GM10136GM5559MDN1
WDR92GM6472DDX46
RPL27ARP59IFI35
MDN1RP518-TAB2
PS3
DDX465-SepDMTF1
AC151275.1RPL35AAC151275.1
GM5561GM12033CPD
2810474O19RIKTRAT1CD44
GM6139NGDNKLF6
UBQLN1IL2RAGM5561
WBP11MRPL32SPARC
PRPF39GNA15EHMT1
EZH1SPAG7NMNAT1
GM100542810407C02RIKPION
SONTMEM179BATXN1
RPL13-CENPLCD27
PS3
ZGPATRPS24SLC2A3
GM5805GM10020GM6139
GM3940DCTN3CASP4
GM7589ACOT9TNFAIP3
WDR70RP510CORO2A
RPL12-RPS7MAF
PS1
QSOX1RPL21SOAT1
HSD3B2RPS21BIRC3
AC110247.1HIST1H2BCNRIP1
AC156282.1RPS13-CCR1
PS1
GM10481TTC39BVP554
TNRC6BHMGN1PRPF39
RPS2-CCDC55RELL1
PS6
GM5619RP516SPIN1
GM3150GM10119FRMD4B
RBM15AC154908.2RBM26
GM8910RPL12AIM1
GM6180CTLA2ASLAMF6
SLC38A6TNFSF11UBL3
UTRNSPNB2INPP4A
CCRN4LERGIC3GM10054
BC005537RPL30SON
TRIM12ADENRANKRD44
RPL21-PECIADAM19
PS3
N5A2RPL7L1FRYL
ACSL4GAPDHARAP2
AL844854.1CCR6CKB
CDKN1AHNRNPA0SQSTM1
4921517L17RIKP4HBWDR70
GM6807MEDI1BCL2A1A
FURINAGPAT3QSOX1
KLF13DHR53CTSD
UPF1GIMAP6CLIC4
ARF5FXYD5CCR7
PRKACADGUOKAPIS2
CT033780.1RPS27A-RPS2-
PS2PS6
WTAPRPL18SOC52
PDE4D5100A112310016C08RIK
GM10695GM10159RUNX1
CAPS2VAMP4NFAT5
GM8815SRGNIGTP
EDEM1GM10359RNF13
BAT2L2PIGXDENND4A
ARF6TRAF3IP3KBTBD11
GM10012ACTR2DCTN4
ZZEF1AEBP2HK2
GM10154IL2RBREL
AC117259.1LAGE3GM8910
TGOLN1GM10335ARL15
GM8991ABLIM1HSF2
4930412F15RIKKLRC1WDFY1
GM5453H2-Q8CCRN4L
GM10063GGNBP2TRIM12A
GM5908CDC42SE1ENTPD7
AC155816.1RPL9-ZBP1
PS6
LRRC58ID2FAM102A
ESCO1ZCRB1ACSL4
RPL36-GM4963CDKN1A
PS3
FNBP1CD37SRSF2IP
UBR4PPP2R5AGM6807
AMD1SNX5FURIN
SEC62BCL2A1DCOQ10B
CFLARGM10263VCPIP1
MACF1DDOSTPRKACA
TXNIPAC129078.1ATPBD4
PPP1R12ARPL151110007C09RIK
MLL5RPL6TNF
SP110CYLDPDE4D
ZMYND8EEF2GCNT2
KRR11810046I19RIKBAT2L2
TNRC6ACCM2UPF2
GM8054SNRPCRALGPS2
AMD2GM5879GM10012
GSK3BRPL9PS4GATAD2A
AC108412.10910001L09RIKAP2B1
SPOPLEIF3AGM10154
UBE2HRAC1AC117259.1
2310035C23RIKGM2606TGOLN1
GM7592FKBP5GM8991
MNDALMYO1GGM5453
SLFN1FOLR4GM10063
RBPJIFI27L2AGM5908
ZFP488RPS6KDM6A
AC142450.1GM6636FOXO1
AC117184.1STARD3NLESCO1
SEMA4BCHMP5AMD1
GM2026ZFRAP1B1
GM7609RPL23AMFSD4
AMD-RPL37MACF1
PS3
GM14434GM9396SAMD9L
EMDTSPAN32FOXP1
IRAK2SEPW1CAMK2D
LY6C1RPL9PPP1R12A
GM3839RPL18ASP110
GM10916RPL37AMT1
NBR1GM10036PDCD11
ZFP187SYPLH2-
T10
A230046K03RIKGM10071ZMYND8
GM9104SIRT2FOXN3
LARS2IL16NFKBIZ
B4GALT1TSPAN31TNRC6A
HNRNPUL15UGT1GM8054
H2-Q6TRA2BAMD2
KH5RPFKBP8ACTN1
GM14391RPS23HELZ
GM11167GM10268CDK13
RAD9GM28332310035C23RIK
H2-AKAP13RBP1
GS10
0610031J06RIKGM10240ZFP488
TECPR1AC124399.1CTNNA1
SPNA2ERMNTMEM71
RC3H1GM9000SEMA4B
GADL1DENND2DAMD-
PS3
FAM113BRPS28EMD
GPBP1L1RPL36NR4A1
PTENCTSWIRF2
GM10566ADRBK1GM10916
SETD2MAT2ARBM47
UBN2ODC1ZFP187
GM10293PPP1R7ARHGAP31
ACQT2CSTBA230046K03RIK
AC159008.1AC114007.1CD9
GM3550TMEM147GM9104
BRWD1NDFIP1ASAH1
RPL7APS3RPS27ARBBP6
AI848100RBMXKH5RP
TRIM24PFKLCT5B
NIPBLGM7536PTEN
RNASET2ABSGZEB2
UBXN11RPL26ACOT2
LNPEPRPL10AISCU
RRM2BMRPL55SMAD7
PRPF4BMAP4K1UBXN11
RNASET2BRPL19GP49A
PPIP5K1CMAHKIF21B
HEXDCLIMD2RRM2B
KDM5BSETD8PRPF4B
PAN3TMEM176APLAC8
NRP1BTLACLJNT1
RPL21-GM54S1PAN3
PS10
RPS6-DGKAMFSD11
PS1
GM5921CST3NRP1
RPGRIP1RPL27RPL21-
PS10
BTG1ITGAVRAB11FIP1
CLASP2HAVCR2GADD45B
LRRC8DGM9846BTG1
CAP1MAPRE2RNF149
LGALS3BPTUBB6LGALS3BP
RSBN1LU2AF1L4TNFRSF1B
RPL7A-VAMP8SKI
PS10
JUNDDGAT1SSH2
DOCK8RPS6KA1MXD1
AP2A2AC119211.2IFI27L2B
DYNC1H1AW112010GAS2L3
4632428N05RIKCTSC
GBP10ANKFY1
IFI27L2BANXA4
GAS2L3GM10362
TTF1TLCD1
ANKFY1SYNRG
DNAJB14ATN1
GM10362LY6I
TLCD1SQD2
RAP2BGBP7
DDX6C330021F23
SYNRGIL7R
CYTH4KTN1
ATN1PPT1
TRPM2ASH1L
GM3222NFIL3
MTMR2ADIPOR1
SQD2BTG2
KIF24PPM1K
C330021F23RIKGM3225
IL7RH2-OA
MS4A4CRUNX3
MLL3MYCBP2
2810422I05RIKSKIL
GBP8SMG1
PBXIP1ABCG1
GM205BRAB10
JHDM1DAL732476.1
KTN1IFRD1
MLL1RPL21-
PS11
ELMOD2SEPP1
ASH1LSIK1
ABHD2TET2
GM103134930470H14RIK
ZCCHC6SRRM2
BTG2CD38
DTX3LSPTY2DI
PPM1KRPL21-
PS6
GM8225NR4A2
RUNX3HMHA1
IGF2RRPL29-
PS2
MYCBP2RRBP1
SKILRASSF2
TRP53INP1LILRB4
SMG1AHNAK
ABCG1PLEK
RAB10FMO1
AL732476.1FQSL2
RPL21-TAXIBP3
PS7
NPC2MS4A6D
RPL21-BIRC5
PS11
SIK1MAP3K1
AC163269.1GM10800
PPP1R15AGM6109
TNRC6CJUN
2610036A22RIKMLL2
TET2JUNB
GBP6INSIG1
E430029J22RIKFQSB
4930470H14RIKCCL5
SRRM2LGMN
GM10718APOE
IFI203NOTCH2
RPL21-SGIP1
PS6
HMHA1NR4A3
MED13LGM4070
RPL29-BMP2K
PS2
CD27ASDC4
RASSF2AIF1
NCOA3LYZ2
KLHL24H2-AA
STAT1GRN
AHNAKC1QB
ARID5BFCER1G
FMO1
GM10291
RPL17-
PS3
SLC39A1
GM10327
BIRC6
IRF2BP2
GM5570
MAP3K1
GM10800
TOB2
GM6316
KDM6B
GM6109
LY6C2
ZFP36L1
JUN
ALKBH5
MLL2
JUNB
PDCD4
MNDA
INSIG1
RNF213
GM8909
C030046E11RIK
PARP4
PSAP
9930111J21RIK2
ARID1B
GM11127
H2-Q2
CDKN1B
NOTCH2
SLFN5
SGIP1
GM4070
PCF11
BMP2K
GM7030
GVIN1
ZFP36
LYZ2
H2-AA
CTSS
FCER1G
TABLE 7
Listed is the fold change (defined as the expression level of the knock out
cells divided by the expression level of corresponding wild type or littermate controls) of all
significantly differentially expressed genes (Experimental Procedures) for a given experimental
condition. Experimental condition information incldes; the knockout mouse (GPR65−/−, PLZP−/−
or TOSO−/−), differentiation condition (TGF-β1 + IL-6 or II-1β + IL-6 + IL-23), and the duration of
differentiation before harvesting for RNA-seq analysis (48 h or 96 h). All differentiations were
conducted as for the single cell in vitro data.
Differentially expressed genes for GPR65−/−, PLZP−/− and TOSO−/− Th17 cells
GPR65-KO-PLZP-KO-TOSO-KO-TOSO-KO-
IL1B + IL6 + IL23-GPR65-KO-IL1B + IL6 + IL23-PLZP-KO-IL1B + IL6 + IL23-IL1B + IL6 + IL23-
96 h-1TGFB1 + IL6-96 h-148 h-1TGFB1 + IL6-48 h-196 h96 h
Fold.ChangeFold.ChangeFold.ChangeFold.ChangeFold.ChangeFold.Change
Gene(KO/WT)Gene(KO/WT)Gene(KO/WT)Gene(KO/WT)Gene(KO/WT)Gene(KO/WT)
CT025533.1638.963LY6G72.0601CR478112.14828.97AC112970.1997.832AC090432.119.4613LY6G20.5027
GM11042219.403CD3G35.7993AC163094.2705.836AC163330.10.00100217GM1099917.1617GM101390.0744158
AC163330.157.6454H2-Q820.2139GM11035469.257AC118017.2691.521FAM132A0.0731972CCDC5612.7227
GM1069552.9557ROMO118.4077AC090563.1181.836GM109740.00177299NDUFC112.6321GM1019212.3271
IL17F20.8104ATP5J16.4856GM10774127.093GM107740.00786822IL240.0840608IL240.0852024
GM1103515.2049MPP115.7176GM1107486.5719GM11074120.52A2LD19.99009PAM160.0870993
2210012G02RIK14.137UFM114.9395GM110320.0235315SND1114.792010107H07RIK9.64576HMGA1-0.0887043
RS1
GM1022212.7776LY6I14.4088CISD30.0267441DEDD0.00957397NHEJ19.40856UCKL10.09253
S100A10.0863747LY6C214.2462IFI27L2A29.7388NUDT159.2046RNF1217.96782PIH1D18.8086
SLC15A311.4418GM1077413.9351TBC1D170.0363317GM102220.017264GM104957.43442GNAQ8.75423
MUTYH10.8353LY6C112.7774AL732569.10.0430873GM62930.0203867NTAN10.152127CCDC98.65022
TEAD210.3068IL17F12.2224EWSR121.7177H2-Q848.6847LSMD10.153423MYCBP0.12853
GM104909.71233CCLS0.0827434AC121566.10.0476118GM1103247.0127MED66.4984FRG10.132368
IFFO28.85699SGK111.4417LIN370.052081ATOX145.8514MED76.43439BCCIP7.46098
TBCB8.749412010107E04RIK11.366FAM36A19.0056AC121566.144.8555CTSE6.370750610037L13RIK0.141592
AC102609.18.69175BANF111.1666GM1072118.9873PFN137.3129TM2D30.160132RABL36.66246
CATSPER48.30689TIMM8B11.0647AC132391.10.0537352AL845291.10.0348296CCDC1016.24144COX6B26.63466
CCBL28.27697VPS3610.7432AC163993.10.05547452310004I24RIK0.0365274SLC12A46.04047MRP5300.150918
GM110748.19721GAA9.860352310030N02RIK18.0003SNXI40.0369612SAP30BP5.90169E130306D19RIK6.53277
LINS8.16618COX7A19.68942GM1116717.8147STRA1326.8007UBASH3B5.82363KLHDC16.48342
1700029F09RIK8.12329AC087540.19.67077GM1010617.0651700054O19RIK25.03578430419L09RIK5.78915FBXO96.32457
MCFD20.123502NDUFC19.66605CCDC3416.96014930423O20RIK24.9749CT030170.25.45632TMEM2096.15855
TMEM337.73635PPP2R5C9.6414.3AC131780.416.68011110051M20RIK0.0422463GOLGA15.358FAM18980.164908
4930425F17RIK7.56473LY6A9.61981LYRM20.060334GCDH0.0422702SRSF95.31367SETD46.03598
CLEC12A7.52948IFI27L2A9.53003WBP1116.5356ARRDC122.4399ZMPSTE245.2634H2-QS5.89529
MCTS17.4317LSMD19.38183CES5A16.1643PAM22.1904TOMM50.190338GM73675.88087
2010107G23RIK7.38844NGFRAP19.37045MLLT1016.0522MED270.0482159TSC22D15.2077MRPS365.83917
UQCC7.377COX5B9.302AC125405.115.7217NMNAT30.048537PGLYRP15.16763LEPREL15.77011
BCCIP7.04936GIMAP39.18091GM1080015.3943NDUFS50.0489343PACSIN35.13269ATF7IP0.177348
XPA7.02998SPAG79.17137RWDD10.0660117PSENEN20.1098ZFP6885.09008WARS0.180546
RAB347.02805GMFG9.11872AC131780.215.0009D8ERTD738E0.0512351PPAN0.196514ZCCHC175.50318
DFFA6.96773TFG8.71842GM1072014.7899MRPS2318.14261700120B22RIK5.073A530032D15RIK0.182743
GNG126.94052XPA0.117077FANCE14.53POLR1D17.8188ZFP5235.04854HINT25.43973
ARL30.146797MRPL28.47401UBE2A0.0707408GMFG17.5188BSDC10.198872GM19685.3797
TDP16.76641CR974466.38.47128CKLF13.7256MRP5517.1812WDFY15.02185GTPBP60.189111
SPG206.7321AC118017.20.11863PRNP13.6676GM103110.0585203MUP114.90405TMUB15.23194
CYTH10.150294RPS6KA30.119074LYRM713.446RNASEK16.9543DIABLO0.205542BCL2L125.09994
GM102386.62969PAIC58.38958GM1071813.44182410015M20RIK0.0598462NUMB4.81845DOK20.196929
HNRNPR6.59627TSPO8.35239A830010M20RIK13.43684225616.5496HMGN34.79371GM104165.05178
DRAM20.153594GM104168.2616GM1071913.3625MRPS18A15.8322FBXO60.210289ACER25.04034
1810020D17RIK0.154415GM52158.11489AEN13.0909RFC515.7095HMGA1-0.213347PYGO20.198435
RS1
CHCHD80.159584NRBP17.90454GM639612.781LSM120.0640507LACTB24.66939CLEC16A5.02465
LZIC6.22633GM77137.86697GM1071712.334218I0035L17RIK0.06432691110051M20RIK4.65484ACSL14.95276
PSMD136.14569ATRX7.859052010107H07RIK11.8908SPC2515.407SERTAD34.65467MLEC0.204293
PPDPF6.0768CCDC109B7.85491CCL311.8036ORC515.3066HRSP124.64663ATPAF20.206001
TCF40.164742PAPOLA0.128481ZFP6680.0856954GM110110.0660117HIAT14.60585FBXW204.84791
FASTK6.03114FUNDC27.75996DPH311.5057IPO90.0670124IL17A0.2174199430002A10RIK4.83616
SAFB25.93824LEPREL17.71451MRPL5211.4306ANAPC1314.8538UBAP14.57328AKAP90.206832
WDR545.77242DBI7.66514POLR2H0.08783082810021J22RIK0.0687575CIC4.56612CBX50.208348
MED285.70363PSMG47.54057PIK3R10.0898843PDCD20.09698MMP164.56577TULP44.79559
MOSPD35.68319RGS197.53778AC025786.10.0900128PAF114.2702PQBP14.55228DOCK74.79084
RENBP5.65082AC112970.10.132869MAPK30.0902236CKLF0.0702488SEC61A24.54133CRTC14.77533
ALDOB5.63858GM58307.43749HMGXB411.0516SLC39A1414.0594RRP84.49549AC154631.14.75579
HELLS5.48094POLR2J7.35784MRPL540.0906724AC132837.113.9914IFT1404.489532310003FI6RIK4.74871
GM114445.45078TARBP27.2882FBXL120.0941317EXOSC30.0716036CCDC109B0.224915DEB14.72541
TNFRSF225.41408RSRC17.253714930431F12RIK10.5765ZCCHC713.8323DSN14.43985DFFA4.66373
AC114625.15.37391HSCB7.21689AC127590.110.53172310061C15RIK13.8171PHF204.435224930431F12RIK4.66082
GM60035.333370610037L13RIK7.19607SEMA4F0.0952178BDH10.0726258NPM34.38892LHPP4.60268
GALE5.25036NOP567.16844MPND0.956693HACL113.7256RCAN34.37872CSNK1G10.217973
GTDC15.21537PIGK7.16261SLCO3A10.0961635CCNE213.6676MKNK14.3396BC0493494.51794
PHF21A0.192406RPL21-7.16OPCML10.354GM97580.0731656EXOC6B4.31007DRAM20.219853
PS6
ARHGAP45.17683ANAPC137.05292ZCCHC100.0984483TMEM10713.5403ENPP20.233365PCID24.5141
DLC15.16934PDRG17.00334AC155646.10.0995017CCDC5513.4463ZC3H104.26288GRAMD1B4.50702
FMNL10.199325ARRDC16.94928PPP2R2B9.89GRCC1013.3862PIGF0.23674RUNX20.22212
PUSL14.98071NAP1L46.94591PDIK1L9.83923SCP213.1902LY6C24.21301GM59004.50071
2610030H06RIK4.94004PQP56.91452EMP39.79589GM163720.0762153TRNAU1AP0.2376821200016B10RIK4.49989
MECR4.898710610037P05RIK6.90089MRPS129.73996RDM10.0768956TRMU4.20472GM163800.222513
TNFAIP80.204512CK51B6.88764GM101929.67502MRPL23-12.9196HIRIP34.18487TRAFD14.49287
PS1
HRSP124.8833A930005H10RIK6.87052GM108019.62219ENTPD112.82492210016L21RIK4.16073FAM165B4.45428
RHOQ0.207277GM105060.145568GM107159.35018INSL60.0791878MDN14.15504TMEM54.44872
GPATCH80.20735CLK16.800991700026D08RIK9.29877AC125405.112.6184SELK0.242008AIM1L4.41539
IFNAR14.82106PRDX46.78434GM108420.107607MRPL1912.6066FBXL124.06574FAM129B0.227632
TAF120.207562WDR756.78281XRCC40.10761GNGT212.4445LLGL24.0643NUP854.36604
RASAL30.207598SMEK26.76563IL90.109536AW11201012.3828MZT24.03573HIST2H3B4.33905
CCL44.78481TMEM856.7621A630001G21RIK0.109616AC102609.112.2887MAD1L10.247878FAM175A0.231044
FAM69A0.209402DPM26.6864ENTPD19.07642ATPBB20.0822643ZCRB10.247973YAF20.232974
ME34.7724UBL46.67639IER3IP19.0603GNG1212.1127DEB14.02094GM103550.23349
MPND4.77191CLEC16A0.151024AC122006.18.8707TMEM2220.0832766KLRC13.98576NAB10.233585
NMB4.67907MPHOSPH86.58917MED70.11446EDF111.9815HIBCH3.97739ADCK30.233826
SLC1A50.216752PCMTD10.152472MMADHC0.115059TIMM1011.9333A530032D15RIK3.96486PEX11B0.234331
CIAPIN14.5998PREB6.55723NSUN38.60642BC05707911.8981MTX10.252901BTBD100.235263
2810432D09RIK4.56386GM83946.54589PHF100.116796GM111100.0850470UNC45A3.93916ACNAT14.24866
POLR2F4.54368FKBP36.5407AC131780.18.51373SLC35A10.0854511KIT3.9252HIST1H4F4.24366
LSM44.53477FAM165B6.54065SNAP478.47662POLR2I0.0856321NPAT3.8405CYSLTR10.235958
PNRC10.222174BCCIP6.50692RAB11A8.44774UBE2811.6779MLLT103.82477PSMB94.21107
PUF604.47952PPIG6.50177EXOC48.44629ASAH10.08637921110005A03RIK0.26263NEBL0.237823
1110001J03RIK4.47334NSMCE4A6.47025HIST1H2BH0.119207MRPL2811.5026DPY19L30.263324HRSP124.20275
MLLT100.223694CEPT16.39179TRAFD18.36382PCID20.087048CDKAL10.263647PDIK1L0.238132
0610010O12RIK4.42709SPCS16.36876ATF70.119705SRP911.48650610010O12RIK3.79286GM104820.238435
GM104820.226172WDR616.367034930470H14RIK8.2969NISCH11.4306C1D0.263877POLR2I4.19095
SLC25A114.39262FANCC6.33444SOD18.20086GM217811.3855MANBA3.78609RPL21-0.239361
PS4
HDAC80.230041RAD23A6.205331700064H15RIK0.121984FUBP111.3077SIN3A3.78214TEME480.239496
THAP74.34098RNF56.20083FASTK0.122115FANCE11.298NASP3.76321CLN30.240092
ECM14.33421CKLF6.13147EVI2A8.17236RAB8A11.2824IQSEC13.7207GABPA0.240205
JUP4.3205CDK5RAP36.11741GALK28.15188RPL30-0.0889991WIBG0.270456ITGAV0.240239
PS6
1110004E09RIK4.31718DCAF170.163649AC131780.38.14542FAM64A11.2121RALGPS13.69553AC114625.14.15154
TOR2A4.30601U2AF1L46.09837DNAJC240.122813TOR1AIP20.0899992ARMC73.68615MBTPS24.12905
ZFP544.29205HMGB16.043544930534B04RIK8.12667PSMG311.0633SNRPC0.2718611810074P20RIK0.242547
GM69900.233534P5MD65.996212310045N01RIK0.123579ALDH7A110.9234CASP93.67579CSNK1E4.11864
AC1SS646.10.233606AC132391.15.97323SERGEF8.03129TSPAN3210.724KLHL153.67081SELENBP24.11852
MTX14.27561LY6K5.94919GGNBP10.124804SSBP210.6665MBTD13.67015STYK14.09169
AL845291.14.2534CD209C0.168595730437N04RIK7.93924PPAPDC1B0.0938321110065P20RIK3.66259UFSP20.24606
GTF2H10.235342DLG45.93042DEB17.91466UBL410.5765NENF0.273259PHLDA30.24686
IER30.235664VILL5.92894MTHF57.87981MED2810.5684EIF4ENIF13.64441KLC10.248587
AKTIP0.235925WDR135.90818GM105767.8766TRIM2810.5317ZFAND33.64348PRL8A14.02006
WBSCR220.241006HFMK15.89641TTC39A0.127576GIMAP70.0953338PDUM10.276107GM121664.01965
LY6K0.241404SLC35A15.83336COX7A17.82902IVD10.48031110007A13RIK3.61404DUSP220.249121
BRIX14.14223NDUFB65.81576HSPA40.128114HIST1H4C10.4707THNSL23.61171CCDC234.00206
DNAJC244.12988PRP515.7819RAB90.128579AC025786.10.0955993MYO1B3.61009NT5DC10.250004
ZMYND84.11359IFI27L15.75922DHODH0.128681UBR410.3681ECE10.27729RNF1850.25078
RNF1410.244327ZMPSTE245.69972PEX197.69973PIGZ0.0967107SIVA10.277827METTL13.97941
DDX494.07132DEK5.68906DHPS7.60305MAGOHB10.2556TIPIN0.277841POLR3G3.95579
SPAG54.06089SERPINB1A5.68805CAP10.13167KCTD910.2384PSIP13.59527H2-Q63.95138
2010107H07RIK0.246574KCNAB25.68376AC102876.17.47393CNDP210.2284USP113.59442GM104950.253919
TRIAP10.247873NPRL25.65002SSSCA17.4544AC163993.110.1311BATF30.278394RAC30.256158
DHDPSL4.030739030619P08RIK5.64316201030SA19RIK7.43256POP10.0992703RALY3.587391700054O19RIK3.89993
CCDC1304.01695MAPRE15.63223NDUFB27.39808DHDDS10.0429MTHFS3.5871TMEM38B3.89356
CPSF3L4.00615UFD1L5.6314GAA7.38646YIPF110.02313110001D03RIK3.56035BCAT20.257108
GUK14.0013IL25.63133HIST1H2BN7.29384RBM4B9.974711500002O20RIK3.55877BATF33.85665
TMEM853.98691CPOS7B5.61304DNAJC197.28404MED249.95916D6MMSE3.554782310061I04RIK3.85025
ACTRT23.96478TEX140.178636CLN30.1375R3HDM29.89625RIT13.55126BMYC0.260139
PPIL30.252903422625.5932PUS7L7.24213MTBP9.84994SYTL33.53773CBX70.26126
RSRC10.253147RNASEH2C5.58174FAM188A7.23481PUS7L9.83923GGT13.5364EGFL70.261514
ZFP683.9485PPOX5.56957IL30.139557TNF0.101767ETFB0.283229AC125405.13.81229
SEL1L3.94649NUDT25.56538GGNBP27.15053SURF20.102144GGPS10.283765NDUFB23.79385
SLC12A63.93412CD270.180344ZFP3537.12168TFPT0.10259ZFP583.51272ING30.264762
YBX10.254705MOCS25.52865VKORC17.08262CERKL0.10267RTEL10.285742THG1L0.265805
ARID5A3.92361RGS15.52352POPS0.142477GM105069.73403BCL33.49539TMEM1473.74181
CCDC520.254901LXN5.51705TXNL4A0.142821TAF120.102745MFSD110.287788ZCCHC103.73683
DULLARD3.91929PTPN65.47051ZCRB10.142824ALGS9.71286SPSB13.47315POLR3GL0.267958
CD209C3.91841GM109990.183075GM144206.99722GM70759.58803PRL7B13.46403CD723.72884
TTC333.90685PES15.45618AL732476.16.98499FAM96B9.58165MAPRE20.2889471110051M20RIK3.72461
RBKS0.256393SNRPD25.44382PARS20.143242MYLPF0.104864CHAF1B0.289552MBD63.71439
PARP33.89675ANKRD375.441572610044O15RIK6.96583KDM4C0.10517AP2A13.43984RNF383.70742
FAM71F20.257088CDCA20.183865AC117259.16.9419CTSW9.48133SCYL33.41864GGT70.270077
281040SK02RIK3.88632E130306D19RIK5.42898GRSF16.931925730469M10RIK9.47232LRIG13.47102C630004H02RIK0.271104
GM107203.8603WDR835.3926BNIP20.144906SH3KBP10.105824RFK3.40993ORC53.67903
CSE1L3.85295EIF2B25.37028PNKD6.87634FBXL179.42315MFSD103.40474PHTF23.67589
ANKRD403.85008MAF15.35371GM102036.8613A330049M08RIK9.37675H2-Q83.402784930425F17RIK3.67327
MCART63.803062310003L22RIK5.35207SLC7A40.146352MAGFD29.37124MAFK3.40214TMEM1990.272294
1700093K21RIK0.26331IFNAR25.34873POLR2I6.82307NCF40.106786HIATL10.29417POLRMT3.66929
MYC3.79416EIF2S3Y0.187055TMEM2236.78242SNRNP259.334236GM89233.39724CNOT6L0.273167
AC131780.23.77596ASNS5.33487TMSB15B10.148173ABAT9.31561ETV60.294593EXOC50.274182
GM107193.75154PDLIM25.33001R3HCC10.148619CD3G9.298772310079N02RIK0.294964ARLSC3.63451
MGAT4C3.74852NAA165.32749TMUB10.150806PIGX9.28618CC2D1B0.296056PHYHD13.61167
MTA23.73852GSOT15.31579CWC270.1516242410017P09RIK0.107927MTCH23.37335BC0553240.277808
MAGOH0.267975GM101205.28972B4GALT30.151711700128F08RIK9.20197POU2F20.296735VHAF1B3.59632
TSPAN43.73086MKNK15.25695AC142104.10.152305PTPN29.18199WWOX3.36062ARIH10.278113
GM111673.72546IFRD15.24842ACIN10.153182POLR2A0.108941SUFU3.35608ROBLD30.280033
CLYBL3.71736SDCBP5.24642SYTL30.153535CCDC99.15689NMI3.34913TNFRSF143.57066
GM107173.71066UBE2NL5.24562DNASE16.49588PTPMT19.1473WDR113.34188FUCA20.280098
ACTR1B3.70995TMEM605.23158GM163720.154587TMEM859.07642GM164163.33749GM112753.54481
BOLA33.70413GM89095.22564MLLT36.46887PLXND19.04785MRPL273.3349HERC33.53754
CEPT13.67288GM97625.20942RBM226.4636GM80540.110711COPZ23.33482PSMD140.283245
LUC7L3.67232LZIC5.1977FKBP1A0.155468SEPSECS0.110944ZFP3183.33439TTC70.284516
LAIR13.67033INSL65.1949PLA2G166.4322COX158.9846APPL13.33289AGPAT23.51269
MRPL173.67024GM109255.17227TMEM126A0.156135P4HA28.97898TCF43.33168AC152721.13.50828
ASAH10.273768CDK11B5.16631METTL11A0.156575EED8.97733TMEM126A3.3264RAD23A3.49641
SMARCD23.64906ANXA55.1584RPL21-6.35423MAT2B8.96162VWA5A3.32388ADAR3.48718
PS7
GM107183.64245GPN35.13763ZFP280C0.158142FUCA28.94968MED100.300958STX4A0.286919
RUVBL23.63161FXR25.13594AC132837.16.32254NDUFB40.111736COX193.32126LIAS0.287101
TTC353.63095TMBIM45.13572MAP3K56.30122ACAD80.112277GM131473.316542210016L21RIK3.47446
TPST23.62056ELF25.09157IL240.159647WBSCR228.88424IRF13.31264IL15RA3.47047
GM77133.60269PDCD55.06988SRCRB4D0.159836SMS0.112965BUB13.302749030617O03RIK0.289013
CCDC1073.59403TTC45.05311HCST6.24208NBR18.8219LYAR3.29979TRPC23.45442
GOSR20.278504MED65.05201GM60966.23421INSIG28.80943KLHL223.29245MPP63.44593
1110003E01RIK3.58679PTP4A25.02647RRP80.160473TMEM1478.8022TSR23.28397TEAD20.290337
FAM175B0.281168FBXO65.02333ALKBH30.1605AC160471.10.1139WFDC123.27084DYNLT1B3.43878
CCNDBP10.281381KCTD130.199541KLHL156.22658POLE30.11417METTL83.26973HIST1H4C3.43302
HCST3.55104AA4671975.00576SLC15A26.215CDC25B8.73177ST6GAL10.306454BX679668.13.42595
ZMPSTE243.51831CREM4.97069GMPPA6.20253MMP168.69991CLEC16A0.307168AQR3.42194
SUCLA20.28494MYCBP0.201252GM106956.2007CAR98.6955FOXJ30.308099GLRX3.42051
IRF53.50011CUL14.96085SPATA240.1613654930425F17RIK8.65193MEN13.23788AW1120103.3997
SNX113.497770610010K14RIK4.954451700128F08RIK6.18163TEAD28.50191CREB10.309475CREB10.29428
CLN63.48154RARS4.95308PRMT10.163087TRPM70.117774WDR913.22232MKI67IP3.38602
HEMK13.48009MLX4.94267CENPT6.11819GM63960.118058RPUSD33.19808PACSIN33.3842
AC139042.10.287734BC0292144.92756VAMP80.163897IFI27L2B8.45495MAN1A23.19268CDCA33.38042
MOSC23.46365HCFC1R14.92552FAM132A6.1003CLPP8.44774KDM4B0.3133014SLCO3A10.295842
ADAMTSL43.44653IDH3G4.9087ANKRD126.080434930431F12RIK8.44682RPS6KA13.18481GABPB10.296055
ENTPD13.44534GM110270.203723MED126.05937GM143260.118615OPCML0.314452PPIL13.36445
HEATR7A3.43505CTSW4.88659USP480.165395GM143990.118719PSPC13.17489GM131453.35155
RBMX20.291348H2-T224.88453ARF26.0275TMEM41B0.118866GEMIN63.16916HNRNPUL10.299266
H2-KE60.291727UBAC14.88373EP4000.165979FAM173A0.118928RSRC10.315601HIST1H4D3.33023
ACADM0.292164LRRC424.8802SEC22A6.02034PARL0.11911PHRF10.316104SRSF90.300436
ACAT10.29297COMMD24.87675POP40.167588PFDN58.37525MTMR23.16217YIPF13.3284
TTC43.41016UFC14.87361CUL4A5.96554DUSP220.119563CBX63.15871110034B05RIK0.301017
MPP10.293362EHMT14.85546RHBDD25.95536USP468.34247NIPSNAP3B0.316638FXR20.301391
DNAJB63.40686TSPAN324.85182MED130.16848RGS108.298ZFP5603.148SEPP13.3074
PEX33.40459NDUFA54.84453GRIPAP10.168836ZNHIT18.28155PENK3.14719CTSE3.30526
TM2D33.39927SHBG0.206932POLB0.168936TMEM688.24996DNAJC123.14601GM164150.302846
ING30.294189NDUFAF14.83055AC117184.15.91584MYD888.23977ALAS13.146LY6C13.29529
BC0033313.3823H2-Q24.82529FAM45A5.91331ADRBK10.12153ATHL13.1458STT3B0.304407
GM107213.37432NAA384.82184ATP8B25.905724933424B01RIK0.121798TRIM233.14356ABHD103.28355
GM72043.36481REXO44.81173HIRIP35.88779SQSTM18.20789RPA30.31917SKINT83.27906
GM111103.35826ADRM14.75358TPRKB0.170218GM110070.122161MYSM10.319182FANCE0.304979
CLUAP10.298264GEMIN74.74963BRP440.170503GM144300.122161PI4K2B3.12907GSR0.306238
CASP23.3411GM163724.7397GNAQ0.170757GM144320.122161AKR1B100.320108IL10RB3.24925
PXT13.34066INPP4B4.73513IMPA15.84994GM20070.122161AP1G23.11828CCDC123.24378
IFT813.34042MRPS334.73216FXR20.170976RNF2148.18285POLR2A3.11809GM97260.308711
INPP5B0.299378DRAM24.73138ZCCHC110.172462BBS58.18151HOMEZ3.106798430419L09RIK0.308795
KIN0.30071H2-Q74.72653YY10.173566PLA2G4C8.17236TCFE33.098820610011L14RIK3.23236
GLUD13.30721PHF5A4.72302ZFP6870.173904TIMM17B8.16695BLVRA0.322872RFC40.309401
ADCK50.30264TANK4.69966ASAH15.73409ITGB48.12447PPP1R103.09185CD693.21508
RANGRF3.3018STOML24.691511110018G07RIK0.174577STAM20.123152FUCA13.0907CCDC580.311259
OBFC10.303249TBCA4.6727MT25.71646RNMT0.123306WHSC10.323879MCEE0.311593
PREB3.27993GDI14.65362COMTD15.68962KIN0.12338CLYBL3.0825GM105763.20521
BRI30.304959FAIM34.63947SNAPC50.176233GNG28.10003SLC10A33.0807RDH93.20507
GM51160.305402ELMOD34.6303EIF3G5.66822HSPB118.08051PTPN53.08056SDF2L13.20341
NINJ13.27329ACBD54.62748RASAL30.176644TRMU8.06635MSL10.324943FAM53B3.19661
ANKRD53.27263MCM74.61003NBR10.176732CCDC538.0588PPP1CC0.324984ZFP6873.19627
NAPA3.27166CDK10.217436MKNK10.176948ZFP1200.124245BOLC1S13.07662TOR1AIP10.313011
PNPK3.2648LIMS14.5884SFXN50.1775692310045N01RIK8.03129RUNX20.325457TSPAN50.313131
MRPL123.2625CD534.58769PRPSAP20.177826WDR540.124942ECHS13.06707CASKIN20.313155
SMS3.26183PCNP4.58745CISH5.61993ZFP2777.98906BECN13.06641TSEN340.313224
FTSJ30.306978LTA4.57147WHSC10.178333GM58307.96854HINT33.06058PLXNA30.313512
ALKBH73.25685LST14.5675PDCD65.60468LST17.96261RIN33.05864DNTTIP20.314457
GTPBP20.307896GM1294.56142A830080D01RIK5.58829GGNBP27.9446STK383.03842DEDD0.314807
GIT23.24272YWHAE4.53645GM98050.179155STX4A0.125957CD740.329393LSM73.17454
EDC43.23745SNRPA14.526851700019E19RIK0.179583ITGA30.126311RIPK23.03454CD209C3.17324
KIF18B0.309756CCDC550.221266MTF10.179724B9D10.126396PLK40.330137NPRL23.17313
MESDC20.311201SIN3B4.50388NUDT15.54265CASP8AP27.91164NSUN50.330782010317E24RIK3.16942
3200002M19RIK3.21272BUD314.49958THAP30.1808122310004N24RIK7.8659CCDC1240.330746COPS80.315522
TOP2B0.311371CAMTA10.222475ABCB80.181246GM101927.84084UBE2L63.021171700128F08RIK3.16253
DCTN33.20361TSPAN314.49444TOP2B0.181282EZH27.83613D2WSU81E3.02012ALDH16A10.31636
2310061I04RIK0.312456GM70754.48701AL844854.15.51339MRPL470.127977NUP850.31187FBLIM13.15478
CTNNBL10.314975NUP434.48542SLMAP0.181459GM105767.80195BC0238293.01789GM53563.15448
RASL2-9-3.17011TMEM2234.47551POLD30.182126GIMAP57.78292CLTC3.00802TRADD0.317215
PS
IDH10.315650610007C21RIK4.47073SCLY5.46754MAPKSP17.76362COG63.00697EBNA1BP20.317965
KIF3A3.16623ZFP684.46896JKAMP0.183242MFHA510.1292442310039H08RIK0.332595ABCC10.318111
LSM123.15031CD24.46617RFT10.183557ARHGAP237.71607MNT3.00475PDCL3.13806
GM2213.14642NUSAP14.46516C1D0.184599STARD40.129674PCYOX10.333752CCDC430.318846
AC131780.33.14591AGPAT34.46119CCDC590.1847041600002K03RIK0.1297B230312A22RIK2.994484930474N05RIK3.1328
FAHD2A3.14433AC156550.14.45543KDELC10.184761SDR39U17.71011CCNE12.98388PDLIM23.12896
DOLPP13.13618CDCA84.44608ADCK35.41193NFYB0.129757NDUFS52.98382CCDC343.12826
STAM3.13571BTF3L44.44441SEPSEC50.184985GRAP7.67211110004E09RIK2.98052SRSF13.12446
TIMM100.319394DDX524.44187VEGFA0.185212LUZP17.63923HMGB12.98022DPP73.12375
GM102033.12599NDUF554.42774SC5D5.39603HCST7.61544GTF2IRD22.97923IL1F93.1218
NUBP10.320316CDC42SE14.41984FNBP15.39015ZFP6377.60926TMED52.97866SLC4A110.320514
NAT90.320397UCHL54.41338AIMP15.38091SRP197.57363FAF22.97736ALG143.11635
RB10.320964PIGYL4.408931810020D17RIK0.18622OSGEPL17.56543CCR40.336944MFSD2A0.321052
H2-GS103.11528PDLIM74.4059ECHDC15.36392TMEM1997.48781NTNG22.9583RB1CC10.321173
MYCBP3.11017VDAC34.40259MTIF35.34337SENP30.133839RNF440.339022FXR10.321184
AC132391.13.10754933424B01RIK4.38958RAPSN5.33494TSEN27.4544NDUFAF30.339379PINX13.11098
IPO130.322049PPP6C4.38589MPHOSPH80.187887GBP27.43783IFT200.340482D4ERTD22E3.11053
PIP4K2B3.10511SUCLA24.37852GM154010.188117CIB17.41262310008H09RIK0.340502GNG23.10972
4930522L14RIK3.10127ENPP24.36614TBCB0.189179IFRD17.40784CAMK2B2.93204ERLEC13.10554
1810043H04RIK3.09951HCST4.353275830405N20RIK0.189513110003A17RIK7.40034COQ20.341951ARMCX13.10231
PSPH0.323327GNPDA14.34901OBFC10.1896332010107H07RIK7.38496MRPL532.91647GSTK13.09987
GM101063.0872BAT1A4.34702AC161001.15.2627ZFP510.135464CD440.342897UBE2D20.322793
ZFP4513.08594ZFP7380.230576AC156282.15.26252ARHGAP157.37653NFKBIL20.343165HIST2H3C13.09785
R3HCC13.08076MBD24.32965GLUL5.25996POLD10.136672RGS12.913138430426H19RIK0.323762
CHCHD23.07856PRR134.32946LIME15.22859TTLL120.13693POLR2K2.9129SMARCD20.323887
PCCA3.07753DPY19L34.32468CCDC430.191352MAN1A20.136938RNPC32.91133BDP13.08415
WDR453.07381GM61804.32311DNAJC15.22394HAUS17.29384ARL5B0.344485MPV173.07741
CFHR13.0702SLC25A194.31144MRPS245.223651700034H14RIK7.27853SLC2A60.344806MPHOSPH60.325279
EMD0.327152TTF20.232143IMMP2L0.191798PGAP27.26564TBC1D9B2.89908CDCA53.07215
BCL2L110.3276RPAIN4.29849COQ60.192374RASA10.137688B230208H17RIK0.3451361700106N22RIK3.07201
AC131780.13.04934CARS4.27862PIGK5.17747JMJD67.24938ZBTB492.89711ACADSB0.325735
VTA10.328216RCC24.26834IL15RA0.193169AC132391.17.20559WDR122.89619AC125099.13.06864
ZMAT53.04487DPF20.234501VPS250.193271COX160.139135PTOV12.89529SELENBP13.06799
PITPNA0.328621PHF104.24475GM51165.16527SDCCAG37.18597SRR2.8933PLA2G163.06358
HNRNPH10.329638BBS90.235876PRM15.150442500003M10RIK7.175772010111I01RIK2.88992HIST1H1B3.06348
STX173.02886RPP210.236253CCLS0.194269SLC25A140.139358BLCAP0.3473861810030N24RIK0.326736
TFAM3.01805VMN2R70.236701GRAMD1B5.1398TCTEX1D20.13985PHF20L12.86916ARID4B0.327308
MXRA83.00578EWSR14.20549CAPS25.137894930447C04RIK7.14502FBXW42.86808NOX43.05421
ACADSB3.00199BLOC1514.2011RPS6KB15.13778MRPL537.13183PHLDB10.348714NEK83.04829
RPL212.99721GOLT1B4.18881TBCA0.194723TMEM39A7.07725RAPH10.349641BLCAP0.328174
HAT12.99666PFKL4.184CLUAP10.194896MTIF27.06029PIH1D12.85572FAM49B0.3283
AC151573.10.333857FAM132A4.17661PUS10.195037SOD17.0561CREG12.85346RHBDD30.328672
PHPT10.334679GSN4.17321NAT95.11575GM143910.142113LMF12.85313ECHDC10.328928
TMEM120A2.98268UGDH4.16843TMEM2190.19559GM165197.03028BC0032672.85291UFD1L0.329148
PEPD0.335518NR2C24.16575ANAPC115.10652ZWILCH7.02727NELF2.83614MGAT4C3.03683
UXT2.97617MECR0.240318SEC630.196009LENEP0.142993TTC9C2.8299SRD5A33.03372
AC131780.42.95956ACAA24.14731EIF2C45.09769BC0311816.98719EPT12.82973ZFP874A0.329847
UCP22.94585GM100284.14033TRNAU1AP5.09474UNC5CI6.96583NEK82.82961UGDH0.329891
PML2.92902REXO24.13396DIABLO0.196302CHUK0.144161MPND0.353903CUL10.330359
GM55312.92779ATM4.13183CEP550.196627SPIC6.92498MOBKL1A2.82546CMAH3.02473
GM107152.91891NOPR14.13177GM70755.05552HOOK30.144541ZFP2872.82431LPL3.02258
POLR3GL2.91406GM102030.242035AIFM25.04453DLG46.91142TBCE2.82358SIDT23.01548
LSM12.91027TAGLN24.11826NUP2100.198274ARMCK60.145354MARK22.81506YBX10.333036
GM111520.343815NBR14.11699FAHD2A0.198849HIST1H1B0.145354GBA22.81383IL90.333116
STAT62.90777CPT24.1068ARHGAP290.19898APIP0.14542FBF12.80213PNPO2.99467
DLG42.90687GM19684.10351MAN2C15.02447DET10.145604MRPS232.8007CENPH2.9921
MTCH10.344018DERL24.09817SPINK105.02384CENPV0.145745MXRA82.80045LTBP12.98882
MAGED20.344848ERGIC24.09157HSPB115.01869GTF2F26.8613GOSR12.80035USP200.335411
SLC10A32.89944PHOSPHO20.2449862810428I15RIK0.199368COMMD66.83834TAF110.357693CD40LG0.33575
CKLF0.345303ATP6V1D4.08007MFF0.199484B4GALT36.82285CHCHD72.79479CLP10.336059
RAE10.345495LSM104.07981TIMD25.0101AARSD10.146637TRIP112.79204CBX80.33665
MED272.8884ZNRD14.07458UFD1L0.19963IFT466.8108SQRDL0.358218TRNT10.336673
CTSE2.88808LGTN4.07031SDHC0.199634MAD2L1BP6.80448TIA12.79049RG9MTD30.336739
IFT572.88579WSB14.069131110001J03RIK5.00685NDUFB66.79376EXOC52.789892610029I01RIK2.95916
GM102170.346567MTIF34.06886GM111750.1999730910001L09RIK0.1472611190007I07RIK2.78892BRE0.338296
CIB10.347105RNF74.05685STYK14.98328GLRX26.7192LYSMD22.78888CHCHD62.95512
HINT30.347157AC116115.10.247894RAD51L14.97228CHD80.149339ZFP3170.358667ZFP4510.338711
TSC22D40.3471712810474O19RIK4.02276MED60.2019691110008F13RIK0.149452CDC340.35947SEC61A20.338805
CDC230.34724LUC7L34.00597CNN20.202084THAP70.149641CCDC28A2.77955MAP3K140.338859
RPL21-2.87725TNFRSF43.9988PLAC84.93497ATP5L-6.67932BRD72.77765SLC25A192.94723
PS14PS1
UPP10.347912OGT3.99743MRRF0.203165MED40.149821SLC39A10.360242RG9MTD22.94721
AI3141800.348002TNNC10.250444DPM20.203226SNX150.149921CRTC22.77523PQLC32.94393
KBTBD40.348425GM129423.978633110001D03RIK4.89187AHSA20.15006ATP6AP22.77394PPL22.94232
2700094K13RIK0.348921JTB3.97698SMAD40.205077PDCD16.62636USP182.77391TMTC22.94055
H2-Q62.85926WBP53.97449RGS100.2056741110058L19RIK6.61022FANCL2.7726MRPL530.340076
ORC42.858284930522L14RIK3.970734933427D14RIK0.205823WFDC120.151538PAFAH1B12.770962610001J05RIK0.3403
SLC4A82.8579PHRF13.96914TOR1A0.205923UFM16.59687SVIL2.76036DYNLT30.340666
SDR39U12.85678CCDC563.96355DUT4.85312MRP5256.5844MRPS252.758664931406P16RIK0.340949
USP32.85498LMAN2L0.252321SNX124.83276CISH0.151934NUDT32.7582DIP2A2.93247
H2-D12.85043ISCA23.9591CAMTA14.82271ACER36.58143ATN12.75811STAB12.92744
SLC1A20.350989PARL3.93771EXOC74.81229SLAMF16.5452OLFR8162.75542BC0176472.92608
CARM12.8438TMEM1350.255381COQ70.207907ACTR1B6.533412310044H10RIK2.75278ELP20.342265
TPK10.352289IFT523.90514RNF1300.208222GM60966.50942CHD62.75194ARHGAP40.342484
GM116782.83596PSAT13.90383FAM58B0.208634ELP46.50917SNRPB20.363667AP3510.34362
ALPL0.352687STX180.256366GPD1L4.79143TM4SF56.50305NUCB10.364479PTPN30.344161
H2AFY2.83378ANXA23.89173SEC24B0.208746PXMP46.49205DLGAP42.74225KDM1A0.344246
MRPL320.353268AEN0.257495MBD24.76196SPATA60.154734DCXR0.364747PVR2.90195
RASSF70.354535TADA33.86131ARMC60.210289SNAPC40.154947AHCY2.74099ERH0.344634
GM144202.81884MAT2B3.86108GM101254.74057AA4671970.1552671110008P14RIK0.365256PPOX0.344666
IGBP10.355248422493.859249930111J21RIK24.73875SUPV3L16.41432TIAM10.365257XLR4B0.345325
NDFIP10.355598NUDT10.2592450610011F06RIK4.73797DHPS0.156039EIF2B12.72536GM29382.88665
UHRF12.81099RHOF3.85418CNPY24.72965DDIT30.1565895830405N20RIK2.7248PAIP2B2.88615
TRIM502.81049IMMP2L3.84533CHCHD84.7235BOD10.156685GM112760.367251FBXW20.348007
CCDC430.355835CELF23.83589ACBD64.716369030625A04RIK6.35456HIST1H2AO0.367251MPST2.8706
GTF2F20.355977DENND2A0.260843ABT14.71447COX5A6.33911RASSF22.72257C22.85795
TNFRSF13B2.80894FAM114A10.2610771810032O08RIK0.212175HERC36.3363CRADD2.72066FAM78A0.350322
TADA2A2.79953EXOSC93.82852JUP4.71286PPIG0.158134SLCO4A10.367988C330021F23RIK2.85429
YIF1B2.79885RPL37-3.8276C130022K22RIK4.70846APITD10.158176ISCA20.368981CDC25B2.85421
PS1
NFKB10.357441NCK10.262234CDK144.701572310008H09RIK6.31859MRPL20.369111ZFP3692.85328
H2-K12.79752SNX153.79972HTRA24.69693MBOAT10.158372STOM0.369478BET10.350498
IFI27L2B2.79246RNASEH2A3.79724EIF2B20.213067LMO40.158933BAG10.370334MRPS220.351299
IDH3B0.359126CNP3.79312ACBD54.688STK190.159019WSB22.70026MTFR10.351334
MRPL550.359918ACADVL3.79189GNGT24.68529PHB6.28423BOP10.370403INPP5D2.83909
CDC400.359945MRPL223.7888706110031J06RIK0.213485UPP10.159191FBXO182.69642DNMT3B2.83849
COMMD52.77285SELK3.7873AI3141800.21373SLC15A26.28071SERPINB6B2.69283D16H22S680E0.352514
STXBP20.361123MRPS240.264476GM104174.66524MOC526.271955730494N06RIK0.372059UGGT22.83121
FAS0.361673ICOS3.77963CTPS20.214964USE16.26846BLOC1S20.372809HRAS12.82848
CTR92.76177CSDE13.77373CLEC4A24.65076DCTN56.26381LAP32.68166PDLIM52.82108
STT3A0.363716SNX23.7713GM76654.64326TLE66.26098CD480.373303TTLL40.354725
H2-T232.74839CNDP23.76083SPINT20.215713STX186.2564CHCHD12.67635ALKBH30.355442
GATAD10.364231SLC25A53.75199SPA170.215945BCL2A1B0.159862MAPK1IP12.67331SFI12.8129
RBM170.366844SEMA4A0.266764TBC1D10.215945CCNDBP16.23421METTL40.374093SYNJ10.355614
TIMM220.367561FBXO43.74721GM143990.216145PHKG26.2254ZFP6052.670434930422I07RIK0.356305
TMEM106A2.71516FXC13.74327PRKRIP14.60596GM104956.21918SLC35A42.67036PRKCZ2.80052
AL732569.10.368325DGAT13.73403ZSCAN20.219106RRP360.161141PEA15A2.66759GGNBP20.35727
SDF20.368758NSMCE13.73357PRIM24.55993POLR1E0.161376IQCE0.375067PRPS22.79576
AC132837.10.369262GBP23.73272QDPR0.219302ARFIP26.16931MTG10.375758NADSYN12.77753
5930416I19RIK0.369793EFTUD13.73038CDCA50.219692KRAS0.16237RALGAPA22.65799NDUFAF10.360265
TUBA82.70237GRHPR3.73034SCFD20.219781MAD2L20.162373NOL70.376463LYSMD22.77269
H2-OB0.370064IL10RB3.72675HADHA0.220075EIF2B26.15565FAIM30.37671NUSAP12.77163
MED60.371113DNAIC153.72299GM48930.2204368430423G03RIK0.162475RAB3D0.377163PXMP20.360913
RSU10.371744IMMP1L3.72249RABB80.22074SGSM30.1625162700094K13RIK0.377586SNX140.361047
TMEM179B0.371842ARMC70.269039STAP10.220747NSMCE4A0.163059LDB10.377679BLOC1522.76953
FLT3L2.68853CYP11A13.71276FAM175A4.51023IPO130.163484ADRBK12.64366HIST1H4I2.7683
TMC42.6825EIF4G10.269833TPST10.222301GM5616.09075EPSTI12.64135MUC22.76605
MORF4L20.373036LGAL593.69407FAM32A4.4939GALK26.08225NRF12.6405PPP1R13L2.76466
DHPS0.373179ECHDC23.69165MTUS20.222665YIPF60.16479PIGT0.378962PARVG2.76232
BC0304992.67153S100A133.69113CCNL14.47916ASTE10.165034TADA12.6381CBARA12.75891
SYCE20.374728RNASEH2B3.67632MLKL0.223832MRPL556.05937CLTB0.379157EXOSC32.7582
ZRSR22.66741ARPC53.66651DNPEP4.46494HSD17B120.165137TSEN342.6373SRSF50.36266
RNMT0.374968CYTIP3.66609GM112764.46139GM68430.165668GM97740.380122FBXL122.75597
GPCPD10.375771RPA20.274174HIST1H2AO4.46139ME36.03518GM28332.624651500001M20RIK0.363356
JAK12.66069MRPL213.64363CDK2APL0.224199ABLIM26.01497WBSCR222.62229MANF2.74877
MT20.375898DYNLRB13.63555ZFP354.45623RPS120.16692410089E03RIK2.61843BECN10.363818
WAC0.376308MUS810.275812SECISBP24.45206VMN1R585.97815UHRF10.382247PSTK2.74776
THOC60.376726GM105663.62112NEK80.224899AKIRIN15.97705UBE2E12.61472TMEM290.364208
USE12.65108NINJ13.6157420704.44642TAF135.97344GSTZ12.61459PUS70.36489
THAP30.377346BLZF13.59783GM54744.436052400001E08RIK5.96702EIF3L0.382819CIT0.365716
GM95740.377779MRPL103.59223TIMM444.433731110001J03RIK5.94875NPLOC42.61217MLLT100.365901
MRPS60.377989PRODH3.58928PPDPF4.43157ALKBH65.94376BC0265852.61077PAQR32.73185
AC160471.10.378247C1D3.58896IFI354.42972DGKZ5.9229VPS290.383109NUDT30.36641
FAM173A2.64377D10WSU52E3.57659CENPL4.41587MUS815.9194GMS6100.383109NUDT30.366644
VEZT2.64154NUPS43.5669CDC74.41328DCUN1D10.168965PDLIM52.60311LBR0.367002
TMUB12.64146OGFOD23.56196AASDH0.226953CLNS1A0.16917SNTB12.60288KCTD132.72178
LITAF2.64108RBM430.281272ZMAT50.227216SLC35C25.9112GPR1070.384602CCDC109A0.367499
CALD12.64051GM55063.55487PPAPDC1B4.3942CNN25.899541810035L17RIK0.384739SSRP12.71716
MAPRE10.378721TXN13.55243110009E18RIK0.228199LRRC405.89635AEN0.385092SPIC2.71485
USP50.378759ASL3.55025GPR194.38073PRIM25.88398USP252.59227PDAP12.71339
PSMG22.6364CD683.54467SLC11A24.37804ARFRP15.87869FAM183B0.38684SNX322.71032
MRPL410.37975GM114443.54297ARL20.22845MND10.170847OAS1A2.584CTSC0.369527
SERHL0.379928CCNL23.54173DCTN30.228615MOBKL30.170936N4BP32.5831NOL60.369607
GCC20.379996DPH33.54104DNAJC120.22918THOC70.171761NCKIPSD2.57772ZWILCH0.370438
CRYGN0.380065C0330039L03RIK0.282425BC0570794.36401UTY0.171829RIOK22.57744OPRM12.69491
ABI10.380254ENTPD10.282635TMEM1884.35828GBA5.81696ASB72.57699MRPS72.69191
XLR4C2.62803H2-Q63.52238ARL34.3536TMEM335.80804ETOHI12.57488MMP162.6919
MBOAT10.380531WDR33.51936FBXL174.3512EIF4EBP15.79065IL1R20.388562PRDM112.69182
TMED32.62714DHX83.51485MUL14.34233GTF2H15.78757CYP4F132.57031CCT6A0.37176
GIMAP32.62097NUBP23.5116NUP1884.33967TUBGCP40.173065NKG72.56692VAV22.6868
NSUNS2.61944GM103243.50986RBL24.33662CD25.77675GM143912.56275DTL2.68598
WIPF10.381938NFIB0.284944MECR4.32577AFF10.173118GRHPR2.55795ELOVL50.372757
CCT42.6093IMPA23.50901AI4624934.32377TANK0.17324PARP32.55783PDPK10.372828
GPS20.38457DCTN53.50543ZFP264.315512310036O22RIK0.173257HDAC50.391103TMEM692.68202
NAA352.60468DDX13.5049GIMAP50.2317224930473A06RIK5.76841SUPT3H2.5565RPA10.37316
RARS20.38393TNFAIP8L23.50285CYBSRL0.231826EPN25.75899STRA60.391227ITM2A0.374049
NGFRAP10.384268ARL6IP43.50197RPS19BP14.31053PNP20.173642EHD12.55424ERCC52.67242
IL1F92.59869TMEM1280.28566CPM0.232819STIM20.173743MRPL170.391555BRD82.67235
TNFAIP8L20.384814AC139042.13.49905GM131474.29271ZFP620.173835TBC1D200.391907CLDN70.374323
TMEM161A0.38506SLAMF80.2867431110021L09RIK4.2791CAPZB0.173926TBCA2.54932SFT2D12.67093
GM108422.5922CDC203.48721PSMD104.27034BCL2A1C0.17394ARHGEF182.54846MRPS100.374692
CTTN2.59036FMNL13.4823VIPAR0.234174TGS10.174071ARL160.392431ACAD112.66233
MLKL0.386417SEPP13.47958CENPA0.234317PPIL30.1740891500011H22RIK0.392571HDAC60.375756
GGTA10.386703RPS173.47038R3HDM24.26772AC166169.10.174396PTPN32.54622RNF60.376193
METT11D12.58361GPR183.46365UBE2W0.234509FUS0.174416SETX2.54107CARKD0.377398
TAF1D0.387516CTLA43.46255TSC22D40.234692PPIH5.71612ZDHHC130.393563SBF22.64709
2310045N01RIK2.57617TMEM93.45673OLA10.234852MED110.174944PHF72.53974OSBPL72.64258
RNASEH2B0.388366EIF4E3.45672TATDN30.234926MANBA5.71117MDFIC2.53954ZBTB7B0.37856
THOC50.388575PIH1D23.44643CHKA4.25282TMEM2235.7077SUSD30.394206RGS32.6388
RPL21-0.389232GM88153.44583RBM140.235463BC0176435.70458RNMT2.53629DULLARD0.379202
PS12
MRE11A0.389622HDAC73.4423CHM4.24299ZZZ35.69466GM129422.53483NUDT140.380537
4632128N05RIK0.390117SLC25A393.44154FAM3C0.235902L7RN65.69185INO80C2.53474TYMS2.62736
IMMP1L0.390261COMMD13.43923MS4A6D4.23668POLK0.175794ZDHHC42.53286SETD62.62561
8430419L09RIK0.390402CSTAD0.290846GM59004.23525NUP435.68573PBK2.53258INPP5F2.6251
CCNC2.56046INSL33.43606HDAC80.236114IFT1400.176033NUDC0.395065CNPY22.62114
PSD40.390742AKR1A43.43343NRK4.22303DDHH5.67803ATPAF20.395464NFIC0.381718
IL15RA0.390915AP1S10.291393VMN1R584.22215SIRT25.63162RACGAP12.52856HPS50.382031
ALKBH10.390984DUSP193.43144ANAPC20.237303MRP5340.177826RFC40.395695GM161812.60756
CINP2.55745PIH1D10.291703BC0553244.2104ST6GAL10.178039METTL102.52633CHMP4B2.60726
PFN12.554494930402H24RIK0.291965IFT200.237727TGMA45.61281CDC452.52427MNS12.60505
E030030I06RIK0.391747LSM53.41724WDR854.19078USP55.60719CASP60.396329DDA10.383999
2700062C07RIK0.392185LAMAS3.41581700047G07RIK4.18925LRRC310.178434PDZD112.52099BUB1B0.384036
GTL32.54873DBP0.292969GM111100.238794SNAPC55.60413FOXRED12.51945MAP3K12.60194
AC087117.12.54855BAD0.293991PFDN54.18382AIP5.59989GM97620.3970142900010M23RIK2.60193
ATF7IP0.392441PFKFB30.294121METTL54.17327PHF205.59826MGLL0.397019REXO10.384764
CEP2500.392457SNRPB23.39522RHBDL24.16203ACP60.1786772310036O22RIK2.51867FKBP22.59551
HIST1H4I2.54658KCTD143.39439AKAP134.15011FAM3A0.179502NMT12.51497CES5A2.59475
TNFRSF252.545TNFRSF183.39091HIBADH4.149542610528E23RIK0.17992410002F23RIK2.51263RBMX0.385616
GMFB0.393779EDF13.39068ING20.241135U2AF1L45.55635USP212.512092500003M10RIK0.385865
PARVG2.53884TM9SF43.38904KIN0.241463MYSM10.180108STAP12.50944ABI30.386054
ACPL20.393929MRPS36-3.38588SNX144.13506SPAG50.180124TMEM120A2.50846PNPT12.59012
PS1
N6AMT20.395451CCNE23.38245BRD70.242433CHAC25.55104LY6I2.50636RPE0.386085
B230208H17RIK0.396264C030048B08RIK3.38235LSM24.12415FAM118B0.180146IKZF32.50618GFM12.58967
3010026O09RIK2.52186TASP13.38082GM710.2425912310003H01RIK5.54767CLK20.399108KPNA30.386292
MTIF32.51817GMNN3.37777SUGP20.24264SUSD35.54412CSDA0.39964DEDD20.386389
BIN22.51792SIT13.37462WDR264.121PJA20.180634IFIT22.50035BTBD90.387014
DCTPP10.397437DAPP13.37257CAB39L4.12023CHST125.52136RFTN12.49718ZZEF12.582
TM9SF40.397746TUBA1A3.37022GM61324.1164ZFP610.181115U2AF10.400739TWSG10.387342
PROP10.397841PLIN23.36978PSMC3IP0.242976A830010M20RIK5.50564LZTR12.49469ASB62.58105
2310003C23RIK0.397982ACTB3.36773IFI27L14.09596LRRC590.1817139030025P20RIK2.49125TRAF32.58057
ATP1B32.51244TMEM973.36432GCC20.244318PJA10.182048RRAS2.49102SUFU2.57907
PHAX0.398858TIMM220.298029TRIP40.244704RBM145.47441NGFRAP10.401506HAUS62.57873
KDM4C0.399245MRPS143.352446330416L07RIK4.08248SNX15.46754TBRG42.49007IRF30.387819
RRAS2.50304WDR543.35107CASP24.08MPDUL5.464151110034B05RIK0.402464E330020D12RIK2.57835
GM64830.39999PHB23.34915PPWD10.245456GM48300.183093H2-M22.48448JMJD52.57693
TCTEX1D22.49548CISD30.29859ISL20.245584PEX195.4617CCDC762.48072TRIAP10.388319
U2AF1L40.401416FKBP1A3.34809AC117232.10.245801H2-Q65.45725ANKRD122.47797LSM22.57473
HMOX10.401474SLC25A113.34521MMP164.06537RBM225.45222ZKSCAN142.4722ZMAT52.57136
AC166169.10.401762AC090563.10.299305APOBEC14.06336MFN20.184369CITED22.47108AC132320.12.57098
SMEK20.40177ATP6AP23.33938CCDC400.246246GM67100.184704ING32.46922UNC45A2.56806
TGDS0.402264PFDN13.33829TIAL14.05923KIF2C5.39762ATP6V1D2.46538ZBTB200.389732
BBC30.402395SNX13.33024MRFAP10.246888TBPL15.38661KCNK70.406665NXF10.391
CBARA12.48476LUC7L3.32473GADL10.247037CDC1235.38034HNRNPL0.406792GMEB10.391028
XRN20.403258EIF4B3.32463SERPINF10.247061RAG1AP15.37712SPG112.45809CIRH1A0.391777
2810428I15RIK0.403471FYB3.31789KIF5B4.046194933421E11RIK5.37001MIPOL12.45699OAS1B2.55216
LGALS30.403629KNG10.302111CORO1B0.247187AC127590.15.35724COL4A32.45583ARRB10.392411
S100A32.4669LPCAT30.302157LRRK14.043494930512M02RIK5.3539HSF2BP2.45561MRPL432.54414
GM63960.405591GGA30.302824TMEM1044.04072TREX15.34849GM127892.45047GM144432.54154
ITGA62.46387ANKRD160.303006ZFP4884.0366MRPL20.18728AU0228702.45027SPAG52.53887
HMGN10.407101ZSCAN210.3030142310001H12RIK0.247861422485.33353VAMP42.44973ZFAND60.393917
EED0.407365VTA13.29714GNB1L0.248459CUL4A5.33054CIAPIN10.408638AC068006.12.53464
DNAJC210.407973SATB13.29571RPS20.248605CENPL0.187671COQ52.44616CD272.53458
NDUFS10.408024NDUFS30.303751ILK0.248648AU0198230.187717TATDN10.408903PLBD22.52362
GM56172.44984JKAMP3.29212COMMD24.01665TRADD5.32189RNF70.408986RAB2A2.52042
WTIP0.408332SKAP23.28684CQQ90.248966SNX120.188894ATR2.44494ATRIP2.52039
CD480.408816H2-Q103.28673D930014E17RIK0.24934LLPH5.29397H2-Q62.44355SEC16A0.396912
MFF0.408963COMMD33.28433AC142450.14.0057TNFRSF13B5.28839PTPN22.4435MED312.5165
SRSF20.410079MYSM13.28378CLCF10.249709MRE11A0.189138ATG4B0.409998PCCA0.397718
SLC39A110.4106551810020D17RIK0.304718AC102609.13.99958IMPA15.27732MED182.43574SNAP230.398389
PPCS2.42455GM108003.28046ORC40.250401GALT0.1896331110049F12RIK2.43525IKBKE2.5098
RPE2.42392TMEM50A3.27816YTHDC10.250938LY6C25.27335SPR0.410802NOP102.50758
BC0493490.412584CAPZA23.27751MRPS233.98124RP23-0.190228TMEM1212.43406D19ERTD386E0.399471
369M17.1
LRRC332.42129MAP2K33.27284TNFSF93.97682LY6I0.190318BAK10.411164CUL4A2.50226
GM49532.42053MDH23.27146LSM123.97426KIF1B0.190499WDR262.43141MRPL122.50001
SMAD20.413406CD3D3.27114POLD43.94804EIF3G5.24109MAPK30.411846NCKAP52.4997
PTPRV0.413851PEX11B3.27019CEP570.253563TMEN2190.191107CD2262.42794GM101252.49936
KLC12.4143AHSA13.26954SAMSN13.942484930529M08RIK5.23014TBC1D132.42405GM56072.49751
CISH2.41248SPINK100.3059272300009A05RIK3.93763H2AFV5.22596TIMM500.412533FANCG0.400578
1700007K09RIK0.415149BC0176433.26754AC156948.13.93241DNAJC95.220931810020D17RIK0.412683FBXO440.400767
PIGZ0.415217A630001G21RIK0.306225S100A13.91733TK15.21873CUX22.42237BIRCC0.400962
PTTG10.415544TBC1D10C3.26649GMDS3.91583RADS1AP10.191965C130026I21RIK2.420042310044H10RIK0.401107
BC0176432.40337PARD6A3.26276CAR50.255555DHX335.20461PRMT70.413402BC0483552.48897
YIF1A0.416415PAM163.2571MRPL213.90612IRF15.20163LUC7L32.418935930416I19RIK2.48462
FBXO50.4165SCN9A0.307586PEA15A0.256257CAT0.192378TM2D10.413569PTPN42.48407
PSEN22.39813MOBKL2A3.25054ACP60.256389CFLAR0.19253SUV420H22.41715ANKRD13C0.402613
LASS22.39778SRSF73.24581DHDDS0.256606BRP440.192542MAPK72.41704DNAJC160.40297
AC135633.12.39558OTUB13.23621RAB7L10.256733ST75.19068NDUFAF40.414396SMARCB12.47527
LAMC12.39358ATP5SL0.309121B9D10.256824MS4A6B5.18831SLC35C22.41267ZEP4882.47522
PQBP10.418668LDHA3.2329GAPTCH83.89108LXN5.18433FBXW172.409561110004E09RIK2.47062
YIPF62.38573CTPS23.23201NTSC30.257359NRD10.193068GM60550.415127DUSP102.40978
PPP1R15A2.38156GLMN3.224482410017P09RIK0.2573891110002B05RIK5.17424COL11A22.407942610030H06RIK0.406121
PHF200.420072ZMAT53.22116UTY3.87871AL844854.15.172491700034H14RIK2.4071SSSCA10.40619
GM97750.420155MAP2K20.3108231110012L19RIK0.258256MCCC25.16899HNRNPM2.40693LGLS3BP2.46169
H2-Q102.37859COMMD43.2157CCNH3.86755MRPL175.15904BOLA10.415547MTM12.45805
PHB22.37696PIGP3.21334ANKRD320.258792RPP380.194425MNDAL2.40581ENTPD50.40683
BTF3L42.37421CNPY23.21207TBC1D70.259321LGTN5.13435BIRC52.40443TBCB2.4579
HSCB2.36574CHCHD83.21086XLR4A3.85399GM161815.13293FOXP12.40424GM21780.406996
A930005H10RIK0.122718HNRNPH13.20272NINJ10.259964ORC40.19501ANKRD13D2.4039CCDC772.45456
PPP2R2C0.422829LCORL3.202422610001J0SRIK0.260073SCARB15.12455AI4521952.40212ZEP2590.407721
ATG132.36443TMEM693.201074930579G24RIK0.260613DOT1L5.11575ARL8A0.416667ZDHHC120.407944
AATF2.36391S100A63.19886GM143260.260714MRPL235.11274ARMCX60.4168GSK3B0.408054
CDK10.423207NFX13.19883STARD3NL3.83561COMMD30.19575TRIM562.39847RMND10.408142
RABGGTB0.423371PDCD6IP3.19865VPS393.83015FLAD10.195774RAB432.39609GM131470.40828
PNRC20.423764ZRSR23.19603ERCC13.82564MRPS115.10387FGD32.39565AIM22.44856
HDAC12.35879ABHD113.19565APPL23.82255IFNGR25.10147TRIM372.39431UHRF12.44582
GTF3C22.35778CNBP3.18437MKRN10.26168COX100.196107NDUFB42.3924DUSP190.409179
PPIL22.35711GM108013.18078PTP4A10.261791ENDOG0.196494DUT0.417992BC0238142.44176
TUBB2C2.35326H2-D13.17421STK113.80655GLS5.08921INSR2.38695TBX212.44142
UBE2W2.35082SPATA53.17178GM104903.80099UBE2E35.08578MTF10.419149LIFR2.43915
NEIL12.34342PSG230.315546TMEM50B0.263502MRPL410.196729FANCE0.419595COMMD50.410351
UBE2A0.426773BRD33.16708GALT0.264371NUBP15.082530610037L13RIK2.38209RGP12.43434
SLC25A390.427532CAPZA13.16705NAA350.264406DGUOK5.0798STK32C2.3794DCAF172.43375
SIL12.33407ADAM330.315962PCIF10.26458TTC10.197229MRM10.420347TAZ0.411156
LY6C12.33251AC131780.23.16492AI4135820.264792NUPL25.06885FKBP52.37896THAP70.411234
H2-KE22.32733A830001N09RIK3.15992MRPL160.265274FKBP1A5.06112RPL21-2.37893TRAPPC30.411335
PS14
0910001L09RIK2.32723GPR190.31673CETN40.266008ABHD105.05799TMEM2090.420712MINK12.42966
RGS12.32238ARHGDIB3.1569RNMTL10.266008GNPDA25.05258PPP2R2D2.37506FBXO110.411741
MFAP30.4308PSPH3.14818LPL3.755914930470H14RIK5.05027SNAP232.37461MCM30.411754
MTMR40.430925GFPT13.14709SPRYD40.266657AMPD25.04453CHD40.421301UAP10.412061
ABHD110.431066RC3H20.31779IFT803.74813ZFP3860.198234ZFP1102.373566330577E15RIK0.412524
THY12.3178PJAK3.146GSN3.74534LMF15.03395VPS26A2.37266SEPW12.42171
1500031L02RIK2.3116TIMM233.14596PDCL3.74384IL20.198661PNKP0.421521BAIAP22.41981
PEMT0.432817PSMA13.14408AGTPBP13.7416ANKHD15.02774TMEM63B0.422363USF20.413563
CDK2AP10.432935FANCE0.318156LUC7L3.73929TSPAN140.199012DNAIB112.36543FOLR42.41563
RAD54L0.434173CDCA70.319438ECE20.267644AC122006.15.01152TYMP2.36431RAB140.414768
SMU10.434279RPP303.127741810074P20RIK3.73582MAP3K55.01152NDUFA100.423107CRYZL10.415122
SMPD20.434936ME23.12344GTF2H43.72869CASP20.199854DARS22.36275BRCC30.415153
IFI27L12.29594NAA203.121191110058L19RIK0.268254TTC234.9978CUL12.36253WDR30.41543
RSL24D12.29428PKP40.320792GM102123.72679GM106954.99638PAPD50.423809FAM60A0.415574
SFRS180.436007FYTTD13.1168TMEM933.72279ETV44.98142700097O09RIK2.35561CAPN20.415688
PIK3CD0.436753PKN13.11594GRCC103.7191HSD3B24.97154D4ERTD22E2.35549ADPGK0.415859
HVCN12.289324933421E11RIK3.11448TFG3.71634ESF10.201313GTF3A0.424646ADSSL10.41591
SEMA4D0.436848CDC233.09954BRCC30.269708SNX174.94502ARNT2.35173MARCKSL10.415994
BC0032662.28841CLDND13.09523PDK33.70624CCND30.202376PARD6A2.3498AGTPBP12.40301
FAM165B0.43747ACTR23.08416GGCT0.269918NSMCE24.93656INTS22.34851RUFY10.417223
IL242.28483E5F13.07944TM9SF13.70468TYM54.93404ATPSK2.34778PROCR0.41754
TBPL12.28311STAT13.07825EDC30.270247KRT194.93225DNAJA12.34478HSD3B22.39353
CPNE80.438106FPR20.325105OSBPL93.69825STXBP3A4.91429ADK0.426564L7RN60.418484
ANKRD372.280061700047G07RIK0.32513ACADM0.270559RHOQ4.90855ABHD112.34376VWA5A0.41858
MSL30.439112PLEKHA23.072792900010J23RIK0.270662CRCP4.90201GM58302.34002TESC2.38873
PIGF0.439876EIF3E3.07275PMS10.2707381700049G17RIK4.89947KPNB12.33972LAP30.419111
EPHX42.27228POR3.072746530401N04RIK3.68326PSTK0.204421IFNAR12.33894BIN32.38331
1500002O20RIK2.26953NSUN50.325629DLGAP40.271558FOXM10.204562DNAJB40.427629ZDHHC212.38218
BCAT10.440655BHMT20.325884PAFAH1B30.271558TLCD24.88197XPO62.33846TRAF3IP32.37848
ZFP580.440674ACAT13.06557UTP30.271746RDH94.8736FAR12.33826SLC25A100.420602
AHSA20.441142SLC12A80.326475GM76093.67655DBFC2B0.205823RAB310.42773WDR732.37632
AC154631.10.441629MRPL233.05711C630004H02RIK0.272053LSM24.85528POLR2L2.33638PIF12.37403
FERMT32.2639MAPK8IP30.327393SIRT40.272685WAC4.8472CYB561D20.429112SUV420H22.36921
PDCD2L0.441746SUMO13.053092700007P21RIK0.2733771700021F05RIK4.83563KPNA20.429464PHYHIPL2.36779
LYRM40.442065TESC3.05301TRIT10.273748DSN14.822715730601F06RIK0.429723TMEM972.36561
PHOSPHO20.4425TMEM9B3.05154TMC53.65153GM98940.207352PIGQ0.429912HOOK20.422724
OIP52.2582ZFP6373.04922GM52443.63364FBXW90.207469AURKB0.430777GMFG2.36501
PGAM52.25599MRPL243.04409GDE10.275214PGAM54.81491TH1L2.32119NOL110.422881
GM62932.25379TBCB3.04279GTF2H33.62942GM55760.207801FAM184A2.31742CLTB2.3623
PDSS10.444757ETS13.04198GNPAT0.27554SNAP234.81051GSTT30.431811FAM136A2.35444
VBP10.445036SDR39U13.04125HSD3B23.6278C2CD34.81043CHD80.432011MOSPD30.425477
IFT460.445227SERPINB1C3.03727DCP1B0.275893HMOX14.80258ODF22.31431PHF72.34785
GPR1740.445264FABP53.03405DHX320.275893HDDC20.208325GM163800.432609ITGB10.426033
TES0.4453571110003E01RIK0.329743GM56173.62459HSPA12B4.79693DHX322.31124TWF12.34335
H2-Q22.24237UQCRC23.02946ZCCHC70.275933CCDC344.79632CELF20.432691CTSO2.33801
GAPVD10.446533MGAT4C0.330276CASP8AP20.276211TMU824.79307TUT12.31019ACP50.427721
PANX10.447449TIPIN3.02717DPF20.276612AC142104.14.79143AFF12.30981RBM432.33786
RBM382.23439RPS6KB13.02517MBD53.61261CDK5RAP10.209144POMP0.432971TMC50.427846
PUM10.448127APOBEC33.02458CERKL3.60774TMBIM14.77378PITRM12.30782GM34352.33728
PER12.22917POLR2F3.02026THUMPD30.277258IL110.209998CYBSD12.3069WASL0.42835
MAEA0.44936TMEM2183.01908LMF13.60375NIPSNAP3B4.75276MED250.434109ANKRD160.429125
RBP70.4497861700123O20RIK3.01794ARRDC13.60208CDC400.210631MTUS22.30321GM55772.32926
6330439K17RIK2.22281OSBPL23.01609GIMAP93.59942ZC3H104.74138AAGAB0.4342761810009A15RIK2.3291
PPIL50.450513RBMXRT3.0158CIZ10.278198DEPDC54.73809GTPBP52.30251LRRC400.429404
TIMM8B0.4519PTMA3.01477ALG90.278323CCDC64.73184SLAIN12.3004420680.429453
TKT2.21139RBM220.331773ADRBK13.59128ZDHHC120.211334ZFP6092.30028ACSS20.429515
GM48770.452772SAT13.01393INSL60.2787024930522L14RIK4.72965TRUB20.434877GM48252.32433
TTC230.4528782410004P03RIK0.332288KANK33.57822METTL80.211536VMAC2.29809H2-Q72.32323
DPYD0.45312ADAMTSL40.332405VPS4B0.279789DCTN34.72301S100A10.435407STARD30.430584
FAM103A10.453256D4WSU53E3.00463PTTG1IP0.279925BCCIP0.211808TOMM40L2.29491MPHOSPH90.431148
CYB5R32.20106GM61040.333215ZFP7383.56755CDKN2AIPNI4.71636INTS120.435833METT5D10.432089
GPR890.454538PRPSAP10.33323NDRG13.5565PIGN4.71511BC0311812.29133MYG10.432492
PICK12.19989GM109790.333439CENPH3.55546PIGQ0.212219ZFP602.29105PPIH2.31213
ARL6IP42.1967ING32.99657MLH10.281258RBM280.212315DBR10.436565EIF50.433605
H2-K22.19661SMARCA52.99274GIT23.5547TARBP24.70756RBM342.29054SNRNP350.433755
GDE10.455548SRP192.98391CDC200.281777AC161001.14.70369KIF21B2.28780610011F06RIK0.43426
AC079644.12.19361INPP5F0.335371EXOSC43.54589CDK144.70157UBN22.28747PPAN0.434394
GM163812.19361STX60.335692TRPM10.282101RAD180.212948BAT52.28739ATP13A30.434517
GM20012.19361GM101232.97672CMAS3.54427DPY19L44.69293RGS192.2834ELOVL10.434824
GM56700.455882CCT52.97616HCFC23.53925HIBADH4.68299TM9SF10.438314GM79350.435003
LEPR2.1916NT5C32.97287ADIG3.53563HNRNPL0.213539XBP10.438542310045N01RIK2.29852
HOPX2.1894PIM22.97224CATSPER43.53563FAM175A4.68088H6PD2.277924930555F03RIK2.2952
CLSPN2.18611LY6E2.96994HERPUD10.282919SYTL34.66871SEMA4A0.438999MRPL160.435779
AKR1B80.457558TTLL42.96786IQCC0.282927GGA34.653RABEP22.27703CYBASC30.436256
GRCC102.18497PTPRC2.95901EIF2B40.283034IFFO24.65076RG9MTD32.27613HI5T1H2BB2.29128
POLE40.457719PKM22.95759S100A60.2837POLB4.647792610020H08RIK0.439923GBP52.29067
GPRASP20.457873SYCP12.95358TGS10.283813GM29384.64434MPDU12.27308WDR770.436786
BC0564740.458157ACOT132.95291PCCB0.283949GRAMD34.64089HEMK12.271661700034H14RIK0.436958
CIDEC0.458486ADAM190.339011FOXK23.521529430023L20RIK4.63579NDOR10.440514RBM70.437622
FNBP10.4585811110065P20RIK2.94794LY6C23.51312ATPBD44.63232BZRAP10.440638BRWD10.437664
SAE12.17964AIMP22.94187METT11D13.50825CREBL24.62929SRBD12.26829WDR462.28303
TFIP110.458874RDM12.9385ARID4B0.285507HDHD30.216275RDH142.267864930534B04RIK2.28142
RPL30-2.17678ZCRB10.340327SGK13.4986GSS4.62133DAZAP10.4410774933427I04RIK2.27929
PS6
ADAR0.459489DAPK20.3407168430423G03RIK3.49655POLD44.61637TRIB30.441663BC0238290.439785
PGS12.17398LRRC410.341072EXTL23.49509DNAJB114.613872810422O20RIK2.26358SGSM32.27323
GPP1070.460142STARD3NL2.93172CENPK0.286116CDK2AP24.60874STX22.26259TOR1B0.440344
TIMM17B2.17137GM111520.341478PAM163.4935VP5364.60218GABPB22.26178FLAD10.440699
STAM22.1672MRPS18A2.91805RALB3.49078CD740.217596FAM126A2.26122VEPH12.26833
GAA0.461615ORMDL30.343151ZBED43.48917TMEM106C4.58509TFB2M2.257776030422M02RIK2.26531
TRAPPC30.461743GHITM2.91234STIM23.48912ZFP3534.58439ECHDC12.25729SCARB20.44166
PAFAH13B2.16551STRN40.3437654930547N16RIK0.286625PHRF14.57943ANKRD322.25421ST6GALNAC62.26353
PRAMEL60.461853AZI22.90738TRPC20.286652PDDC10.218373EPHA20.444115NRF10.442264
LPHN30.462371GM70302.90617ING33.4874CORO74.57843NSUN30.444483GJC32.26072
PCBP32.16243RTP30.34424DGCR63.48344GTF2H44.57703SHARPIN2.24975PPPDE20.442814
SRSF30.46284COPS22.90125BOLA10.287478TTC354.57584LRRC8C2.24954L1CAM0.442979
PET112L0.465325GM104510.344691HIST1H4D0.2878596030408B16RIK4.56696ATP2B42.2494RPAP22.25699
1500012F01RIK0.465366CALM22.90089GM29383.46952JAK14.55797RASL1182.2486DPY19L40.443354
SHISA52.14857ICAM12.89977PSAP3.45928PRAMEF84.55729TTI12.24819MFN20.443758
SH2D3C0.466011HSPA142.89926AC161211.23.45693GTPBP84.55576RFXAP2.24717CCDC840.444341
MRPS280.466172MED142.8974SLC16A60.289278FAM162A0.219795LRRC332.24323NR4A20.444708
IL40.467198EBP2.89522GNPDA20.289466CNOT6L0.219928AC101875.12.23945PARVA2.24781
HNRNPC0.467546ACAT32.89508COX173.44155MTUS24.54291CDK5RAP12.23785CCPG10.445004
RTF12.135722310035K24RIK2.89501MPDU13.44092ZMYND114.53646SETDB10.447154H2AFX2.2465
IDH3G2.13392BC0570790.345461PNPLA73.4408SFPQ0.220524TELO20.447155MRPL12.24561
MF5D2A0.469366CRISP40.345759COX100.291276THUMPD30.22081VTA12.23592900097C17RIK2.2443
CLN30.470116SNRNP250.346171SETD53.43074DNAJB64.52642ZFP4262.23532ADI12.24225
CYP510.470341ARRB10.346338TNF3.42383CENPH0.221034MSL32.23499GRAP20.446283
CARS2.12414GM107192.88708TRAPPC6B0.292286STK38L4.51851SSNA12.23311IKZF32.24007
ACAT30.471553AL603711.10.346453ERI33.4132ZFP1100.221331SNRPG0.448137UTP60.44674
ETFB2.11968SLC25A12.88624USP330.29313ZDHHC64.51423SLC28A20.448712LCORL0.447019
ATRIP0.472654CLK22.88431DIAP10.293347GMS6230.221694EXOSC72.22748SEC23B2.23703
NSMCE12.11554GM110420.346709PKP30.293441HIST1H4K4.51023HELZ0.44939LEPREL22.23611
DHRS10.473178LGALS40.347111DCBLD23.40187UBE2K0.221837MGAT4A2.22469GM97620.447916
GM102500.473386CCDC972.87776IKBKB0.293957AL732476.14.5064C330027C09RIK2.22406SLC25A230.448019
SVOP2.11244ITGA32.87471PRPF30.294636RPF10.222192FAM33A2.22079MRPS332.23185
GBP30.473443BC0265850.347865FNBP43.39347EFTUDI0.222611DIS3L22.22056CDRO2A0.448298
TSPO2.11212NDUFB112.87217PHOSPHO20.294693METTL60.222665PRPS20.450339STK17B0.448479
FAM45A0.473528SLC5A110.34834NFYC0.294786AGA0.222796ELP42.21858YKT60.448781
NEK22.1112NDUFA82.86842MCOLN23.3836MGST24.486GLRX22.21715RCBTB20.449053
DGAT10.474097BUB1B2.86674PDAP10.295633PMPC84.47916TCP11L12.21687GIT12.2222
CENPH0.474097RHBDL20.349214NFYB3.37877LZTFL10.223606NFS12.21653AC156948.12.22018
SGSM30.474555CYBS2.86303MRPL20.296363DTWD14.47201TMC60.451846LEO10.450433
TRIM30B0.474604PDCD12.86295DTWD10.29648REPS14.46966MYEOV22.21222MVP0.45048
FDXR0.47544CAPRIN20.349369GM100333.37291REXD44.46788PFDN20.452543RDM12.21862
TOMM202.10061DHRS10.349492STRN43.36855MRPS154.46494TMEM161A2.20829FAM192A2.2176
PDAP10.477104SH3GLB12.85718SEC61A20.296884RAC10.223967CHRM42.2041TBL32.21522
PTPMT12.09393TCF40.350483ACER23.3672EIF4ENIF14.43929E130309D02RIK0.4537871110008L16RIK2.21368
SIGMAR10.478621TRIAP12.85065BUB1B0.297187NRF14.43836NPEPPS2.20295UVRAG0.452127
BBS70.47905FUBP32.84969GTDC13.36386SPINT20.225426DNAJB20.454667GLRX52.20846
TNFSF13B0.479792CENPF0.351001GADD45G3.36234PLOD24.43373GM21780.4547562510003E04RIK0.452882
PARP22.08299LY6F2.84688TM2D20.297412NDUFAF24.43157MS4A6B2.19789NUFIP20.453053
NUDT32.08262GM141810.35151TOMM343.35824ABHD60.225748DOS2.19472TK10.453355
TTC52.08224TPI12.84474DYNLL20.297932GTF3C54.42774TBX212.19429PPP1R12A0.453602
LRRC240.480779LMNA2.83893MTERFD13.35647TXNIP4.41587FBXO440.456012MAX2.20405
NAA200.48164TMEM55B0.352678TFAM3.35624SNX30.226596CTLA2B2.1924PLIN20.453764
EIF1AX0.481816IFI472.82703FLT3L3.34759TM9SF44.410674921517L17RIK2.19238DNAJA20.453795
MRPS360.481983GMS1452.82597NOL70.298838BBS94.40793AC165266.10.456577MTF20.453888
COX6B20.482287ADK2.82127CTSE3.34344SEC23A4.40537PPRC12.18911F2RL10.455044
GTPBP80.482307AC149585.10.354732810422J05RIK3.34306UBLCP14.40451BCAS30.457248FBXO30.455736
CHI3L10.482918NAT92.81543MIA13.34135NT5C4.40436PSMB60.457575GM104172.19193
SIGIRR2.07058XRN22.81516EIF4H0.299847POLR2H4.40262TMEM120B0.457765ZER10.456295
GM112732.06922SCMH10.355375THAP70.300809CDC42SE14.40229CDK162.1831PREX10.456446
GM98300.483586GM51602.81271CREB13.32323TNFAIP30.2273582310011J03RIK2.18273RPL21-0.456737
PS7
DBR10.483831HFM10.355716GM28330.300988PRR150.227365GPR890.458367IGSF80.456869
LEPREL10.483856D18ERTD653E2.80732SRSF90.301296TNFSF13B4.3957ARL5C2.18109MAPK30.457086
CRYZL10.4840854933427I04RIK0.356243PFDN20.301424NUDC0.227573GSTK10.458555730469M10RIK2.1868
CCDC1270.484708ARHGAP42.80704PIGYL3.31608ZFPL14.3942DSTN2.18006SEMA4D0.457713
RNF72.05833PRAMEF82.80697GM80553.31475C20.227783SEC23B0.458803MYCBP22.18452
ACTC12.05784CCR72.80169REST0.30191NGRN0.227815FTSJ12.17933STX82.17767
GM88152.05722G3BP12.80063SP1003.31134CRYZL14.38778MEF2A0.459317NOL122.17683
TBC1D10C2.05628ADAMTSL50.357385OAS1G0.302131PSMD54.38291CDK2AP12.17715TOP3B0.460001
OSCAR0.486345HSDL22.79789RASA13.30054CBLL10.229251TANK2.1771HECTD20.460161
GM89092.05336SDHD2.79732MAPKAPK53.29877FOLR44.36204AC125221.10.459349IKBKAP0.460335
NCOA72.05066LRRK10.35776SLC4A1AP0.303347PRMT14.36011MPHOSPH62.17579DGUOK0.460441
TRNT10.487822PSMD52.79458SQSTM13.29316OPCML4.35887GM73672.1738R3HDM20.460494
AIRE2.04966HSD17B122.79424COX193.29302CD2000.229479AC163101.10.460169STIM22.17149
MRPS18B0.48936KIF18B0.357954GM121840.303672HSD17B74.34864CALD12.17236IPO90.460607
AC113307.10.490348GTF2E22.79364MAPKAP10.304115OTUD7B4.34571ZFP1252.17183TCP11L12.17006
PA2G40.490583RP23-2.79357TRMU0.304377ZCCHC94.3401ALG50.460528UQCRC10.46127
147O14.1
VPS80.490681ACNAT12.79048ITGB10.30453ITGAM0.230433CNIH42.17113DYNC1H12.16781
UBE2F0.490797GOSR22.789858430410A17RIK3.28293TIAL10.230539GM101802.17074TM7SF32.16685
DDX500.491492SNRPE2.78815TMEM106B3.27349KATNAL24.33361NAPG0.460711PAPOLG2.16558
LCTL0.4915213110057O12RIK2.78673TUBD10.305922FTD0.231057CCNK0.460907UBEIY12.16452
PWP12.03349TBPL12.78564GET43.26735SLC12A84.32621110014N23RIK0.461185COPG0.462215
TMEM1670.4918295730437N04RIK2.78518ZFP5600.306077GM66244.32377NDEL12.16644CREB30.46359
TRABD2.0272FGGY0.359534RG9MTD30.307657CEP630.231391TOM1L22.16555DHX322.15693
PCNA2.02689MAP4K22.77986RPS6KB23.24669TM9SF30.231488VARS22.16514PHRF12.15662
SFT2D10.493485DIAP12.779621500011B03RIK0.308119ASCC14.31718BBS90.461886RNF2202.15494
IFRD10.494308TUBA1C2.7781MAP2K50.308611TBCE4.31053ERH0.461997DNAJB60.464138
RPS6KA60.495289AI4624932.77233GMS8900.308934ELMOD24.30615EVL2.16263BCLAF10.464892
FBXO40.495816N6AMT22.77103LSM60.30901SMARCD24.3011FAM58B2.16142210012G02RIK0.464973
IRF62.01593PPIA2.76671SESTD10.309995BUB34.29961810014F10RIK0.462829TFPT2.14718
TIMM132.0151A430093F15RIK2.76654AIG13.22462SLC20A14.29733BPNT10.463089H2-DMA2.14258
HEATR30.497245TSR12.76595SLC25A140.310115GPN20.233055AKAP92.15875UQCRQ2.14201
CNN30.497368AC120410.12.76426TMEM39A3.22291SLU70.233292SLC30A42.15757RBBP62.14017
GM63510.498605TGOLN12.763990610010K14RIK3.21932M54A6D4.27783UBTF2.15703WBSCR270.467399
RTN32.005471810012P15RIK0.361885AC132397.10.311155VTI1B4.27576TSR10.463691NLRC32.13944
OLFR3450.499759GM49790.362023WWOX3.21153PI4KA4.27384INTS90.463911NAAA2.13651
CCDC550.500381TMED70.362038RP93.20928GM102080.234478AC132391.12.15509SRR0.468115
GAR10.502431TRP532.758CHCHD50.311661MLX4.25504FKBP152.15391BC0164230.468265
CCR81.98996CETN32.75738RANGAP10.311673HAUS70.235016GM133082.15066TMPRSS11BNL2.13355
HSDL21.9894CTNNBL12.75612FYN0.311934ARGLU14.25041TXNRD20.46555MCM60.468971
RTCD10.502788USMG52.75505GPLD13.2021TGIF10.235529PWP10.465791GABARAP122.13081
2900092E17RIK1.98882ORF190.363004DNAJA13.1971GTF3C20.235537TMEM2202.14674MYC2.12935
ACLY1.9886RP23-0.363122422530.312944ADM0.235992PDE7A2.14661P5ENEN2.1288
389D15.1
1110059E24RIK0.503225CORO1C0.363195IL23A0.313055DSCR30.236114CGRRF10.466117ADCK42.12453
CAPRIN10.503311AC131780.12.75298PRL8A13.19363RNF134.23063IL17F0.4664762610020H08RIK2.1236
FAM129B1.98337KBTBD42.75195SEPP10.313428PPAP2C4.22014HIST4H40.466639COQ60.470918
MTHFS0.504917RPL7A-2.75035NDUFB73.18801GM1290.237507ALDH4A10.466655TRRAP2.12216
PS10
STAU11.977012610204G22RIK0.364174WDR350.31374CRTC24.20833MRPL202.14273ERGIC20.471759
TLE60.505982GM107500.364482CSF20.313826ANKRD464.20651CLEC4A20.466949HYOU10.471895
1190002H23RIK1.97612IKZF50.364538RER10.314012TOR1A0.237885UBXN2A2.13985PTPRCAP2.1184
CD40LG1.97553NPEPPS2.73802RECQL3.18209ZNF512B4.19972FAM82B0.467589TOMM70A0.472127
STAT5A0.5065354932425I24RIK0.36533STAG10.314267SPRED10.238232HIST1H1B0.467605TCIRG10.472379
FHDC10.506963GNL22.73438NKAP3.18169MRPL504.19615MAP2K52.13721MRPL352.11517
NRBP10.507055UGT1A6A0.366222PTGR23.1815ZC3H150.238561STRN2.13357BRP162.1119
RHOC0.507238STAG10.366399SIRT33.18125GINS40.238992GM107362.13349CYB5R10.473998
SIDT20.507307UBE2I20.366474CCBL10.3145231700020C11RIK0.239037CDKN2C2.1312PFKP0.474076
LPCAT40.507401NIPSNAP10.366488KIF3A3.17297KDELR30.239351EPS152.13044TIMM220.474165
1700009P17RIK0.50749UBC2.725812310061C15RIK0.315197DUSP230.2394682510002D24RIK0.469557PRDX10.474435
GPN30.508025PDIK1L0.367074PDHX3.17102ACAD114.17327VTI1A2.12789TOP1MT2.10729
POP71.96773PFKFB20.36714GALNT60.316141CLCC14.17103CCR80.469985COX150.474648
TMEM106C0.508505CCDC930.367484ALG10.316257NDUFA104.16873IRGM12.126834933421E11RIK0.475088
GBA20.509279ZFP2602.72025ORAOV10.316266SEPP14.16486UBE2M0.47037AIF1L2.10471
ING10.509737RNF380.367695PEX30.316448ATG134.16056RELT0.470413PATZ10.475465
ATP5G20.50999ADD12.71941TRIM12C3.15835ING24.15707GBP82.12493NDRG10.476038
ZMYND150.510139EEF1G2.71874CR974466.33.1556GM1354D0.24064MFSD50.471448GM64042.09989
RAMP11.95994MARK20.368465WIPI20.316989H2-M30.240682LCMT10.471778SLC35C10.476217
TUBE11.95881KLF72.71385TRIB20.317126ERP440.240825KPNA62.1164EPB4.10.476245
COMMD20.5108985730403B10RIK0.368507HTT0.317342OVGP14.14954TMX12.116IL5RA2.09889
FAM76A0.511198TMEM176B2.713GM103550.317373TEX2640.241296BET1L2.1144DPH32.09784
OSGIN10.511961IL1F90.36898PABPC10.317586GSPT14.14142ADARB10.473036MED300.476857
GM104790.512029RNH12.709METTL13.14705MRPL244.14044RPL30-0.473359FGF130.477104
PS6
CCDC1550.512097TXNDC172.70692BIN30.317891NARFL4.13729FBXL82.11176LRCH12.09545
AP2S10.513282ARI32.70455EIF1AD0.318045HMBOX10.241991CTSL0.47388PHACTR40.477394
GM53561.94757NAPG2.70085SLC7A30.318191MRPL404.132210610007C21RIK2.10994ENTPD12.09064
GM20040.513559COX5A2.69935ACSL63.14156AP3M10.242416AMDHD20.473971ELF40.478486
ZMYM11.94678ARFGAP32.69573TIMP13.14129RILPL24.12217IFITM72.107845133401N09RIK2.08776
YIPF31.94037B230208H17RIK0.371107H2-M30.318527BC0564740.242985PRKD32.10658GM52442.08734
NDUFB41.93997CCT22.69382HNRNPD0.318867LAMC10.243258DPP70.474707TXNDC50.479354
SLC5A61.9379EXTL10.371383SMARCE10.318939C1GALT1C10.243391AHCYL10.475079DBR10.479424
SLPI0.5161842210418O10RIK0.371465FYTTD10.318977UTP64.10415SNRPE0.475442PSME22.08388
STXBP3B0.516692PAK20.371564ZFP680.319157HELQ0.243841KDM1A2.10326GLB10.481116
ODF21.93096MANIB10.371606GRK43.13139CNPY24.0997ASAH12.10298PYGL0.481326
MYO1B1.92966ABHD14A2.68887NCALD3.12826CTSE4.09769NBEAL22.1018ZNRD12.07589
PABPN10.51825AQP32.68602VDAC20.320477FUNDC24.09626TMEM2232.1006DDB10.482269
FAM119A0.519745GM144430.372325WDR53.11549AATF0.244143BC0164952.09905RDH12.07068
HSP90B10.519761PTS2.68215PIGN3.11357BAZ2B4.09403MTMR140.4770071810006K21RIK2.06959
FAAH1.92212COX7A22.675934933411K20RIK3.10909NPRL20.244258TMEM194B2.09601SCAI2.06911
GNAQ1.92071TMX12.67553UBFD10.321659STRN30.244485ANK2.0958GMPPA2.06901
YWHAZ0.521058LIMD20.373978USF10.321783RBMX24.08875PPP1R82.09564OTUB12.06728
FAM98B1.91469SEC14L30.374268EPB4.10.322107TMEM161B0.244574GM110922.09303MRPL542.06611
SYNGR10.523142GM132470.37523DNAJC93.10335RHOT14.08433ZHX20.477808TNFSF92.06593
SHARPIN0.523917ABI12.6648SPEN0.322519MOBKL2B4.08057IDE0.478085TPCN20.484625
PSMA41.90774FAM53A2.66272MCEE0.322623ANKLE14.0788HSBP10.478165GPS22.0626
AMZ20.525351SECI32.65611CENPO0.322861HTATIP20.245456BC0291272.091APPL22.06132
GM55900.525698SUN10.376637EBI33.09731CORO1B4.07192PLSCR10.478294GMIP0.485289
PXMP40.525848GTDC10.376912NDUFS33.09465D030074E01RIK0.245584MAVS0.478734EIF2AK40.485579
ESRRG0.5259934933427D14RIK2.65234ASH2L0.32334SERTAD24.06939GM1292.08859TMLEM1230.485769
PFDN11.90048UIMC12.6522NAGK3.08875ITGA60.246102TFPT0.478798UBE3B0.486341
CCDC211.89879PSMB22.64775WDR370.323944SPEN4.056534931429L15RIK2.08706SEC11A0.486873
MUS811.89522SNX122.64757MOBKL2A3.08572DAP0.246516BC0564740.4791574933439F18RIK0.486967
RBM30.52776GM56232.64667PPP1R73.08366DGCR64.0543FAM96A0.479384OLFR6132.0521
DLGAP40.52777TEX130.377913MOBKL30.324337GRAMD1B0.246709TAF62.08572KCTD102.05136
PSMG10.528081GM102220.3780322410017P07RIK3.08185ATPSS0.246942BRCC30.479527CAST0.487554
ABCF20.528096HIST1H4D2.64458TMC63.08041SEL1L4.045630610007P08RIK2.08457RAPGEF22.05031
A430005L14RIK1.89358OLFR5920.378312RCC10.324706LTBP14.04427THYN10.480007RPL23A-2.05018
P51
PARS21.89236DEF8360.378563FAM98A0.324901BC0020590.247311PLRG10.480248PKP42.05014
DDX230.529333MAGEB180.378797GSTO13.07349FKBP24.04349PEX190.480576TTF20.487781
TRADD0.529472PRKRA2.63855ADI13.07244PIH1D10.24734MSRB20.48109SNX110.488012
BRWD10.529774ZCWPW12.63355CAD3.07003CAMK40.247613SGSM30.481319AKIRIN10.489518
HOOK10.529863TECR2.63226PRKAB10.326859EPB4.14.03552GOLPH30.482346SHPRH0.48972
BZW10.530277ESCO20.380025IDS3.05883TMEM120A4.03342TNFRSF1B0.482373MS4A6B2.03976
CIZ10.531406PPID2.63042PIGS0.327095ACY14.03144NUDT10.48239TAF62.03951
LPIN30.531659SRP682.62526UBE2K3.05691FBXO70.24829PAG10.482728STK250.490473
RHOG1.87916TXNRD22.62157DHTKD10.3271492700062C07RIK4.0248EAPP2.06911RGS142.03743
TDRD70.534047493025F17RIK0.381826PNPO0.327168SLAMF70.248459ADHS0.483443APEX10.491194
BRCC31.87235ODF20.381901ATOX13.0533ECHDC10.248583CHEK22.0683WDR370.49142
NME20.534089EEF1A12.61766MTA13.05263INPPSD4.0219ZDHHC50.483838BC0056242.03429
COMMD90.534538GM46092.61683MPP70.327679OGFOD14.02036SPATA20.483905TAX1BP10.4917
CUL10.534786EIF2512.61263ENO30.327796PPIL50.248734AKR1B80.484074VAPA0.491756
FGFR1OP21.86828REPS12.61087CTLA2B0.328106CD840.248964TMEM1600.484123MFSD40.492919
GM54950.535808HEXDC0.383138TRMT53.0478AC142450.14.01427TADA2A2.06517C130026I21RIK2.02849
STARD40.536393NUBPL0.383279L7RN60.328111TUBB44.0125BFAR2.06511GTF2H10.49316
SLC4A20.536914H2-K12.60702FBXO180.328343HIGD2A4.01059CD552.06327GUK12.02764
ACBD70.5370823110003A17RIK2.60608OBFC2B0.328937ITPRIPL10.249459CDYL22.0612BAT40.493262
NUP1880.537166SLC12A90.384014UBE2R20.329711BOLA24.006435730460C07RIK2.05794PXN0.494138
CCDC670.537188CDADC12.60389JAGN13.02432TUBA3A0.2496045830418K08RIK2.05734BOLA30.494476
SCO20.537268ATP6V1A2.6038DNASE2A3.02216UNC504.00364LARP1B2.05711INSIG10.494544
RPL7A-0.537795MLF20.384558STX70.331134PHF140.250137NRD12.05564CARM12.02201
PS8
SYNGR30.538227MGST32.6002PI4KA3.01903FAM114A20.250261GPT20.486763LGALS42.01707
6720456B07RIK0.538249CTSD2.59728WASF23.01724AMT3.99346LGALS80.486918STIM10.496023
SBDS0.539336FIGNL10.385147RRBP10.331763DHRS130.250712G6PDX2.05221FAF10.496116
SRFBP10.5393871110054O05RIK0.385579LRPPRC0.332031AC117259.13.98473R3HDM22.051980610030E20RIK2.0156
MANBA0.539715STXBP3A2.58956FAH3.00849FAM103A13.98124ATP5H2.05144TUSC30.496643
MARK20.540156RPS62.58683SPC243.00563ALKBH13.97894TRAF3IP30.487575BZW10.497008
CRNKL10.542027GSTT22.58677IPP0.333073CYSLTR13.97682GNG120.487806CYP4X12.00703
RAB8B0.542064TUBA1B2.58618SFMBT13.00142DRAM20.251573SLC25A100.488253EROIL0.498334
CREBL20.542531TEC2.58595CSTF23.00105CKMT10.251619B9D10.488281LAMC10.498656
CRLF30.543038OLFR570.386892TCP11L13.000889930111J21RIK23.97353MAPK1IP1L0.4884441110038D17RIK2.00509
MBD60.543651ZFPS82.58133BCAS33.00008AIM23.97193ETL42.04704CD520.498934
MPHOSPH80.544274GM18402.57924WBSCR220.333521TASP13.96539ABR2.04692ACSL40.499223
ORAOV10.545472OPHN12.57923XIAP2.99495TRIP133.95438SMPDL3A2.04613LETMD12.003
EFTUD10.546074CENPH0.388323CTLA2A2.9872IDH3B3.95381PSMD60.488833CIAD10.499491
SYNE20.546379422530.388438CCDC302.98622PRAMEL63.94997GATA30.488935NARS0.499662
GM165190.546936GM83252.57395ESF10.335338H2-AB13.94804MFN20.489162GM108451.99974
GZMA0.547503CDKN2AIPNL2.57245RBBP92.98171KPNA60.253314RPP212.14171ATP5SL0.500147
SSBP30.547555RASA12.57058FRYL0.335625PSMB43.94607PARK70.489843TNK20.500251
AC154908.20.548873MMAB2.57045WSB12.97883FOXP10.253422PTPN72.14112TRPM70.500309
TEX100.549138HNRNPA2B12.56681GTF3C52.97865PCCB0.25368VTI1B2.04051HEXDC1.99764
ENTPD80.54997DYNC1LI10.390009MAN1A22.97706CASZ10.253707SYPL2.03922C794070.500689
CLU0.550086ACOT80.390187CHURC12.97412310061I04RIK3.94086SLC35C10.490401SMG50.500951
ATP6AP10.550153GM65782.56122APOO2.97331EDA3.94086GM102260.490733ERCC10.501052
EXOC40.550339RCAN32.56117SPARC2.9712PDSSA0.25396FBXO222.036ALKBH61.99384
AC121959.10.55083PIGU0.390626RABL30.337272CLEC16A3.93383BNIP3L2.03506GARS0.501551
CLDND10.550984A430078G23RIK0.390774AC163269.10.337421URM13.92678SUV420H12.03379CINP0.502082
PELP10.552241CRIP22.55862MDM20.337925CDK23.92492WDR772.03303PHF200.502194
IAH10.552825DPP60.391064BC0040042.955742900062L11RIK0.255086WDR472.03231CBX60.502539
UFSP21.80831ZFP7720.3912241810006K21RIK0.338721TMCO43.91832SUGP10.492191PI151.98937
PSAT10.553274MRPS52.55298SMARCA50.339057YIPF33.91359GFER2.03044HTATSF11.9889
RPL21-1.8074TIMM132.55225SMC40.33909GM65313.90986TNFAIP32.02887MTHFD1L0.502804
PS10
ATAD3A0.553998WDR700.39246TLCD12.94781TADA33.90802SLC19A20.493046CTPS20.502812
FANCC0.554128RPS8-PS12.54258ZMYM42.94475AC157595.13.907GGA22.02625RPL311.98629
RPL7A-0.556195CIZ10.393323CR1L2.93625RIN33.9052MARK40.493923IPO81.98393
PS3
DTWD10.556841PDCD2L2.54149AC154908.22.929NDUFV33.90298ATP11A0.494052GM79640.504708
SOD10.558599HAT10.39379TRAT10.341417SLC29A10.256339KATNAL22.02335SLC7A111.97586
SPEN0.55987UROS0.393838ARL12.92184TOR1AIP10.25649TPRKB2.02206DPF20.506625
FAM58B0.561243CENPM2.53785FH10.342486DPF10.256606RABGGTA0.495179GM99240.97165
KLHDC100.56306KIF1B0.394297MSL12.91958GEMIN43.89371HEG12.01904AC159008.11.96929
MMADHC0.564054TNNI30.394472SLC4A110.342529ARMC73.89219CHD22.01572UBE4B0.508236
GNA130.564115DRAP10.394833GEMIN60.342677WARS0.257273ATF10.496124STAM0508625
1110001A16RIK1.77005DCUNID12.53234PDXDC10.3433552610001J05RIK3.88639GZMB2.01541SERPINF10.509771
AC112970.10.565013RADS22.5261TRAPPC20.344281AC154908.20.257344IKBIP2.01523CAPN70.51015
MRPL470.565422TNIP20.395921CTSA2.90317EBAG90.257504HPVC-PS2.01226UPA1L10.510379
BCORL10.5655GM49452.52316CDK5RAP10.344497MFNG3.88175MFAP1B0.496986SF3A31.95914
GM165140.566314CHST122.52284CIAO12.89879HK13.88042KAT2B0.497207DTX30.510586
DENR0.567381CSN30.396734SMYD42.89716MRP5100.257769PIN42.01112CTXN10.510613
ZBTB200.567625DRG22.52055GRHPR0.345239PIGYL3.87436SPRED22.0094ATP13A20.510873
IPO40.5679814930431F12RIK0.396971BATF0.345388RBM170.258107CPM2.0084KPNA10.511448
CSTF10.568261GM122160.397362IFT462.89292310001H12RIK0.258201CRYZ2.00822NUP1600.511725
DNALC10.569127VEGFB2.51584HEXDC2.89157CDADC10.258521PRDM92.00768DOHH0.511747
PPOX0.570301NDUFV12.51426LIMS12.89128EIF3K0.258656D17WSU104E0.498499CD840.95194
RP23-1.75336WAC2.50759MTM12.889849330129D05RIK3.86585SLC25A230.498819PPME10.51334
378I13.5
GSTT11.75265PSMD72.50723EMID20.346445NADK3.86219SIT10.498906GM81131.94732
UBAC10.570886SET2.50644VPS360.346491CISD33.85639H2AFX2.00404RELT0.513763
FAM114A21.75083DAZAP22.50634CSNK1G12.886012610021521RIK3.85623MED290.499274SIN3A1.94621
ATP6V1D0.571885DPCD2.50611MRPS92.88388TNNC13.84669SPECC1L2.00133MAP2K20.514433
NUP2100.572376MYG12.50566AC163101.12.87474COPG23.84545CFLAR2.00132GAD10.515378
FKBP40.573039TRAP62.50285CTSS2.87188GPS10.260265POLK0.4997942010106G01RIK0.516083
SF3B51.744222410002F23RIK0.399637ABCF30.348441TWF20.260267STX1A0.500013PIGX1.93751
GNAS0.57567SLC1A52.50163ATF20.348658TRIAP13.83902AAK11.997212510039O18RIK0.516461
1600002K03RIK0.57703KATNAL10.399924SND10.34901GM121840.260714OSBPL30.500811TRAPPC40.516634
TRIM271.73294SHI5A52.49998GM49782.86399CNOT30.260816TES0.501326PYCR20.517569
MTA30.577892PLXNA20.400344KBTBD42.86249IER33.83412FAM76A0.501609GM73341.93156
CDKN1A1.7286ENSA2.49638PDE7A0.349459PUM13.8327THUMPD30.501862VPS240.517816
LY6I1.72847PTPN22.49413RPL300.349535MRPS93.83118ADORA2B1.9918ZBTB440.518369
MRPL41.72501CCR82.49156SRD5A30.349732GLUL0.261028DLAT0.502327ZBTB251.92887
STK160.582641GPR1712.49065CCDC1010.349885TAF1D0.261034RCBTB21.98949CDCA7L1.92825
FAM19A10.582755EAF20.401732ZFP8280.3507272700060E02RIK3.8289BC0033311.98914DPP80.5189
1700022I11RIK1.71165LYZL60.401936CNOT6L2.84999RAD520.261605CYFIP11.98797FOXRED10.519651
CCDC580.58524SIGLEC52.48747BET12.84451CELF23.819352400001E08RIK1.98759PSG281.92329
CWC271.70688NUMB0.402097ATP5J22.843652410002D22RIK0.261916RNPEP0.503512CDCA40.520308
NPC20.586183SMOX2.48543MTA20.351893PTTG1IP0.262322KIF2A1.98526NMT20.520384
CASC10.587311PRKRIP12.48495TSR22.84151LRP1B3.812CNOT71.98499SLC25A30.520812
FIGNL10.5877061700040L02RIK2.48404APOO-PS2.8409317000841I2RIK0.262448ACER21.98398TBCE0.520816
GM109470.588948HOMER30.402929SRP90.352207AM220.262804CTNNB10.504068FGFR1OP0.521589
USP40.59223AKT1S10.402934CHD60.352869TWF10.2629022310001H12RIK0.504076UPB11.91671
IPO90.592784CCDC522.48127ST130.3531819130011E15RIK0.2630891200016B10RIK0.504231ACO20.521775
GLUL0.593623MLXIPL0.403803GM101260.353969PGM23.79693COQ90.504426ARID1A1.91419
IK0.594284FAM96B2.47507YME1L10.35435ZFP119B0.263371GM99201.98207SCO10.522445
SMN10.598734FAM192A2.47499LARS20.35468MS4A4C0.263921LSR0.504704STK191.91279
RPF10.600117D2WSU81E2.47422XRCC22.81685BSCL23.78787A230046K03RIK1.98068SLC9A71.9123
EIF3F0.600944KARS2.4733PPT10.35564GPAA13.78783PAPI20.505006MEF2A0.523401
STAMBPL10.601806ZEB22.47291ATP6V1G10.355986SLC6A93.78783NAB20.5051614732465J04RIK1.90773
NAP1L40.601861EIF3I2.47183LRRC590.356549RABEPK0.264159IBTK1.97881TRIM261.90526
SUMO31.66089CCT72.47169DAP0.366992POLR3G0.264569SCMH10.505617PDLIM70.525343
ZFYVE201.65953H2AFZ2.46948E130309D02RIK0.357077PHB23.77815BC0313531.9776RAB840.525506
SNX61.64709CLIP10.405064HMGB30.357559VPS253.77655UPF3A1.97507FAM172A1.90216
TMEM2080.608069FLNA2.46297USP450.358041APPL23.77447FDXACB11.97505HSP90B10.526021
CDYL21.64066CMAH0.406825UBE2G20.358728NAGA0.264986LY6C11.97364TRAF21.90049
MRPS231.62324PSMB32.45744SLC13A40.35893ZFP4440.265217RBBP61.97358RTN30.526287
SDCCAG80.618133NUP1882.45588DCTN60.359605BTD3.7705DNAJC150.506779HAT10.526603
GM101800.6231TMEM50B0.407658BC0055370.359731ERCC80.265289TBX61.97285AI4806530.52694
NFKBIL20.62363PDIA62.451164930473A06RIK0.360025231001I103RIK0.265376IRS21.97257WDR130.527264
TREX11.60079SLC2A90.408452NUP350.360156SLC3A23.76824ZFP2600.506964RPS121.89526
NMT10.629225FBXO182.44665DUS1L0.360992ADI10.265536A630010A05RIK1.97099H2-GS101.89495
BOLA21.58747IL2RG2.44447RNF252.76455GST210.265792SYTL10.507613RBPSUH-0.527976
RS3
RPS12-0.635083SNRNP2002.44421ATP6V1D0.362458MTG10.265886LYN1.96963CTNNA10.528139
PS3
EIF3K0.640023APLF0.409141AGK0.362486PPM1M3.76093ZMYND80.507888POLD10.528232
RNF80.640552TTC160.409214EIF4E30.362549MYBBP1A0.265955TGTP21.96875FNDC3A0.530053
GIMAPS0.641094FAM171A20.409287PNO10.363285TUSC20.2660081600014C10RIK0.507938ECT20.530097
ICOS1.55747RDH112.44145RPAP22.74578CCDC400.266274COG20.508092ZBTB480.531218
AAAS0.645299GM98670.409753CRYBG32.74507RCCD13.75553EIF1B1.96748AIMP20.531318
AACS1.54713SH3GL20.410064YBX10.364351UBE2G23.75208AKR7A50.508268GEM0.532475
CLTB0.646466TGDS2.43712BBS92.74435ZCCHC113.75014A430033K04RIK0.508386SMOX0.532485
TSTA30.64683GM123552.43594CCNC0.364551RFT13.74624GNPTG1.96637GRK11.87782
GLTPD10.647385SLC17A10.410597ORC62.74063BFAR3.74384CDC42SE21.96594HSPH10.532596
USP330.652032CHCHD22.43431PSTK2.74002MLL50.267206UBAC10.509005EEF20.532776
HSF2BP0.657262310004I24RIK0.411066PHF200.365273AB0418030.267228STT3A1.96405SESN30.53345
EIF2B21.51353RFC42.43237GBP42.7348EIF4E1B3.74213MEA11.96388TMEM16B0.534151
GM98460.661306GM54492.43162ATP2A20.365747NUP540.267326ALG61.96135UBE2O20.534491
AC068006.11.51037RNMT2.42932CSDA0.36588TMEM1113.74061MAP2K40.510181RASSF71.86971
BCL2A1D0.663782KIN2.42826CBR40.366082GYG0.267383DAPK31.95949MAVS0.535261
EPHX10.664393CRX0.412069CCDC1112.73013WAPAL0.267453GM61321.95886FAM32A1.86815
NOSTRIN2.42669MBTPS20.366424POLA13.73713LRP10.511141SMG70.535301
TPMT2.42108GLA0.36763SCFD23.73631VMN1R151.95617CBLB1.86343
RIN20.414077TUBGCP42.70223ZBTB250.2676442010005H15RIK0.511384VPS33B0.537968
MRPS212.41435PTPN222.70201CCDC1373.73303PUS11.955RERE0.538233
LSS0.41427FAM82B0.370392GM163803.73135HOXB11.95471RAB70.539009
ERCC6L0.414813OCIAD10.370722ZFP8700.268254PTPN10.511705TMEM2221.8546
CDH70.415362PHF140.371514BC0216143.72679TOP3A1.9542CDC260.539305
FLT10.415599BC0176430.371595CENPQ3.72435420661.95403ARRB20.540067
NHLRC32.40576TAF122.6907RGS110.268513FAM65A1.95385REEP40.540384
RAC22.4055B230208H17RIK2.68825SIDT20.268642TRA2A1.95347NFKBIL10.5408
TTC350.416079SMOX2.68797BHMT23.72138CCDC341.95296LUC7L1.84785
SERTAD22.40336SNX10.372894PRPSAP10.26896SEC61B0.512119GM72631.84588
BCL32.40316GM104912.67982ZNRD13.71802UBTD10.512319SGIP10.541977
QRAOV10.416262GLIPR12.67168ZFP5660.269243BCAP290.5124971810029B16RIK1.83701
GM101920.416457CCDC552.66151TFG0.269249F730047E07RIK0.512933GPR981.8369
GM105760.416531BCKDK2.655PIH1D23.7134GTF2E20.512934SYPL1.83272
1810062G17RIK0.416542OLFR6132.65279ATG4A3.71131BPTF1.94905TARDBP0.546266
ATF70.416542MRPS280.378022GRINA0.269574SURF60.513101PAFAH1B31.82962
PYGL0.416898GOSR22.64246SIL10.269867CDK41.94802SNAPC10.547069
B4GALT72.39738SNX100.379113FAM54B3.70403OAT0.513348PNRC10.547422
SHKBP10.417187PTPN72.63711H2-Q73.70298HSPBP10.513387DHCR240.547732
NEIL30.417589RPL21-2.6325LGAL543.70086RP23-0.51361EPT10.548464
PS671J17.1
ARHGAP230.417865CDK2AP20.380398FZR10.27031MINK10.513728SERINC31.82304
CCDC730.417948LRRC330.38045PAFAH1B33.69605GPN20.513745TRIM160.549268
SERINC32.39241PXMP42.62462NFKB10.270657LANCL10.514143EIF4H0.549483
IRF32.39082MAP3K12.62401TAF83.69444RNF2140.514319SERINC10.55107
REEP30.418667LCLAT10.381754CD440.270938NEURL31.94432NFE2L20.551235
NAPA2.38692TADA2A2.61838SLC12A63.68795GJA11.94426PSG161.80771
RCCD12.38312SBF22.61665ADPRHL13.68326CTPS0.514334PSD40.553898
ZBTB480.419638MED110.382379GSTT20.271538EPHB60.514382BRP44L1.80508
ENO12.38291SDR39U10.382638NDUFS33.68165SC4MOL1.94234NDUFS81.79949
SRA12.38251FLII0.38277WWOX0.271617GOLGB11.94176PRKAG10.556035
NRN12.3825CCDC582.60645GALNT13.67991FAM53B1.94035VEGFA0.557177
RBMX22.38229DCAF172.60515AK1573020.271878AZIN10.515598PML0.557223
PLSCR20.419896DPYSL52.60017VP5520.271994TBC1D71.93891ZFP2771.79357
MRPL272.37937D17WSU104E2.59814TPRGL0.272112GMS1480.516057EAPP0.557704
GM99200.420306CRBN0.384986SDCCAG380.272155GM154460.516669UBR11.79211
SNX110.420689COMMD32.59712JMY0.272284RPS19BP10.517113NUP2100.558386
CCDC1272.37554GARS0.385447ZFP683.67263BAT2L1.93341TRAF40.559497
GM121662.37455ADD10.385754KDELR13.66723THOC21.93184NSMCE4A1.78532
CHCHD52.37221IGF2BP12.5918PRPSAP20.272685GM72040.51812TUBG10.560497
PSMC12.371294930422I07RIK0.3859534921521F21RIK3.66495N4BP2L11.92934HERC40.560835
CDCA32.37031SLC5A62.59072AMDHD20.272858DDB20.518393TMEM1280.561113
HIGD1A2.3696STK38L2.58657SREK10.272866UBR10.518747ACP60.561274
HK22.36845PIGQ2.58532SPATA53.6635CCDC641.92739RAE11.77899
HAX12.36709CCNE20.387262YWHAB3.653TIMM17A0.519287CRK1.77824
GM66160.422601RNFT10.387617AGPAT43.65153NOL60.519525PKM20.563075
GOLGA22.36616WDR832.5794C130022K22RIK3.64762SNF80.519544RAB3D0.563213
IDH3A2.36555TMEM2082.57819AAMP0.274362ZFAND2B0.519692ERI20.565008
TUFT10.422771LDHB2.57679BTNL70.274362MAP2K61.92378RAD91.76762
IARS2.36517HIGD2A0.388263FARS20.274534RPA21.92376TRIM12C0.566937
SNRPC0.423025TRMT2A2.57556TMEM1383.63884FLCN0.519922BHLHE400.567591
TAGAP12.363051810029B16RIK0.388697DHRS10.274822CCDC109A1.92333GOLGA30.568098
ESRRB2.36259WDR112.57205FAM45A0.274896MICAL11.92329DHODH0.568201
EGLN20.423727PRPF40.389138LRRC513.63652YTHDF10.520077CD2BP20.568371
GNB30.423751GM1292.56113HBP13.635276330512M04RIK1.92214NUP500.568502
CETN22.35631RNF80.390462TM9SF10.275183ARFGAP31.92052RBBP40.570096
SRSF22.35537ENOPH12.55864GM101253.63363AHSA10.520838SYCE20.57057
GM69842.35439CCDC212.55839VPS4B3.63172KCTD201.91971SDHB0.57114
ZDHHC20.424965POLR3C2.5568SMYD40.275947OLFR3091.91784IKZF10.571504
ACTR52.35309LZIC0.391656KDELC10.2759471200011M11RIK1.91752TPM30.571603
BNIP10.425006SMARCAL12.54702RIOK23.62388FUZ0.52165GNPDA20.572931
FUNDC12.34964CASP90.392795ACTG20.276442FBXL200.521882BBS50.573259
TMEM106C2.3483ATAD3A2.54526ACSL53.6173CHCHD41.91611WIPI10.575804
RPL27A-2.34643CREM0.393101IFT803.61578CCDC991.91608GM101261.72328
PS2
2610020H08RIK2.3461NUSAP12.54381C1Q8P3.61566CNOT80.522017EIF2B30.581782
ALOXE30.426314INTS42.54359SAR1A0.276578RMND5A0.522092UBAP11.71853
HELZ2.34235UBE3B2.54343EIF50.276583VRK30.52243SPSB10.582234
FAM58B2.340332210012G02RIK2.54314ZDHHC43.61425TMEM700.52249ALG10.583562
TMEM292.33863DCLRE1C2.54308RHEB3.61261WDR621.91337EIF4G10.585375
CCT82.33681HUS10.393319DEGS10.27711PLA2G4C0.52267GM101541.69708
BRD70.42797UROS0.39347SETD30.277125APLP21.91309ARPC20.590214
PSMD42.33652CDCA22.54124SNX53.60658HEXB0.522921SIN3B0.590385
AC087117.12.33628ATP5G20.3936221700026D08RIK3.60065ITGA70.523022NADK0.591149
CCNB12.33545CHCHD22.53764F730047E07RIK3.59942DDX3X0.523083SNX150.59177
GLRX20.428297RNF72.53293AC102876.13.59924LY751.90982SLIGP10.592918
PRKAR1A2.33459ZFP8710.394855STRADA3.59782AMN10.523642WBP110.593785
FER1L40.428392HDAC32.5311MTCH13.59294GM101261.9084EIF4B0.594521
SERF12.33264GM114440.395169CLUAP10.278452TBC1D141.90609KPNA60.599164
GLB12.33197TRMT60.395306TXNL4A3.59025TTC141.90396EIF3G0.603438
RPL17-2.33185PSMB40.3957132010002N04RIK3.5887POLR2I1.90363CHCHD30.603518
PS3
PCID22.32949PSMB102.52609WDR833.58864CORO2A0.525557BLMH0.604504
SLC35A52.32855FDPS0.396001ALG13.58642CTSF1.90247NDUFV20.608703
ACOT92.32667PUS70.396696SLC25A200.27883CPEB41.90227AC121959.10.60898
XPO62.32366H2-T100.396761C1D0.27899ACAD111.901421700021K19RIK0.612295
DULLARD0.430551CDK62.52008WDR5B0.27904ACADL1.90024JUNB0.61636
MBTPS20.430966STXBP3A0.397055FBXW170.279342USP341.89966GM163720.620202
GLRX32.31916PRDX40.397077MRPS23.57822HECTD21.89902TRIOBP0.622737
FBXO70.431201CAMK2D0.397755PARP20.279468FAM18B1.89846SLC23A20.623297
RFC12.31754SURF60.397919PVR0.27983SUB10.527024TIMP10.62437
BACH20.431518NMT10.398118SLC38A63.57343GM49531.89734NG20.627746
EDIL30.431846MRPL122.50676CCDC1170.280162ORAOV10.527263TM92F20.630655
MAPKAPK32.31328UBA32.50203SRSF13.56743ARL13B0.527358BCL2L10.631318
ACSL32.31253CCDC530.400055CCDC370.280558VPS37A0.527394ZFP680.632526
EIF2B12.31248ARPC42.49874NDUFA50.280768RAD23B1.893562410002F23RIK0.637736
RAB340.432762SIP12.49633LAPTM4A0.280976KLHDC11.89321TIMD20.65466
HIST1H1B2.30999DUSP110.40093RHBDD33.55902ACTN11.89312GM100920.662806
FAM862.30926AQR0.401972CHURC13.55842RNASEH10.528473
USP200.433214PRPF62.48721SRP720.281468MFSD40.528688
ARRDC42.30811SMCHD12.4853VAPA3.54427PHKB1.89121
GNB1L2.30491TOE10.402448VCAM10.282146CR974466.30.529469
OXSM0.433947ETF10.402612C130026I21RIK0.282669FEN10.529593
KDELR12.30246CSNK2B0.402876TAX1BP13.535HK20.529764
PPWD10.434447MRPL230.403481H2-KE23.53448ZFP641.88746
MTCH10.43445PIP4K2B2.47823LRRC570.282927CBFA2T20.529815
UBE2K2.30119GEMIN50.403858MCFD20.282948NUDT21.88732
MCM32.30089MPP62.47593RPUSD40.283046TRIM261.88656
RAB262.29972CHCHD32.47563AHCTF13.53299CAPN11.88608
COQ52.29929BCAP290.4041562610015P09RIK0.28318GPRASP20.530205
PPP1R12B0.43505HAX10.404298AC161211.23.53132WDR830.53028
GM16730.435322RAB10.404338GM108453.52711ATP6V0A10.530309
BAT42.296182310008H09RIK2.47301RRP93.526321810012P15RIK0.5304
HHAT0.436199PLEKHA20.404372HKAMP3.52345GNPDA21.88521
IL110.4364MRPS112.47198BEND50.2838811200011I18RIK1.88463
TERF2IP0.436546GM102470.40515RBM183.52106RNF2201.88316
TOP10.43657RFC32.46815PFN20.284257D11WSU47E0.531055
PIGO0.43692C130026I21RIK2.46687COQ30.284301UBXN110.531091
GAK2.28802ETFDH0.40565CYC13.51628GSPT11.88263
SLC35B42.285512810474O19RIK2.46509GRINL1A3.51311FUS1.88159
RWDD12.28476ZMYM10.405929CMTM53.51179MAPK60.531495
ARFGAP12.28443MYH92.46303UVRAG0.2849382810006K23RIK1.88005
RNG2070.437931RNF1352.46229SLC2A13.50806TUBB60.532162
4933434E20RIK2.28268GBP22.46228DCUN1D53.50632BC0032661.87822
1700049G17RIK0.438122RABGGTB0.406451RMND13.50373ZKSCAN51.87779
MYST20.438131BCL2L112.4598PRKCQ0.285425PPARD0.532756
HNRNPLJ0.438283CORO70.407067BOLA13.50029TOMM70A0.532804
A2LD10.43857CAB390.4072572900010I23RIK0.285879ZCCHC101.87629
WDR670.438638PFKFB42.45371CLSPN3.49183LPIN20.533743
MAPKAPK50.438737THOC42.45298NUDT193.48855MOCOS1.87267
MRPL23-2.27896R3HDM10.407704TRP53BP10.286692CCDC170.534254
PS1
BC0463310.43885LAMC10.407777USP80.286742PIK3AP10.534386
GM46660.43888RBM4B0.4079372310008H04RIK0.286929SPNS10.534407
ADSL2.27559SERF20.408298NR2C20.286973EIF3B30.534832
VSIG100.439492CINP2.44822CD973.48148ACD0.534847
ATP2812.27493KPNB10.408474PGM30.287426PTPMT11.8695
UCP22.27177TAP10.409023CLEC4A23.47531UBA50.535227
GM82790.440209EIF3F2.44425FCRL10.287744SOCS11.86833
SSR42.271552700094K13RIK2.43966RAD51L10.287889TRIM12A1.86742
IGSF80.440973CPNE82.43946HIBCH3.47339KANK31.866
DAGLB0.44098AP1G22.43878METTL7A10.288049CSTF30.535954
UBB2.26697CNN30.410269FAM58B3.4704RREB10.535999
KLHL180.441715NUP932.43383GM55070.288649GM107491.86475
SNRNP350.441746SNAP230.410876KCNQS3.46359NCOA21.86434
DPM32.26299IL17A0.411163D630004N19RIK0.288787ABCD11.86396
BCD493490.442184SCD32.43123SLC25A393.46276FOXO30.536658
HECTD32.26147RRP360.412123110001D03RIK3.46125ASH2L0.536841
MUM10.442346WDR770.412209ACRBP3.45928CD1D11.862
GM58792.261110059E24RIK2.425552610301B20RIK0.289335EXOSC31.86032
ANAPC52.25775RAB4B2.42419TIMM223.45622700073G19RIK1.85974
MSN2.25766FAS0.412615ERCC30.289451CLP10.537757
PRKAB10.44338CDV30.41273TTLL10.289466GFM10.53782
OBFC2B0.443448TARS0.412962CYP4X13.45405ANGEL10.537921
2010002N04RIK2.25414MCTS12.42132FANCC0.289578TMEM55B0.537935
GBA2.25281ADK2.41902ACP20.289725AI3149760.537974
WFDC120.444838LUM0.413837BC0682810.2898FRG11.85879
HSD178102.24676DDB20.414031STARD73.45065IFI471.85754
RSF10.44561CHCHD10.414192PLACB3.44922SLC39A140.538616
DHR530.445627HAUSS0.414267RPL21-3.44832RPL10A-0.539089
PS4PS2
APEX12.2438GRAMD30.414562AA9604360.290163MRPL121.85427
TULP40.445693STAG22.41193CBR13.44506RAPGEF61.8531
KLHL62.2427KIF232.41134UROD0.290278ETFA1.85182
TSTD20.445908FANCG0.4148151110018G07RIK3.44241IL4RA0.540125
SFXN50.445941422492.40807GABARAP0.290549BCL2L111.85096
A530064D06RIK0.445959MRPS52.40689RFTN10.290802CKAP51.8501
RG9MTD20.4463279330129D05RIK0.415476TOR1B3.43761PPP1R111.84947
POLR3K2.24048MYCBP2.40489SIRT60.290991MGAT10.540891
ZFAND12.24028SMYD52.3976HYAL20.291252ACTL6A0.540991
CHN20.446849ZFP6052.39591AA4153980.291482PRR30.541067
GNA132.23782POLR2F0.418289LAPTM4B0.291771TSPAN30.541238
LRRC572.23676TCTN22.3902ACOT93.42735SDHA0.541254
RORA2.23615ZC3HAV12.38878RIT10.291982IRAK10.541435
STK380.4475012410002I01RIK0.419639GATAD2B0.292071GM163720.541465
SDF42.23205TOMM72.38206GM4543.42131PRR130.541563
EMG12.23131TBC1D9B2.38033RUSC10.292286PHF21A1.84613
FAM69A0.448401AC166253.12.378462610029G23RIK0.292435CCNDBP11.84601
IKBKG0.448482ID20.420475MAX3.41678WBSCR160.5418
AC170752.12.22847NUP430.420972CIR13.41407SURF20.542203
IAH10.448838CLINT10.421182SLC45A43.41407HEATR7A0.542304
ARMC62.22769BAZ2B2.37372LEPROT3.41029UPF21.84338
RIMKLB0.449006NFS10.421373DTWD20.29323ZDHHC21.84324
ZNF512B0.449096CD40LG0.421523TMEM126A3.40481ZSWIM70.542856
PSMB12.22377SEPHS10.421566TSPAN63.40187SNAPIN1.84112
SPIC0.449745MSH22.370211700052N19RIK3.40139H2-OA0.543218
NDUFB52.222661110001A16RIK2.36988ZFP2393.40051HIST1H3G0.543763
ACTL6A0.45008IL162.369622410002I01RIK3.39923SH3BPS1.83895
PLA2G2C0.450326SEC11A2.36893CST33.39793ECT21.83892
RPL32.21903UXT0.422554SLC9A3R10.2942974933421E11RIK0.544024
CLUAP12.21787DECR22.3663ZFP823.39489CYB5R10.544334
S100A7A0.45141DPCD0.422754CABLES23.39347GM103490.544527
USP70.451836POLE0.422899RNF383.39295PDCD10.544637
2900092E17RIK0.452171INPP5K0.4229822410002F23RIK3.38695PPP2R5E1.83563
TUBG10.452948FOXRED12.36406HMGN50.295251WHRN0.545202
PECI2.20688VPS4A2.36323GALNT70.295262WDYHV10.545738
DHX402.20161BIRC32.3623CRK0.295838GNPNAT11.83227
PAQR30.45431GM103950.4233511810063B05RIK3.379456330416G13RIK0.545776
AL592187.10.4544993110043O21RIK0.423484PPP2R2D0.295906QDPR0.545929
MIF4GD2.20019SATB10.424146AHNAK0.295931ZRANB21.83121
ABAT0.454636AC132320.12.35533PTK2B0.296081GM711.83056
PBX10.455434QRICH10.424793ATP6AP23.37424FBXW110.546354
MLPH0.455592TRP530.425101WEE10.29648GIMAP41.83026
WDR770.455613SLC25A362.35234LRRC413.37149AGFG11.82946
FBXO442.19411PIGX2.35214CENPQ0.296665AMIGO10.546986
PFDN20.455822ASB130.425366ATP6V1B20.297223NDUFB90.547145
SULF20.456066POP10.425439NGFRAP13.36353TMEM430.5472
4632433K11RIK0.456427TTC190.425544GM49220.297412EPHX10.547367
AP1B10.456439AC087117.12.34081HAUS30.297412RALB0.547377
MRPL202.18844ZDHHC212.34042IL1RL20.297412SPATA71.82669
HINT12.18827TCHP0.4272952610018G03RIK0.297753KHK0.547649
6330439K17RIK0.457064DPY302.33847RPL13-3.35846CYTH21.82538
PS3
ANP32A0.457393NTAN10.4278093110009E18RIK0.297797FAAH0.5479
H2-T232.18171PFDN40.428353LDHC3.35647R3HCC10.548164
SAG0.458565SH3KBP12.33449NHEDC20.2984411700057G04RIK0.548299
CTSZ2.1802GM100630.428916MKLN10.298486AHR1.82235
GM106990.458737CAML0.428973FBXL43.34996RTCD11.82138
DGUOK2.17906LRRFIP10.429017GM68163.34996PLOD31.82037
PARP12.17543SEMA4D2.33063CORO1C3.34844PSMD100.549959
UNC45B0.459794TRAPPC12.32803SETX0.298789USMG50.551139
H2-K22.17147ZFP6550.429566USP210.298838CCDC1270.551167
POLD42.17121CD209C2.32591APEX23.3449BRIX11.81416
ZFP532.16957PPPDE12.32456TMEM693.34327BGLAP-1.81269
RS1
VP5542.16948DEPDC50.431528LRPPRC0.299236POLD10.551766
H2-OA0.460969EIF2B52.3157CMTM63.33853YWHAZ0.551889
CCDC1240.46117AP1B12.31379RER13.33853PRPF30.551942
TLCD20.461632RNASEH2B0.432267CDK80.29975HIST1H2BG1.81161
LIMCH10.461684RNF200.432732KPNB10.2998054930455C21RIK0.55247
CLYBL2.1649GNPDA12.30957GSN3.335042810417H13RIK1.80977
2610028H24RIK2.16231VTA10.4332422010107G23RIK3.33144CHURC11.80936
POT1B0.462653NISCH2.30736CCNL10.300171DNAJA30.552777
GM103592.161INPP5B0.434037EIF2C43.32825TMEM330.552949
FAM48A2.16008VEZT2.301352310039H08RIK3.32527CRTC10.552987
SLC35A30.463086DDX560.434647GM47690.300727MAN2C10.553075
VP5282.15942MRPS18C2.29971CDKN33.32437TBC1D2B1.80746
GM162230.463771TSSC12.29637ALG80.300988TMEM1090.553279
PPIL10.463844NIF3L10.435506NDUFAF13.30854DPH10.553309
SEC23A2.15533ACOT90.435567BC0265903.30791ZFP6171.8071
KCTD200.465161RTN42.29576VPS450.302474GABPB10.553583
GM104910.465733ICAM10.43576RPL21-0.302832PBX41.80478
PS6
GAPDH2.14683IFT520.436114932425I24RIK3.30203RPS6KB20.554403
EML10.466GM109410.437625ZUFSP0.303095WASF21.80256
GM111270.466028CUL20.43766FBXO250.3033231810063B05RIK0.5548
CTSL2.14528RABEPK0.437762CHCHD33.29527GTF3C50.555342
FCGBP0.466372CCNA20.4381AC156282.13.29316NCOR11.80056
PMPCB2.14362NFU10.4384APOO3.29302UFD1L0.555419
GM116960.4670284930512M02RIK2.28097TUBB2A3.29053INCENP0.555529
EXPI0.467245MEMO10.43848AC154727.10.304576BC0040041.79969
CD700.467704AP2A12.28027METT11D13.28058ELOF10.555867
MRPL130.4678339130011J15RIK0.438852TMEM55B0.305017ANKRD391.79863
FAM188A2.13708NAE10.43911GM77920.305222PEX51.79857
HELLS2.1366SET0.439536TBC1D143.2763PML1.79766
ZDHHC120.468237PYCARD0.43988PRKACA0.305346PDGFA0.556287
GM68430.468988FADS62.27168CHCHDS3.27182ZMYM10.556484
H2-KE22.13071NDUFB30.440946PUF603.26996RC3H20.556597
TTC39B2.12776CTNNB10.441276B230315N10RIK0.305922IFI300.556767
GM91042.12771PGAP20.441622CASP93.26881WDR740.556829
IFFO20.470055SLC1A70.441665CSMD33.26735MAPKAPK50.557062
UNC1190.4701111110007A13RIK2.26398GIT23.26251PLCXD10.557075
FARSA2.12375PARVG2.26374PARVG3.26132ABI10.55722
CDKSRAP10.471003CCNG10.441766JMJD1C0.306647PISD0.557802
GM104812.121931810043G02RIK2.2607EXT10.306879PRDX10.55786
HCN30.471413CENPN2.26031STRBP3.25784CEPT10.558251
WDR262.11983AHSA22.25986HSD11B13.25586GPSM31.79072
ZSCAN20.471782NF12.25905TSC22D30.307254EGFR0.558788
RABL20.472485TIMM92.25653FXR10.307377CASZ11.78932
LRRC8D0.47293FAM192A2.2561M6PR0.307753SAR1B1.78855
CPNE50.47295CPA62.25489CDIPT3.24906COMMD10.559244
WFDC50.473411ACO12.25226GM101200.30795SMC40.559449
1110008J03RIK0.473831SUCLA22.24747ME23.24437NEDD91.78684
TPM10.474032CCS2.24671UBE2R23.24431GALT0.559741
TAF110.474295HDAC62.24613NUP353.24336FZR10.560072
1110049F12RIK0.474416BCL2A1D0.44523PRP523.24211LRSAM10.560257
CCDC1012.10597SS182.243565730403B10RIK3.24185FAM126B0.560396
8430410A17RIK0.475727RPUSD40.446656GJC33.24033ZFP7831.78415
GM164092.10033STOML22.23691RRP153.2377RHEBL10.560621
4930423O20RIK0.476418TRUB22.23626HERC40.309176DLD0.560748
IPO40.47698LIAS0.447179PPIE0.309526AGPAT61.78293
OGFOD10.477246EME12.23473BRMS13.2304CCDC88C0.560936
GM162530.477298PAPD52.23455TOP2B3.22874TFG0.561088
AC087229.10.477627MKKS0.448136FBXO223.22729GM64041.78103
FTL12.09329HDAC72.23062LISP73.22585NUFIP21.77858
GM61772.092512610002J02RIK0.448358ST130.31005PAM160.562297
EIF1AX0.478225GM102220.448984CCDC473.22504ACBD40.562414
OXSR10.478424COX7B0.449612AC158559.10.310331PICK10.562446
GM110110.478426ARPP190.449695CDK160.3108356LRP1B0.562624
ZWILCH2.08967PYGB2.222232410022L05RIK3.21695TMEM1410.562888
APH1B2.085491110004E09RIK0.451634IGFBP40.310854GADD45A0.562989
FNDC70.479946MRPS360.4520485RSF23.21548PCGF10.563208
NUDT16L10.479973P4HB0.452445D1BWG0212E0.311108MGAT4C1.77522
AL589878.10.480025FAM103A12.20774FEN10.311256NVL1.77516
2010106G01RIK0.480718MCFD22.20704YBX10.311269PRPF40A1.77313
AC153594.10.480831SLC35A20.453334CCNG13.21088TMEM1010.564739
RPL21-2.07755HAUS70.453406FAM40A3.20928CDCA30.564975
PS11
ATF42.07678TMEM492.205025830433M19RIK0.311726SLFN81.76958
EMD2.07543SMAD30.454636CTS53.20795FUNDC20.565211
ABHD42.06755MADD2.19501CLIP13.206921810013D10RIK0.565296
PATZ10.483944ZFP2772.19468GM104820.311832RNASEH20.565395
1700061G19RIK0.483965930416I19RIK2.19421PRAMEL53.20611GSN0.565571
ERH2.06622HDGF0.455778GM100883.20581PLCG11.768
SNX170.4842CHRAC12.18984ATP5L0.3121791600002H07RIK0.56563
RHBDD20.484413NUP2142.18975ZC3HAV13.20328SYNGR10.565632
ILF22.06414AGPAT62.18953ING30.312252MRPL131.76635
GHM50452.06252CUL10.456742UPF3B3.2021CLPTM1L0.566179
TRPM10.485193FAM48A2.18865GM48853.19919ATP5G10.566213
FURIN0.486329ADAMTSL40.45754D2WSU81E3.19707ERN10.566827
GM79642.05608PRDM112.1834SMC40.312889SMYD31.76394
GPKOW2.05294BC0265852.18302POT1A0.313051PLAGL10.567379
IRGM12.05135AKAP90.458226PRKAB13.1937MARCKSL11.762
METTLS2.05114DSTN0.458411BANF13.19157ALDOART20.567735
PGAM12.0511GOT20.459847CDC203.18913SELP0.567803
MYBBP1A0.487549POLR2J2.1702TRMT61A0.313689DCTN40.568542
NUDT72.0507GM158870.461531MKKS3.18457CDK20.568548
2410017P09RIK0.487772CREB30.4632331110002N22RIK3.1836PLA2G160.568624
NXT10.487864NUP540.463314930555F03RIK3.181796720489N17RIK0.569815
HNRNPAB0.487941GM104952.15552CGN3.179721110007C09RIK0.570179
PPP1R3F2.04769INPPSF2.14687KRT2223.17941USE10.570337
LEO12.04707LGALS12.14651SARNP3.17917VPS390.570866
CMTM62.04648TIMM17A0.466884ARL63.17793ATG9A0.570933
MFF0.48865SURF40.467147P2RX43.17721ILF21.75104
PCBP30.488893PSMC60.467773COX183.17615420680.571351
KLC10.489024NDUFB112.1347TADA2A0.314846YIF1A0.571751
GM98082.04453PSMD130.468583TUFT10.314984CIB11.74844
CBX12.0445SSB0.475177RIOK13.17343TNFRSF141.74806
IL4RA2.04176SDF40.479001NUDT16L10.31515AC090123.11.74715
2310045N01RIK0.489897RPL22L10.4846880610009B22RIK3.17238AMFR0.572472
CCT42.03971NME12.05839NAPA3.17114MAPKAP10.572544
BDH12.03947RPS15A0.489007FNBP13.16923GM131541.74582
GM108450.49045ANP32A0.495753CTPS3.16817PAFAH20.573079
NUDC2.03722GM100360.501126FAM195A3.16733EVI2A0.57309
T5FM2.03532KDM5A1.98696ATP6V1E10.316043CD690.573096
UHRF10.491655MRPL201.98423C330021F23RIK3.163594930453N24RIK0.573176
CHD30.491889UBA10.5085062810004N23RIK3.15863PLEK0.573959
PI4KA0.491991CRIP11.94192FUT80.316765PIK3CD0.573988
CD2470.492074AT5B0.516133PSMB50.316776AKTIP0.574124
PSG290.492256TOPMM51.92215RNF140.316879NUP500.574382
DDXS60.492881VPS291.89828GM64983.15505GM101080.574638
MGST20.493199LY6A1.89024PPOX3.15469SF3B40.57473
PIPSK1A0.493439GPI10.529736LIAS0.317121BC0520401.73993
SCD22.02514APEX10.536689LIN370.317155MFSD2A1.73981
TNNI10.4940421810009A15RIK0.537057CACNA1F3.15288PHLDA30.574792
SAA10.494437FBXO183.15288GFPT10.574973
GM110920.494518ARHGDIA3.15234CDC261.73847
OLFR3160.49502BCL33.15086CYP11A10.575584
MARCKSL10.495066NUBPL3.1491MKKS0.576672
CCDC610.496047NARS23.14837TMEM1231.73129
HIST1H1E0.496819POP40.317653SF3A20.577604
SIGMAR10.496855RNF343.14724RNF1250.57771
EIF4G30.49691EIF2853.14567A630033E08RIK0.577835
NFKBID0.496946MYG10.31794CIR10.577934
UNC500.496963M54A150.317994RCSD10.577976
AI3149762.01113DDX410.318146MANEA1.72905
TRIM43A0.4973ARL33.14104GIMAP90.578676
RAB7L10.497891AEN3.13723TMEM1380.578809
PI160.498177BPGM0.318753JMJD60.579051
1110007A13RIK0.498318ARMC100.318867ALDH7A10.57939
BTBD110.498889SNUPN3.1361LZIC0.579408
WDR690.4992666330416G13RIK3.13603NAT91.72561
CDK20.499306GORA5P20.319013UN13D0.5797
SEPW10.499344WDR533.13378MSI21.72493
ZBTB430.499355CCDC580.319208UBE2B0.579806
RELB2.00243KDM1A3.13242STK160.580011
RPL102.00217BC0114260.319263RAB140.58029
AL845291.10.499614TMEM1640.31954AA4671970.581573
GM48830.499929MBTPS23.12778EPN20.581642
FAM160A20.500259QDPR3.12635MTMR11.71705
SLC22A230.501166TFIP113.12476FLII0.582556
ECHDC10.501544BC0032673.12306A630007B06RIK1.71539
EFCAB10.5017292210404I11RIK0.320322GPR981.71429
CIAPIN10.502094NSG20.320617ISYNA10.583808
PGAM50.502382SGIP13.11884SNRNP2001.71092
ZDHHC190.502393GIMAP63.11626HIST1H3C1.7103
PRDM101.99024ATG16L23.11474TFPI0.585092
RPL39L0.502504NUPR13.11474COX6A10.586233
RDH90.50263GM103430.321806GFM20.586276
ITPA1.98861TSPAN183.10729PPIL30.586625
PTGES31.98596KIF5B0.321931810032O08RIK1.70368
PTMS0.503584RPL27A-3.10625KHDRBS10.587185
PS1
RNF1350.50392VPS723.10624TMEM1591.70133
MRPL501.98425GM49780.322117ALDOC1.70114
BRAP0.504061FASTKD20.322465SMAP10.588077
TMEM45B0.504185LUC7L33.10094TM9SF41.7001
COMMD90.504361STX110.322483SUPT5H0.58832
CNTN10.504447NME73.09872TMEM1491.69819
ANO30.504602TGFBR13.09731ATP6V1H0.589251
DCTN40.5047035HQ13.09603KCTD110.589528
MAPRE20.504727LMAN13.09465SOCS40.589616
HIST4H40.505159HIP1R3.09349WASL1.69599
1500032L24RIK0.505228CSTB3.09201SMPD40.58987
DOK20.505314GM51453.08822FAM125A0.590039
LIN371.97879PDIA33.08642SIGMAR11.69479
DCXR1.97873KYNU3.0849UHRF1BP1L0.590182
RPS6-PS11.9786CHD40.324318EZH10.590285
PMS10.505608AC117184.13.08207SDCCAG81.69368
GPI11.97771SERINC10.324744PSMB91.69186
INSIG21.97708UBE2E13.07896MRPL190.591074
CEP2500.505932YWHAH3.07799A130022J15RIK0.591458
TRMU0.50683OXNAD13.07753DNAJC110.591491
AU0174550.50733TTC50.325023SRSF40.591655
8430426H19RIK0.50749RWDD4A3.07464GM89730.591773
9030625A04RIK0.507881RPL26-3.07285ARHGAP40.592326
PS2
ELMOD20.508684PDHX3.07277SEPHS11.68819
MFN10.508852GALE3.07244IL20.592404
GNGT11.96518PHOH3.07071PRNP1.68801
LRRTM40.509206TAFIB3.06934LSP10.592415
HBXIP1.96377GM109160.325935QPRT0.592438
OBSL10.509404CCDC1320.326324C809130.592481
RRP90.509527SMCHD13.06355LRRC240.59293
SR11.96225CRIP23.06351YTHDF20.592945
4930579K19RIK0.509665GRPEL20.326535PYGB0.593102
1700016D06RIK0.509699PARP43.06245SEMA4F0.593194
SEPHS10.509782M5L30.326656RILPL20.593397
OXNAD10.509827AAR50.326762ATIC0.593821
RPE0.51997TMEM179B3.06001CPNE31.68383
RPL7A-1.954PYCRL0.327028IKBKG0.594093
PS8
SLC15A30.511777LPL3.05767VHL0.594121
GM5610.5119220030046E11RIK3.05746MRPL351.68226
FBXO31.95304ZC3H12D0.327301H470.594489
OSGIN20.512062700007P21RIK0.327512ZNHIT10.594596
PXMP40.5121824930583H14RIK3.05263ITPR21.68152
FXYD30.512375ACAP20.327587GP49A1.67993
PLEKHG20.512695CPNE80.327879XLR4C0.595291
MDH11.9488LCMT10.327899KPNA41.67867
LMO30.513707CES2B3.04897DPF10.595754
THAP71.94632MARK23.0478ZFYVE200.595924
SLC1A70.513853CDK2AP10.328236FAF10.596011
PHPT10.514348PLEK0.328688POLB0.596191
TOMM51.94408THOC10.328704RPL371.67707
HNRPDL1.94367GTPBP23.04092MOCS10.596294
WDR310.514637CBWD10.329216GNAI20.596532
TOR1AIP20.514874BBS120.329239YME1L11.67359
MYO1B0.515039TMEM1670.32943GPAA10.597772
RNF1251.93985CSDA0.329624INSL30.597842
2310016C08RIK1.93825CCDC220.329876DNLZ1.67102
NARFL0.516157VAMP43.02859CLK41.66998
APEX20.516321VPS163.02752APBB1IP1.66987
RANBP11.93556SH3GLB13.02432MRPS110.599032
HMCN10.517013ZC3H140.330652MAGED20.599116
AAGAB0.517197TRMT110.330748ESCO10.599233
PSG160.517263ABI30.331024AC151578.11.66877
2610044O15RIK0.517356HBA-A23.02035GPN10.60056
TMEM491.93192NOP143.02006UTRN0.602289
FCER1G0.517759ENOPH13.01903BDP11.65868
KIF240.518046SLC44A10.331232AC148768.10.603
MEA10.51844GM56143.01688RPL351.65822
DHODH0.518678GM82250.332032ENO21.65807
GM95740.519645CD473.00969DRAM21.65765
HNRNPK1.92367FTSJ10.332414ATXN20.603542
NOC4L0.5201741700030K09RIK0.33275ABHD100.603967
AW1461540.520334PPP1CC3.00449TPRGL0.605694
INTU0.520955NOL80.333129OSGIN20.605746
YPEL51.91937WSB13.00142APOO-PS0.605872
PTOV10.521626WBP110.333203RPL340.60602
GM110570.521738MTERFD10.333307GM165140.607024
4930429B21RIK0.521955VPS26A0.333475GNL3L0.607071
LAPTM51.91483ADAM172.99801FXYD70.607867
NTNG20.522288NUP1880.333567LIMK20.608276
CCM20.522776ZFAND60.333577ELAC20.608326
RPL91.9115HPS50.334144AW1120101.64378
MS4A6D0.523227NUP850.334404KIF2C1.64323
USH2A0.523684GM55282.99039GM140851.6432
PANX10.523705PEX11B0.334418MTOR0.608838
5430437P03RIK1.90784AL593857.10.334998IMPA20.60909
DDX280.524218CYFIP10.33539RIC80.609158
PDXDC10.5245054930451C15RIK2.98157GPR1080.609424
1700025C18RIK0.52592SERBP10.335462CD631.64047
PIN41.90123PRL8A12.96933EIF2S21.63999
9130011J15RIK1.9008GIMAP30.336894TBCB1.63952
NEK110.526292SCFD10.337001USP6NL0.610349
1700057G04RIK0.526551KDMSC0.337333PIK3R50.610793
CSF2RA0.527THYN10.337668RABIF0.610904
CDC14B0.527155RARS20.337682YBX11.63684
ARID1A0.527197MLH30.337695IFT520.61108
ABTB20.527331RUVBL22.95935CCS0.611143
GLIPR10.527729GADL12.95707ADRM10.611145
ABL10.52888SMARCB10.338306FAM69A1.63587
LRRC310.528966HYOU10.339143LRRC611.63562
PTN0.5293476030422M02RIK2.94463GM102571.63531
CTSH1.88706SPC240.3399SDCBP0.611715
STXBP21.88643PAPD52.9409DGKZ0.612086
CHMP4B0.530156EIF2S20.340636ZFP1130.61223
ZBTB7B0.530163EPHA22.93381YWHAE1.63332
THNSL11.88547RPL21-0.341839GM23821.6316
PS13
BCHE0.530597RALGPS10.343168H130.612962
NPNT0.530949WDR342.91354TPST20.613058
SLC25A121.8827TCOF10.343472UTP181.63081
GM117440.531659RAMP10.3436DPF20.613245
MEN10.531763AC132320.12.91022SRSF101.62946
TDG1.880371810046I19RIK2.90959GM67231.62727
SLCO1A40.532108GM100712.90557RPL21-0.614851
PS4
GM31501.87932GTF2A22.90346MRPL230.61524
DHTKD10.532265RSRC12.90081CKLF1.62516
WFDC30.532408ZFP7380.345153BCL2L120.61548
LY6G6C0.532747SEPW12.89617SLC25A350.615825
SARS1.87699ICOS0.345799FABP50.615904
SMYD51.87569CHSY10.346137PRPF190.616463
CC2D1B0.533576LSM60.346562ACAD91.6219
DLEC10.533793AU0222522.88303HSF20.617283
INVS0.534027MYO190.346902SDC10.617848
COPA0.534307TULP42.88204GM75511.61851
HHEX0.534463SCD10.347362CRELD10.618095
TMEM430.534548CD830.347481IL210.618323
TMSB4X1.8707SIN3A0.348585LSG10.618479
NDUFAF20.534784TMEM1280.348728BNIP10.618645
NUDT190.534909ARF20.349221SLC2SA140.618958
GM101250.534953YME1L10.349654PSMG21.61508
SLC12A60.535677PLEKHA10.350085RWDD11.61433
0610011F06RIK1.86601CDC232.855134930431F12RIK0.619471
TMEM1491.86387CWC220.350444FAM53A1.6121
GPR1430.536753RHOF0.3505059130011J15RIK0.620392
LRPAP10.537166HMGN22.85272AMD-PS30.621165
AIP1.86093PFDN10.350644XKRX1.60901
CCDC1420.537379DMTF10.350683ZFP3820.622107
ITSN10.537442CCDC562.84927COMMD100.622673
PRAMEL60.537628ANAPC112.84924COPA0.623015
COPE1.8586PPP2R3C0.351018IMMP1L0.623121
SYNE10.538565KBTBD40.351941AC114007.11.6038
HBP11.85527ATP11A0.3520032210012G02RIK0.624415
YPEL10.539064CD2260.352127HIPK30.624904
TMX21.85357CEP972.83567ZEB10.62508
5730403M16RIK0.540161FDPS0.352866C230096C10RIK0.62563
TECTB0.540828BRCA10.353625CCDC450.62605
AC132837.11.84883ZFP71-2.82321CCPG10.626144
RS1
NDUFAF40.541102DNAJA30.354683HRAS10.626281
GCDH0.541261BAZ1B2.81913EIF2B50.626283
SCARB10.541408SMC30.355663RELB1.59645
UBASH3A1.8468DHODH2.80861CCDC840.626489
ZZZ30.541756INO80E0.356211ARF20.626727
MEGF60.543478SELPLG0.356485AP1S10.628649
RPL9-PS61.83817BBS40.356769ZFP6400.628656
AWAT20.5445532700050L05RIK0.356786PRMT101.58977
BTBD160.544948WDR430.356853GTF3C20.62908
GCNT21.83502NUDCD30.356972DMTF11.58942
ARSK0.545347RARS2.79838GOSR20.629196
AASDH0.545482CYBASC30.357406SAAL10.62955
TRMT2B0.545657BCKDK0.357639PTMS0.629922
HIST1H4A1.8326PAIP20.357925PSMD10.630357
EFTUD20.546041RNMTL10.358145CD721.58599
DTWD20.546128LSG12.7903EIF2S3Y1.58457
GM104170.5461551700008F21RIK2.7852NCBP20.631746
NGDN0.546662CPA62.78487COG80.632996
HOXB10.547072700029M09RIK0.359429GM63960.633102
D11WSU47E0.547455WDR122.77938ERP290.633491
GM106910.547725NAT60.360026NUBPL0.634143
DHRS20.548037GM76270.360486ATP5L-0.634633
PS1
SRGN1.82428AP1B10.360615ASNS0.6348
GM144200.548174DYNC1H10.360627DTNB1.57523
NUP2100.548315ANAPC12.77169GM68431.5748
TMEM661.82364ARAF2.76739TPT11.57464
4931408A02RIK0.54868GDI12.76562LYRM20.635092
CCDC600.54869RPL210.361663WAC0.635559
VTI1B0.549696ADK0.36299TRIOBP0.63562
PCYT21.81917AIFM10.363256GSDMD0.636216
RPL13A-0.549725PSD42.73921NFKBIA0.636642
PS1
GM63201.81274H2-K12.7367PLEKHF20.636936
UBE2A0.551724CEP570.365624ZMIZ10.637048
TOP380.551816USP480.365786DFFA0.637145
TRAPPC6A0.552441NDUFA130.365819THOP10.637365
RPL71.81007PPP2R5D0.366459GSS0.63771
DAZAP10.552963COMT12.72784BANF10.63781
CHD60.552991EYA32.72297MAP2K20.637887
SPRR1A0.553582PECR0.367285WSB10.638066
PHF201.80464CFDP10.368433CUL51.56587
VPS720.554352IL4RA0.368673SHKBP10.638955
1700057K13RIK0.55457SDF20.369416TECR0.639414
TRIM240.5555224732418C07RIK2.69688TMEM290.639476
GM142960.55609ZFP4460.370858TWF10.640301
TXNDC110.556915VGLL40.37087HYOU10.641183
1700093K21RIK0.557875COG60.3714141810049H13RIK0.641337
SPP10.55804COMMD10.372521NFIA0.641587
IVD0.558086CDC270.372858DERL20.641603
YY10.560429RPL26-0.373203AKR1B31.55771
PS4
ACI25405.10.560436SLC11A20.373532TSEN150.642102
CCDC180.561413TUBA80.374051ZFP5931.55675
CTSC0.5620115RPR0.374362IL10RB1.55656
GM49531.77831STXBP20.37487BID0.642473
AL672068.10.563077IKZF50.375049SLC4A20.642706
PSRC11.77182RNF200.37522HSD17B121.55536
KAT2B0.565RPS12-0.375391SNRPB0.643816
PS2
TMED41.76822EIF1AX0.37563PRDM20.644808
OLFR10550.565775NAT100.375687PSMF10.645237
ME30.56733GPATCH40.375755TMEM106B0.645351
ETL40.567722PFAS0.375796BCAS20.645699
LRRC330.56973SLC35B10.375953EBNA1BP20.645891
FBXL210.569879BLVRA0.376773RORC0.646421
2810417H13RIK1.75076KPNA32.65139SMYD21.54653
DCBLD20.571483STAG20.377564GGA10.647033
RALGDS0.57227CRNKL12.64761PSME30.647243
SYF21.747SVOP0.378SEL1L1.54484
ALG130.572541I0C0044D17RIK0.378094BCCIP0.647653
FDXR1.74544TMEM802.63584SRA10.648219
TCTEX1D20.573273UQCC2.63526SERPINB1A0.648369
SLC25A420.573647CCL200.379952PRKB10.648723
ID21.73951ISY10.380229SYT111.54006
1110008P14RIK0.57645IFI472.62916ENTPD40.649691
METTL140.577951ASNS0.3804NDUFB50.650471
TXNDC160.580239NAA400.380451TRP53BP10.651022
RP571.72318CCNE10.380776PIP5K1C0.651315
FAM184A0.580454D330012F22RIK0.381723CMC10.652247
SNAP471.7222CDK5RAP20.382085RPS6KC10.652501
RAD181.720441700123O20RIK0.383244PAPOLA1.53209
MAP3K71.72015T5G1012.607051110031I02RIK0.653034
H2-AB11.71789MTHFS2.60336IL15RA0.653215
COLEC120.583908RTN4IP10.38439DDT0.653474
LIAS0.584048ADAMTSL40.384451LXN0.653775
VGLL41.71021POLE0.384793CAML0.654098
STAM20.584817BCAP290.384893NME60.654537
E230001N04RIK0.585026CD50.385786GHDC0.655175
SEL1L0.585038GLE10.385815NT5C3L1.52508
H2AFY1.70815SMC50.386445DDX180.655943
R3HDM20.585866MPI2.58682900010J23RIK0.656538
SPEN0.586272ARIH12.58613RB10.656729
NCSTN0.58805OXCT10.387007HIST1H3H0.656763
PRL8A10.588641PDPK12.57756DPP30.657155
MRPS151.69495PRODH0.388288DLG20.657191
GIGYF20.591335DDX470.388346ZFP7031.52155
DERA1.68952610507B11RIK2.57237AC090563.11.52069
GM120331.068909DPM10.389039HCFC10.657862
TM95F10.594558ANXA70.389075MRPS300.657906
UCHL40.594589KEAP10.389438NBR10.658347
XRCC40.595784930453N24RIK2.56778BC0292140.65973
AC068006.10.597766CREM0.38953CARS20.659736
AUTS20.598334RPP140.389539SNAP470.659889
NPDC10.599026IFT202.56423D8ERTD738E0.660079
CT033780.10.5990511810022K09RIK0.390263RBM330.66011
1110001J03RIK1.66896GALM0.390284DYNLT31.51481
AUH0.599389GFM10.390367HNRNPAB0.66032
GBP50.601504PDAP10.391077MRPS36-0.661543
PS1
OAS30.602776CUX10.391626PPP5C0.661659
MTG10.606137SP1002.54971CLN60.661741
PNPLA80.606206PPP4R20.392231MEMO10.661783
1500011B03RIK0.606396CAND12.54938LSM70.661841
ZFP5750.606493CBX32.54808ELK31.51042
RPL30-0.608551BUD130.393549CUTA0.662154
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  • Ahmed, M., and Gaffen, S. L. (2010). IL-17 in obesity and adipogenesis. Cytokine & growth factor reviews 21, 449-453.
  • Amit, I., Citri, A., Shay, T., Lu, Y., Katz, M., Zhang, F., Tarcic, G., Siwak, D., Lahad, J., Jacob-Hirsch, J., et al. (2007). A module of negative feedback regulators defines growth factor signaling. Nature genetics 39, 503-512.
  • Amit, I., Garber, M., Chevrier, N., Leite, A. P., Donner, Y., Eisenhaure, T., Guttman, M., Grenier, J. K., Li, W., Zuk, O., et al. (2009). Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257-263.
  • Annunziato, F., Cosmi, L., Santarlasci, V., Maggi, L., Liotta, F., Mazzinghi, B., Parente, E., Fili, L., Ferri, S., Frosali, F., et al. (2007). Phenotypic and functional features of human Th17 cells. The Journal of experimental medicine 204, 1849-1861.
  • Antebi, Y. E., Reich-Zeliger, S., Hart, Y., Mayo, A., Eizenberg, I., Rimer, J., Putheti, P., Pe'er, D., and Friedman, N. (2013). Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates. PLoS biology 11, e1001616.
  • Arpaia, N., Campbell, C., Fan, X., Dikiy, S., van der Veeken, J., deRoos, P., Liu, H., Cross, J. R., Pfeffer, K., Coffer, P. J., et al. (2013). Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 504, 451-455.
  • Aust, G., Kamprad, M., Lamesch, P., and Schmucking, E. (2005). CXCR6 within T-helper (Th) and T-cytotoxic (Tc) type 1 lymphocytes in Graves' disease (GD). European journal of endocrinology/European Federation of Endocrine Societies 152, 635-643.
  • Awasthi, A., Riol-Blanco, L., Jager, A., Korn, T., Pot, C., Galileos, G., Bettelli, E., Kuchroo, V. K., and Oukka, M. (2009). Cutting edge: IL-23 receptor gfp reporter mice reveal distinct populations of IL-17-producing cells. Journal of immunology 182, 5904-5908.
  • Bachmann, M. F., Barner, M., and Kopf, M. (1999). CD2 sets quantitative thresholds in T cell activation. The Journal of experimental medicine 190, 1383-1392.
  • Baeten, D. L., and Kuchroo, V. K. (2013). How Cytokine networks fuel inflammation: Interleukin-17 and a tale of two autoimmune diseases. Nature medicine 19, 824-825.
  • Bending, D., De la Pena, H., Veldhoen, M., Phillips, J. M., Uyttenhove, C., Stockinger, B., and Cooke, A. (2009). Highly purified Th17 cells from BDC2.5NOD mice convert into Th1-like cells in NOD/SCID recipient mice. The Journal of clinical investigation 119, 565-572.
  • Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological), 289-300.
  • Berod, L., Friedrich, C., Nandan, A., Freitag, J., Hagemann, S., Harmrolfs, K., Sandouk, A., Hesse, C., Castro, C. N., Bahre, H., el al. (2014). De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells. Nature medicine 20, 1327-1333.
  • Bettelli, E., Carrier, Y., Gao, W., Korn, T., Strom, T. B., Oukka, M., Weiner, H. L., and Kuchroo, V. K. (2006). Reciprocal developmental pathways for the generation of pathogenic effector TH 17 and regulatory T cells. Nature 441, 235-238.
  • Bettelli, E., Pagany, M., Weiner, H. L., Linington, C., Sobel, R. A., and Kuchroo, V. K. (2003). Myelin oligodendrocyte glycoprotein-specific T cell receptor transgenic mice develop spontaneous autoimmune optic neuritis. The Journal of experimental medicine 197, 1073-1081.
  • Blaschitz, C., and Raffatellu, M. (2010). Th17 cytokines and the gut mucosal barrier. Journal of clinical immunology 30, 196-203.
  • Brenner, D., Brustle, A., Lin, G. H., Lang, P. A., Duncan, G. S., Knobbe-Thomsen, C. B., St Paul, M., Reardon, C., Tusche, M. W., Snow, B., el al. (2014). Toso controls encephalitogenic immune responses by dendritic cells and regulatory T cells. Proceedings of the National Academy of Sciences of the United States of America 111, 1060-1065.
  • Cellot, S., and Sauvageau, G. (2007). Zfx: at the crossroads of survival and self-renewal. Cell 129, 239-241.
  • Chai, J. G., and Lechler, R. I. (1997). Immobilized anti-CD3 mAb induces anergy in murine naïve and memory CD4+ T cells in vitro. International immunology 9, 935-944.
  • Chen, L., Wu, G., and Ji, H. (2011). hmChIP: a database and web server for exploring publicly available human and mouse ChIP-seq and ChIP-chip data. Bioinformatics 27, 1447-1448.
  • Cho, J. H. (2008). The genetics and immunopathogenesis of inflammatory bowel disease. Nature reviews Immunology 8, 458-466.
  • Chung, Y., Chang, S. H., Martinez, G. J., Yang, X. O., Nurieva, R., Kang, H. S., Ma, L., Watowich, S. S., Jetten, A. M., Tian, Q., el al. (2009). Critical regulation of early Th17 cell differentiation by interleukin-1 signaling. Immunity 30, 576-587.
  • Ciofani, M., Madar, A., Galan, C., Sellars, M., Mace, K., Pauli, F., Agarwal, A., Huang, W., Parkurst, C. N., Muratet, M., el al. (2012a). A validated regulatory network for Th17 cell specification. Cell 151, 289-303.
  • Ciofani, M., Madar, A., Galan, C., Sellars, M., Mace, K., Pauli, F., Agarwal, A., Huang, W., Parkurst, Christopher N., Muratet, M., el al. (2012b). A Validated Regulatory Network for Th17 Cell Specification. Cell.
  • Codarri, L., Gyulveszi, G., Tosevski, V., Hesske, L., Fontana, A., Magnenat, L., Suter, T., and Becher, B. (2011). RORgammat drives production of the cytokine GM-CSF in helper T cells, which is essential for the effector phase of autoimmune neuroinflammation. Nature immunology 12, 560-567.
  • Crawford, A., Angelosanto, J. M., Kao, C., Doering, T. A., Odorizzi, P. M., Barnett, B. E., and Wherry, E. J. (2014). Molecular and transcriptional basis of CD4(+) T cell dysfunction during chronic infection. Immunity 40, 289-302.
  • Dang, E. V., Barbi, J., Yang, H. Y., Jinasena, D., Yu, H., Zheng, Y., Bordman, Z., Fu, J., Kim, Y., Yen, H. R., et al. (2011). Control of T(H)17/T(reg) balance by hypoxia-inducible factor 1. Cell 146, 772-784.
  • De Rosa, S. C., Herzenberg, L. A., Herzenberg, L. A., and Roederer, M. (2001). 11-color, 13-parameter flow cytometry: identification of human naïve T cells by phenotype, function, and T-cell receptor diversity. Nature medicine 7, 245-248.
  • Deng, Q., Ramskold, D., Reinius, B., and Sandberg, R. (2014). Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193-196.
  • Dolfi, D. V., Boesteanu, A. C., Petrovas, C., Xia, D., Butz, E. A., and Katsikis, P. D. (2008). Late signals from CD27 prevent Fas-dependent apoptosis of primary CD8+ T cells. Journal of immunology 180, 2912-2921.
  • El-Behi, M., Ciric, B., Dai, H., Yan, Y., Cullimore, M., Safavi, F., Zhang, G. X., Dittel, B. N., and Rostami, A. (2011). The encephalitogenicity of T(H)17 cells is dependent on IL-1- and IL-23-induced production of the cytokine GM-CSF. Nature immunology 12, 568-575.
  • Esfandiari, E., McInnes, I. B., Lindop, G., Huang, F. P., Field, M., Komai-Koma, M., Wei, X., and Liew, F. Y. (2001). A proinflammatory role of IL-18 in the development of spontaneous autoimmune disease. Journal of immunology 167, 5338-5347.
  • Fang, X., Huang, Z., Zhou, W., Wu, Q., Sloan, A. E., Ouyang, G., McLendon, R. E., Yu, J. S., Rich, J. N., and Bao, S. (2014). The zinc finger transcription factor ZFX Is required for maintaining the tumorigenic potential of glioblastoma stem cells. Stem cells.
  • Franke, A., McGovern, D. P., Barrett, J. C., Wang, K., Radford-Smith, G. L., Ahmad, T., Lees, C. W., Balschun, T., Lee, J., Roberts, R., et al. (2010). Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nature genetics 42, 1118-1125.
  • Galan-Caridad, J. M., Harel, S., Arenzana, T. L., Hou, Z. E., Doetsch, F. K., Mirny, L. A., and Reizis, B. (2007). Zfx controls the self-renewal of embryonic and hematopoietic stem cells. Cell 129, 345-357.
  • Gaffen, S. L., Hernandez-Santos, N., and Peterson, A. C. (2011). IL-17 signaling in host defense against Candida albicans. Immunologic research 50, 181-187.
  • Gattinoni, L., Zhong, X. S., Palmer, D. C., Ji, Y., Hinrichs, C. S., Yu, Z., Wrzesinski, C., Boni, A., Cassard, L., Garvin, L. M., et al. (2009). Wnt signaling arrests effector T cell differentiation and generates CD8+ memory stem cells. Nature medicine 15, 808-813.
  • Genovese, M. C., Van den Bosch, F., Roberson, S. A., Bojin, S., Biagini, I. M., Ryan, P., and Sloan-Lancaster, J. (2010). LY2439821, a humanized anti-interleukin-17 monoclonal antibody, in the treatment of patients with rheumatoid arthritis: A phase I randomized, double-blind, placebo-controlled, proof-of-concept study. Arthritis and rheumatism 62, 929-939.
  • Ghoreschi, K., Laurence, A., Yang, X. P., Tato, C. M., McGeachy, M. J., Konkel, J. E., Ramos, H. L., Wei, L., Davidson, T. S., Bouladoux, N., et al. (2010). Generation of pathogenic T(H)17 cells in the absence of TGF-beta signalling. Nature 467, 967-971.
  • Ghosh, S., Elder, A., Guo, J., Mani, U., Patane, M., Carson, K., Ye, Q., Bennett, R., Chi, S., Jenkins, T., et al. (2006). Design, synthesis, and progress toward optimization of potent small molecule antagonists of CC chemokine receptor 8 (CCR8). Journal of medicinal chemistry 49, 2669-2672.
  • Gilmore, T. D., and Gerondakis, S. (2011). The c-Rel Transcription Factor in Development and Disease. Genes & cancer 2, 695-711.
  • Hamann, I., Zipp, F., and Infante-Duarte, C. (2008). Therapeutic targeting of chemokine signaling in Multiple Sclerosis. Journal of the neurological sciences 274, 31-38.
  • Harant, H., and Lindley, I. J. (2004). Negative cross-talk between the human orphan nuclear receptor Nur77/NAK-1/TR3 and nuclear factor-kappaB. Nucleic acids research 32, 5280-5290.
  • Harel, S., Tu, E. Y., Weisberg, S., Esquilin, M., Chambers, S. M., Liu, B., Carson, C. T., Studer, L., Reizis, B., and Tomishima, M. J. (2012). ZFX controls the self-renewal of human embryonic stem cells. PloS one 7, e42302.
  • Harrington, L. E., Janowski, K. M., Oliver, J. R., Zajac, A. J., and Weaver, C. T. (2008). Memory CD4 T cells emerge from effector T-cell progenitors. Nature 452, 356-360.
  • Hendriks, J., Gravestein, L. A., Tesselaar, K., van Lier, R. A., Schumacher, T. N., and Borst, J. (2000). CD27 is required for generation and long-term maintenance of T cell immunity. Nature immunology 1, 433-440.
  • Hendriks, J., Xiao, Y., and Borst, J. (2003). CD27 promotes survival of activated T cells and complements CD28 in generation and establishment of the effector T cell pool. The Journal of experimental medicine 198, 1369-1380.
  • Hernandez-Santos, N., and Gaffen, S. L. (2012). Th17 cells in immunity to Candida albicans. Cell host & microbe 11, 425-435.
  • Hilliard, B. A., Mason, N., Xu, L., Sun, J., Lamhamedi-Cherradi, S. E., Liou, H. C., Hunter, C., and Chen, Y. H. (2002). Critical roles of c-Rel in autoimmune inflammation and helper T cell differentiation. The Journal of clinical investigation 110, 843-850.
  • Hitoshi, Y., Lorens, J., Kitada, S. I., Fisher, J., LaBarge, M., Ring, H. Z., Francke, U., Reed, J. C., Kinosh*ta, S., and Nolan, G. P. (1998). Toso, a cell surface, specific regulator of Fas-induced apoptosis in T cells. Immunity 8, 461-471.
  • Hock, H., Meade, E., Medeiros, S., Schindler, J. W., Valk, P. J., Fujiwara, Y., and Orkin, S. H. (2004). Tel/Etv6 is an essential and selective regulator of adult hematopoietic stem cell survival. Genes & development 18, 2336-2341.
  • Hueber, W., Sands, B. E., Lewitzky, S., Vandemeulebroecke, M., Reinisch, W., Higgins, P. D., Wehkamp, J., Feagan, B. G., Yao, M. D., Karczewski, M., et al. (2012). Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn's disease: unexpected results of a randomised, double-blind placebo-controlled trial. Gut 61, 1693-1700.
  • Hundt, M., Tabata, H., Jeon, M. S., Hayashi, K., Tanaka, Y., Krishna, R., De Giorgio, L., Liu, Y. C., f*ckata, M., and Altman, A. (2006). Impaired activation and localization of LAT in anergic T cells as a consequence of a selective palmitoylation defect. Immunity 24, 513-522.
  • Huntley, R. P., Binns, D., Dimmer, E., Barrell, D., O'Donovan, C., and Apweiler, R. (2009). QuickGO: a user tutorial for the web-based Gene Ontology browser. Database: the journal of biological databases and curation 2009, bap010.
  • Ichii, H., Sakamoto, A., Arima, M., Hatano, M., Kuroda, Y., and Tokuhisa, T. (2007). Bcl6 is essential for the generation of long-term memory CD4+ T cells. International immunology 19, 427-433.
  • International Genetics of Ankylosing Spondylitis, C., Cortes, A., Hadler, J., Pointon, J. P., Robinson, P. C., Karaderi, T., Leo, P., Cremin, K., Pryce, K., Harris, J., el al. (2013). Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nature genetics 45, 730-738.
  • International Multiple Sclerosis Genetics, C., Wellcome Trust Case Control, C., Sawcer, S., Hellenthal, G., Pirinen, M., Spencer, C. C., Patsopoulos, N. A., Moutsianas, L., Dilthey, A., Su, Z., et al. (2011). Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214-219.
  • Ioannidis, V., Beermann, F., Clevers, H., and Held, W. (2001). The beta-catenin-TCF-1 pathway ensures CD4(+)CD8(+) thymocyte survival. Nature immunology 2, 691-697.
  • Ivanov, II, Atarashi, K., Manel, N., Brodie, E. L., Shima, T., Karaoz, U., Wei, D., Goldfarb, K. C., Santee, C. A., Lynch, S. V., et al. (2009). Induction of intestinal Th17 cells by segmented filamentous bacteria. Cell 139, 485-498.
  • Jager, A., Dardalhon, V., Sobel, R. A., Bettelli, E., and Kuchroo, V. K. (2009). Th1, Th17, and Th9 effector cells induce experimental autoimmune encephalomyelitis with different pathological phenotypes. Journal of immunology 183, 7169-7177.
  • Jarboe, J. S., Anderson, J. C., Duarte, C. W., Mehta, T., Nowsheen, S., Hicks, P. H., Whitley, A. C., Rohrbach, T. D., McCubrey, R. O., Chiu, S., et al. (2012). MARCKS regulates growth and radiation sensitivity and is a novel prognostic factor for glioma. Clinical cancer research: an official journal of the American Association for Cancer Research 18, 3030-3041.
  • Jhun, J. Y., Yoon, B. Y., Park, M. K., Oh, H. J., Byun, J. K., Lee, S. Y., Min, J. K., Park, S. H., Kim, H. Y., and Cho, M. L. (2012). Obesity aggravates the joint inflammation in a collagen-induced arthritis model through deviation to Th17 differentiation. Experimental & molecular medicine 44, 424-431.
  • Jin, L., Martynowski, D., Zheng, S., Wada, T., Xie, W., and Li, Y. (2010). Structural basis for hydroxycholesterols as natural ligands of orphan nuclear receptor RORgamma. Molecular endocrinology 24, 923-929.
  • Johnson, W. E., Li, C., and Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118-127.
  • Jostins, L., Ripke, S., Weersma, R. K., Duerr, R. H., McGovern, D. P., Hui, K. Y., Lee, J. C., Schumm, L. P., Sharma, Y., Anderson, C. A., el al. (2012). Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119-124.
  • Kandasamy, K., Mohan, S. S., Raju, R., Keerthikumar, S., Kumar, G. S., Venugopal, A. K., Telikicherla, D., Navarro, J. D., Mathivanan, S., Pecquet, C., el al. (2010). NetPath: a public resource of curated signal transduction pathways. Genome biology 11, R3.
  • Kaplan, M. H., Sun, Y. L., Hoey, T., and Grusby, M. J. (1996). Impaired IL-12 responses and enhanced development of Th2 cells in Stat4-deficient mice. Nature 382, 174-177.
  • Komatsu, N., Okamoto, K., Sawa, S., Nakashima, T., Oh-hora, M., Kodama, T., Tanaka, S., Bluestone, J. A., and Takayanagi, H. (2014). Pathogenic conversion of Foxp3+ T cells into TH17 cells in autoimmune arthritis. Nature medicine 20, 62-68.
  • Konkel, J. E., and Chen, W. (2011). Balancing acts: the role of TGF-beta in the mucosal immune system. Trends in molecular medicine 17, 668-676.
  • Korn, T., Bettelli, E., Gao, W., Awasthi, A., Jager, A., Strom, T. B., Oukka, M., and Kuchroo, V. K. (2007). IL-21 initiates an alternative pathway to induce proinflammatory T(H)17 cells. Nature 448, 484-487.
  • Korn, T., Bettelli, E., Oukka, M., and Kuchroo, V. K. (2009). IL-17 and Th17 Cells. Annu Rev Immunol 27, 485-517.
  • Kryczek, I., Zhao, E., Liu, Y., Wang, Y., Vatan, L., Szeliga, W., Moyer, J., Klimczak, A., Lange, A., and Zou, W. (2011). Human TH17 cells are long-lived effector memory cells. Science translational medicine 3, 104ra100.
  • Kurachi, M., Barnitz, R. A., Yosef, N., Odorizzi, P. M., DiIorio, M. A., Lemieux, M. E., Yates, K., Godec, J., Klatt, M. G., Regev, A., et al. (2014). The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nature immunology 15, 373-383.
  • Lachmann, A., Xu, H., Krishnan, J., Berger, S. I., Mazloom, A. R., and Ma'ayan, A. (2010). ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438-2444.
  • Lalmansingh, A. S., Arora, K., Demarco, R. A., Hager, G. L., and Nagaich, A. K. (2013). High-throughput RNA FISH analysis by imaging flow cytometry reveals that pioneer factor Foxa1 reduces transcriptional stochasticity. PloS one 8, e76043.
  • Lamb, JR., Zanders, E. D., Sewell, W., Crumpton, M. J., Feldmann, M., and Owen, M. J. (1987). Antigen-specific T cell unresponsiveness in cloned helper T cells mediated via the CD2 or CD3/Ti receptor pathways. European journal of immunology 17, 1641-1644.
  • Lang, K. S., Lang, P. A., Meryk, A., Pandyra, A. A., Boucher, L. M., Pozdeev, V. I., Tusche, M. W., Gothert, J. R., Haight, J., Wakeham, A., et al. (2013). Involvement of Toso in activation of monocytes, macrophages, and granulocytes. Proceedings of the National Academy of Sciences of the United States of America 110, 2593-2598.
  • Latta, M., Mohan, K., and Issekutz, T. B. (2007). CXCR6 is expressed on T cells in both T helper type 1 (Th1) inflammation and allergen-induced Th2 lung inflammation but is only a weak mediator of chemotaxis. Immunology 121, 555-564.
  • Laurence, A., Tato, C. M., Davidson, T. S., Kanno, Y., Chen, Z., Yao, Z., Blank, R. B., Meylan, F., Siegel, R., Hennighausen, L., et al. (2007). Interleukin-2 signaling via STAT5 constrains T helper 17 cell generation. Immunity 26, 371-381.
  • Lee, Y., Awasthi, A., Yosef, N., Quintana, F. J., Xiao, S., Peters, A., Wu, C., Kleinewietfeld, M., Kunder, S., Hafler, D. A., et al. (2012). Induction and molecular signature of pathogenic TH17 cells. Nature immunology 13, 991-999.
  • Lee, Y. K., Turner, H., Maynard, C. L., Oliver, J. R., Chen, D., Elson, C. O., and Weaver, C. T. (2009). Late developmental plasticity in the T helper 17 lineage. Immunity 30, 92-107.
  • Lees, C. W., Barrett, J. C., Parkes, M., and Satsangi, J. (2011). New IBD genetics. common pathways with other diseases. Gut 60, 1739-1753.
  • Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdottir, H., Tamayo, P., and Mesirov, J. P. (2011). Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740. Leonardi, C., Matheson, R., Zachariae, C., Cameron, G., Li, L., Edson-Heredia, E., Braun, D.,
  • and Banerjee, S. (2012). Anti-interleukin-17 monoclonal antibody ixekizumab in chronic plaque psoriasis. The New England journal of medicine 366, 1190-1199.
  • Lin, L., Ibrahim, A. S., Xu, X., Farber, J. M., Avanesian, V., Baquir, B., Fu, Y., French, S. W., Edwards, J. E., Jr., and Spellberg, B. (2009). Th1-Th17 cells mediate protective adaptive immunity against Staphylococcus aureus and Candida albicans infection in mice. PLoS pathogens S, e1000703.
  • Linhart, C., Halperin, Y., and Shamir, R. (2008). Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome research 18, 1180-1189.
  • Liu, Y., Wang, X., Jiang, J., Cao, Z., Yang, B., and Cheng, X. (2011). Modulation of T cell cytokine production by miR-144* with elevated expression in patients with pulmonary tuberculosis. Molecular immunology 48, 1084-1090.
  • Mahad, D. J., and Ransohoff, R. M. (2003). The role of MCP-1 (CCL2) and CCR2 in multiple sclerosis and experimental autoimmune encephalomyelitis (EAE). Seminars in immunology 15, 23-32.
  • Maity, A., and Koumenis, C. (2006). HIF and MIF-a nifty way to delay senescence? Genes & development 20, 3337-3341.
  • Martinez, V. G., Escoda-Ferran, C., Tadeu Simoes, I., Arai, S., Orta Mascaro, M., Carreras, E., Martinez-Florensa, M., Yelamos, J., Miyazaki, T., and Lozano, F. (2014). The macrophage soluble receptor AIM/Api6/CD5L displays a broad pathogen recognition spectrum and is involved in early response to microbial aggression. Cellular & molecular immunology 11, 343-354.
  • Mathews, J. A., Wurmbrand, A. P., Ribeiro, L., Neto, F. L., and Shore, S. A. (2014). Induction of IL-17A Precedes Development of Airway Hyperresponsiveness during Diet-Induced Obesity and Correlates with Complement Factor D. Frontiers in immunology 5, 440.
  • Maynard, C. L., Harrington, L. E., Janowski, K. M., Oliver, J. R., Zindl, C. L., Rudensky, A. Y., and Weaver, C. T. (2007). Regulatory T cells expressing interleukin 10 develop from Foxp3+ and Foxp3− precursor cells in the absence of interleukin 10. Nature immunology 8, 931-941.
  • McGeachy, M. J., Chen, Y., Tato, C. M., Laurence, A., Joyce-Shaikh, B., Blumenschein, W. M., McClanahan, T. K., O'Shea, J. J., and Cua, D. J. (2009). The interleukin 23 receptor is essential for the terminal differentiation of interleukin 17-producing effector T helper cells in vivo. Nature immunology 10, 314-324.
  • Miaw, S. C., Choi, A., Yu, E., Kishikawa, H., and Ho, I. C. (2000). ROG, repressor of GATA, regulates the expression of cytokine genes. Immunity 12, 323-333.
  • Miyazaki, T., Hirokami, Y., Matsuhashi, N., Takatsuka, H., and Naito, M. (1999). Increased susceptibility of thymocytes to apoptosis in mice lacking AIM, a novel murine macrophage-derived soluble factor belonging to the scavenger receptor cysteine-rich domain superfamily. The Journal of experimental medicine 189, 413-422.
  • Mo, C., Chearwae, W., O'Malley, J. T., Adams, S. M., Kanakasabai, S., Walline, C. C., Stritesky, G. L., Good, S. R., Perumal, N. B., Kaplan, M. H., et al. (2008). Stat4 isoforms differentially regulate inflammation and demyelination in experimental allergic encephalomyelitis. Journal of immunology 181, 5681-5690.
  • Monk, J. M., Hou, T. Y., Turk, H. F., McMurray, D. N., and Chapkin, R. S. (2013). n3 PUFAs reduce mouse CD4+ T-cell ex vivo polarization into Th17 cells. J Nutr 143, 1501-1508.
  • Monk, J. M., Jia, Q., Callaway, E., Weeks, B., Alaniz, R. C., McMurray, D. N., and Chapkin, R. S. (2012). Th17 cell accumulation is decreased during chronic experimental colitis by (n-3) PUFA in Fat-1 mice. J Nutr 142, 117-124.
  • Muranski, P., Borman, Z. A., Kerkar, S. P., Klebanoff, C. A., Ji, Y., Sanchez-Perez, L., Sukumar, M., Reger, R. N., Yu, Z., Kern, S. J., et al. (2011). Th17 cells are long lived and retain a stem cell-like molecular signature. Immunity 35, 972-985.
  • Nakae, S., Iwakura, Y., Suto, H., and Galli, S. J. (2007). Phenotypic differences between Th1 and Th17 cells and negative regulation of Th1 cell differentiation by IL-17. Journal of leukocyte biology 81, 1258-1268.
  • Nguyen, X. H., Lang, P. A., Lang, K. S., Adam, D., Fattakhova, G., Foger, N., Kamal, M. A., Prilla, P., Mathieu, S., Wagner, C., et al. (2011). Toso regulates the balance between apoptotic and nonapoptotic death receptor signaling by facilitating RIP1 ubiquitination. Blood 118, 598-608.
  • Nishikomori, R., Usui, T., Wu, C. Y., Morinobu, A., O'Shea, J. J., and Strober, W. (2002). Activated STAT4 has an essential role in Th1 differentiation and proliferation that is independent of its role in the maintenance of IL-12R beta 2 chain expression and signaling. Journal of immunology 169, 4388-4398.
  • Novershtern, N., Subramanian, A., Lawton, L. N., Mak, R. H., Haining, W. N., McConkey, M. E., Habib, N., Yosef, N., Chang, C. Y., Shay, T., el al. (2011). Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296-309.
  • Nurieva, R., Yang, X. O., Martinez, G., Zhang, Y., Panopoulos, A. D., Ma, L., Schluns, K., Tian, Q., Watowich, S. S., Jetten, A. M., et al. (2007). Essential autocrine regulation by IL-21 in the generation of inflammatory T cells. Nature 448, 480-483.
  • Palmer, M. T., and Weaver, C. T. (2010). Autoimmunity: increasing suspects in the CD4+ T cell lineup. Nature immunology 11, 36-40.
  • Papp, K. A., Leonardi, C., Menter, A., Ortonne, J. P., Krueger, J. G., Kricorian, G., Aras, G., Li, J., Russell, C. B., Thompson, E. H., et al. (2012). Brodalumab, an anti-interleukin-17-receptor antibody for psoriasis. The New England journal of medicine 366, 1181-1189.
  • Patel, D. D., Lee, D. M., Kolbinger, F., and Antoni, C. (2013). Effect of IL-17A blockade with secukinumab in autoimmune diseases. Annals of the rheumatic diseases 72 Suppl 2, iii 16-123.
  • Pe'er, D., Regev, A., and Tanay, A. (2002). Minreg: inferring an active regulator set. Bioinformatics 18 Suppl 1, S258-267.
  • Pepper, M., Linehan, J. L., Pagan, A. J., Zell, T., Dileepan, T., Cleary, P. P., and Jenkins, M. K. (2010). Different routes of bacterial infection induce long-lived TH1 memory cells and short-lived TH17 cells. Nature immunology 11, 83-89.
  • Perfetto, S. P., Chattopadhyay, P. K., and Roederer, M. (2004). Seventeen-colour flow cytometry: unravelling the immune system. Nature reviews Immunology 4, 648-655.
  • Peters, A., Burkett, P. R., Sobel, R. A., Buckley, C. D., Watson, S. P., Bettelli, E., and Kuchroo, V. K. (2014). Podoplanin negatively regulates CD4+ effector T cell responses. The Journal of clinical investigation.
  • Pruitt, K. D., Tatusova, T., and Maglott, D. R. (2007). NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35, D61-65.
  • Quintana, F. J., Jin, H., Burns, E. J., Nadeau, M., Yeste, A., Kumar, D., Rangachari, M., Zhu, C., Xiao, S., Seavitt, J., et al. (2012). Aiolos promotes TH17 differentiation by directly silencing 112 expression. Nature immunology 13, 770-777.
  • Ramskold, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012). Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature biotechnology 30, 777-782.
  • Reya, T., Duncan, A. W., Ailles, L., Domen, J., Scherer, D. C., Willert, K., Hintz, L., Nusse, R., and Weissman, I. L. (2003). A role for Wnt signalling in self-renewal of haematopoietic stem cells. Nature 423, 409-414.
  • Risso, D., Schwartz, K., Sherlock, G., and Dudoit, S. (2011). GC-content normalization for RNA-Seq data. BMC bioinformatics 12, 480.
  • Rocha, P. P., Scholze, M., Bleiss, W., and Schrewe, H. (2010). Med12 is essential for early mouse development and for canonical Wnt and Wnt/PCP signaling. Development 137, 2723-2731.
  • Sallusto, F., Lenig, D., Forster, R., Lipp, M., and Lanzavecchia, A. (1999). Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401, 708-712.
  • Salminen, A., and Kaarniranta, K. (2011). Control of p53 and NF-kappaB signaling by WIP1 and MIF: role in cellular senescence and organismal aging. Cellular signalling 23, 747-752.
  • Santori, F. R., Huang, P., van de Pavert, S. A., Douglass, E. F., Jr., Leaver, D. J., Haubrich, B. A., Keber, R., Lorbek, G., Konijn, T., Rosales, B. N., el al. (2015). Identification of natural RORgamma ligands that regulate the development of lymphoid cells. Cell metabolism 21, 286-297.
  • Sarkar, S., Kalia, V., Haining, W. N., Konieczny, B. T., Subramaniam, S., and Ahmed, R. (2008). Functional and genomic profiling of effector CD8 T cell subsets with distinct memory fates. The Journal of experimental medicine 205, 625-640.
  • Sarrias, M. R., Gronlund, J., Padilla, O., Madsen, J., Holmskov, U., and Lozano, F. (2004). The Scavenger Receptor Cysteine-Rich (SRCR) domain: an ancient and highly conserved protein module of the innate immune system. Critical reviews in immunology 24, 1-37.
  • Segal, E., Shapira, M., Regev, A., Pe'er, D., Botstein, D., Koller, D., and Friedman, N. (2003). Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature genetics 34, 166-176.
  • Sester, U., Presser, D., Dirks, J., Gartner, B. C., Kohler, H., and Sester, M. (2008). PD-1 expression and IL-2 loss of cytomegalovirus-specific T cells correlates with viremia and reversible functional anergy. American journal of transplantation: official journal of the American Society of Transplantation and the American Society of Transplant Surgeons 8, 1486-1497.
  • Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Gnirke, A., et al. (2013). Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature in press.
  • Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., et al. (2014). Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 509, 363-369.
  • Shi, L. Z., Wang, R., Huang, G., Vogel, P., Neale, G., Green, D. R., and Chi, H. (2011). HIF1 alpha-dependent glycolytic pathway orchestrates a metabolic checkpoint for the differentiation of TH17 and Treg cells. The Journal of experimental medicine 208, 1367-1376.
  • Shin, H. J., Lee, J. B., Park, S. H., Chang, J., and Lee, C. W. (2009). T-bet expression is regulated by EGR1-mediated signaling in activated T cells. Clinical immunology 131, 385-394.
  • Shinohara, M. L., Kim, J. H., Garcia, V. A., and Cantor, H. (2008). Engagement of the type I interferon receptor on dendritic cells inhibits T helper 17 cell development: role of intracellular osteopontin. Immunity 29, 68-78.
  • Snyder, C. M., Cho, K. S., Bonnett, E. L., van Dommelen, S., Shellam, G. R., and Hill, A. B. (2008). Memory inflation during chronic viral infection is maintained by continuous production of short-lived, functional T cells. Immunity 29, 650-659.
  • Song, Y., and Jacob, C. O. (2005). The mouse cell surface protein TOSO regulates Fas/Fas ligand-induced apoptosis through its binding to Fas-associated death domain. The Journal of biological chemistry 280, 9618-9626.
  • Soroosh, P., Wu, J., Xue, X., Song, J., Sutton, S. W., Sablad, M., Yu, J., Nelen, M. I., Liu, X., Castro, G., et al. (2014). Oxysterols are agonist ligands of RORgammat and drive Th17 cell differentiation. Proceedings of the National Academy of Sciences of the United States of America 111, 12163-12168.
  • Stumhofer, J. S., Silver, J. S., Laurence, A., Porrett, P. M., Harris, T. H., Turka, L. A., Ernst, M., Saris, C. J., O'Shea, J. J., and Hunter, C. A. (2007). Interleukins 27 and 6 induce STAT3-mediated T cell production of interleukin 10. Nature immunology 8, 1363-1371.
  • Sutton, C., Brereton, C., Keogh, B., Mills, K. H., and Lavelle, E. C. (2006). A crucial role for interleukin (IL)-1 in the induction of IL-17-producing T cells that mediate autoimmune encephalomyelitis. The Journal of experimental medicine 203, 1685-1691.
  • Symons, A., Budelsky, A. L., and Towne, J. E. (2012). Are Th17 cells in the gut pathogenic or protective? Mucosal immunology 5, 4-6.
  • Thierfelder, W. E., van Deursen, J. M., Yamamoto, K., Tripp, R. A., Sarawar, S. R., Carson, R. T., Sangster, M. Y., Vignali, D. A., Doherty, P. C., Grosveld, G. C., et al. (1996). Requirement for Stat4 in interleukin-12-mediated responses of natural killer and T cells. Nature 382, 171-174.
  • Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., and Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature biotechnology 32, 381-386.
  • Trapnell, C., Pachter, L., and Salzberg, S. L. (2009). TopHat. discovering splice junctions with RNA-Seq. In Bioinformatics, pp. 1105-1111.
  • Trimble, L. A., Kam, L. W., Friedman, R. S., Xu, Z., and Lieberman, J. (2000). CD3zeta and CD28 down-modulation on CD8 T cells during viral infection. Blood 96, 1021-1029.
  • Tsuzuki, S., and Seto, M. (2013). TEL (ETV6)-AML1 (RUNX1) initiates self-renewing fetal pro-B cells in association with a transcriptional program shared with embryonic stem cells in mice. Stem cells 31, 236-247.
  • Veldhoen, M., Hocking, R. J., Atkins, C. J., Locksley, R. M., and Stockinger, B. (2006). TGFbeta in the context of an inflammatory cytokine milieu supports de novo differentiation of IL-17-producing T cells. Immunity 24, 179-189.
  • Waite, J. C., and Skokos, D. (2012). Th17 response and inflammatory autoimmune diseases. International journal of inflammation 2012, 819467.
  • Wang, H., Geng, J., Wen, X., Bi, E., Kossenkov, A. V., Wolf, A. I., Tas, J., Choi, Y. S., Takata, H., Day, T. J., et al. (2014). The transcription factor Foxp1 is a critical negative regulator of the differentiation of follicular helper T cells. Nature immunology 15, 667-675.
  • Wei, G., Wei, L., Zhu, J., Zang, C., Hu-Li, J., Yao, Z., Cui, K., Kanno, Y., Roh, T. Y., Watford, W. T., et al. (2009). Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 30, 155-167.
  • Weisberg, S. P., Smith-Raska, M. R., Esquilin, J. M., Zhang, J., Arenzana, T. L., Lau, C. M., Churchill, M., Pan, H., Klinakis, A., Dixon, J. E., et al. (2014). ZFX controls propagation and prevents differentiation of acute T-lymphoblastic and myeloid leukemia. Cell reports 6, 528-540.
  • Welford, S. M., Bedogni, B., Gradin, K., Poellinger, L., Broome Powell, M., and Giaccia, A. J. (2006). HIF1alpha delays premature senescence through the activation of MIF. Genes & development 20, 3366-3371.
  • Wells, A. D., Walsh, M. C., Bluestone, J. A., and Turka, L. A. (2001). Signaling through CD28 and CTLA-4 controls two distinct forms of T cell anergy. The Journal of clinical investigation 108, 895-903.
  • Wherry, E. J., Ha, S. J., Kaech, S. M., Haining, W. N., Sarkar, S., Kalia, V., Subramaniam, S., Blattman, J. N., Barber, D. L., and Ahmed, R. (2007). Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670-684.
  • Willinger, T., Freeman, T., Herbert, M., Hasegawa, H., McMichael, A. J., and Callan, M. F. (2006). Human naïve CD8 T cells down-regulate expression of the WNT pathway transcription factors lymphoid enhancer binding factor I and transcription factor 7 (T cell factor-1) following antigen encounter in vitro and in vivo. Journal of immunology 176, 1439-1446.
  • Winer, S., Paltser, G., Chan, Y., Tsui, H., Engleman, E., Winer, D., and Dosch, H. M. (2009). Obesity predisposes to Th17 bias. European journal of immunology 39, 2629-2635.
  • Wu, C., Yosef, N., Thalhamer, T., Zhu, C., Xiao, S., Kishi, Y., Regev, A., and Kuchroo, V. K. (2013). Induction of pathogenic TH17 cells by inducible salt-sensing kinase SGK1. Nature 496, 513-517.
  • Xiao, S., Yosef, N., Yang, J., Wang, Y., Zhou, L., Zhu, C., Wu, C., Baloglu, E., Schmidt, D., Ramesh, R., et al. (2014). Small-molecule RORgammat antagonists inhibit T helper 17 cell transcriptional network by divergent mechanisms. Immunity 40, 477-489.
  • Xu, J., Yang, Y., Qiu, G., Lal, G., Wu, Z., Levy, D. E., Ochando, J. C., Bromberg, J. S., and Ding, Y. (2009). c-Maf regulates IL-10 expression during Th17 polarization. Journal of immunology 182, 6226-6236.
  • Yosef, N., Shalek, A. K., Gaublomme, J. T., Jin, H., Lee, Y., Awasthi, A., Wu, C., Karwacz, K., Xiao, S., Jorgolli, M., et al. (2013). Dynamic regulatory network controlling TH17 cell differentiation. Nature 496, 461-468.
  • Zhou, L., Ivanov, II, Spolski, R., Min, R., Shenderov, K., Egawa, T., Levy, D. E., Leonard, W. J., and Littman, D. R. (2007). IL-6 programs T(H)-17 cell differentiation by promoting sequential engagement of the IL-21 and IL-23 pathways. Nature immunology 8, 967-974.
  • Zingoni, A., Soto, H., Hedrick, J. A., Stoppacciaro, A., Storlazzi, C. T., Sinigaglia, F., D'Ambrosio, D., O'Garra, A., Robinson, D., Rocchi, M., et al. (1998). The chemokine receptor CCR8 is preferentially expressed in Th2 but not Th1 cells. Journal of immunology 161, 547-551.

The invention is further described by the following numbered paragraphs:

1. A method of diagnosing, prognosing and/or staging an immune response involving T cell balance, comprising detecting a first level of expression, activity and/or function of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l or one or more products of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l and comparing the detected level to a control of level of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Sc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l or gene product expression, activity and/or function, wherein a difference in the detected level and the control level indicates that the presence of an immune response in the subject.
2. A method of monitoring an immune response in a subject comprising detecting a level of expression, activity and/or function of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l or one or more products of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5 at a first time point, detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm. Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l at a second time point, and comparing the first detected level of expression, activity and/or function with the second detected level of expression, activity and/or function, wherein a change in the first and second detected levels indicates a change in the immune response in the subject.
3. A method of identifying a patient population at risk or suffering from an immune response comprising detecting a level of expression, activity and/or function of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l or one or more products of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the patient population and comparing the level of expression, activity and/or function of one or more signature genes or one or more products of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr6S, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in a patient population not at risk or suffering from an immune response, wherein a difference in the level of expression, activity and/or function of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5 or any combination of Gpr65, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l or one or more products of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more thereof Gpr65, Plzp or Cd5l or any combination of Gpr6S, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the patient populations identifies the patient population as at risk or suffering from an immune response.
4. A method for monitoring subjects undergoing a treatment or therapy specific for a target gene selected from the group consisting of candidates comprising a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l for an aberrant immune response to determine whether the patient is responsive to the treatment or therapy comprising detecting a level of expression, activity and/or function of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the absence of the treatment or therapy and comparing the level of expression, activity and/or function of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, (Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the presence of the treatment or therapy, wherein a difference in the level of expression, activity and/or function of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Doll, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21. Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l or one or more products of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gam, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more thereof Gpr65, Plzp or Cd5l or any combination of Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in the presence of the treatment or therapy indicates whether the patient is responsive to the treatment or therapy.
5. The method of any one of numbered paragraphs 1 to 4 wherein the immune response is an autoimmune response or an inflammatory response.
6. The method of numbered paragraph 5 wherein the inflammatory response is associated with an autoimmune response, an infectious disease and/or a pathogen-based disorder.
7. The method of any one of numbered paragraphs 1 to 6 wherein the signature genes are Th17-associated genes.
8. The method of any one of numbered paragraphs 4 to 7, wherein the treatment or therapy is an antagonist as to expression of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells.
9. The method of any one of numbered paragraphs 4 to 7, wherein the treatment or therapy is an agonist that enhances or increases the expression of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce T cell differentiation toward Th17 cells.
10. The method of numbered paragraphs 4 to 7, wherein the treatment or therapy is an antagonist of a target gene selected from the group consisting of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2h, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature.
11. The method of numbered paragraphs 4 to 7, wherein the treatment or therapy is antagonist that enhances or increases the expression of a target gene selected from the group consisting of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Doll, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med2l, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a non-pathogenic to a pathogenic signature.
12. The method according to any one of numbered paragraphs 8 to 11, wherein the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent.
13. A method of modulating T cell balance, the method comprising contacting a T cell or a population of T cells with a T cell modulating agent in an amount sufficient to modify differentiation, maintenance and/or function of the T cell or population of T cells by altering balance between Th17 cells, regulatory T cells (Tregs) and other T cell subsets as compared to differentiation, maintenance and/or function of the T cell or population of T cells in the absence of the T cell modulating agent; wherein the T cell modulating agent is an antagonist for or of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13. Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65. Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells, or wherein the T cell modulating agent is an agonist that enhances or increases the expression of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65. Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce T cell differentiation toward Th17 cells, or wherein the T cell modulating agent is specific for a target gene selected from the group consisting of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l, or wherein the T cell modulating agent is an antagonist of a target gene selected from the group consisting of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature, or wherein the T cell modulating agent is an agonist that enhances or increases the expression of a target gene selected from the group consisting of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a non-pathogenic to a pathogenic signature.
14. The method according to numbered paragraph 13, wherein the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent.
15. The method according to numbered paragraph 13, wherein the T cells are naïve T cells, partially differentiated T cells, differentiated T cells, a combination of naïve T cells and partially differentiated T cells, a combination of naïve T cells and differentiated T cells, a combination of partially differentiated T cells and differentiated T cells, or a combination of naïve T cells, partially differentiated T cells and differentiated T cells.
16. A method of enhancing Th17 differentiation in a cell population, increasing expression, activity and/or function of one or more Th17-associated cytokines or one or more Th17-associated transcription regulators selected from interleukin 17F (IL-17F), interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-related orphan receptor C (RORC), and/or decreasing expression, activity and/or function of one or more non-Th17-associated cytokines or non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Doll, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l.
17. The method of numbered paragraph 16, wherein the agent enhances expression, activity and/or function of at least Toso.
18. The method of numbered paragraphs 16 or 17, wherein the agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist.
19. The method of numbered paragraph 18, wherein the agent is an antibody.
20. The method of numbered paragraph 19 wherein the antibody is a monoclonal antibody.
21. The method of numbered paragraph 20, wherein the antibody is a chimeric, humanized or fully human monoclonal antibody.
22. Use of an antagonist for or of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acal3, Adi1, Dot1l, Mett10d, Sirt6, Sc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells for treating or Drug Discovery of or formulating or preparing a treatment for an aberrant immune response in a patient.
23. Use of an agonist that enhances or increases the expression of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to induce T cell differentiation toward Th17 cells for treating or Drug Discovery of or formulating or preparing a treatment for an aberrant immune response in a patient.
24. Use of an antagonist of a target gene selected from the group consisting of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature for treating or Drug Discovery of or formulating or preparing a treatment for an aberrant immune response in a patient.
25. Use of an agonist that enhances or increases the expression of a target gene selected from the group consisting of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino8Mc, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more thereof Gpr65, Plzp or Cd5 or any combination of Gpr65, Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l in an amount sufficient to switch Th17 cells from a non-pathogenic to a pathogenic signature for treating or Drug Discovery of or formulating or preparing a treatment for an aberrant immune response in a patient.
26. A treatment method or Drug Discovery method or method of formulating or preparing a treatment comprising any one of the methods or uses of any of the preceding numbered paragraphs.
27. The method of numbered paragraph 26 or the use of numbered paragraph 27 wherein an agent, agonist or antagonist of any of the preceding numbered paragraphs is a putative drug or treatment in Drug Discovery or formulating or preparing a treatment; and formulating or preparing a treatment comprises admixing the agent, agonist or antagonist with a pharmaceutically acceptable carrier or excipient.
28. A method of drug discovery for the treatment of a disease or condition involving an immune response involving T cell balance in a population of cells or tissue which express one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Beall, Zfp36, Acsl4, Acat3, Adi1, Dot1I, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr6s, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l comprising the steps of:
(a) providing a compound or plurality of compounds to be screened for their efficacy in the treatment of said disease or condition;
(b) contacting said compound or plurality of compounds with said population of cells or tissue;
(c) detecting a first level of expression, activity and/or function of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65. Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5l or one or more products of one or more of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Ctla2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21, Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination thereof Gpr65. Plzp or Cd5l in any combination of Gpr65, Plzp, Toso or Cd5l;
(d) comparing the detected level to a control of level of a gene in a herein Table or a combination of genes in herein Table(s) or Toso, Cd1a2b, Gatm, Bdh2, Bcat1, Zfp36, Acsl4, Acat3, Adi1, Dot1l, Mett10d, Sirt6, Slc25a13, Chd2, Ino80c, Med21. Pdss1, Galk1, Gnpda2 or Mtpap or any one of the foregoing or any combination thereof with one or more of Gpr65, Plzp or Cd5l or any combination of Gpr65, Plzp or Cd5l in any combination thereof Gpr65, Plzp, Toso or Cd5 or gene product expression, activity and/or function; and,
(e) evaluating the difference between the detected level and the control level to determine the immune response elicited by said compound or plurality of compounds.
29. A method of diagnosing, prognosing and/or staging an immune response involving Th17 T cell balance in a subject, comprising detecting a first level of expression of one or more of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) in Th17 cells, and comparing the detected level to a control level of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA), wherein a change in the first level of expression and the control level detected indicates a change in the immune response in the subject.
30. The method of numbered paragraph 29, further comprising determining the ratio of SFA to PUFA and comparing the ratio to a control level, wherein a shift in the ratio indicates a change in the immune response in the subject.
31. The method of numbered paragraphs 29 or 30, wherein a shift towards polyunsaturated fatty acids (PUFA) and/or away from saturated fatty acids (SFA) indicates a non-pathogenic Th17 response.
32. A method for monitoring subjects undergoing a treatment or therapy involving T cell balance comprising, detecting a first level of expression of one or more of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) in Th17 cells in the absence of the treatment or therapy and comparing the detected level to a level of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) in the presence of the treatment or therapy, wherein a difference in the level of expression in the presence of the treatment or therapy indicates whether the subject is responsive to the treatment or therapy.
33. The method of numbered paragraph 32, wherein the treatment or therapy involving T cell balance is for a subject undergoing treatment or therapy for cancer or an autoimmune disease.
34. A method of drug discovery for the treatment of a disease or condition involving an immune response involving Th17 T cell balance in a population of cells or tissue comprising:
(a) providing a compound or plurality of compounds to be screened for their efficacy in the treatment of said disease or condition;
(b) contacting said compound or plurality of compounds with said population of cells or tissue; (c) detecting a first level of expression of one or more of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA) in Th17 cells;
(d) comparing the detected level to a control level of saturated fatty acids (SFA) and/or polyunsaturated fatty acids (PUFA); and,
(e) evaluating the difference between the detected level and the control level to determine the immune response elicited by said compound or plurality of compounds.
A method of treatment of a disease or condition involving an immune response involving Th17 T cell balance comprising administering at least one lipid to a patient in need thereof, wherein the at least one lipid is sufficient to cause a shift in the ratio of SFA to PUFA, whereby there is a change in T cell balance.

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.

T cell balance gene expression, compositions of matters and methods of use thereof (2024)
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