WO2014134351A2

WO2014134351A2 - T cell balance gene expression, compositions of matters and methods of use thereof

Info

Publication number: WO2014134351A2
Application number: PCT/US2014/019127
Authority: WO
Inventors: Aviv Regev; Vijay Kuchroo; Hongkun Park; Nir YOSEF; Alexander K. SHALEK; Jellert GAUBLOMME; Nicole C. JOLLER; Chuan WU; Ana Carrizosa ANDERSON
Original assignee: The Broad Institute, Inc.; President And Fellows Of Harvard College; The Brigham And Women's Hospital, Inc.
Priority date: 2013-02-27
Filing date: 2014-02-27
Publication date: 2014-09-04
Also published as: WO2014134351A3; CA2902940A1; KR20150126882A; EP2961849A2; CN105593373A; RU2015140941A3; IL240881A0; RU2015140941A; US20150361396A1; AU2014223344A1; JP2016525873A; US10822587B2

Abstract

This invention relates generally to compositions and methods for identifying the regulatory network that modulates, controls or otherwise influences T cell balance, for example, Thl7 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.

Description

T CELL BALANCE GENE EXPRESSION, COMPOSITIONS OF MATTERS AND

METHODS OF USE THEREOF

RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No.

61/770,036, filed February 27, 2013, which is incorporated herein by reference in its entirety.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with Government support under the following grants: Pioneer Grant DP1OD003958-01 awarded by National Institute of Health; Pioneer Grant DP1OD003958-03 awarded by National Institute of Health; Centers of Excellence in Genomics Science Grant 1P50HG006193-01 awarded by National Institute of Health; Grant 1P01HG005062-01 awarded by National Human Genome Research Institute; Grant DP1OD003893 awarded by National Institute of Health, Grant NS30843 awarded by National Institute of Health, Grant NS045937 awarded by National Institute of Health, Grant AI 73748 awarded by National Institute of Health, Grant AI45757 awarded by National Institute of Health, Grant P01AI056299 awarded by National Institute of Health and Grant RG2571 awarded by National MS Society, New York. The Government has certain rights in the invention.

FIELD OF THE INVENTION

[0003] This invention relates generally to compositions and methods for identifying the regulatory network that modulates, controls or otherwise influences T cell balance, for example, Thl7 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. BACKGROUND OF THE INVENTION

[0004] Despite their importance, the molecular circuits that control the balance of T cells, including the differentiation of na^'ive 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 Thl7 cell differentiation, maintenance and function, and means for exploiting this network in a variety of therapeutic and diagnostic methods.

SUMMARY OF THE INVENTION

[0005] 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.

[0006] 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 Thl7 cell differentiation; controlling or otherwise influencing the networks that regulate Thl7 cell maintenance, for example, over the lifespan of a Thl7 cell; controlling or otherwise influencing the networks that regulate Thl7 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 Thl7 cells to other T cell types such as Tregs or other CD4+ T cells; manipulating or otherwise influencing the ratio of different types of Thl7 cells such as, for example, pathogenic Thl7 cells and nonpathogenic Thl7 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 Thl7 cell; and/or manipulating or otherwise influencing at least one function or biological activity of another CD4+ T cell.

[0007] 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 Thl7 and other T cell types, for example, regulatory T cells (Tregs), and/or Thl7 activity and inflammatory potential. As used herein, terms such as "Thl7 cell" and/or "Thl7 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 "Thl cell" and/or "Thl phenotype" and all grammatical variations thereof refer to a differentiated T helper cell that expresses interferon gamma (IFNy). 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.

[0008] 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 Thl 7 phenotypes, and/or Thl7 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.

[0009] 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 Thl7 cell types, e.g., between pathogenic and non-pathogenic Thl7 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 Thl7 activity.

[0010] 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 Thl7 cells, with or without a specific pathogenic distinction, or away from Thl7 cells, with or without a specific pathogenic distinction.

[0011] 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- Thl7 T cell subset or away from a non-Thl7 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 Thl7 cells into a different subtype, or into a new state.

[0012] 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 Thl7 cells into a different subtype, or into a new state.

[0013] 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.

[0014] In some embodiments, the T cells are na^'ive 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 na^'ive T cells and differentiated T cells. In some embodiments, the T cells are mixture of na^'ive 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 na^'ive T cells, partially differentiated T cells, and differentiated T cells.

[0015] 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 Thl7-related perturbations. These target genes are identified, for example, by contacting a T cell, e.g., na^'ive 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 the specification.

[0016] In some embodiments, the target gene is one or more Thl7-associated cytokine(s) or receptor molecule(s) selected from those listed in Table 3 of the specification. In some embodiments, the target gene is one or more Thl7-associated transcription regulator(s) selected from those shown in Table 4 of the specification.

[0017] In some embodiments, the target gene is one or more Thl7-associated transcription regulator(s) selected from those shown in Table 5 of the specification. In some embodiments, the target gene is one or more Thl7-associated receptor molecule(s) selected from those listed in Table 6 of the specification. In some embodiments, the target gene is one or more Thl7-associated kinase(s) selected from those listed in Table 7 of the specification. In some embodiments, the target gene is one or more Thl7-associated signaling molecule(s) selected from those listed in Table 8 of the specification. In some embodiments, the target gene is one or more Thl7-associated receptor molecule(s) selected from those listed in Table 9 of the specification.

[0018] In some embodiments, the target gene is one or more target genes involved in induction of Thl7 differentiation such as, for example, IRF1, IRF8, IRF9, STAT2, STAT3, IRF7, STAT1, ZFP281, IFI35, REL, TBX21, FLU, BATF, IRF4, one or more of the target genes listed in Table 5 as being associated with the early stage of Thl7

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, FLU, FOXOl, GAT A3, GATAD2B, HIF1A, ID2, IFI35, IKZF4, IRF1, IRF2, IRF3, IRF4, IRF7, IRF9, JMJDIC, JUN, LEFl, LRRFIPl, MAX, NCOA3, NFE2L2, NFIL3, NFKBl, NMI, NOTCHl, NR3C1, PHF21A, PML, PRDMl, REL, RELA, RUNXl, SAP18, SATB1, SMAD2, SMARCA4, SP100, SP4, STAT1, STAT2, STAT3, STAT4, STAT5B, STAT6, TFEB, TP53, TRIM24, and/or ZFP161, or any combination thereof.

[0019] In some embodiments, the target gene is one or more target genes involved in onset of Thl7 phenotype and amplification of Thl7 T cells such as, for example, IRF8, STAT2, STAT3, IRF7, JUN, STAT5B, ZPF2981, CHD7, TBX21, FLU, SATB1, RUNX1, BATF, RORC, SP4, one or more of the target genes listed in Table 5 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, BATF, BCL11B, BCL3, BCL6, CBFB, CBX4, CDC5L, CEBPB, CHD7, CREBl, CREB3L2, CREM, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, ETV6, EZH1, FLU, FOSL2, FOXJ2, FOXOl, FUS, HIF1A, HMGB2, ID1, ID2, IFI35, IKZF4, IRF3, IRF4, IRF7, IRF8, IRF9, JUN, JUNB, KAT2B, KLF10, KLF6, KLF9, LEF1, LRRFIP1, MAFF, MAX, MAZ, MINA, MTA3, MYC, MYST4, NCOAl, NCOA3, NFE2L2, NFIL3, NFKBl, NMI, NOTCHl, NR3C1, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, RELA, RORA, RUNX1, SAP18, SATB1, SKI, SKIL, SMAD2, SMAD7, SMARCA4, SMOX, SP1, SP4, SSI 8, STAT1, STAT2, STAT3, STAT5A, STAT5B, STAT6, SUZ12, TBX21, TFEB, TLE1, TP53, TRIM24, TRIM28, TRPS1, VAV1, ZEB1, ZEB2, ZFP161, ZFP62, ZNF238, ZNF281, and/or ZNF703, or any combination thereof.

[0020] In some embodiments, the target gene is one or more target genes involved in stabilization of Thl7 cells and/or modulating Thl7-associated interleukin 23 (IL-23) signaling such as, for example, STAT2, STAT3, JUN, STAT5B, CHD7, SATB1, RUNX1, BATF, RORC, SP4 IRF4, one or more of the target genes listed in Table 5 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1 , ATF3, ATF4, BATF, BATF3,

BCL11B, BCL3, BCL6, C210RF66, CBFB, CBX4, CDC5L, CDYL, CEBPB, CHD7, CHMPIB, CIC, CITED2, CREBl, CREB3L2, CREM, CSDA, DDIT3, E2F1, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, EZH1, FLU, FOSL2, FOXJ2, FOXOl, FUS, GAT A3, GATAD2B, HCLS1, HIF1A, ID1, ID2, IFI35, IKZF4, IRF3, IRF4, IRF7, IRF8, IRF9, JARID2, JMJD1C, JUN, JUNB, KAT2B, KLFIO, KLF6, KLF7, KLF9, LASS4, LEF1, LRRFIP1, MAFF, MAX, MEN1, MINA, MTA3, MXI1, MYC, MYST4, NCOAl, NCOA3, NFE2L2, NFIL3, NFKBl, NMI, NOTCHl, NR3C1, PHF13, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, REL, RELA, RNF11, RORA, RORC, RUNX1, RUNX2, SAP 18, SAP30, SATB1, SERTAD1, SIRT2, SKI, SKIL, SMAD2, SMAD4, SMAD7, SMARCA4, SMOX, SPl, SPlOO, SP4, SS18, STATl, STAT3, STAT4, STAT5A, STAT5B, STAT6, SUZ12, TBX21, TFEB, TGIF1, TLE1, TP53, TRIM24, TRPS1, TSC22D3, UBE2B, VAV1, VAX2, XBP1, ZEB1, ZEB2, ZFP161, ZFP36L1, ZFP36L2, ZNF238, ZNF281, ZNF703, ZNRF1, and/or ZNRF2, or any combination thereof.

[0021] In some embodiments, the target gene is one or more of the target genes listed in Table 6 as being associated with the early stage of Thl7 differentiation, maintenance and/or function, e.g., FAS, CCR5, IL6ST, IL17RA, IL2RA, MYD88, CXCR5, PVR, IL15RA, IL12RB1, or any combination thereof.

[0022] In some embodiments, the target gene is one or more of the target genes listed in Table 6 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., IL7R, ITGA3, IL1R1, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, CCR8, DDR1, PROCR, IL2RA, IL12RB2, MYD88, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRD1,

IRAKI BP 1, PVR, IL12RB1, IL18R1, TRAF3, or any combination thereof.

[0023] In some embodiments, the target gene is one or more of the target genes listed in Table 6 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., IL7R, ITGA3, IL1R1, FAS, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, DDR1, PROCR, IL2RA, IL12RB2, MYD88, BMPR1A, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRD1,

IRAKI BP 1, PVR, IL15RA, TLR1, ACVR1B, IL12RB1, IL18R1, TRAF3, IFNGR1, PLAUR, IL21R, IL23R, or any combination thereof.

[0024] In some embodiments, the target gene is one or more of the target genes listed in Table 7 as being associated with the early stage of Thl7 differentiation, maintenance and/or function, e.g., EIF2AK2, DUSP22, HK2, RIPK1, RNASEL, TEC, MAP3K8, SGK1, PRKCQ, DUSP16, BMP2K, PIM2, or any combination thereof.

[0025] In some embodiments, the target gene is one or more of the target genes listed in Table 7 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., PSTPIP1, PTPN1, ACP5, TXK, RIPK3, PTPRF, NEK4, PPME1, PHACTR2, HK2, GMFG, DAPP1, TEC, GMFB, PIM1, NEK6, ACVR2A, FES, CDK6, ZAK, DUSP14, SGK1, JAK3, ULK2, PTPRJ, SPHK1, TNK2, PCTK1, MAP4K3, TGFBR1, HK1, DDR1, BMP2K, DUSP10, ALPK2, or any combination thereof. [0026] In some embodiments, the target gene is one or more of the target genes listed in Table 7 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., PTPLA, PSTPIP1, TKl, PTEN, BPGM, DCK, PTPRS, PTPNl 8, MKNK2, PTPNl, PTPRE, SH2D1A, PLK2, DUSP6, CDC25B, SLK, MAP3K5, BMPRIA, ACP5, TXK, RIPK3, PPP3CA, PTPRF, PACSIN1, NEK4, PIP4K2A, PPME1, SRPK2, DUSP2, PHACTR2, DCLK1, PPP2R5A, RIPK1, GK, RNASEL, GMFG, STK4, HINT3, DAPP1, TEC, GMFB, PTPN6, RIPK2, PIM1, NEK6, ACVR2A, AURKB, FES, ACVR1B, CDK6, ZAK, VRK2, MAP3K8, DUSPl 4, SGKl, PRKCQ, JAK3, ULK2, HIPK2, PTPRJ, INPP1, TNK2, PCTK1, DUSPl, NUDT4, TGFBR1, PTP4A1, HK1, DUSPl 6, ANP32A, DDR1, ITK, WNKl, NAGK, STK38, BMP2K, BUB1, AAK1, SIK1, DUSP10, PRKCA, PIM2, STK17B, TK2, STK39, ALPK2, MST4, PHLPP1, or any combination thereof

[0027] In some embodiments, the target gene is one or more of the target genes listed in Table 8 as being associated with the early stage of Thl7 differentiation,

maintenance and/or function, e.g., HK2, CDKNIA, DUT, DUSPl, NADK, LIMK2, DUSPl 1, TAOK3, PRPSl, PPP2R4, MKNK2, SGKl, BPGM, TEC, MAPK6, PTP4A2, PRPF4B, ACPI, CCRN4L, or any combination thereof

[0028] In some embodiments, the target gene is one or more of the target genes listed in Table 8 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., HK2, ZAP70, NEK6, DUSPl 4, SH2D1A, ITK, DUT, PPPlRl l, DUSPl, PMVK, TKl, TAOK3, GMFG, PRPSl, SGKl, TXK, WNKl, DUSPl 9, TEC, RPS6KA1, PKM2, PRPF4B, ADRBK1, CKB, ULK2, PLK1, PPP2R5A, PLK2, or any combination thereof.

[0029] In some embodiments, the target gene is one or more of the target genes listed in Table 8 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., ZAP70, PFKP, NEK6, DUSPl 4, SH2D1A, INPP5B, ITK, PFKL, PGK1, CDKNIA, DUT, PPPlRl l, DUSPl, PMVK, PTPN22, PSPH, TKl, PGAM1, LIMK2, CLK1, DUSPl 1, TAOK3, RIOK2, GMFG, UCKL1, PRPSl, PPP2R4, MKNK2, DGKA, SGKl, TXK, WNKl, DUSPl 9, CHP, BPGM, PIP5K1A, TEC, MAP2K1, MAPK6, RPS6KA1, PTP4A2, PKM2, PRPF4B, ADRBK1, CKB, ACPI, ULK2, CCRN4L, PRKCH, PLK1, PPP2R5A, PLK2, or any combination thereof

[0030] In some embodiments, the target gene is one or more of the target genes listed in Table 9 as being associated with the early stage of Thl7 differentiation,

maintenance and/or function, e.g., CD200, CD40LG, CD24, CCND2, ADAM 17, BSG, ITGAL, FAS, GPR65, SIGMAR1, CAP1, PLAUR, SRPRB, TRPV2, IL2RA, KDELR2, TNFRSF9, or any combination thereof.

[0031] In some embodiments, the target gene is one or more of the target genes listed in Table 9 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., CTLA4, CD200, CD24, CD5L, CD9, IL2RB, CD53, CD74, CAST, CCR6, IL2RG, ITGAV, FAS, IL4R, PROCR, GPR65, TNFRSF18, RORA, IL1RN, RORC, CYSLTR1, PNRC2, LOC390243, ADAMIO, TNFSF9, CD96, CD82, SLAMF7, CD27, PGRMC1, TRPV2, ADRBK1, TRAF6, IL2RA, THY1, IL12RB2, TNFRSF9, or any combination thereof.

[0032] In some embodiments, the target gene is one or more of the target genes listed in Table 9 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., CTLA4, TNFRSF4, CD44, PDCD1, CD200, CD247, CD24, CD5L, CCND2, CD9, IL2RB, CD53, CD74, ADAM 17, BSG, CAST, CCR6, IL2RG, CD81, CD6, CD48, ITGAV, TFRC, ICAM2, ATP1B3, FAS, IL4R, CCR7, CD52, PROCR, GPR65, TNFRSF18, FCRLl, RORA, IL1RN, RORC, P2RX4, SSR2, PTPN22, SIGMAR1, CYSLTR1, LOC390243, ADAMIO, TNFSF9, CD96, CAP1, CD82, SLAMF7, PLAUR, CD27, SIVA1, PGRMC1, SRPRB, TRPV2, NR1H2, ADRBK1, GABARAPL1, TRAF6, IL2RA, THY1, KDELR2, IL12RB2, TNFRSF9, SCARB1, IFNGR1, or any combination thereof.

[0033] 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 Thl7 differentiation and/or Thl7 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 Thl7 differentiation, use of an antagonist that interacts with those target genes will shift differentiation away from the Thl7 phenotype, while use of an agonist that interacts with those target genes will shift differentiation toward the Thl7 phenotype. For target genes that are identified as negative regulators of Thl7 differentiation, use of an antagonist that interacts with those target genes will shift differentiation toward from the Thl7 phenotype, while use of an agonist that interacts with those target genes will shift differentiation away the Thl7 phenotype. For example, for target genes that are identified as positive regulators of Thl7 maintenance, use of an antagonist that interacts with those target genes will reduce the number of cells with the Thl7 phenotype, while use of an agonist that interacts with those target genes will increase the number of cells with the Thl7 phenotype. For target genes that are identified as negative regulators of Thl7 differentiation, use of an antagonist that interacts with those target genes will increase the number of cells with the Thl7 phenotype, while use of an agonist that interacts with those target genes will reduce the number of cells with the Thl7 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.

[0034] In some embodiments, the positive regulator of Thl7 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 Thl7 differentiation is a target gene selected from MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS and

combinations thereof.

[0035] In some embodiments, the negative regulator of Thl7 differentiation is a target gene selected from SP4, ETS2, IKZF4, TSC22D3, IRFl and combinations thereof. In some embodiments, the negative regulator of Thl7 differentiation is a target gene selected from SP4, IKZF4, TSC22D3 and combinations thereof.

[0036] 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 Thl7 cells. As shown herein, expression of FAS in T cell populations induced or otherwise influenced differentiation toward Thl7 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 na^'ive 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 na^'ive T cells and differentiated T cells. In some embodiments, the T cells are mixture of na^'ive 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 na^'ive T cells, partially differentiated T cells, and differentiated T cells.

[0037] 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 Thl7 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Thl 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, Sjogren's syndrome, uveitis, and rheumatoid arthritis, asthma, systemic lupus erythematosus, transplant rejection including allograft rejection, and combinations thereof. In addition, enhancement of Thl 7 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 na^'ive 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 na^'ive T cells and differentiated T cells. In some embodiments, the T cells are mixture of na^'ive 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 na^'ive T cells, partially differentiated T cells, and differentiated T cells.

[0038] 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 Thl 7 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Thl 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 na^'ive 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 na^'ive T cells and differentiated T cells. In some embodiments, the T cells are mixture of na^'ive 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 na^'ive T cells, partially differentiated T cells, and differentiated T cells.

[0039] 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 Thl7 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Thl 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 na^'ive 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 na^'ive T cells and differentiated T cells. In some embodiments, the T cells are mixture of na^'ive 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 na^'ive T cells, partially differentiated T cells, and differentiated T cells.

[0040] 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 Thl 7 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Thl 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 na^'ive 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 na^'ive T cells and differentiated T cells. In some embodiments, the T cells are mixture of na^'ive 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 na^'ive T cells, partially differentiated T cells, and differentiated T cells.

[0041] 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 Thl7 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Thl 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

[0042] 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 Thl 7 cell or population of cells, for example, by influencing a na^'ive T cell or population of cells to differentiate to a pathogenic or non-pathogenic Thl 7 cell or population of cells, by causing a pathogenic Thl 7 cell or population of cells to switch to a non-pathogenic Thl 7 cell or population of T cells (e.g., populations of na^'ive T cells, partially differentiated T cells, differentiated T cells and combinations thereof), or by causing a non-pathogenic Thl7 cell or population of T cells (e.g., populations of na^'ive T cells, partially differentiated T cells, differentiated T cells and combinations thereof) to switch to a pathogenic Thl7 cell or population of cells.

[0043] The terms "pathogenic" or "non-pathogenic" as used herein are not to be construed as implying that one Thl7 cell phenotype is more desirable than the other. As described herein, there are instances in which inhibiting the induction of pathogenic Thl7 cells or modulating the Thl7 phenotype towards the non-pathogenic Thl7 phenotype is desirable. Likewise, there are instances where inhibiting the induction of non-pathogenic Thl7 cells or modulating the Thl7 phenotype towards the pathogenic Thl7 phenotype is desirable.

[0044] As used herein, terms such as "pathogenic Thl7 cell" and/or "pathogenic

Thl7 phenotype" and all grammatical variations thereof refer to Thl7 cells that, when induced in the presence of TGF-P3, express an elevated level of one or more genes selected from CxcB, IL22, IL3, Ccl4, Gzmb, Lrmp, Ccl5, Caspl , Csf2, Ccl3, Tbx21 , Icos, IL17r, Stat4, Lgals3 and Lag, as compared to the level of expression in a TGF-P3-induced Thl7 cells. As used herein, terms such as "non-pathogenic Thl7 cell" and/or "non-pathogenic Thl7 phenotype" and all grammatical variations thereof refer to Thl7 cells that, when induced in the presence of TGF-P3, express a decreased level of one or more genes selected from IL6st, ILlrn, Ikzf3, Maf, Ahr, IL9 and IL10, as compared to the level of expression in a TGF-p3-induced Thl7 cells.

[0045] 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 Thl7 cells. As shown herein, expression of PROCR in Thl7 cells reduced the pathogenicity of the Thl7 cells, for example, by switching Thl7 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.

[0046] 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 Thl7 cells switches non-pathogenic Thl7 cells to pathogenic Thl7 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.

[0047] In some embodiments, the invention provides a method of inhibiting Thl7 differentiation, maintenance and/or function in a cell population and/or increasing expression, activity and/or function of one or more non-Thl7-associated cytokines, one or more non-Thl7 associated receptor molecules, or non-Thl7-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GAT A3, 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 na^'ive 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-Thl7 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-Thl7 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 Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Thl7 T cell to become and/or produce a CD4+ T cell phenotype other than a Thl7 T cell phenotype. In some embodiments, the T cell is a Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Thl7 T cell to become and/or produce a shift in the Thl7 T cell phenotype, e.g., between pathogenic or non-pathogenic Thl7 cell phenotype.

[0048] In some embodiments, the invention provides a method of inhibiting Thl7 differentiation in a cell population and/or increasing expression, activity and/or function of one or more non-Thl7-associated cytokines, one or more non-Thl7-associated receptor molecules, or non-Thl7-associated transcription factor selected from FOXP3, interferon gamma (IFN-γ), GAT A3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRFl 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 na^'ive 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-Thl7 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-Thl7 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 Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Thl7 T cell to become and/or produce a CD4+ T cell phenotype other than a Thl7 T cell phenotype. In some embodiments, the T cell is a Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Thl7 T cell to become and/or produce a shift in the Thl7 T cell phenotype, e.g., between pathogenic or non-pathogenic Thl7 cell phenotype.

[0049] In some embodiments, the invention provides a method of enhancing Thl7 differentiation in a cell population increasing expression, activity and/or function of one or more Thl7-associated cytokines, one or more Thl7-associated receptor molecules, or one or more Thl7-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-Thl7- associated cytokines, one or more Thl7-associated receptor molecules, or one or more non- Thl7-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GAT A3, STAT4 and TBX21, comprising contacting a T cell with an agent that inhibits expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRFl 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 na^'ive 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 Thl7 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 Thl7 T cell phenotype. In some embodiments, the T cell is a CD4+ T cell other than a Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the non- Thl7 T cell to become and/or produce a Thl7 T cell phenotype. In some embodiments, the T cell is a Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Thl7 T cell to become and/or produce a shift in the Thl7 T cell phenotype, e.g., between pathogenic or non-pathogenic Thl7 cell phenotype.

[0050] In some embodiments, the invention provides a method of enhancing Thl7 differentiation in a cell population, increasing expression, activity and/or function of one or more Thl7-associated cytokines, one or more Thl7-associated receptor molecules, and/or one or more Thl7-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-Thl7-associated cytokines, one or more Thl7-associated receptor molecules, or one or more non-Thl7-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GAT A3, 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, GAT A3, STAT4 and/or TBX21 expression, activity and/or function. In some embodiments, the T cell is a na^'ive 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 Thl7 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 Thl7 T cell phenotype. In some embodiments, the T cell is a CD4+ T cell other than a Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the non-Thl7 T cell to become and/or produce a Thl7 T cell phenotype. In some embodiments, the T cell is a Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Thl7 T cell to become and/or produce a shift in the Thl7 T cell phenotype, e.g., between pathogenic or non-pathogenic Thl7 cell phenotype.

[0051] In some embodiments, the invention provides a method of identifying genes or genetic elements associated with Thl7 differentiation comprising: a) contacting a T cell with an inhibitor of Thl7 differentiation or an agent that enhances Thl7 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 Thl7 differentiation or an agent that enhances Thl7 differentiation; and d) identifying a gene whose expression is modulated by step c). In some embodiments, the inhibitor of Thl7 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 Thl7 differentiation is an agent that enhances expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRFl 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 Thl7 differentiation is an agent that inhibits expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRFl or combinations thereof. In some embodiments, wherein the agent that enhances Thl7 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.

[0052] In some embodiments, the invention provides a method of modulating induction of Thl7 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 IRFl, IRF8, IRF9, STAT2, STAT3, IRF7, STAT1, ZFP281, IFI35, REL, TBX21, FLU, BATF, IRF4, one or more of the target genes listed in Table 5 as being associated with the early stage of Thl7 differentiation, maintenance and/or function, e.g., AES, AHR, ARID5A, BATF, BCL11B, BCL3, CBFB, CBX4, CHD7, CITED2, CREBl, E2F4, EGR1, EGR2, ELL2, ETS1, ETS2, ETV6, EZH1, FLU, FOXOl, GAT A3, GATAD2B, HIF1A, ID2, IFI35, IKZF4, IRFl, IRF2, IRF3, IRF4, IRF7, IRF9, JMJD1C, JUN, LEF1, LRRFIP1, MAX, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCHl, NR3C1, PHF21A, PML, PRDMl, REL, RELA, RUNXl, SAP18, SATBl, SMAD2, SMARCA4, SP100, SP4, STAT1, STAT2, STAT3, STAT4, STAT5B, STAT6, TFEB, TP53, TRIM24, and/or ZFP161, or any combination thereof.

[0053] In some embodiments, the invention provides a method of modulating onset of Thl7 phenotype and amplification of Thl7 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 IRF8, STAT2, STAT3, IRF7, JUN, STAT5B, ZPF2981, CHD7, TBX21, FLU, SATBl, RUNXl, BATF, RORC, SP4, one or more of the target genes listed in Table 5 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., AES, AHR, ARID3A, ARID5A, AR TL, ASXL1, BATF, BCL11B, BCL3, BCL6, CBFB, CBX4, CDC5L, CEBPB, CHD7, CREB1, CREB3L2, CREM, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, ETV6, EZH1, FLU, FOSL2, FOXJ2, FOXOl, FUS, HIF1A, HMGB2, ID1, ID2, IFI35, IKZF4, IRF3, IRF4, IRF7, IRF8, IRF9, JUN, JUNB, KAT2B, KLF10, KLF6, KLF9, LEF1, LRRFIPI, MAFF, MAX, MAZ, MINA, MTA3, MYC, MYST4, NCOAl, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF21A, PML, POU2AF1, POU2F2, PRDMl, RARA, RBPJ, RELA, RORA, RUNXl, SAP18, SATB1, SKI, SKIL, SMAD2, SMAD7, SMARCA4, SMOX, SP1, SP4, SS18, STAT1, STAT2, STAT3, STAT5A, STAT5B, STAT6, SUZ12, TBX21, TFEB, TLE1, TP53, TRIM24, TRIM28, TRPS1, VAV1, ZEB1, ZEB2, ZFP161, ZFP62, ZNF238, ZNF281, and/or ZNF703, or any combination thereof.

[0054] In some embodiments, the invention provides a method of modulating stabilization of Thl7 cells and/or modulating Thl7-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 STAT2, STAT3, JUN, STAT5B, CHD7, SATB1, RUNXl, BATF, RORC, SP4 IRF4, one or more of the target genes listed in Table 5 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, ATF3, ATF4, BATF, BATF3, BCL11B, BCL3, BCL6, C210RF66, CBFB, CBX4, CDC5L, CDYL, CEBPB, CHD7, CHMP1B, CIC, CITED2, CREBl, CREB3L2, CREM, CSDA, DDIT3, E2F1, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, EZH1, FLU, FOSL2, FOXJ2, FOXOl, FUS, GAT A3,

GATAD2B, HCLS1, HIF1A, ID1, ID2, IFI35, IKZF4, IRF3, IRF4, IRF7, IRF8, IRF9, JARID2, JMJD1C, JUN, JUNB, KAT2B, KLF10, KLF6, KLF7, KLF9, LASS4, LEF1, LRRFIPI, MAFF, MAX, MENl, MINA, MTA3, MXll, MYC, MYST4, NCOAl, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF13, PHF21A, PML, POU2AF1, POU2F2, PRDMl, RARA, RBPJ, REL, RELA, RNFl 1, RORA, RORC, RUNXl, RUNX2, SAP 18, SAP30, SATB1, SERTAD1, SIRT2, SKI, SKIL, SMAD2, SMAD4, SMAD7, SMARCA4, SMOX, SP1, SP100, SP4, SS18, STAT1, STAT3, STAT4, STAT5A,

STAT5B, STAT6, SUZ12, TBX21, TFEB, TGIF1, TLE1, TP53, TRIM24, TRPS1,

TSC22D3, UBE2B, VAV1, VAX2, XBP1, ZEB1, ZEB2, ZFP161, ZFP36L1, ZFP36L2, ZNF238, ZNF281, ZNF703, ZNRF1, and/or ZNRF2, or any combination thereof [0055] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 6 as being associated with the early stage of Thl7 differentiation, maintenance and/or function, e.g., FAS, CCR5, IL6ST, IL17RA, IL2RA, MYD88, CXCR5, PVR, IL15RA, IL12RB1, or any combination thereof

[0056] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 6 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., IL7R, ITGA3, IL1R1, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, CCR8, DDR1, PROCR, IL2RA, IL12RB2, MYD88, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRD1, IRAKI BP 1, PVR, IL12RB1, IL18R1, TRAF3, or any combination thereof.

[0057] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 6 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., IL7R, ITGA3, IL1R1, FAS, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, DDR1, PROCR, IL2RA, IL12RB2, MYD88,

BMPR1A, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB,

TNFRSF12A, CXCR4, KLRD1, IRAKI BP 1, PVR, IL15RA, TLR1, ACVR1B, IL12RB1, IL18R1, TRAF3, IFNGR1, PLAUR, IL21R, IL23R, or any combination thereof

[0058] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 7 as being associated with the early stage of Thl7 differentiation, maintenance and/or function, e.g., EIF2AK2, DUSP22, HK2, RIPKl, RNASEL, TEC, MAP3K8, SGKl, PRKCQ, DUSP16, BMP2K, PIM2, or any combination thereof.

[0059] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 7 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., PSTPIP1, PTPN1, ACP5, TXK, RIPK3, PTPRF, NEK4, PPME1, PHACTR2, HK2, GMFG, DAPP1, TEC, GMFB, PIM1, NEK6, ACVR2A, FES, CDK6, ZAK, DUSP14, SGKl, JAK3, ULK2, PTPRJ, SPHK1, TNK2, PCTK1, MAP4K3, TGFBR1, HK1, DDR1 , BMP2K, DUSP10, ALPK2, or any combination thereof.

[0060] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 7 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., PTPLA, PSTPIP1, TK1, PTEN, BPGM, DCK, PTPRS, PTPN18, MKNK2, PTPN1, PTPRE, SH2D1A, PLK2, DUSP6, CDC25B, SLK, MAP3K5, BMPR1A, ACP5, TXK, RIPK3, PPP3CA, PTPRF, PACSIN1, NEK4, PIP4K2A, PPME1, SRPK2, DUSP2, PHACTR2, DCLK1, PPP2R5A, RIPK1, GK,

RNASEL, GMFG, STK4, HINT3, DAPP1, TEC, GMFB, PTPN6, RIPK2, PIM1, NEK6, ACVR2A, AURKB, FES, ACVR1B, CDK6, ZAK, VRK2, MAP3K8, DUSP14, SGKl, PRKCQ, JAK3, ULK2, HIPK2, PTPRJ, INPP1, TNK2, PCTK1, DUSP1, NUDT4,

TGFBR1, PTP4A1, HK1, DUSP16, ANP32A, DDR1, ITK, WNKl, NAGK, STK38, BMP2K, BUB1, AAK1, SIK1, DUSP10, PRKCA, PIM2, STK17B, TK2, STK39, ALPK2, MST4, PHLPP1, or any combination thereof.

[0061] In some embodiments, the invention provides a method of modulating is one or more of the target genes listed in Table 8 as being associated with the early stage of Thl7 differentiation, maintenance and/or function, e.g., HK2, CDKNIA, DUT, DUSP1, NADK, LIMK2, DUSP11, TAOK3, PRPSl, PPP2R4, MKNK2, SGKl, BPGM, TEC, MAPK6, PTP4A2, PRPF4B, ACPI, CCRN4L, or any combination thereof.

[0062] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 8 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., HK2, ZAP70, NEK6, DUSP14, SH2D1A, ITK, DUT, PPP1R11, DUSP1, PMVK, TK1, TAOK3, GMFG, PRPSl, SGKl, TXK, WNKl, DUSP19, TEC, RPS6KA1, PKM2, PRPF4B, ADRBK1, CKB, ULK2, PLK1, PPP2R5A, PLK2, or any combination thereof.

[0063] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 8 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., ZAP70, PFKP, NEK6, DUSP14,

SH2D1A, INPP5B, ITK, PFKL, PGK1, CDKNIA, DUT, PPP1R11, DUSP1, PMVK, PTPN22, PSPH, TK1, PGAM1, LIMK2, CLK1, DUSP11, TAOK3, RIOK2, GMFG, UCKLl, PRPSl, PPP2R4, MKNK2, DGKA, SGKl, TXK, WNKl, DUSP19, CHP, BPGM, PIP5K1A, TEC, MAP2K1, MAPK6, RPS6KA1, PTP4A2, PKM2, PRPF4B, ADRBK1, CKB, ACPI, ULK2, CCRN4L, PRKCH, PLK1, PPP2R5A, PLK2, or any combination thereof.

[0064] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 9 as being associated with the early stage of Thl7 differentiation, maintenance and/or function, e.g., CD200, CD40LG, CD24, CCND2, ADAM 17, BSG, ITGAL, FAS, GPR65, SIGMAR1, CAP1, PLAUR, SRPRB, TRPV2, IL2RA, KDELR2, TNFRSF9, or any combination thereof.

[0065] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 9 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function, e.g., CTLA4, CD200, CD24, CD5L, CD9, IL2RB, CD53, CD74, CAST, CCR6, IL2RG, ITGAV, FAS, IL4R, PROCR, GPR65, TNFRSF18, RORA, IL1RN, RORC, CYSLTR1, PNRC2, LOC390243, ADAM 10,

TNFSF9, CD96, CD82, SLAMF7, CD27, PGRMCl, TRPV2, ADRBKl, TRAF6, IL2RA, THY1, IL12RB2, TNFRSF9, or any combination thereof

[0066] In some embodiments, the invention provides a method of modulating one or more of the target genes listed in Table 9 as being associated with the late stage of Thl7 differentiation, maintenance and/or function, e.g., CTLA4, TNFRSF4, CD44, PDCD1, CD200, CD247, CD24, CD5L, CCND2, CD9, IL2RB, CD53, CD74, ADAM 17, BSG, CAST, CCR6, IL2RG, CD81, CD6, CD48, ITGAV, TFRC, ICAM2, ATP1B3, FAS, IL4R, CCR7, CD52, PROCR, GPR65, TNFRSF18, FCRL1, RORA, IL1RN, RORC, P2RX4, SSR2, PTPN22, SIGMAR1, CYSLTR1, LOC390243, ADAM 10, TNFSF9, CD96, CAP1, CD82, SLAMF7, PLAUR, CD27, SIVAl, PGRMCl, SRPRB, TRPV2, NR1H2, ADRBKl, GABARAPL1, TRAF6, IL2RA, THY1, KDELR2, IL12RB2, TNFRSF9, SCARB1, IFNGR1, or any combination thereof.

[0067] 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; cisp latin; 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.

[0068] 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 Table 2 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.

[0069] 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 Table 2 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 Table 2 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.

[0070] 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.

[0071] 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 AJ, Peggs KS, Allison JP, "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.

[0072] 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.

[0073] 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 the specification.

[0074] 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.

BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figures 1A, lB-1, 1B-2, 1C and ID are a series of graphs and illustrations depicting genome wide temporal expression profiles of Thl7 differentiation. Figure 1A depicts an overview of approach. Figures lB-1 and 1B-2 depict gene expression profiles during Thl7 differentiation. Shown are the differential expression levels for genes (rows) at 18 time points (columns) in Thl7 polarizing conditions (TGF-βΙ and IL-6; left panel, Z- normalized per row) or Thl7 polarizing conditions relative to control activated ThO cells (right panel, log2(ratio)). The genes are partitioned into 20 clusters (C1-C20, color bars, right). Right: mean expression (Y axis) and standard deviation (error bar) at each time point (X axis) for genes in representative clusters. Cluster size ("n"), enriched functional annotations ("F"), and representative genes ("M") are denoted. Figure 1C depicts three major transcriptional phases. Shown is a correlation matrix (red (right side of correlation scale): high; blue (left side of correlation scale): low) between every pair of time points. Figure ID depicts transcriptional profiles of key cytokines and receptor molecules. Shown are the differential expression levels (log2(ratio)) for each gene (column) at each of 18 time points (rows) in Thl7 polarizing conditions (TGF-βΙ and IL-6; left panel, Z-normalized per row) vs. control activated ThO cells.

[0076] Figures 2 A, 2B, 2C, 2D, 2E-1, 2E-2 and 2E-3 are a series of graphs and illustrations depicting a model of the dynamic regulatory network of Thl7 differentiation. Figure 2A depicts an overview of computational analysis. Figure 2B depicts a schematic of temporal network 'snapshots'. Shown are three consecutive cartoon networks (top and matrix columns), with three possible interactions from regulator (A) to targets (B, C & D), shown as edges (top) and matrix rows (A→B - top row; A→C - middle row; A→D - bottom row). Figure 1C depicts 18 network 'snapshots'. Left: each row corresponds to a TF-target interaction that occurs in at least one network; columns correspond to the network at each time point. A purple entry: interaction is active in that network. The networks are clustered by similarity of active interactions (dendrogram, top), forming three temporally consecutive clusters (early, intermediate, late, bottom). Right: a heatmap denoting edges for selected regulators. Figure ID depicts dynamic regulator activity. Shown is, for each regulator (rows), the number of target genes (normalized by its maximum number of targets) in each of the 18 networks (columns, left), and in each of the three canonical networks (middle) obtained by collapsing (arrows). Right: regulators chosen for perturbation (pink), known Thl7 regulators (grey), and the maximal number of target genes across the three canonical networks (green, ranging from 0 to 250 targets). Figures lE-1, 1E-2, and 1E-3 depict that at the heart of each network is its 'transcriptional circuit', connecting active TFs to target genes that themselves encode TFs. The transcription factor circuits shown (in each of the 3 canonical networks) are the portions of each of the inferred networks associating transcription regulators to targets that themselves encode transcription regulators. Yellow nodes denote transcription factor genes that are over-expressed (compared to ThO) during the respective time segment. Edge color reflects the data type supporting the regulatory interaction (legend).

[0077] Figures 3A, 3B, 3C and 3D are a series of graphs and illustrations depicting knockdown screen in Thl7 differentiation using silicon nanowires. Figure 3 A depicts unbiased ranking of perturbation candidates. Shown are the genes ordered from left to right based on their ranking for perturbation (columns, top ranking is leftmost). Two top matrices: criteria for ranking by 'Network Information' (topmost) and 'Gene Expression Information'. Purple entry: gene has the feature (intensity proportional to feature strength; top five features are binary). Bar chart: ranking score. 'Perturbed' row: dark grey: genes successfully perturbed by knockdown followed by high quality mRNA quantification; light grey: genes where an attempt to knockdown was made, but could not achieve or maintain sufficient knockdown or did not obtain enough replicates; Black: genes perturbed by knockout or for which knockout data was already available. Known row: orange entry: a gene was previously associated with Thl7 function (this information was not used to rank the genes; Fig. 10A, 10B). Figure 3B depicts scanning electron micrograph of primary T cells (false colored purple) cultured on vertical silicon nanowires. Figure 3C depicts delivery by silicon nanowire neither activates nor induces differentiation of na^'ive T cells and does not affect their response to conventional TCR stimulation with anti-CD3/CD28. Figure 3D depicts effective knockdown by siRNA delivered on nanowires. Shown is the % of mRNA remaining after knockdown (by qPCR, Y axis: mean ± standard error relative to non-targeting siRNA control, n = 12, black bar on left) at 48hrs after introduction of polarizing cytokines.

[0078] In Figures 3 A and Figure 2D, the candidate regulators shown are those listed in Table 5. In Figure 3 A, the candidate regulators are listed on the x axis and are, in order from left to right, RORC, SATB1, TRPS1, SMOX, RORA, ARID5A, ETV6, ARNTL, ETS1, UBE2B, BATF, STAT3, STAT1, STAT5A, NR3C1, STAT6, TRIM24, HIF1A, IRF4, IRF8, ETS2, JUN, RUNX1, FLU, REL, SP4, EGR2, NFKB1, ZFP281, STAT4, RELA, TBX21, STAT5B, IRF7, STAT2, IRF3, XBP1, FOXOl, PRDM1, ATF4, IRF1, GAT A3, EGR1, MYC, CREBl, IRF9, IRF2, FOXJ2, SMARCA4, TRP53, SUZ12, POU2AF1, CEBPB, ID2, CREM, MYST4, MXI1, RBPJ, CHD7, CREB3L2, VAX2, KLF10, SKI, ELK3, ZEB1, PML, SERTAD1, NOTCH 1, LRRFIP1, AHR,

1810007M14RIK, SAP30, IDl, ZFP238, VAVl, MINA, BATF3, CDYL, IKZF4, NCOAl, BCL3, JUNB, SS18, PHF13, MTA3, ASXL1, LASS4, SKIL, DDIT3, FOSL2, RUNX2, TLE1, ATF3, ELL2, AES, BCL11B, JARID2, KLF9, KAT2B, KLF6, E2F8, BCL6, ZNRF2, TSC22D3, KLF7, HMGB2, FUS, SIRT2, MAFF, CHMP1B, GATAD2B,

SMAD7, ZFP703, ZNRF1, JMJD1C, ZFP36L2, TSC22D4, NFE2L2, RNF11, ARID3A, MEN1, RARA, CBX4, ZFP62, CIC, HCLS1, ZFP36L1, TGIF1.

[0079] Figures 4A, 4B, 4C and 4D are a series of graphs and illustrations depicting coupled and mutually-antagonistic modules in the Thl7 network. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/naturel 1981. Figure 4A depicts the impact of perturbed genes on a 275-gene signature. Shown are changes in the expression of 275 signature genes (rows) following knockdown or knockout (KO) of 39 factors (columns) at 48hr (as well as IL-21r and IL-17ra KO at 60 hours). Blue (left side of Fold change (log2) scale): decreased expression of target following perturbation of a regulator (compared to a non-targeting control); red (right side of Fold change (log2) scale): increased expression; Grey: not significant; all color (i.e., non-grey) entries are significant (see Methods in Example 1). 'Perturbed' (left): signature genes that are also perturbed as regulators (black entries). Key signature genes are denoted on right. Figure 4B depicts two coupled and opposing modules. Shown is the perturbation network associating the 'positive regulators' (blue nodes, left side of x-axis) of Thl7 signature genes, the 'negative regulators' (red nodes, right side of x-axis), Thl7 signature genes (grey nodes, bottom) and signature genes of other CD4+ T cells (grey nodes, top). A blue edge from node A to B indicates that knockdown of A downregulates B; a red edge indicates that knockdown of A upregulates B. Light grey halos: regulators not previously associated with Thl7

differentiation. Figure 4C depicts how knockdown effects validate edges in network model. Venn diagram: compare the set of targets for a factor in the original model of Fig. 2a (pink circle) to the set of genes that respond to that factor's knockdown in an RNA-Seq experiment (yellow circle). Bar chart on bottom: Shown is the -loglO(Pvalue) (Y axis, hypergeometric test) for the significance of this overlap for four factors (X axis). Similar results were obtained with a non-parametric rank-sum test (Mann- Whitney U test, see Methods in Example 1). Red dashed line: P=0.01. Figure 4D depicts how global knockdown effects are consistent across clusters. Venn diagram: compare the set of genes that respond to a factor's knockdown in an RNA-Seq experiment (yellow circle) to each of the 20 clusters of Fig. lb (purple circle). The knockdown of a hl7 positive' regulator was expected to repress genes in induced clusters, and induce genes in repressed clusters (and vice versa for hl7 negative' regulators). Heat map: For each regulator knockdown (rows) and each cluster (columns) shown are the significant overlaps (non grey entries) by the test above. Red (right side of Fold enrichment scale): fold enrichment for up-regulation upon knockdown; Blue (left side of Fold enrichment scale): fold enrichment for down regulation upon knockdown. Orange entries in the top row indicate induced clusters.

[0080] Figures 5A, 5B, 5C, and 5D are a series of graphs and illustrations depicting that Mina, Fas, Pou2afl, and Tsc22d3 are key novel regulators affecting the Thl7 differentiation programs. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461- 468 (2013)/doi: 10.1038/naturel l981. Figures 5A-5D, left: Shown are regulatory network models centered on different pivotal regulators (square nodes): (Fig. 5 A) Mina, (Fig. 5B) Fas, (Fig. 5C) Pou2afl, and (Fig. 5D) Tsc22d3. In each network, shown are the targets and regulators (round nodes) connected to the pivotal nodes based on perturbation (red and blue dashed edges), TF binding (black solid edges), or both (red and blue solid edges). Genes affected by perturbing the pivotal nodes are colored (blue: target is down-regulated by knockdown of pivotal node; red: target is up-regulated). (Figs. 5A-5C, middle and right) Intracellular staining and cytokine assays by ELISA or Cytometric Bead Assays (CBA) on culture supernatants at 72h of in vitro differentiated cells from respective KO mice activated in vitro with anti- CD3 + anti-CD28 with or without Thl7 polarizing cytokines (TGF-β + IL-6). (Fig. 5D, middle) ChlP-Seq of Tsc22d3. Shown is the proportion of overlap in bound genes (dark grey) or bound regions (light grey) between Tsc22d3 and a host of Thl7 canonical factors (X axis). All results are statistically significant (P<10^~6; see Methods in Example 1).

[0081] Figures 6A, 6B, 6C, and 6D are a series of graphs and illustrations depicting treatment of Na^'ive CD4+ T-cells with TGF-βΙ and IL-6 for three days induces the differentiation of Thl7 cells. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461- 468 (2013)/doi: 10.1038/naturel 1981. Figure 6A depicts an overview of the time course experiments. Na^'ive T cells were isolated from WT mice, and treated with IL-6 and TGF-βΙ . Microarrays were then used to measure global mRNA levels at 18 different time points (0.5hr-72hr, see Methods in Example 1). As a control, the same WT na^'ive T cells under ThO conditions harvested at the same 18 time points were used. For the last four time points (48hr - 72hr), cells treated with IL-6, TGF-βΙ, and IL-23 were also profiled. Figure 6B depicts generation of Thl7 cells by IL-6 and TGF-βΙ polarizing conditions. FACS analysis of na^'ive T cells differentiated with TGF-βΙ and IL-6 (right) shows enrichment for IL-17 producing Thl7 T cells; these cells are not observed in the ThO controls. Figure 6C depicts comparison of the obtained microarray profiles to published data from na^'ive T-cells and differentiated Thl7 cells (Wei et. al, 2009; Langmead, B., Trapnell, C, Pop, M. & Salzberg, S. L. in Genome Biol Vol. 10 R25 (2009)). Shown is the Pearson correlation coefficient (Y axis) between each of the 18 profiles (ordered by time point, X axis) and either the na^'ive T cell profiles (blue) or the differentiated Thl7 profiles (green). The expression profiles gradually transition from a na^'ive-like state (at t=0.5hr, r2>0.8, p<10^"10) to a Thl7 differentiated state (at t=72hr, r2>0.65, p<10^"10). Figure 6D depicts expression of key cytokines. Shown are the mR A levels (Y axis) as measured at each of the 18 time points (X axis) in the Thl7 polarizing (blue) and ThO control (red) conditions for the key Thl7 genes RORc (left) and IL-17a (middle), both induced, and for the cytokine IFN-γ, unchanged in the time course.

[0082] Figure 7 is a series of graphs depicting clusters of differentially expressed genes in the Thl7 time course data. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/naturel 1981. For each of the 20 clusters in Fig. lb shown are the average expression levels (Y axis, ± standard deviation, error bars) at each time point (X axis) under Thl7 polarizing (blue) and ThO (red) conditions. The cluster size ("n"), enriched functional annotations ("F"), and representative member genes ("M") are denoted on top.

[0083] Figures 8A and 8B are a series of graphs depicting transcriptional effects of

IL-23. Figure 8A depicts transcriptional profiles of key genes. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/naturel 1981. Shown are the expression levels (Y axis) of three key genes (IL-22, RORc, IL-4) at each time point (X axis) in Thl7 polarizing conditions (blue), ThO controls (red), and following the addition of IL-23 (beginning at 48hr post differentiation) to the Thl7 polarizing conditions (green). Figure 8B depicts IL-23 -dependent transcriptional clusters. Shown are clusters of differentially expressed genes in the IL-23r ^{~ ~} time course data (blue) compared to WT cells, both treated with Thl7 polarizing cytokines and IL23 (red). For each cluster, shown are the average expression levels (Y axis, ± standard deviation, error bars) at each time point (X axis) in the knockout (blue) and wildtype (red) cells. The cluster size ("n"), enriched functional annotations ("F"), and representative member genes ("M") are denoted on top.

[0084] Figures 9A and 9B are a series of graphs depicting predicted and validated protein levels of ROR-γί during Thl7 differentiation. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/naturel l981. Figure 9A shows RORyt mRNA levels along the original time course under Thl7 polarizing conditions, as measured with microarrays (blue). A sigmoidal fit for the mRNA levels (green) is used as an input for a model (based on Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342, doi: 10.1038/naturel0098 (2011)) that predicts the level of RORyt protein at each time point (red). Figure 9B depicts distribution of measured ROR-yt protein levels (x axis) as determined by FACS analysis in Thl7 polarizing conditions (blue) and ThO conditions (red) at 4, 12, 24, and 48hr post stimulation.

[0085] Figures 10A and 10B are a series of graphs depicting predictive features for ranking candidates for knockdown. Shown is the fold enrichment (Y axis, in all cases, p <10^~3, hypergeometric test) in a curated list of known Thl7 factors for different (Fig. 10A) network-based features and (Fig. 10B) expression-base features (as used in Fig. 3a).

[0086] Figures 11 A, 1 IB, and 11C are a series of graphs depicting Nanowire activation on T-cells, knockdown at lOh, and consistency of NW-based knockdowns and resulting phenotypes. Figure 11 A depicts how Nanowires do not activate T cells and do not interfere with physiological stimuli. Shown are the levels of mRNA (mean ± standard error, n = 3) for key genes, measured 48hr after activation by qPCR (Y axis, mean and standard error of the mean), in T cells grown in petri dishes (left) or on silicon nanowires (right) without polarizing cytokines ('no cytokines') or in the presence of Thl7 polarizing cytokines ('TGF-βΙ + IL6'). Figure 1 IB depicts effective knockdown by siRNA delivered on nanowires. Shown is the % of mRNA remaining after knockdown (by qPCR, Y axis: mean ± standard error relative to non-targeting siRNA control, n = 12, black bar on left) at 10 hours after introduction of polarizing cytokines. The genes presented are a superset of the 39 genes selected for transcriptional profiling. Figure 11c. Consistency of NW-based knockdowns and resulting phenotypes. Shown are average target transcript reductions and phenotypic changes (as measured by IL-17f and IL-17a expression) for three different experiments of NW-based knockdown (from at least 2 different cultures) of 9 genes at 48 hours post stimulation. Light blue bars: knockdown level (%remaining relative to siRNA controls); dark grey and light green bars: mRNAs of IL-17f and IL-17a, respectively, relative to siRNA controls.

[0087] Figures 12A and 12B are a series of graphs depicting cross-validation of the

Nanostring expression profiles for each nanowire-delivered knockdown using Fluidigm 96x96 gene expression chips. Figure 12A depicts a comparison of expression levels measured by Fluidigm (Y axis) and Nanostring (X axis) for the same gene under the same perturbation. Expression values were normalized to control genes as described in Example 1. Figure 12B depicts how analysis of Fluidigm data recapitulates the partitioning of targeted factors into two modules of positive and negative Thl7 regulators. Shown are the changes in transcription of the 82 genes out of the 85 gene signature (rows) that

significantly responded to at least one factor knockdown (columns).

[0088] Figure 13 is a graph depicting rewiring of the Thl7 "functional" network between lOhr to 48hr post stimulation. For each regulator that was profiled at lOhr and 48hr, the percentage of "edges" (i.e., gene A is affected by perturbation of gene B) that either appear in the two time points with the same activation/repression logic (Sustained); appear only in one time point (Transient); or appear in both networks but with a different activation/repression logic (Flipped) were calculated. In the sustained edges, the

perturbation effect (fold change) has to be significant in at least one of the time point (see Methods in Example 1), and consistent (in terms of activation/repression) in the other time point (using a more permissive cutoff of 1.25 fold).

[0089] Figure 14 is an illustration depicting "chromatic" network motifs. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi:

10.1038/naturel 1981. A 'chromatic' network motif analysis was used to find recurring sub networks with the same topology and the same node and edge colors. Shown are the four significantly enriched motifs (p<0.05). Red nodes: positive regulators; blue nodes: negative regulator; red edges from A to B: knockdown of A downregulates B; blue edge: knockdown of A upregulates B. Motifs were found using the FANMOD software (Wernicke, S. & Rasche, F. FANMOD: a tool for fast network motif detection. Bio informatics 22, 1152- 1153, doi: 10.1093/bioinformatics/btl038 (2006)).

[0090] Figures 15 A, 15B, and 15C are a series of graphs depicting RNA-seq analysis of nanowire-delivered knockdowns. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/naturel 1981. Figure 15A depicts a correlation matrix of knockdown profiles. Shown is the Spearman rank correlation coefficient between the R A-Seq profiles (fold change relative to NT si NA controls) of regulators perturbed by knockdowns. Genes that were not significantly differentially expressed in any of the samples were excluded from the profiles. Figure 15B depicts knockdown effects on known marker genes of different CD4+ T cell lineages. Shown are the expression levels for canonical genes (rows) of different T cell lineages (labeled on right) following knockdown of each of 12 regulators (columns). Red/Blue: increase/decrease in gene expression in knockdown compared to non-targeting control (see Methods in Example 1). Shown are only genes that are significantly differentially expressed in at least one knockdown condition. The experiments are hierarchically clustered, forming distinct clusters for Thl7-positive regulators (left) and Thl7-negative regulators (right). Figure 15C depicts knockdown effects on two subclusters of the T-regulatory cell signature, as defined by Hill et al, Foxp3 transcription- factor-dependent and -independent regulation of the regulatory T cell transcriptional signature. Immunity 27, 786-800, doi:S1074-7613(07)00492-X [pii]

10.1016/j.immuni.2007.09.010 (2007). Each cluster (annotated in Hill et al as Clusters 1 and 5) includes genes that are over expressed in Tregs cells compared to conventional T cells. However, genes in Cluster 1 are more correlated to Foxp3 and responsive to Foxp3 transduction. Conversely, genes in cluster 1 are more directly responsive to TCR and IL-2 and less responsive to Foxp3 in Treg cells. Knockdown of Thl7-positive regulators strongly induces the expression of genes in the 'Foxp3 ' Cluster 1. The knockdown profiles are hierarchically clustered, forming distinct clusters for Thl7-positive regulators (left) and Thl7-neagtive regulators (right). Red/Blue: increase/decrease in gene expression in knockdown compared to non-targeting control (see Methods in Example 1). Shown are only genes that are significantly differentially expressed in at least one knockdown condition.

[0091] Figures 16A, 16B, 16C, and 16D are a series of graphs depicting

quantification of cytokine production in knockout cells at 72h of in- vitro differentiation using Flow cytometry and Enzyme-linked immunosorbent assay (ELISA). All flow cytometry figures shown, except for Octl, are representative of at least 3 repeats, and all ELISA data has at least 3 replicates. For Octl, only a limited amount of cells were available from reconstituted mice, allowing for only 2 repeats of the Octl deficient mouse for flow cytometry and ELISA. (Fig. 16A, left) Mina^_/~ T cells activated under ThO controls are controls for the graphs shown in Fig. 5a. (Fig. 16A, right) TNF secretion by Mina^_/~ and WT cells, as measured by cytometric bead assay showing that Mina^_/~ T cells produce more TNF when compared to control. Figure 15B depicts intracellular cytokine staining of Pou2afT ^~ and WT cells for IFN-γ and IL-17a as measured by flow cytometry. (Fig. 15C, left) Flow cytometric analysis of Fas '^" and WT cells for Foxp3 and 11-17 expression. (Fig. 15C, right) IL-2 and Tnf secretion by Fas '^" and WT cells, as measured by a cytokine bead assay ELISA. (Fig 15D, left). Flow cytometry on Octl^_/" and WT cells for IFN-γ and IL-17a, showing an increase in IFN-γ positive cells in the ThO condition for the Octl deficient mouse. (Fig. 15D, right) II- 17a, IFN-γ, IL-2 and TNF production by Octl^"7" and WT cells, as measured by cytokine ELISA and cytometric bead assay. Statistical significance in the ELISA figures is denoted by: * p < 0.05, ** p < 0.01, and *** p <0.001.

[0092] Figures 17A and 17B are a series of illustrations depicting that Zebl,

Smarca4, and Sp4 are key novel regulators affecting the Thl7 differentiation programs. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi:

10.1038/naturel 1981. Shown are regulatory network models centered on different pivotal regulators (square nodes): (Fig. 17A) Zebl and Smarca4, and (Fig. 17B) Sp4. In each network, shown are the targets and regulators (round nodes) connected to the pivotal nodes based on perturbation (red and blue dashed edges), TF binding (black solid edges), or both (red and blue solid edges). Genes affected by perturbing the pivotal nodes are colored (red: target is up-regulated by knockdown of pivotal node; blue: target is down-regulated).

[0093] Figure 18 is a graph depicting the overlap with ChlP-seq and RNA-seq data from Ciofani et al (Cell, 2012). Fold enrichment is shown for the four TF that were studied by Ciofani et al using ChlP-seq and RNA-seq and are predicted as regulators in the three network models (early, intermediate (denoted as "mid"), and late). The results are compared to the ChlP-seq based network of Ciofani et al. (blue) and to their combined ChlP- seq/RNA-seq network (taking a score cutoff of 1.5, as described by the authors; red). In all cases the p-value of the overlap (with ChlP-seq only or with the combined ChlP-seq/ RNA- seq network) is below 10^{" 10} (using Fisher exact test), but the fold enrichment is particularly high in genes that are both bound by a factor and affected by its knockout, the most functional edges.

[0094] Figures 19 A, 19B, 19C, and 19D are a series of graphs depicting that

PROCR is specifically induced in Thl7 cells induced by TGF-βΙ with IL-6. Figure 19A depicts how PROCR expression level was assessed by the microarray analysis under ThO and Thl7 conditions at 18 different time points. Figure 19B depicts how kinetic expression of PROCR mRNA was measured by quantitative RT-PCR analysis in Thl7 cells

differentiated with TGF-βΙ and IL-6. Figure 19C depicts how PROCR mRNA expression was measured by quantitative RT-PCR analysis in different T cell subsets 72hr after stimulation by each cytokine. Figure 19D depicts how PROCR protein expression was examined by flow cytometry in different T cell subsets 72hr after stimulation with each cytokine.

[0095] Figures 20A, 20B, 20C, and 20D are a series of graphs depicting that

PROCR stimulation and expression is not essential for cytokine production from Thl7 cells. Figure 20A depicts how na^'ive CD4+ T cells were differentiated into Thl7 cells by anti- CD3/anti-CD28 stimulation in the presence of activated protein C (aPC, 300nM), the ligand of PROCR. On day 3, cells were stimulated with PMA and Ionomycin for 4hr, stained intracellularly for IFN-γ and IL-17 and analyzed by flow cytometry. Figure 20B depicts IL- 17 production from Thl7 cells (TGF-β + IL-6) differentiated with or without activated protein C (aPC and Ctl, respectively) was assessed by ELISA on Day 3 and 5. Figure 20C depicts how na^'ive CD4+ T cells were polarized under Thl7 conditions (TGF-β + IL-6), transduced with either GFP control retrovirus (Ctl RV) or PROCR-expressing retrovirus (PROCR RV). Intracellular expression of IFN-γ and IL-17 in GFP+ cells were assessed by flow cytometry. Figure 20D depicts how na^'ive CD4+ T cells from EPCR δ/δ mice and control mice were polarized under Thl7 conditions with TGF-βΙ and IL-6. Intracellular expression of IFN-γ and IL-17 were assessed by flow cytometry.

[0096] Figures 21 A and 2 IB are a series of graphs depicting that PROCR

expression only induces minor changes in the expression of co-stimulatory molecules on Thl7 cells. Figure 21 A depicts how na^'ive CD4⁺ T cells were polarized under Thl7 conditions (TGF-β + IL-6), transduced with either GFP control retrovirus (Ctl GFP) or PROCR-expressing retrovirus (PROCR RV) and expression of ICOS, CTLA-4, PD-1, Pdp and Tim-3 was analyzed by flow cytometry. Figure 2 IB depicts how na^'ive wild type (WT) or EPCR 5/5 C ⁺ T cells were differentiated into Thl7 cells by anti-CD3/anti-CD28 stimulation in the presence of TGF-βΙ and IL-6. Expression of ICOS, CTLA-4, PD-1, Pdp and Tim-3 was assessed by flow cytometry.

[0097] Figures 22A, 22B, and 22C are a series of graphs depicting that PROCR is expressed in non-pathogenic Thl7 cells. Figure 22A depicts genes for Thl7 cells differentiated with TGF^3 + IL-6 (pathogenic) or TGF-βΙ + IL-6 (non-pathogenic) and comparison of their expression levels in these two subsets. Figures 22B and 22C depict how na^'ive CD4⁺ T cells were differentiated with TGF-βΙ and IL-6, TGF- 3 and IL-6 or IL-Ιβ and IL-6 and PROCR expression was assessed by (Fig. 22B) quantitative RT-PCR analysis (Fig. 22C) and protein expression was determined by flow cytometry.

[0098] Figures 23 A, 23B, and 23C are a series of graphs depicting that PROCR stimulation or expression impairs some pathogenic signature genes in Thl7 cells. Figure 23A depicts quantitative RT-PCR analysis of mRNA expression of several pathogenic signature genes in Thl7 cells differentiated with TGF i and IL-6 in the presence of activated protein C (aPC) for 3 days in vitro. Figure 23B depicts quantitative RT-PCR analysis of mRNA expression of several pathogenic signature genes in na^'ive CD4⁺ T cells polarized under Thl7 conditions, transduced with either GFP control retrovirus (Control RV) or PROCR-expressing retrovirus (PROCR RV) for 3 days. Figure 23C depicts quantitative RT-PCR analysis of mRNA expression of several pathogenic signature genes in Thl7 cells from E Ci? δ/δ mice and control mice differentiated with TGF i and IL-6 for 3 days in vitro.

[0099] Figures 24A, 24B, 24C, and 24D are a series of graphs depicting that Roryt induces PROCR expression under Thl7 conditions polarized with TGF-βΙ and IL-6. Figure 24A depicts ChlP-Seq of Roryt. The PROCR genomic region is depicted. Figure 24B depicts how the binding of Roryt to the Procr promoter in Thl7 cells was assessed by chromatin immunoprecipitation (ChIP). ChIP was performed using digested chromatin from Thl7 cells and anti-Roryt antibody. DNA was analyzed by quantitative RT-PCR analysis. Figure 24C depicts how na^'ive CD4+ T cells from Roryt-/- mice and control mice were polarized under Thl7 conditions with TGF-βΙ and IL-6 and under ThO conditions (no cytokines) and PROCR expression was analyzed on day 3 by flow cytometry. Figure 24D depicts how na^'ive CD4+ T cells polarized under Thl7 conditions were transduced with either GFP control retrovirus (Ctl RV) or Roryt -expressing retrovirus (Roryt RV) for 3 days. PROCR mRNA expression was measured by quantitative RT-PCR analysis and PROCR protein expression was assessed by flow cytometry.

[00100] Figures 25 A, 25B, and 25C are a series of graphs depicting that IRF4 and

STAT3 bind to the Procr promoter and induce PROCR expression. Figure 25 A depicts how binding of IRF4 or STAT3 to the Procr promoter was assessed by chromatin

immunoprecipitation (ChlP)-PCR. ChIP was performed using digested chromatin from

Thl7 cells and anti-IRF4 or anti-STAT3 antibody. DNA was analyzed by quantitative RT- PCR analysis. Figure 25B depicts how na^'ive CD4+ T cells from Cd4^CreSTATf^/J1 mice (STAT3 KO) and control mice (WT) were polarized under Thl 7 conditions with TGF-βΙ with IL-6 and under ThO condition with no cytokines. On day 3, PROCR expression was determined by quantitative PCR. Figure 25 C depicts how na^'ive CD4+ T cells from

Cd^^IRF^ mice and control mice were polarized under Thl 7 conditions with TGF-βΙ and IL-6 and under ThO condition with no cytokines. On day 3, PROCR expression was determined by flow cytometry.

[00101] Figures 26A, 26B, 26C, and 26D are a series of graphs and illustrations depicting that PROCR deficiency exacerbates EAE severity. Figure 26A depicts frequency of CD4+ T cells expressing IL-17 and PROCR isolated from EAE mice 21d after immunization with MOG₃₅-55. Figure 26B depicts how EAE was induced by adoptive transfer of MOG₃₅_55-specific 2D2 cells transduced with a control retrovirus (Ctl GFP) or a PROCR-expression retrovirus (PROCR RV) and differentiated into Thl 7 cells. Mean clinical scores and summaries for each group are shown. Results are representative of one of two experiments. Figure 26C depicts how Ragl-/- mice were reconstituted with either PROCR-deficient (EPCR £/£→Ragl-/-) or WT T cells (WT→Ragl-/-) and immunized with MOG₃₅_55 to induce EAE. The mean clinical score of each group is shown. Results are representative of one of two experiments. Figure 26D depicts a schematic representation of PROCR regulation. Roryt, IRF4, and STAT3 induce PROCR expression. PROCR ligation by activated protein C induces a downregulation of the pathogenic signature genes IL-3, CXCL3, CCL4 and Pdp and reduced pathogenicity in EAE.

[00102] Figures 27A, 27B, and 27C are a series of graphs depicting that FAS promotes Thl 7 differentiation. Na^'ive CD4⁺ T cells from wild type (WT) or FAS-deficient (LPR) mice were differentiated into Thl 7 cells by anti-CD3/anti-CD28 stimulation in the presence of IL-Ιβ, IL-6 and IL-23. On day 4, cells were (Fig. 27A) stimulated with PMA and Ionomycin for 4hr, stained intracellularly for IFN-γ and IL-17 and analyzed by flow cytometry and (Fig. 27B) IL-17 production was assessed by ELISA. Figure 27C depicts how RNA was extracted and expression of IL17a and Il23r mRNA was determined by quantitative PCR.

[00103] Figures 28A, 28B, and 28C are a series of graphs depicting that FAS inhibits

Thl differentiation. Na^'ive CD4⁺ T cells from wild type (WT) or FAS-deficient (LPR) mice were differentiated into Thl cells by anti-CD3/anti-CD28 stimulation in the presence of IL- 12 and anti-IL-4. On day 4, cells were (Fig. 28A) stimulated with PMA and Ionomycin for 4hr, stained intracellularly for IFN-γ and IL-17 and analyzed by flow cytometry and (Fig. 28B) IFN-γ production was assessed by ELISA. Figure 28C depicts how RNA was extracted and expression of Ifng mRNA was determined by quantitative PCR.

[00104] Figures 29A and 29B are a series of graphs depicting that FAS inhibits Treg differentiation. Na^'ive CD4⁺ T cells from wild type (WT) or FAS-deficient (LPR) mice were differentiated into Tregs by anti-CD3/anti-CD28 stimulation in the presence of TGF-β. On day 4, cells were (Fig. 29A) stimulated with PMA and Ionomycin for 4hr, stained intracellularly for IL-17 and Foxp3 and analyzed by flow cytometry and (Fig. 29B) IL-10 production was assessed by ELISA.

[00105] Figures 30A and 30B are a series of graphs depicting that FAS-deficient mice are resistant to EAE. Wild type (WT) or FAS-deficient (LPR) mice were immunized with 10(^g MOG₃₅-55 in CFA s.c. and received pertussis toxin i.v. to induce EAE. Figure 30A depicts mean clinical score ± s.e.m. of each group as shown. Figure 30B depicts how on day 14 CNS infiltrating lymphocytes were isolated, re-stimulated with PMA and Ionomycin for 4 hours and stained intracellularly for IL-17, IFN-γ, and Foxp3. Cells were analyzed by flow cytometry.

[00106] Figures 31 A, 3 IB, 31C and 3 ID are a series of graphs and illustrations depicting that PROCR is expressed on Thl7 cells. Figure 31A depicts a schematic representation of PROCR, its ligand activated protein C and the signaling adapter

PARI . Figure 3 IB depicts how na^'ive CD4+ T cells were differentiated under polarizing conditions for the indicated T helper cell lineages. Expression of PROCR was determined by quantitative PCR on day 3. Figure 31C depicts how mice were immunized for EAE, cells were isolated at peak of disease, and cytokine production (IL-17) and PROCR expression were analyzed by flow cytometry. Figure 3 ID depicts how na^'ive and memory cells were isolated from WT and PROCRd/d mice and stimulated with anti-CD3/CD28. Na^'ive cells were cultured under Thl7 polarizing conditions as indicated; memory cells were cultured in the presence or absence of IL-23. After 3 days IL-17A levels in supernatants were analyzed by ELISA.

[00107] Figures 32A, 32B, 32C and 32D are a series of graphs depicting how

PROCR and PD-1 expression affects Thl7 pathogenicity. Figure 32A depicts signature genes of pathogenic and non-pathogenic Thl7 cells. Na^'ive CD4+ T cells were differentiated into non-pathogenic (TGFpi+IL-6) or pathogenic (TGFP3+IL-6 or IL-pi+IL-6) Thl7 cells and PROCR expression was determined by quantitative PCR. Figure 32B depicts how na^'ive WT or PROCRd/d CD4+ T cells were stimulated under Thl7 polarizing conditions (TGFpi+IL-6) in the presence or absence of aPC. Quantitative expression of three pathogenic signature genes was determined on day 3. Figure 32C depicts how na^'ive 2D2 T cells were transduced with a retrovirus encoding for PROCR or a control (GFP), differentiated into Thl7 cells in vitro, and transferred into na^'ive recipients. Mice were monitored for EAE. Figure 32D depicts how na^'ive 2D2 T cells were differentiated into Thl7 cells in vitro with TGFpi+IL-6 + IL-23 and transferred into WT or PD-L1-/- recipients. Mice were monitored for EAE.

[00108] Figures 33A and 33B are a series of graphs depicting that PROCR expression is enriched in exhausted T cells. Figure 33A depicts how C57BL/6 or BalbC mice were implanted with B16 melanoma or CT26 colon cancer cells respectively. Tumor Infiltrating Lymphocytes were isolated 3 weeks after tumor implantation, sorted based on PD-1 and Tim3 expression and analyzed for PROCR expression using real time PCR. Effector memory (CD44hiCD62Llo) CD8 T cells were sorted from na^'ive mice. Figure 33B) depicts how PROCR, PD-1 and Tim3 expression on antigen-specific CD8 T cells were measured by FACS from acute (Armstrong) and chronic (Clone 13) LCMV infection at different times points as indicated.

[00109] Figure 34 is a graph depicting B16 tumor inoculation of PROCR mutant mice. 7 week old wild type or PROCR mutant (EPCR delta) C57BL/6 mice were inoculated with 5xl0⁵ B16F10 melanoma cells.

DETAILED DESCRIPTION

[00110] 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.

[00111] 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 Thl7 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 Thl7 activity and inflammatory potential. As used herein, terms such as "Thl7 cell" and/or "Thl7 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 inter leukin 17A (IL-17A), interleukin 17F (IL-17F), and interleukin 17A/F heterodimer (IL17-AF). As used herein, terms such as "Thl cell" and/or "Thl phenotype" and all grammatical variations thereof refer to a differentiated T helper cell that expresses interferon gamma (IFNy). 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.

[00112] 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 Thl 7 and other T cell types, for example, regulatory T cells (Tregs).

[00113] 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 Thl 7 cell types, e.g., between pathogenic and non-pathogenic Thl 7 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 Thl 7 cell phenotype, with or without a specific pathogenic distinction, or away from the Thl 7 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 Thl 7 cell phenotype, with or without a specific pathogenic distinction, or away from the Thl 7 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 Thl 7 cells, for example toward the pathogenic Thl 7 cell phenotype or away from the pathogenic Thl 7 cell phenotype, or toward the non-pathogenic Thl7 cell phenotype or away from the nonpathogenic Thl7 cell phenotype. These compositions and methods use T cell modulating agents to influence or otherwise impact the maintenance of a population of Thl7 cells, for example toward the pathogenic Thl7 cell phenotype or away from the pathogenic Thl7 cell phenotype, or toward the non-pathogenic Thl7 cell phenotype or away from the nonpathogenic Thl7 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-Thl7 T cell subset or away from a non-Thl7 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-Thl7 T cell subset or away from a non-Thl7 cell subset.

[00114] As used herein, terms such as "pathogenic Thl7 cell" and/or "pathogenic

Thl7 phenotype" and all grammatical variations thereof refer to Thl7 cells that, when induced in the presence of TGF-P3, express an elevated level of one or more genes selected from CxcB, IL22, IL3, Ccl4, Gzmb, Lrmp, Ccl5, Caspl, Csf2, Ccl3, Tbx21, Icos, IL17r, Stat4, Lgals3 and Lag, as compared to the level of expression in a TGF-P3-induced Thl7 cells. As used herein, terms such as "non-pathogenic Thl7 cell" and/or "non-pathogenic Thl7 phenotype" and all grammatical variations thereof refer to Thl7 cells that, when induced in the presence of TGF-P3, express a decreased level of one or more genes selected from IL6st, ILlrn, Ikzf3, Maf, Ahr, IL9 and IL10, as compared to the level of expression in a TGF-p3-induced Thl7 cells.

[00115] 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 Thl7 cell or Thl7 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-Thl7 T cell or non-Thl7 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 Thl7 cells into a different subtype, or into a new state. [00116] 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 Thl7 differentiation. See e.g., Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/naturel l981, 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 Thl7 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 Thl7 differentiation.

[00117] A "Thl7-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 Thl7 cells, co-localizes on the genome with key Thl7 regulators. The "Thl7 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 Pou2afl was found to up-regulate IFN-γ production in stimulated na^'ive cells, and perturbation of the TNF receptor Fas was found to up-regulate IL-2 production in stimulated na^'ive cells. All three factors also control IL-17 production in Thl7 cells.

[00118] Effective coordination of the immune system requires careful balancing of distinct pro- inflammatory and regulatory CD4+ helper T cell populations. Among those, pro- inflammatory IL-17 producing Thl7 cells play a key role in the defense against extracellular pathogens and have also been implicated in the induction of several autoimmune diseases (see e.g., Bettelli, E., Oukka, M. & Kuchroo, V. K. T(H)-17 cells in the circle of immunity and autoimmunity. Nat Immunol 8, 345-350, doi: 10.1038/ni0407- 345 (2007)), including for example, psoriasis, ankylosing spondylitis, multiple sclerosis and inflammatory bowel disease. Thl7 differentiation from na^'ive T-cells can be triggered in vitro by the cytokines TGF-βΙ and IL-6. While TGF-βΙ alone induces Foxp3+ regulatory T cells (iTreg) (see e.g., Zhou, L. et al. TGF-beta- induced Foxp3 inhibits T(H)17 cell differentiation by antagonizing RORgammat function. Nature 453, 236-240, doi:nature06878 [pii] 10.1038/nature06878 (2008)), the presence of IL-6 inhibits iTreg and induces Thl7 differentiation (Bettelli et al, Nat Immunol 2007).

[00119] While TGF-βΙ is required for the induction of Foxp3+ induced Tregs

(iTregs), the presence of IL-6 inhibits the generation of iTregs and initiates the Thl7 differentiation program. This led to the hypothesis that a reciprocal relationship between pathogenic Thl7 cells and Treg cells exists (Bettelli et al, Nat Immunol 2007), which may depend on the balance between the mutually antagonistic master transcription factors (TFs) ROR-γί (in Thl7 cells) and Foxp3 (in Treg cells) (Zhou et al, Nature 2008). Other cytokine combinations have also been shown to induce ROR-γί and differentiation into Thl7 cells, in particular TGF-βΙ and IL-21 or IL-Ιβ, TGF-P3 + IL-6, IL-6, and IL-23 (Ghoreschi, K. et al. Generation of pathogenic T(H)17 cells in the absence of TGF-beta signaling. Nature 467, 967-971, doi: 10.1038/nature09447 (2010)). Finally, although a number of cytokine combinations can induce Thl7 cells, exposure to IL-23 is critical for both stabilizing the Thl7 phenotype and the induction of pathogenic effector functions in Thl7 cells.

[00120] Much remains unknown about the regulatory network that controls Thl7 cells (O'Shea, J. et al. Signal transduction and Thl7 cell differentiation. Microbes Infect 11, 599-611 (2009); Zhou, L. & Littman, D. Transcriptional regulatory networks in Thl7 cell differentiation. Curr Opin Immunol 21, 146-152 (2009)). Developmentally, as TGF-β is required for both Thl7 and iTreg differentiation, it is not understood how balance is achieved between them or how IL-6 biases toward Thl7 differentiation (Bettelli et al, Nat Immunol 2007). Functionally, it is unclear how the pro -inflammatory status of Thl7 cells is held in check by the immunosuppressive cytokine IL-10 (O'Shea et al, Microbes Infect 2009; Zhou & Littman, Curr Opin Immunol 2009). Finally, many of the key regulators and interactions that drive development of Thl7 remain unknown (Korn, T., Bettelli, E., Oukka, M. & Kuchroo, V. K. IL-17 and Thl7 Cells. Annu Rev Immunol 27, 485-517,

doi: 10.1146/annurev. immuno 1.021908.13271010.1146/annurev. immuno 1.021908. 132710 [pii] (2009)).

[00121] Recent studies have demonstrated the power of coupling systematic profiling with perturbation for deciphering mammalian regulatory circuits (Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257-263, doi: 10.1126/science.1179050 (2009); Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296-309, doi: 10.1016/j.cell.2011.01.004 (2011); Litvak, V. et al. Function of C/EBPdelta in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals. Nat. Immunol. 10, 437-443, doi: 10.1038/ni. l721 (2009); Suzuki, H. et al. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat Genet 41, 553-562 (2009); Amit, I., Regev, A. & Hacohen, N. Strategies to discover regulatory circuits of the mammalian immune system. Nature reviews. Immunology 11, 873-880, doi: 10.1038/nri3109 (2011); Chevrier, N. et al.

Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell 147, 853-867, doi: 10.1016/j.cell.2011.10.022 (2011); Garber, M. et al. A High-Throughput Chromatin Immunoprecipitation Approach Reveals Principles of Dynamic Gene Regulation in Mammals. Molecular cell, doi: 10.1016/j.molcel.2012.07.030 (2012)). Most of these studies have relied upon computational circuit-reconstruction algorithms that assume one 'fixed' network. Thl7 differentiation, however, spans several days, during which the components and wiring of the regulatory network likely change. Furthermore, na^'ive T cells and Thl7 cells cannot be transfected effectively in vitro by traditional methods without changing their phenotype or function, thus limiting the effectiveness of perturbation strategies for inhibiting gene expression.

[00122] These limitations are addressed in the studies presented herein by combining transcriptional profiling, novel computational methods, and nanowire-based siRNA delivery (Shalek, A. K. et al. Vertical silicon nanowires as a universal platform for delivering biomolecules into living cells. Proc. Natl. Acad. Sci. U.S.A. 107, 1870-1875,

doi: 10.1073/pnas.0909350107 (2010) (Fig. la) to construct and validate the transcriptional network of Thl7 differentiation. Using genome-wide profiles of mRNA expression levels during differentiation, a model of the dynamic regulatory circuit that controls Thl7 differentiation, automatically identifying 25 known regulators and nominating 46 novel regulators that control this system, was built. Silicon nanowires were used to deliver siRNA into na^'ive T cells (Shalek et al, Proc. Natl. Acad. Sci. U.S.A. 2010) to then perturb and measure the transcriptional effect of 29 candidate transcriptional regulators and 10 candidate receptors on a representative gene signature at two time points during

differentiation. Combining this data, a comprehensive validated model of the network was constructed. In particular, the circuit includes 12 novel validated regulators that either suppress or promote Thl7 development. The reconstructed model is organized into two coupled, antagonistic, and densely intra-connected modules, one promoting and the other suppressing the Thl7 program. The model highlights 12 novel regulators, whose function was further characterized by their effects on global gene expression, DNA binding profiles, or Thl7 differentiation in knockout mice. The studies provided herein demonstrate an unbiased systematic and functional approach to understanding the development of the Thl7 T cell subset.

[00123] The methods provided herein combine a high-resolution transcriptional time course, novel methods to reconstruct regulatory networks, and innovative nanotechnology to perturb T cells, to construct and validate a network model for Thl7 differentiation. The model consists of three consecutive, densely intra-connected networks, implicates 71 regulators (46 novel), and suggests substantial rewiring in 3 phases. The 71 regulators significantly overlap with genes genetically associated with inflammatory bowel disease (Jostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119-124, doi: 10.1038/naturel 1582 (2012)) (11 of 71, p<10^"9). Building on this model, 127 putative regulators (80 novel) were systematically ranked, and top ranking ones were tested experimentally.

[00124] It was found that the Thl7 regulators are organized into two tightly coupled, self-reinforcing but mutually antagonistic modules, whose coordinated action may explain how the balance between Thl7, Treg, and other effector T cell subsets is maintained, and how progressive directional differentiation of Thl7 cells is achieved. Within the two modules are 12 novel factors (Fig. 4 and 5), which were further characterized, highlighting four of the factors (others are in Fig. 17 A, 17B).

[00125] This validated model highlights at least 12 novel regulators that either positively or negatively impact the Thl7 program (Fig. 4 and 5). Remarkably, these and known regulators are organized in two tightly coupled, self-reinforcing and mutually antagonistic modules, whose coordinated action may explain how the balance between Thl7, Treg, and other effector T cells is maintained, and how progressive directional differentiation of Thl7 cells is achieved while repressing differentiation of other T cell subsets. The function of four of the 12 regulators - Mina, Fas, Pou2afl, and Tsc22d3 - was further validated and characterized by undertaking Thl7 differentiation of T cells from corresponding knockout mice or with ChlP-Seq binding profiles.

[00126] 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 Thl7-related perturbations. These target genes are identified, for example, by contacting a T cell, e.g., na^'ive 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 shown below.

Table 1. Signature Genes

IL17A IL21R CCL1 PSTPIP1

IL7R BCL3 CD 247 IER3

IRF4 DPP4 PROCR FZD7

CXCL10 TGFBR1 RELA GLIPRl

IL12RB1 CD83 HIF1A AIM1

TBX21 RBPJ PRNP CD4

ZNF281 CXCR3 IL17RA LMNB1

IL10RA NOTCH2 STAT1 MGLL

CXCR4 CCL4 LRRFIP1 LSP1

TNFRSF13B TAL2 KLRD1 GJA1

ACVR1B IL9 RUNX1 LGALS3BP

TGIF1 FAS ID2 ARHGEF3

ABCG2 SPRY1 STAT5A BCL2L11

REL PRF1 TNFRSF25 TGM2

ID3 FASLG BATF UBIAD1

ZEB1 MT2A KAT2B MAP3K5

MYD88 POU2AF1 NFATC2 RAB33A

EGR2 IFNG CD70 CAS PI

AES PLAC8 LITAF FOXP1

PML IL17F IL27RA MTA3

TGFBR3 DDR1 IL22 IFIH1

CCR8 IL4 MINA RASGRP1

ZFP161 CD28 XBP1 XRCC5

IRF1 TNFSF9 PRDM1 NCF1C

CCR6 SMARCA4 AHR NUDT4

SMOX VAX2 SLAMF7 PDCD1LG2

ITGB1 IL21 IL1RN PYCR1

CASP6 SAP30 MBNL3 AQ.P3

NFKBIE CD9 ARID5A SEMA7A

LAMP2 IL24 TRIM24 PRC1

GATA3 STAT5B CSF2 IFIT1

RORA SKI NFE2L2 DNTT

SGK1 BCL6 IL23R PMEPA1

IL2RA ELK3 KLF6 GAP43 MT1A CD74 ACVR2A PRICKLEl

JAK3 STAT6 NR3C1 OAS2

IL4R TNFSF8 CCR4 ERRFI1

NAMPT IL3 CXCR5 LAD1

ITGA3 TGFB1 SKAP2 TMEM126A

TGFB3 ETV6 PLEKHF2 LILRBl, LILRB2,

LILRB3, LILRB4, LILRB5

INHBA CASP4 STAT2 KATNA1

KLF7 CEBPB IRF7 B4GALT1

RUNX3 TRAF3 FLU ANXA4

NFKBIZ TRPS1 IRF9 SULT2B1

SERPINE2 JUN GFI1 PHLDA1

RXRA STAT4 MXI1 PRKD3

SERTAD1 CMTM6 IFI35 TAP1

MAF SOCS3 MAX TRIM5

IL10 TSC22D3 ZNF238 FLNA

BMPR1A LIF CHD7 GUSB

PTPRJ DAXX FOXM1 C140RF83

STAT3 KLF9 BCL11B VAV3

CCR5 IL6ST RUNX2 ARL5A

CCL20 CLCF1 EMP1 GRN

SPP1 NFIL3 PELI2 PRKCA

CD80 IKZF4 SEMA4D PECI

RORC ISG20 STARD10 ARMCX2

SERPINB1 CD86 TIMP2 SLC2A1

IL12RB2 IL2RB KLF10 RPP14

IFNGR2 NCOA1 CTSW PSMB9

SMAD3 NOTCH1 GEM CASP3

FOXP3 TNFRSF12A TRIM25 TRAT1

CD24 CD274 HLA-A P LAG LI

CD5L MAFF MYST4 RAD51AP1

CD2 ATF4 FRMD4B NKG7

TNFSF11 ARNTL RFK IFITM2

ICOS IL1R1 CD44 HIP1R

IRF8 FOXOl ERCC5

Table 2. Subset of Signature Genes

CASP6 IFNG KLRD1 SGK1

CCL20 IL10 LIF SKAP2

CCL4 IL10RA LTA SKI

CCR5 IL17A MAF SMOX

CCR6 IL17F MAFF SOCS3

CD24 IL17RA MINA STAT1

CD5L IL2 MYC STAT3

CD80 IL21 NFATC2 STAT4

CEBPB IL21R NFE2L2 TBX21

CLCF1 IL22 NFIL3 TGFBR1

CSF2 IL23R N0TCH1 TGIF1

CXCR3 IL24 NUDT4 TNFRSF25

EGR2 IL2RA PML TNFSF8

ELK3 IL7R POU2AF1 TRIM24

ETV6 IL9 PROCR TRPS1

FAS INHBA PSMB9 TSC22D3

F0XP3 IRF1 RBPJ ZFP36L1

GATA3

[00127] In some embodiments, the target gene is one or more Thl7-associated cytokine(s) or receptor molecule(s) selected from those listed in Table 3. In some embodiments, the target gene is one or more Thl7-associated transcription regulator(s) selected from those shown in Table 4.

Table 3. Thl7-Associated Receptor Molecules

Table 4. Thl7-Associated Transcription Regulators

TRPS1 SMARCA4 CDYL SIRT2

SMOX ZFP161 IKZF4 MAFF

ARNTL TP53 NCOA1 CHMP1B

UBE2B SUZ12 SS18 GATAD2B

NR3C1 POU2AF1 PHF13 ZNF703 TRIM24 MYST4 MTA3 ZNRF1

FLU MXI1 ASXL1 JMJD1C

SP4 CHD7 LASS4 ZFP36L2

EGR2 CREB3L2 SKIL TSC22D4

ZNF281 VAX2 F0SL2 NFE2L2

RELA KLF10 RUNX2 RNF11

IRF7 SKI TLE1 ARID3A

STAT2 ELK3 ELL2 MEN1

IRF3 ZEB1 BCL11B CBX4

XBP1 LRRFIP1 KAT2B ZFP62

PRDM1 PAXBP1 KLF6 CIC

ATF4 ID1 E2F8 HCLS1

CREB1 ZNF238 ZNRF2 ZFP36L1

IRF9 VAV1 TSC22D3 TGIF1

IRF2 MINA HMGB2

F0XJ2 BATF3 FUS

[00128] In some embodiments, the target gene is one or more Thl7-associated transcription regulator(s) selected from those shown in Table 5. In some embodiments, the target gene is one or more Thl7-associated receptor molecule(s) selected from those listed in Table 6. In some embodiments, the target gene is one or more Thl7-associated kinase(s) selected from those listed in Table 7. In some embodiments, the target gene is one or more Thl7-associated signaling molecule(s) selected from those listed in Table 8. In some embodiments, the target gene is one or more Thl7-associated receptor molecule(s) selected from those listed in Table 9.

Table 5. Candidate Regulators

TBX21 0 1 0 OVER-EXPR

POU2F2 0 1 0 OVER-EXPR

ZNF281 0 1 0 UNDER-EXPR

NFIL3 0.611111111 0.611111111 1

SMARCA4 0.805825243 0.757281553 1 OVER-EXPR

CSDA 0 0 1 OVER-EXPR

STAT3 0.855392157 0.970588235 1 UNDER-EXPR

FOXOl 0.875 1 0.875

NCOA3 0.875 1 0.9375

LEF1 0.380952381 0.904761905 1 UNDER-EXPR

SUZ12 0 1 0 OVER-EXPR

CDC5L 0 1 0 UNDER-EXPR

CHD7 1 0.860465116 0.686046512 UNDER-EXPR

HIF1A 0.733333333 0.666666667 1 UNDER-EXPR

RELA 0.928571429 1 0.880952381 UNDER-EXPR

STAT2 1 0.821428571 0

STAT5B 1 0.848484848 0.515151515 UNDER-EXPR

RORC 0 0 1 UNDER-EXPR

STAT1 1 0.635658915 0 UNDER-EXPR

MAZ 0 1 0

LRRFIP1 0.9 0.8 1

REL 1 0 0 OVER-EXPR

CITED2 1 0 0 UNDER-EXPR

RUNX1 0.925149701 0.925149701 1 UNDER-EXPR

ID2 0.736842105 0.789473684 1

SATB1 0.452380952 0.5 1 UNDER-EXPR

TRIM28 0 1 0

STAT6 0.54 0.64 1 OVER-EXPR

STAT5A 0 0.642241379 1 UNDER-EXPR

BATF 0.811732606 0.761255116 1 UNDER-EXPR

EGR1 0.857142857 1 0 OVER-EXPR

EGR2 0.896428571 0.839285714 1 OVER-EXPR

AES 0.888888889 1 0.777777778

IRF8 0 1 0.824786325 OVER-EXPR

SMAD2 0.806060606 0.781818182 1

NFKB1 0.266666667 0.706666667 1 UNDER-EXPR

PHF21A 1 0.533333333 0.933333333 UNDER-EXPR

CBFB 0.35 0.9 1

ZFP161 0.818181818 0.714876033 1 OVER-EXPR

ZEB2 0 0.411764706 1

SP1 0 0.740740741 1

FOXJ2 0 1 1

IRF1 1 0 0

MYC 0 0.595505618 1 UNDER-EXPR I RF2 1 0 0

EZH1 1 0.8 0.44 UNDER-EXPR

RUNX2 0 0 1

JUN 0.647058824 0.647058824 1 OVER-EXPR

STAT4 1 0 0 UNDER-EXPR

MAX 0.947368421 0.789473684 1

TP53 0.292307692 0.615384615 1 UNDER-EXPR

I RF3 1 0.485294118 0.235294118 UNDER-EXPR

BCL11B 0.666666667 0.611111111 1

E2F1 0 0 1 OVER-EXPR

I RF9 1 0.440433213 0 UNDER-EXPR

GATA3 1 0 0 OVER-EXPR

TRI M24 0.965517241 1 0.965517241 UNDER-EXPR

E2F4 0.083333333 0.5 1

NR3C1 1 1 0 UNDER-EXPR

ETS2 1 0.925925926 0.864197531 OVER-EXPR

CREB1 0.802197802 0.706959707 1

I RF7 1 0.777777778 0 OVER-EXPR

TFEB 0.8 0.6 1

TRPS1 OVER-EXPR UNDER-EXPR

SMOX OVER-EXPR OVER-EXPR UNDER-EXPR

RORA OVER-EXPR OVER-EXPR UNDER-EXPR

ARI D5A OVER-EXPR OVER-EXPR OVER-EXPR OVER-EXPR

ETV6 OVER-EXPR OVER-EXPR

ARNTL OVER-EXPR UNDER-EXPR

UBE2B OVER-EXPR UNDER-EXPR

XBP1 OVER-EXPR

PRDM 1 OVER-EXPR OVER-EXPR UNDER-EXPR

ATF4 OVER-EXPR OVER-EXPR

POU2AF1 OVER-EXPR UNDER-EXPR

CEBPB OVER-EXPR OVER-EXPR UNDER-EXPR

CREM OVER-EXPR OVER-EXPR UNDER-EXPR

MYST4 OVER-EXPR OVER-EXPR UNDER-EXPR

MXI 1 OVER-EXPR UNDER-EXPR

RBPJ OVER-EXPR OVER-EXPR OVER-EXPR

CREB3L2 OVER-EXPR OVER-EXPR UNDER-EXPR

VAX2 OVER-EXPR OVER-EXPR

KLF10 OVER-EXPR OVER-EXPR

SKI OVER-EXPR OVER-EXPR UNDER-EXPR

ELK3 OVER-EXPR OVER-EXPR

ZEB1 OVER-EXPR OVER-EXPR OVER-EXPR

PM L OVER-EXPR OVER-EXPR UNDER-EXPR

SERTAD1 OVER-EXPR UNDER-EXPR

NOTCH1 OVER-EXPR OVER-EXPR OVER-EXPR AHR OVER-EXPR OVER-EXPR OVER-EXPR UNDER-EXPR

C210RF66 OVER-EXPR UNDER-EXPR

SAP30 OVER-EXPR OVER-EXPR

I D1 OVER-EXPR OVER-EXPR OVER-EXPR

ZNF238 OVER-EXPR OVER-EXPR

VAV1 OVER-EXPR UNDER-EXPR

M I NA OVER-EXPR OVER-EXPR UNDER-EXPR

BATF3 OVER-EXPR OVER-EXPR

CDYL UNDER-EXPR

I KZF4 OVER-EXPR OVER-EXPR OVER-EXPR OVER-EXPR

NC0A1 OVER-EXPR OVER-EXPR

BCL3 OVER-EXPR OVER-EXPR OVER-EXPR UNDER-EXPR

JUNB OVER-EXPR UNDER-EXPR

SS18 OVER-EXPR OVER-EXPR

PHF13 OVER-EXPR

MTA3 OVER-EXPR UNDER-EXPR

ASXL1 OVER-EXPR OVER-EXPR

LASS4 OVER-EXPR UNDER-EXPR

SKI L OVER-EXPR OVER-EXPR OVER-EXPR

DDIT3 OVER-EXPR OVER-EXPR

FOSL2 OVER-EXPR OVER-EXPR

TLE1 OVER-EXPR OVER-EXPR

ATF3 OVER-EXPR

ELL2 OVER-EXPR OVER-EXPR OVER-EXPR

JARI D2 OVER-EXPR OVER-EXPR

KLF9 OVER-EXPR OVER-EXPR OVER-EXPR

KAT2B OVER-EXPR UNDER-EXPR

KLF6 OVER-EXPR OVER-EXPR UNDER-EXPR

E2F8 OVER-EXPR OVER-EXPR OVER-EXPR

BCL6 OVER-EXPR UNDER-EXPR

ZNRF2 UNDER-EXPR

TSC22D3 OVER-EXPR UNDER-EXPR

KLF7 OVER-EXPR

HMGB2 OVER-EXPR

FUS OVER-EXPR OVER-EXPR

SI RT2 OVER-EXPR

MAFF OVER-EXPR OVER-EXPR OVER-EXPR

CHM P1B OVER-EXPR UNDER-EXPR

GATAD2B OVER-EXPR OVER-EXPR

SMAD7 OVER-EXPR OVER-EXPR

ZNF703 OVER-EXPR OVER-EXPR

ZNRF1 OVER-EXPR OVER-EXPR

JMJD1C OVER-EXPR UNDER-EXPR

ZFP36L2 OVER-EXPR UNDER-EXPR TSC22D4

NFE2L2 OVER-EXPR OVER-EXPR OVER-EXPR UNDER-EXPR

RNF11 OVER-EXPR

ARI D3A OVER-EXPR OVER-EXPR UNDER-EXPR

M EN1 OVER-EXPR OVER-EXPR

RARA OVER-EXPR OVER-EXPR UNDER-EXPR

CBX4 OVER-EXPR OVER-EXPR OVER-EXPR

ZFP62 OVER-EXPR

CIC OVER-EXPR

HCLS1 UNDER-EXPR

ZFP36L1 UNDER-EXPR

TGI F1 UNDER-EXPR

SMAD4 OVER-EXPR

I L7R OVER EXPR OVER EXPR UNDER EXPR

ITGA3 OVER EXPR OVER EXPR

I L1R1 OVER EXPR OVER EXPR UNDER EXPR

FAS OVER EXPR UNDER EXPR

CCR5 OVER EXPR OVER EXPR OVER EXPR UNDER EXPR

CCR6 OVER EXPR OVER EXPR

ACVR2A OVER EXPR OVER EXPR UNDER EXPR

I L6ST OVER EXPR OVER EXPR UNDER EXPR

I L17RA OVER EXPR OVER EXPR UNDER EXPR

CCR8 OVER EXPR

DDR1 OVER EXPR OVER EXPR UNDER EXPR

PROCR OVER EXPR OVER EXPR OVER EXPR

I L2RA OVER EXPR OVER EXPR OVER EXPR OVER EXPR

I L12RB2 OVER EXPR OVER EXPR UNDER EXPR

MYD88 OVER EXPR OVER EXPR UNDER EXPR

BM PR1A OVER EXPR UNDER EXPR

PTPRJ OVER EXPR OVER EXPR OVER EXPR

TNFRSF13B OVER EXPR OVER EXPR UNDER EXPR

CXCR3 OVER EXPR UNDER EXPR

I L1RN OVER EXPR OVER EXPR UNDER EXPR

CXCR5 OVER EXPR OVER EXPR OVER EXPR UNDER EXPR

CCR4 OVER EXPR OVER EXPR UNDER EXPR

I L4R OVER EXPR OVER EXPR UNDER EXPR

I L2RB OVER EXPR OVER EXPR

TNFRSF12A OVER EXPR OVER EXPR OVER EXPR

CXCR4 OVER EXPR OVER EXPR UNDER EXPR

KLRD1 OVER EXPR OVER EXPR

I RAK1BP1 OVER EXPR OVER EXPR

PVR OVER EXPR OVER EXPR OVER EXPR UNDER EXPR

Table 6. Candidate Receptor Molecules

%Differential expression (compared to ThO)

Symbol Early Intermediate Late IL23R knockout (late)

PTPLA UNDER EXPR

PSTPI P1 OVER EXPR OVER EXPR UNDER EXPR

TK1 UNDER EXPR

EI F2AK2 OVER EXPR

PTEN UNDER EXPR

BPGM UNDER EXPR

DCK OVER EXPR

PTPRS OVER EXPR

PTPN18 OVER EXPR

M KNK2 OVER EXPR

PTPN1 OVER EXPR UNDER EXPR

PTPRE UNDER EXPR

SH2D1A OVER EXPR

DUSP22 OVER EXPR

PLK2 OVER EXPR

DUSP6 UNDER EXPR

CDC25B UNDER EXPR

SLK OVER EXPR UNDER EXPR

MAP3K5 UNDER EXPR

BM PR1A OVER EXPR UNDER EXPR

ACP5 OVER EXPR OVER EXPR UNDER EXPR

TXK OVER EXPR OVER EXPR UNDER EXPR

RI PK3 OVER EXPR OVER EXPR UNDER EXPR

PPP3CA OVER EXPR

PTPRF OVER EXPR OVER EXPR OVER EXPR

PACSI N1 OVER EXPR

NEK4 OVER EXPR UNDER EXPR

PI P4K2A UNDER EXPR PPM E1 OVER EXPR OVER EXPR UNDER EXPR

SRPK2 UNDER EXPR

DUSP2 OVER EXPR

PHACTR2 OVER EXPR OVER EXPR

HK2 OVER EXPR OVER EXPR

DCLK1 OVER EXPR

PPP2R5A UNDER EXPR

RI PK1 OVER EXPR UNDER EXPR

GK OVER EXPR

RNASEL OVER EXPR OVER EXPR

GM FG OVER EXPR OVER EXPR OVER EXPR

STK4 UNDER EXPR

HI NT3 OVER EXPR

DAPP1 OVER EXPR UNDER EXPR

TEC OVER EXPR OVER EXPR OVER EXPR UNDER EXPR

GM FB OVER EXPR OVER EXPR

PTPN6 UNDER EXPR

RI PK2 UNDER EXPR

PI M 1 OVER EXPR OVER EXPR OVER EXPR

NEK6 OVER EXPR OVER EXPR UNDER EXPR

ACVR2A OVER EXPR OVER EXPR UNDER EXPR

AURKB UNDER EXPR

FES OVER EXPR OVER EXPR

ACVR1B OVER EXPR OVER EXPR

CDK6 OVER EXPR OVER EXPR UNDER EXPR

ZAK OVER EXPR OVER EXPR UNDER EXPR

VRK2 UNDER EXPR

MAP3K8 OVER EXPR UNDER EXPR

DUSP14 OVER EXPR UNDER EXPR

SGK1 OVER EXPR OVER EXPR OVER EXPR UNDER EXPR

PRKCQ OVER EXPR UNDER EXPR

JAK3 OVER EXPR UNDER EXPR

ULK2 OVER EXPR UNDER EXPR

HI PK2 OVER EXPR OVER EXPR

PTPRJ OVER EXPR OVER EXPR OVER EXPR

SPHK1 OVER EXPR

I NPP1 UNDER EXPR

TNK2 OVER EXPR OVER EXPR OVER EXPR

PCTK1 OVER EXPR OVER EXPR OVER EXPR

DUSP1 OVER EXPR

NUDT4 UNDER EXPR

MAP4K3 OVER EXPR

TGFBR1 OVER EXPR OVER EXPR OVER EXPR

Table 7. Candidate Kinases

Table 8. Candidate Signaling Molecules From Single Cell Analysis

CD 247 UNDER EXPR UNDER EXPR

CD14

ITGAV

FCER1G

I L2RG OVER EXPR UNDER EXPR

Table 9. Candidate Receptor Molecules From Single Cell Analysis

[00129] Among the novel hl7 positive' factors is the zinc finger E-box binding homeobox 1 Zebl, which is early- induced and sustained in the Thl7 time course (Fig. 17a), analogous to the expression of many known key Thl7 factors. Zebl knockdown decreases the expression of Thl7 signature cytokines (including IL-17A, IL-17F, and IL-21) and TFs (including Rbpj, Maff, and Mina) and of late induced cytokine and receptor molecule genes (p<10^~4, cluster C19). It is bound in Thl7 cells by ROR-γί, Batf and Stat3, and is down- regulated in cells from Stat3 knockout mice (Fig. 17a). Interestingly, Zebl is known to interact with the chromatin factor Smarca4/Brgl to repress the E-cadherin promoter in epithelial cells and induce an epithelial-mesenchymal transition (Sanchez-Tillo, E. et al. ZEB1 represses E-cadherin and induces an EMT by recruiting the SWI/SNF chromatin- remodeling protein BRG1. Oncogene 29, 3490-3500, doi: 10.1038/onc.2010.102 (2010)). Smarca4 is a regulator in all three network models (Fig. 2d,e) and a member of the 'positive module' (Fig. 4b). Although it is not differentially expressed in the Thl7 time course, it is bound by Batf, Irf4 and Stat3 (positive regulators of Thl7), but also by Gata3 and Stat5 (positive regulators of other lineages, Fig. 17a). Chromatin remodeling complexes that contain Smarca4 are known to displace nucleosomes and remodel chromatin at the IFN-γ promoter and promote its expression in Thl cells (Zhang, F. & Boothby, M. T helper type 1 -specific Brgl recruitment and remodeling of nucleosomes positioned at the IFN-gamma promoter are Stat4 dependent. J. Exp. Med. 203, 1493-1505, doi: 10.1084/jem.20060066 (2006)). There are also potential Smarca4 binding DNA sequences within the vicinity of the IL-17a promoter (Matys, V. et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 31, 374-378 (2003)). Taken together, this suggests a model where chromatin remodeling by Smarca4, possibly in interaction with Zebl, positive regulates Thl 7 cells and is essential for IL-17 expression.

[00130] Conversely, among the novel 'Thl7 negative' factors is Sp4, an early- induced gene, predicted in the model as a regulator of ROR-γί and as a target of ROR-γί, Batf, Irf4, Stat3 and Smarca4 (Fig. 17b). Sp4 knockdown results in an increase in ROR-γί expression at 48h, and an overall stronger and "cleaner" Thl 7 differentiation as reflected by an increase in the expression of Thl 7 signature genes, including IL-17, IL-21 and Irf4, and decrease in the expression of signature genes of other CD4+ cells, including Gata3, Foxp3 and Stat4.

[00131] These novel and known regulatory factors act coordinately to orchestrate intra- and intermodules interactions and to promote progressive differentiation of Thl 7 cells, while limiting modules that inhibit directional differentiation of this subset and promote differentiation of T cells into other T cell subsets. For instance, knockdown of Smarca4 and Zebl leads to decrease in Mina (due to all-positive interactions between Thl 7 'positive regulators'), while knockdown of Smarca4 or Mina leads to increase in Tsc22d3 31 expression, due to negative cross-module interactions. As shown using RNAseq, these effects extend beyond the expression of regulatory factors in the network and globally affect the Thl 7 transcriptional program: e.g. knock-down of Mina has substantial effects on the progression of the Thl 7 differentiation network from the intermediate to the late phase, as some of its affected down-regulated genes significantly overlap the respective temporal clusters (p<10^~5, e.g., clusters C9, C19). An opposite trend is observed for the negative regulators Tsc22d3 and Sp4. For example, the transcriptional regulator Sp4 represses differentiating Thl 7 cells from entering into the late phase of differentiation by inhibiting the cytokine signaling (CI 9; p<10^"7) and heamatopoesis (C20; p<10^"3) clusters, which include Ahr, Batf, ROR-γί, etc. These findings emphasize the power of large-scale functional perturbation studies in understanding the action of complex molecular circuits that govern Thl7 differentiation.

[00132] In a recent work, Ciofani et al. (Ciofani, M. et al. A Validated Regulatory

Network for Thl7 Cell Specification. Cell, doi: 10.1016/j.cell.2012.09.016 (2012)) systematically ranked Thl7 regulators based on ChlPSeq data for known key factors and transcriptional profiles in wild type and knockout cells. While their network centered on known core Thl7 TFs, the complementary approach presented herein perturbed many genes in a physiologically meaningful setting. Reassuringly, their core Thl7 network significantly overlaps with the computationally inferred model (Fig. 18).

[00133] The wiring of the positive and negative modules (Fig. 4 and 5) uncovers some of the functional logic of the Thl7 program, but likely involve both direct and indirect interactions. The functional model provides an excellent starting point for deciphering the underlying physical interactions with DNA binding profiles (Glasmacher, E. et al. A

Genomic Regulatory Element That Directs Assembly and Function of Immune-Specific AP-l-IRF Complexes. Science, doi: 10.1126/science.1228309 (2012)) or protein-protein interactions (Wu, C, Yosef, N. & Thalhamer, T. SGK1 kinase regulates Thl7 cells maintenance through IL-23 signaling pathway. (Submitted)). The regulators identified are compelling new targets for regulating the Thl7/Tregs balance and for switching pathogenic Thl7 into non-pathogenic ones.

Automated Procedure for Selection of Signature Genes

[00134] 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 (>20k 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.

[00135] 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).

[00136] An exemplary embodiment of this procedure is the selection of the 275-gene signature (Table 1), 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 Thl7 microarray signature (comparing to other CD4+ T cells, see e.g., Wei et al, in Immunity vol. 30 155-167 (2009)), see Methods described herein); 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

[00137] The invention provides T cell related gene signatures for use in a variety of diagnostic and/or therapeutic indications. For example, the invention provides Thl7 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.

[00138] Exemplary signatures are shown in Tables 1 and 2 and are collectively referred to herein as, inter alia, "Thl7-associated genes," "Thl7-associated nucleic acids," "signature genes," or "signature nucleic acids."

[00139] 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 Table 2 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.

[00140] 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 Table 2 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 Table 2 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.

[00141] 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. 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.

[00142] 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 homologous 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, radio labels, 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

[00143] 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 below in Table 10.

Table 10. T cell Modulating Agents

CCR5 cholesterol, cyclosporin a, glutamine, methionine, guanine, simvastatin, threonine, indinavir, lipoxin A4, cysteine, prostaglandin E2, zinc, dapta, 17-alpha-ethinylestradiol, polyacrylamide, progesterone, zidovudine, rapamycin, rantes, glutamate, alanine, valine, ccl4, quinine, NSC 651016, methadone, pyrrolidine dithiocarbamate, palmitate, nor-binaltorphimine, interferon beta- la, vitamin-e, tak779, lipopolysaccharide, cisp latin, albuterol, fluvoxamine, vicriviroc, bevirimat, carbon tetrachloride, galactosylceramide, ATP-gamma-S, cytochalasin d, hemozoin, CP 96345, tyrosine, etravirine, vitamin d, mip 1 alpha, ammonium, tyrosine sulfate, iso leucine, isopentenyl diphosphate, il 10, serine, N-acetyl-L- cysteine, histamine, cocaine, ritonavir, tipranavir, aspartate, atazanavir, tretinoin, ATP, ribavirin, butyrate, N-nitro-L-arginine methyl ester, larc, buthionine sulfoximine, DAPTA, aminooxypentane-rantes, triamcinolone acetonide, shikonin, actinomycin d, bucladesine, aplaviroc, nevirapine, N-formyl-Met-Leu-Phe, cyclosporin A, lipoarabinomannan, nucleoside, sirolimus, morphine, mannose, calcium, heparin, c-d4i, pge2, beta- estradiol, mdms, dextran sulfate, dexamethasone, arginine, ivig, mcp 2, cyclic amp, U 50488H, N-methyl-D-aspartate, hydrogen peroxide, 8- carboxamidocyclazocine, latex, groalpha, xanthine, ccl3, retinoic acid, Maraviroc, sdf 1, opiate, efavirenz, estrogen, bicyclam, enfuvirtide, filipin, bleomycin, polysaccharide, tare, pentoxifylline, E. coli B5 lipopolysaccharide, methylcellulose, maraviroc

ITGA3 SP600125, paclitaxel, decitabine, e7820, retinoid, U0126, serine, retinoic acid, tyrosine, forskolin, Ca2+

IRF4 prostaglandin E2, phorbol myristate acetate, lipopolysaccharide, A23187, tacrolimus, trichostatin A, stallimycin, imatinib, cyclosporin A, tretinoin, bromodeoxyuridine, ATP-gamma-S, ionomycin

BATF Cyclic AMP, serine, tacrolimus, beta-estradiol, cyclosporin A, leucine

RBPJ zinc, tretinoin

PROCR lipopolysaccharide, cisp latin, fibrinogen, 1, 10-phenanthroline, 5-N- ethylcarboxamido adenosine, cystathionine, hirudin, phospholipid, Drotrecogin alfa, vegf, Phosphatidylethanolamine, serine, gamma- carboxyglutamic acid, calcium, warfarin, endotoxin, curcumin, lipid, nitric oxide

ZEB1 resveratrol, zinc, sulforafan, sorafenib, progesterone, PD-0332991,

dihydrotestosterone, silibinin, LY294002, 4-hydroxytamoxifen, valproic acid, beta-estradiol, forskolin, losartan potassium, fulvestrant, vitamin d

POU2AF1 terbutaline, phorbol myristate acetate, bucladesine, tyrosine, ionomycin,

KT5720, H89

EGR1 ghrelin, ly294002, silicone, sodium, propofol, 1, 25 dihydroxy vitamin d3, tetrodotoxin, threonine, cyclopiazonic acid, urea, quercetin, ionomycin, 12-o-tetradecanoylphorbol 13-acetate, fulvestrant, phenylephrine, formaldehyde, cysteine, leukotriene C4, prazosin, LY379196, vegf, rapamycin, leupeptin, pd 98, 059, ruboxistaurin, pCPT- cAMP, methamphetamine, nitroprusside, H-7, Ro31-8220, phosphoinositide, lysophosphatidylcholine, bufalin, calcitriol, leuprolide, isobutylmethylxanthine, potassium chloride, acetic acid, cyclothiazide, quinolinic acid, tyrosine, adenylate, resveratrol, topotecan, genistein, thymidine, D-glucose, mifepristone, lysophosphatidic acid, leukotriene D4, carbon monoxide, poly rI:rC-RNA, sp 600125, agar, cocaine, 4- nitroquinoline-1 -oxide, tamoxifen, lead, fibrinogen, tretinoin, atropine, mithramycin, K+, epigallocatechin-gallate, ethylenediaminetetraacetic acid, h2o2, carbachol, sphingosine-1 -phosphate, iron, 5- hydroxytryptamine, amphetamine, SP600125, actinomycin d, SB203580, cyclosporin A, norepinephrine, okadaic acid, ornithine, LY294002, pge2, beta-estradiol, glucose, erlotinib, arginine, 1 -alpha, 25 -dihydroxy vitamin D3, dexamethasone, pranlukast, phorbol myristate acetate, nimodipine, desipramine, cyclic amp, N-methyl-D-aspartate, atipamezole, acadesine, losartan, salvin, methylnitronitrosoguanidine, EGTA, gf 109203x, nitroarginine, 5-N-ethylcarboxamido adenosine, 15-deoxy-delta-12, 14 - PGJ 2, dbc-amp, manganese superoxide, di(2-ethylhexyl) phthalate, egcg, mitomycin C, 6, 7-dinitroquinoxaline-2, 3-dione, GnRH-A, estrogen, ribonucleic acid, imipramine, bapta, L-triiodothyronine, prostaglandin, forskolin, nogalamycin, losartan potassium, lipid, vincristine, 2-amino-3-phosphonopropionic acid, prostacyclin, methylnitrosourea, cyclosporin a, vitamin K3, thyroid hormone, diethylstilbestrol, D-tubocurarine, tunicamycin, caffeine, phorbol, guanine, bisindolylmaleimide, apomorphine, arachidonic acid, SU6656, prostaglandin E2, zinc, ptxl, progesterone, cyclosporin H, phosphatidylinositol, U0126, hydroxyapatite, epoprostenol, glutamate, 5fluorouracil, indomethacin, 5-fluorouracil, RP 73401, Ca2+, superoxide, trifluoperazine, nitric oxide, lipopolysaccharide, cisplatin, diazoxide, tgf betal, calmidazolium, anisomycin, paclitaxel, sulindac sulfide, ganciclovir, gemcitabine, testosterone, ag 1478, glutamyl-Se- methylselenocysteine, doxorubicin, tolbutamide, cytochalasin d,

PD98059, leucine, SR 144528, cyclic AMP, matrigel, haloperidol, serine, sb 203580, triiodothyronine, reverse, N-acetyl-L-cysteine, ethanol, s- nitroso-n-acetylpenicillamine, curcumin, 1-nmma, H89, tpck, calyculin a, chloramphenicol, A23187, dopamine, platelet activating factor, arsenite, selenomethylselenocysteine, ropinirole, saralasin, methylphenidate, gentamicin, reserpine, triamcinolone acetonide, methyl

methanesulfonate, wortmannin, thapsigargin, deferoxamine, calyculin A, peptidoglycan, dihydrotestosterone, calcium, phorbol- 12-myristate, ceramide, nmda, 6-cyano-7-nitroquinoxaline-2, 3-dione, hydrogen peroxide, carrageenan, sch 23390, linsidomine, oxygen, clonidine, fluoxetine, retinoid, troglitazone, retinoic acid, epinephrine, n acetylcysteine, KN-62, carbamylcholine, 2-amino-5-phosphonovaleric acid, oligonucleotide, gnrh, rasagiline, 8-bromo-cAMP, muscarine, tacrolimus, kainic acid, chelerythrine, inositol 1, 4, 5 trisphosphate, yohimbine, acetylcholine, atp, 15-deoxy-delta-12, 14-prostaglandin j2, ryanodine, CpG oligonucleotide, cycloheximide, BAPTA-AM, phenylalanine

ETV6 lipopolysaccharide, retinoic acid, prednisolone, valproic acid, tyrosine, cerivastatin, vegf, agar, imatinib, tretinoin

IL17RA rantes, lipopolysaccharide, 17-alpha-ethinylestradiol, camptothecin, E.

coli B5 lipopolysaccharide EGR2 phorbol myristate acetate, lipopolysaccharide, platelet activating factor, carrageenan, edratide, 5-N-ethylcarboxamido adenosine, potassium chloride, dbc-amp, tyrosine, PD98059, camptothecin, formaldehyde, prostaglandin E2, leukotriene C4, zinc, cyclic AMP, GnRH-A, bucladesine, thapsigargin, kainic acid, cyclosporin A, mifepristone, leukotriene D4, LY294002, L-triiodothyronine, calcium, beta-estradiol, H89, dexamethasone, cocaine

SP4 betulinic acid, zinc, phorbol myristate acetate, LY294002, methyl 2- cyano-3, 12-dioxoolean-l, 9-dien-28-oate, beta-estradiol, Ca2+

IRF8 oligonucleotide, chloramphenicol, lipopolysaccharide, estrogen,

wortmannin, pirinixic acid, carbon monoxide, retinoic acid, tyrosine

NFKB1 Bay 11-7085, Luteolin, Triflusal, Bay 11-7821, Thalidomide, Caffeic acid phenethyl ester, Pranlukast

TSC22D3 phorbol myristate acetate, prednisolone, sodium, dsip, tretinoin, 3- deazaneplanocin, gaba, PD98059, leucine, triamcinolone acetonide, prostaglandin E2, steroid, norepinephrine, U0126, acth, calcium, ethanol, beta-estradiol, lipid, chloropromazine, arginine, dexamethasone

PML lipopolysaccharide, glutamine, thyroid hormone, cadmium, lysine,

tretinoin, bromodeoxyuridine, etoposide, retinoid, pic 1, arsenite, arsenic trioxide, butyrate, retinoic acid, alpha-retinoic acid, h2o2, camptothecin, cysteine, leucine, zinc, actinomycin d, proline, stallimycin, U0126

IL12RB1 prostaglandin E2, phorbol myristate acetate, lipopolysaccharide,

bucladesine, 8-bromo-cAMP, gp 130, AGN194204, galactosylceramide- alpha, tyrosine, ionomycin, dexamethasone, il-12

IL21R azathioprine, lipopolysaccharide, okadaic acid, E. coli B5

lipopolysaccharide, calyculin A

NOTCH 1 interferon beta- la, lipopolysaccharide, cisp latin, tretinoin, oxygen,

vitamin B12, epigallocatechin-gallate, isobutylmethylxanthme, threonine, apomorphine, matrigel, trichostatin A, vegf, 2-acetylaminofluorene, rapamycin, dihydrotestosterone, poly rI:rC-RNA, hesperetin, valproic acid, asparagine, lipid, curcumin, dexamethasone, glycogen, CpG oligonucleotide, nitric oxide

ETS2 oligonucleotide

MINA phorbol myristate acetate, 4-hydroxytamoxifen

SMARCA4 cyclic amp, cadmium, lysine, tretinoin, latex, androstane, testosterone, sucrose, tyrosine, cysteine, zinc, oligonucleotide, estrogen, steroid, trichostatin A, tpmp, progesterone, histidine, atp, trypsinogen, glucose, agar, lipid, arginine, vancomycin, dihydrofolate FAS hoechst 33342, ly294002, 2-chlorodeoxyadenosine, glutamine, cd 437, tetrodotoxin, cyclopiazonic acid, arsenic trioxide, phosphatidylserine, niflumic acid, gliadin, ionomycin, safrole oxide, methotrexate, rubitecan, cysteine, propentofylline, vegf, boswellic acids, rapamycin, pd 98, 059, captopril, methamphetamine, vesnarinone, tetrapeptide, oridonin, raltitrexed, pirinixic acid, nitroprusside, H-7, beta-boswellic acid, adriamycin, concanamycin a, etoposide, trastuzumab, cyclophosphamide, ifii-alpha, tyrosine, rituximab, selenodiglutathione, chitosan, omega-N- methylarginine, creatinine, resveratrol, topotecan, genistein, trichostatin A, decitabine, thymidine, D-glucose, mifepristone, tetracycline, Sn50 peptide, poly rI:rC-R A, actinomycin D, sp 600125, doxifluridine, agar, ascorbic acid, acetaminophen, aspirin, tamoxifen, okt3, edelfosine, sulforafan, aspartate, antide, n, n-dimethylsphingosine, epigallocatechin- gallate, N-nitro-L-arginine methyl ester, h2o2, cerulenin, sphingosine-1- phosphate, SP600125, sodium nitroprusside, glycochenodeoxycholic acid, ceramides, actinomycin d, SB203580, cyclosporin A, morphine, LY294002, n(g)-nitro-l-arginine methyl ester, 4-hydroxynonenal, piceatannol, valproic acid, beta-estradiol, 1 -alpha, 25 -dihydroxy vitamin D3, arginine, dexamethasone, sulfadoxine, phorbol myristate acetate, beta-lapachone, nitrofurantoin, chlorambucil,

methylnitronitrosoguanidine, CD 437, opiate, egcg, mitomycin C, estrogen, ribonucleic acid, fontolizumab, tanshinone iia, recombinant human endostatin, fluoride, L-triiodothyronine, bleomycin, forskolin, nonylphenol, zymosan A, vincristine, daunorubicin, prednisolone, cyclosporin a, vitamin K3, diethylstilbestrol, deoxyribonucleotide, suberoylanilide hydroxamic acid, orlistat, 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide, rottlerin, arachidonic acid, ibuprofen, prostaglandin E2, toremifene, depsipeptide, ochratoxin A, (glc)4, phosphatidylinositol, mitomycin c, rantes, sphingosine, indomethacin, 5fluorouracil, phosphatidylcholine, 5-fluorouracil, mg 132, thymidylate, trans-cinnamaldehyde, sterol, polyadenosine diphosphate ribose, nitric oxide, vitamin e succinate, lipopolysaccharide, cisplatin, herbimycin a, 5- aza-2'deoxycytidine, proteasome inhibitor PSI, 2, 5-hexanedione, epothilone B, caffeic acid phenethyl ester, glycerol 3-phosphate, tgf betal, anisomycin, paclitaxel, gemcitabine, medroxyprogesterone acetate, hymecromone, testosterone, ag 1478, doxorubicin, S-nitroso-N- acetylpenicillamine, adpribose, sulforaphane, vitamin d, annexin-v, lactate, reactive oxygen species, sb 203580, serine, N-acetyl-L-cysteine, dutp, infliximab, ethanol, curcumin, cytarabine, tpck, calyculin a, dopamine, gp 130, bromocriptine, apicidin, fatty acid, citrate, glucocorticoid, arsenite, butyrate, peplomycin, oxaliplatin, camptothecin, benzyloxycarbonyl-Leu-Leu-Leu aldehyde, clofibrate, carbon, wortmannin, fludarabine, N-(3 -(amino methyl)benzyl)acetamidine, sirolimus, peptidoglycan, c2ceramide, dihydrotestosterone, 7- aminoactinomycin d, carmustine, heparin, ceramide, paraffin, mitoxantrone, docosahexaenoic acid, vitamin a, ivig, hydrogen peroxide, 7-ethyl-lO-hydroxy-camptothecin, oxygen, pydrin, bortezomib, retinoic acid, 1, 4-phenylenebis(methylene)selenocyanate, teriflunomide, epinephrine, n acetylcysteine, noxa, irinotecan, oligonucleotide, d-api, rasagiline, 8-bromo-cAMP, atpo, agarose, fansidar, clobetasol propionate, teniposide, aurintricarboxylic acid, polysaccharide, CpG oligonucleotide, cycloheximide

IRF 1 tamoxifen, chloramphenicol, polyinosinic-polycytidylic acid, inosine monophosphate, suberoylanilide hydroxamic acid, butyrate, iron, gliadin, zinc, actinomycin d, deferoxamine, phosphatidylinositol, adenine, ornithine, rantes, calcium, 2', 5'-oligoadenylate, pge2, poly(i-c), indoleamine, arginine, estradiol, nitric oxide, etoposide, adriamycin, oxygen, retinoid, guanylate, troglitazone, ifn-alpha, retinoic acid, tyrosine, adenylate, am 580, guanosine, oligonucleotide, estrogen, thymidine, tetracycline, serine, sb 203580, pdtc, lipid, cycloheximide

MYC cd 437, 1, 25 dihydroxy vitamin d3, phenethyl isothiocyanate, threonine, arsenic trioxide, salicylic acid, quercetin, prostaglandin El, ionomycin, 12-o-tetradecanoylphorbol 13-acetate, fulvestrant, phenylephrine, fisetin, 4-coumaric acid, dihydroartemisinin, 3-deazaadenosine, nitroprusside, pregna-4, 17-diene-3, 16-dione, adriamycin, bromodeoxyuridine, AGN 194204, STA-9090, isobutylmethylxanthine, potassium chloride, docetaxel, quinolinic acid, 5, 6, 7, 8-tetrahydrobiopterin, propranolol, delta 7-pgal, topotecan, AVI-4126, trichostatin A, decitabine, thymidine, D-glucose, mifepristone, poly rI:rC-R A, letrozole, L-threonine, 5- hydroxytryptamine, bucladesine, SB203580, Γ-acetoxychavicol acetate, cyclosporin A, okadaic acid, dfmo, LY294002, hmba, piceatannol, 2', 5'- oligoadenylate, 4-hydroxytamoxifen, butylbenzyl phthalate, dexamethasone, ec 109, phosphatidic acid, grape seed extract, phorbol myristate acetate, coumermycin, tosylphenylalanyl chloromethyl ketone, CD 437, di(2-ethylhexyl) phthalate, butyrine, cytidine, sodium arsenite, tanshinone iia, L-triiodothyronine, niacinamide, glycogen, daunorubicin, vincristine, carvedilol, bizelesin, 3-deazaneplanocin, phorbol, neplanocin a, panobinostat, [alcl], phosphatidylinositol, U0126,

dichlororibofuranosylbenzimidazole, flavopiridol, 5-fluorouracil, verapamil, cyclopamine, nitric oxide, cisplatin, hrgbetal, 5, 6-dichloro-l- beta-d-ribofuranosylbenzimidazole, amsacrine, gemcitabine, aristeromycin, medroxyprogesterone acetate, gambogic acid, leucine, alpha-naphthyl acetate, cyclic AMP, reactive oxygen species, PD

180970, curcumin, chloramphenicol, A23187, crocidolite asbestos, 6- hydroxydopamine, cb 33, arsenite, gentamicin, benzyloxycarbonyl-Leu- Leu-Leu aldehyde, clofibrate, wortmannin, sirolimus, ceramide, melphalan, 3M-001, linsidomine, CP-55940, hyaluronic acid, ethionine, clonidine, retinoid, bortezomib, oligonucleotide, methyl 2-cyano-3, 12- dioxoolean-1, 9-dien-28-oate, tacrolimus, embelin, methyl-beta- cyclodextrin, 3M-011, folate, ly294002, PP1, hydroxyurea, aclarubicin, phenylbutyrate, PD 0325901, methotrexate, Cd2+, prazosin, vegf, rapamycin, alanine, phenobarbital, pd 98, 059, trapoxin, 4- hydroperoxycyclophosphamide, methamphetamine, s-(l, 2- dichlorovinyl)-l-cysteine, aphidicolin, vesnarinone, ADI PEG20,

pirinixic acid, wp631, H-7, carbon tetrachloride, bufalin, 2, 2- dimethylbutyric acid, etoposide, calcitriol, trastuzumab,

cyclophosphamide, harringtonine, tyrosine, N(6)-(3-iodobenzyl)-5'-N- methylcarboxamidoadenosine, resveratrol, thioguanine, genistein, S- nitroso-N-acetyl-DL-penicillamine, zearalenone, lysophosphatidic acid, Sn50 peptide, roscovitine, actinomycin D, propanil, agar, tamoxifen, acetaminophen, imatinib, tretinoin, mithramycin, ATP, epigallocatechin- gallate, ferric ammonium citrate, acyclic retinoid, L-cysteine, nitroblue tetrazolium, actinomycin d, sodium nitroprusside, 1, 2- dimethylhydrazine, dibutyl phthalate, ornithine, 4-hydroxynonenal, beta- estradiol, 1 -alpha, 25 -dihydroxy vitamin D3, cyproterone acetate, nimodipine, nitrofurantoin, temsirolimus,

15-deoxy-delta-12, 14 -PGJ 2, estrogen, ribonucleic acid, ciprofibrate, alpha-amanitin, SB 216763, bleomycin, forskolin, prednisolone, cyclosporin a, thyroid hormone, tunicamycin, phosphorothioate, suberoylanilide hydroxamic acid, pga2, 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide, benzamide riboside,

bisindolylmaleimide, SU6656, prostaglandin E2, depsipeptide, zidovudine, cerivastatin, progesterone, sethoxydim, indomethacin, mg 132, mezerein, pyrrolidine dithiocarbamate, vitamin e succinate,

herbimycin a, 5-aza-2'deoxycytidine, lipopolysaccharide, diazoxide, anisomycin, paclitaxel, sodium dodecylsulfate, nilotinib, oxysterol, doxorubicin, lipofectamine, PD98059, steroid, delta- 12-pgj2, serine, H-8, N-acetyl-L-cysteine, ethanol, n-(4-hydroxyphenyl)retinamide, tiazofurin, cytarabine, H89, 10-hydroxycamptothecin, everolimus, lactacystin, n(l), n(12)-bis(ethyl)spermine, silibinin, glucocorticoid, butyrate, camptothecin, triamcinolone acetonide, tocotrienol, n-ethylmaleimide, phorbol 12, 13-didecanoate, thapsigargin, deferoxamine, R59949, bryostatin 1, paraffin, romidepsin, vitamin a, docosahexaenoic acid, hydrogen peroxide, droloxifene, saikosaponin, fluoxetine, retinoic acid, n acetylcysteine, dithiothreitol, cordycepin, agarose, 8-bromo-cAMP, D- galactosamine, tachyplesin i, theophylline, metoprolol, SU6657, 15- deoxy-delta-12, 14-prostaglandin j2, dmso, 2-amino-5-azotoluene,

cycloheximide

[00144] 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 semisolid 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 WN "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 Techno 1. 52:238-311 (1998) and the citations therein for additional information related to formulations, excipients and carriers well known to pharmaceutical chemists.

[00145] 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, Thl7 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, Thl7 T cell differentiation is contemplated for augmenting Thl7 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, Thl7 cells might also play an important role. {See e.g., Abadja F, Sarraj B, Ansari MJ., "Significance of T helper 17 immunity in transplantation." Curr Opin Organ Transplant. 2012 Feb;17(l):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 Thl7/Treg balance has also been implicated in these indications. For example, some studies have suggested that IL-23 and Thl7 cells play a role in some cancers, such as, by way of non- limiting example, colorectal cancers. {See e.g., Ye J, Livergood RS, Peng G. "The role and regulation of human Thl7 cells in tumor immunity." Am J Pathol. 2013 Jan;182(l): 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 Thl7 cell production, for example, patients that do not produce Thl7 cells naturally.

[00146] 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.

[00147] Autoimmune diseases include, for example, Acquired Immunodeficiency

Syndrome (AIDS, which is a viral disease with an autoimmune component), alopecia areata, ankylosing spondylitis, antiphospho lipid 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 hepetiformis; 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, pernacious 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)), Sjogren's syndrome, stiff- man syndrome, systemic lupus erythematosus, Takayasu arteritis, temporal arteritis/giant cell arteritis, ulcerative colitis, uveitis, vitiligo and Wegener's granulomatosis.

[00148] 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 Thl7 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.

[00149] 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.

[00150] 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.

[00151] 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.

[00152] 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.

[00153] 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.

[00154] 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.

[00155] 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, chemo therapeutic agent, or growth-inhibitory agent. Such molecules are suitably present in combination in amounts that are effective for the purpose intended.

[00156] All publications and patent documents cited herein are incorporated herein by reference as if each such publication or document was specifically and individually indicated to be incorporated herein by reference. Citation of publications and patent documents is not intended as an admission that any is pertinent prior art, nor does it constitute any admission as to the contents or date of the same. The invention having now been described by way of written description, those of skill in the art will recognize that the invention can be practiced in a variety of embodiments and that the foregoing description and examples below are for purposes of illustration and not limitation of the claims that follow.

EXAMPLES

[00157] The following examples, including the experiments conducted and results achieved are provided for illustrative purposes only and are not to be construed as limiting upon the present invention.

EXAMPLE 1: Materials and Methods

[00158] Briefly, gene expression profiles were measured at 18 time points (0.5hr to

72 days) under Thl7 conditions (IL-6, TGF-βΙ) or control (ThO) using Affymetrix microarrays HT MG-430A. Differentially expressed genes were detected using a consensus over four inference methods, and cluster the genes using k-means, with an automatically derived k. Temporal regulatory interactions were inferred by looking for significant (p< 5* 10^"5 and fold enrichment > 1.5) overlaps between the regulator's putative targets (e.g., based on ChlPseq) and the target gene's cluster (using four clustering schemes). Candidates for perturbation were ordered lexicographically using network-based and expression-based features. Perturbations were done using Si W for siR A delivery. These methods are described in more detail below.

[00159] Mice: C57BL/6 wild-type (wt), Mt^~ , IrfT^/_, Fas^"7", Irf4^fl/fl, and Cd4^Cre mice were obtained from Jackson Laboratory (Bar Harbor, ME). Statl ^{- ~} and 129/Sv control mice were purchased from Taconic (Hudson, NY). IL-12rpr^/_ mice were provided by Dr. Pahan Kalipada from Rush University Medical Center. IL-17Ra ^{~ ~} mice were provided by Dr. Jay Kolls from Louisiana State University/University of Pittsburgh. Irf8^fl/fl mice were provided by Dr. Keiko Ozato from the National Institute of Health. Both Irf4^fl/fl and Irf8^fl/fl mice were crossed to Cd4^Cre mice to generate Cd4^CrexIrf4^fl/fl and Cd4^CrexIrf8^fl/fl mice. All animals were housed and maintained in a conventional pathogen-free facility at the Harvard Institute of Medicine in Boston, MA (IUCAC protocols: 0311-031-14 (VKK) and 0609- 058015 (AR)). 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, MA). In addition, spleens from Mina^_/~ mice were provided by Dr. Mark Bix from St. Jude Children's Research Hospital (IACUC Protocol: 453). Pou2afl^_/" mice were obtained from the laboratory of Dr. Robert Roeder (Kim, U. et al. The B-cell-specific transcription coactivator OCA-B/OBF-l/Bob-1 is essential for normal production of immunoglobulin isotypes. Nature 383, 542-547, doi: 10.1038/383542a0 (1996)). Wild-type and Octl^"7" fetal livers were obtained at day El 2.5 and transplanted into sub-lethally irradiated Ragl^_/~ mice as previously described (Wang, V. E., Tantin, D., Chen, J. & Sharp, P. A. B cell development and immunoglobulin transcription in Oct- 1 -deficient mice. Proc. Natl. Acad. Sci. U.S.A. 101, 2005-2010, doi: 10.1073/pnas.0307304101 (2004)) (IACUC Protocol: 11- 09003).

[00160] Cell sorting and in vitro T-cell differentiation in Petri dishes: 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-Cd621-APC, and anti-Cd44-PE antibodies (all Biolegend, CA).

[00161] Na^'ive Cd4⁺ Cd621^high Cd44^low T cells were sorted using the BD FACSAria cell sorter. Sorted cells were activated with plate bound anti-Cd3 (2μg/ml) and anti-Cd28 (2μg/ml) in the presence of cytokines. For Thl7 differentiation: 2ng/mL rhTGF-βΙ

(Miltenyi Biotec), 25ng/mL rmIl-6 (Miltenyi Biotec), 20ng/ml rmIl-23 (Miltenyi Biotec), and 20ng/ml rmIL-βΙ (Miltenyi Biotec). Cells were cultured for 0.5 - 72 hours and harvested for RNA, intracellular cytokine staining, and flow cytometry.

[00162] Flow cytometry and intracellular cytokine staining (ICC): Sorted na^'ive T cells were stimulated with phorbol 12-myristate 13-aceate (PMA) (50ng/ml, Sigma-aldrich, MO), ionomycin (^g/ml, Sigma-aldrich, MO) and a protein transport inhibitor containing monensin (Golgistop) (BD Biosciences) for four hours prior to 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, i.e. Brilliant Violet 650™ anti-mouse IFN-γ (XMG1.2) and allophycocyanin-anti-IL-17A (TCI 1-18H10.1), diluted in Perm/Wash buffer as described (Bettelli, E. et al. Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells. Nature 441, 235-238 (2006)) (Fig. 5, Fig. 16). To measure the time-course of RORyt protein expression, a phycoerythrin-conjugated anti- Retinoid-Related Orphan Receptor gamma was used (B2D), also from eBioscience (Fig 16). FOXP3 staining for cells from knockout mice was performed with the FOXP3 staining kit by eBioscience (00-5523-00) in accordance with their "Onestep protocol for intracellular (nuclear) proteins". Data was collected using either a FACS Calibur or LSR II (Both BD Biosciences), then analyzed using Flow Jo software (Treestar) (Awasthi, A. et al. A dominant function for interleukin 27 in generating interleukin 10-producing antiinflammatory T cells. Nature immunology 8, 1380-1389, doi: 10.1038/nil541 (2007);

Awasthi, A. et al. Cutting edge: IL-23 receptor gfp reporter mice reveal distinct populations ofIL-17-producing cells. J Immunol 182, 5904-5908, doi: 10.4049/jimmuno 1.0900732 (2009)).

[00163] Quantification of cytokine secretion using Enzyme-Linked Immunosorbent

Assay (ELISA): Na^'ive T cells from knockout mice and their wild type controls were cultured as described above, their supernatants were collected after 72 h, 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 (Fig. 5, Fig. 16).

[00164] Microarray data: Na^'ive T cells were isolated from WT mice, and treated with IL-6 and TGF-βΙ . Affymetrix microarrays HT MG-430A were used to measure the resulting mRNA levels at 18 different time points (0.5 - 72 h; Fig. lb). In addition, cells treated initially with IL-6, TGF-βΙ and with addition of IL-23 after 48hr were profiled at five time points (50 - 72 h). As control, time- and culture-matched WT na^'ive T cells stimulated under ThO conditions were used. Biological replicates were measured in eight of the eighteen time points (lhr, 2hr, lOhr, 20hr, 30hr, 42hr, 52hr, 60hr) with high

reproducibility (r²>0.98). For further validation, the differentiation time course was compared to published microarray data of Thl7 cells and na^'ive T cells (Wei, G. et al. in Immunity Vol. 30 155-167 (2009)) (Fig. 6c). In an additional dataset na^'ive T cells were isolated from WT and I123r mice, and treated with IL-6, TGF-βΙ and IL-23 and profiled at four different time points (49hr, 54hr, 65hr, 72hr). Expression data was preprocessed using the RMA algorithm followed by quantile normalization (Reich, M. et al. GenePattem 2.0. Nature genetics 38, 500-501, doi: 10.1038/ng0506-500 (2006)).

[00165] Detecting differentially expressed genes: Differentially expressed genes

(comparing to the ThO control) were found using four methods: (1) Fold change. Requiring a 2-fold change (up or down) during at least two time points. (2) Polynomial fit. The EDGE software (Storey, J., Xiao, W., Leek, J., Tompkins, R. & Davis, R. in Proc. Natl. Acad. Sci. U.S.A. vol. 102 12837 (2005); Leek, J. T., Monsen, E., Dabney, A. R. & Storey, J. D.

EDGE: extraction and analysis of differential gene expression. Bioinformatics 22, 507-508, doi: 10.1093/bioinformatics/btk005 (2006)), designed to identify differential expression in time course data, was used with a threshold of q- value < 0.01. (3) Sigmoidal fit. An algorithm similar to EDGE while replacing the polynomials with a sigmoid function, which is often more adequate for modeling time course gene expression data (Chechik, G. & Koller, D. Timing of gene expression responses to environmental changes. J Comput Biol 16, 279-290, doi: 10.1089/cmb.2008.13TT10.1089/cmb.2008.13TT [pii] (2009)), was used. A threshold of q- value < 0.01. (4) ANOVA was used. Gene expression is modeled by: time (using only time points for which there was more than one replicate) and treatment ("TGF- βΐ + IL-6" or "ThO"). The model takes into account each variable independently, as well as their interaction. Cases in which the p-value assigned with the treatment parameter or the interaction parameter passed an FDR threshold of 0.01 were reported.

[00166] Overall, substantial overlap between the methods (average of 82% between any pair of methods) observed. The differential expression score of a gene was defined as the number of tests that detected it. As differentially expressed genes, cases with differential expression score >3 were reported.

[00167] For the I123r^_/" time course (compared to the WT T cells) methods 1.3 (above) were used. Here, a fold change cutoff of 1.5 was used, and genes detected by at least two tests were reported.

[00168] Clustering: several ways for grouping the differentially expressed genes were considered, based on their time course expression data: (1) For each time point, two groups were defined: (a) all the genes that are over-expressed and (b) all the genes that are under- expressed relative to ThO cells (see below); (2) For each time point, two groups were defined: (a) all the genes that are induced and (b) all the genes that are repressed, comparing to the previous time point; (3) K-means clustering using only the Thl7 polarizing conditions. The minimal k was used, such that the within-cluster similarity (average Pearson correlation with the cluster's centroid) was higher than 0.75 for all clusters; and, (4) K- means clustering using a concatenation of the ThO and Thl7 profiles.

[00169] For methods (1, 2), to decide whether to include a gene, its original mRNA expression profiles (ThO, Thl7), and their approximations as sigmoidal functions (Chechik, G. & Koller, D. Timing of gene expression responses to environmental changes. J Comput Biol 16, 279-290, doi: 10.1089/cmb.2008.13TT10.1089/cmb.2008.13TT [pii] (2009)) (thus filtering transient fluctuations) were considered. The fold change levels (compared to ThO (method 1) or to the previous time point (method 2)) were required to pass a cutoff defined as the minimum of the following three values: (1) 1.7; (2) mean + std of the histogram of fold changes across all time points; or (3) the maximum fold change across all time points. The clusters presented in Fig. lb were obtained with method 4.

[00170] Regulatory network inference: potential regulators of Thl7 differentiation were identified by computing overlaps between their putative targets and sets of differentially expressed genes grouped according to methods 1-4 above, regulator-target associations from several sources were assembled: (1) in vivo DNA binding profiles (typically measured in other cells) of 298 transcriptional regulators (Linhart, C, Halperin, Y. & Shamir, R. Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome research 18, 1180-1189, doi: 10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integrated platform of mammalian transcription factors. Bio informatics 24, 2416-2417,

doi: 10.1093/bioinformatics/btn439 (2008); Wilson, N. K. et al. Combinatorial

transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532-544, doi:S1934-5909(10)00440-6

[pii] 10.1016/j.stem.2010.07.016 (2010); Lachmann, A. et al. in Bioinformatics Vol. 26 2438-2444 (2010); Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0.

Bioinformatics 27, 1739-1740, doi: 10.1093/bioinformatics/btr260 (2011); Jiang, C, Xuan, Z., Zhao, F. & Zhang, M. TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35, D137-140 (2007)); (2) transcriptional responses to the knockout of 11 regulatory proteins (Awasthi et al, J. Immunol 2009; Schraml, B. U. et al. The AP-1 transcription factor Batf controls T(H)17 differentiation. Nature 460, 405-409, doi:nature08114 [pii] 10.1038/nature08114 (2009); Shi, L. Z. et al. 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, doi: 10.1084/jem.20110278 (2011); Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nature immunology 12, 247-254, doi: 10.1038/ni. l995 (2011); Durant, L. et al. Diverse Targets of the Transcription Factor STAT3 Contribute to T Cell Pathogenicity and Homeostasis. Immunity 32, 605-615, doi: 10.1016/j.immuni.2010.05.003 (2010); Jux, B., Kadow, S. & Esser, C. Langerhans cell maturation and contact hypersensitivity are impaired in aryl hydrocarbon receptor-null mice. Journal of immunology (Baltimore, Md.: 1950) 182, 6709- 6717, doi: 10.4049/jimmuno 1.0713344 (2009); Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257-263, doi: 10.1126/science.1179050 (2009); Xiao, S. et al. Retinoic acid increases Foxp3+ regulatory T cells and inhibits development of Thl7 cells by enhancing TGF-beta-driven Smad3 signaling and inhibiting IL-6 and IL-23 receptor expression. J Immunol 181, 2277- 2284, doi: 181/4/2277 [pii] (2008)); (3) additional potential interactions obtained by applying the Ontogenet algorithm (Jojic et al, under review; regulatory model available at: to data from the mouse ImmGen consortium (January 2010 release (Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nature immunology 9, 1091-1094, doi: 10.1038/nil008-1091 (2008)), which includes 484 microarray samples from 159 cell subsets from the innate and adaptive immune system of mice; (4) a statistical analysis of cis-regulatory element enrichment in promoter regions (Elkon, Pv., Linhart, C, Sharan, R., Shamir, R. & Shiloh, Y. in Genome Research Vol. 13 773-780 (2003); Odabasioglu, A., Celik, M. & Pileggi, L. T. in Proceedings of the 1997 IEEE/ ACM international conference on Computer-aided design 58-65 (IEEE Computer Society, San Jose, California, United States, 1997)); and, (5) the TF enrichment module of the IPA software. 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 was computed. Cases where p< 5* 10^"5 and the fold enrichment > 1.5 were included.

[00171] Each edge in the regulatory network was assigned a time stamp based on the expression profiles of its respective regulator and target nodes. For the target node, the time points at which a gene was either differentially expressed or significantly induced or repressed with respect to the previous time point (similarly to grouping methods 1 and 2 above) were considered. A regulator node was defined as 'absent' at a given time point if: (i) it was under expressed compared to ThO; or (ii) the expression is low (<20% of the maximum value in time) and the gene was not over-expressed compared to ThO; or, (iii) up to this point in time the gene was not expressed above a minimal expression value of 100. As an additional constraint, protein expression levels were estimated using the model from Schwanhausser, B. et al. (Global quantification of mammalian gene expression control. Nature 473, 337-342, doi: 10.1038/naturel0098 (2011)) and using a sigmoidal fit (Chechik & Koller, J Comput Biol 2009) for a continuous representation of the temporal expression profiles, and the ProtParam software (Wilkins, M. R. et al. Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol. 112, 531-552 (1999)) for estimating protein half-lives. It was required that, in a given time point, the predicted protein level be no less than 1.7 fold below the maximum value attained during the time course, and not be less than 1.7 fold below the ThO levels. The timing assigned to edges inferred based on a time-point specific grouping (grouping methods 1 and 2 above) was limited to that specific time point. For instance, if an edge was inferred based on enrichment in the set of genes induced at lhr (grouping method #2), it will be assigned a "lhr" time stamp. This same edge could then only have additional time stamps if it was revealed by additional tests.

[00172] Selection of Nano string signature genes: The selection of the 275-gene signature (Table 1) 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 Thl7 microarray signature (comparing to other CD4+ T cells (Wei et al, in Immunity vol. 30 155-167 (2009)), see Methods described herein); that are included as regulators in the network and have a differential expression score>l; or that are strongly differentially expressed (differential expression score=4); (2) it must include at least 10 representatives from each cluster of genes that have similar expression profiles (using clustering method (4) above); (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 across all 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. To this list 18 genes whose expression showed no change (in time or between treatments) in the microarray data were added.

[00173] The 85-gene signature (used for the Fluidigm BioMark qPCR assay) is a subset of the 275-gene signature, selected to include all the key regulators and cytokines discussed. To this list 10 control genes (2900064A13RIK, API5, CAND1, CSNK1A1, EIF3E, EIF3H, FIP1L1, GOLGA3, HSBP1, KHDRBS1, MED24, MKLN1, PCBP2, SLC6A6, SUFU, TMED7, UBE3A, ZFP410) were added. [00174] Selection of perturbation targets: an unbiased approach was used to rank candidate regulators - transcription factor or chromatin modifier genes - of Thl7

differentiation. The ranking was based on the following features: (a) whether the gene encoding the regulator belonged to the Thl7 microarray signature (comparing to other CD4+ T cells (Wei et al, in Immunity vol. 30 155-167 (2009)), see Methods described herein); (b) whether the regulator was predicted to target key Thl7 molecules (IL-17, 1L-21, IL23r, and ROR-γί); (c) whether the regulator was detected based on both perturbation and physical binding data from the IP A software; (d) whether the regulator was included in the network using a cutoff of at least 10 target genes; (e) whether the gene encoding for the regulator was significantly induced in the Thl7 time course. Only cases where the induction happened after 4 hours were considered to exclude non-specific hits; (f) whether the gene encoding the regulator was differentially expressed in response to Thl7-related

perturbations in previous studies. For this criterion, a database of transcriptional effects in perturbed Thl7 cells was assembled, including: knockouts of Batf (Schraml et al, Nature 2009), ROR-γΐ (Xiao et al, unpublished), Hifla (Shi et al, J. Exp. Med. (2011)), Stat3 and Stat5 (Yang et al, Nature Immunol (2011); Durant, L. et al. in Immunity Vol. 32 605-615 (2010), Tbx21 (Awasthi et al, unpublished), IL23r (this study), and Ahr (Jux et al, J.

Immunol 2009)). Data from the Thl7 response to Digoxin (Huh, J. R. et al. Digoxin and its derivatives suppress TH17 cell differentiation by antagonizing RORgammat activity. Nature 472, 486-490, doi: 10.1038/nature09978 (2011)) and Halofuginone (Sundrud, M. S. et al. Halofuginone inhibits TH17 cell differentiation by activating the amino acid starvation response. Science (New York, N.Y.) 324, 1334-1338, doi: 10.1126/science.1172638 (2009)), as well as information on direct binding by ROR-γί as inferred from ChlP-seq data (Xiao et al, unpublished) was also included. The analysis of the published expression data sets is described in the Methods described herein. For each regulator, the number of conditions in which it came up as a significant hit (up/ down-regulated or bound) was counted; for regulators with 2 to 3 hits (quantiles 3 to 7 out of 10 bins), a score of 1 was then assign; for regulators with more than 3 hits (quantiles 8-10), a score of 2 (a score of 0 is assigned otherwise) was assigned; and, (g) the differential expression score of the gene in the Thl7 time course.

[00175] The regulators were ordered lexicographically by the above features according to the order: a, b, c, d, (sum of e and f), g - that is, first sort according to a then break ties according to b, and so on. Genes that are not over-expressed during at least one time point were excluded. As an exception, predicted regulators (feature d) that had additional external validation (feature f) were retained. To validate this ranking, a supervised test was used: 74 regulators that were previously associated with Thl7 differentiation were manually annotated. All of the features are highly specific for these regulators (p<10^~3). Moreover, using a supervised learning method (Naive Bayes), the features provided good predictive ability for the annotated regulators (accuracy of 71%, using 5 -fold cross validation), and the resulting ranking was highly correlated with the unsupervised lexicographic ordering (Spearman correlation > 0.86).

[00176] This strategy was adapted for ranking protein receptors. To this end, feature c was excluded and the remaining "protein-level" features (b and d) were replaced with the following definitions: (b) whether the respective ligand is induced during the Thl7 time course; and, (d) whether the receptor was included as a target in the network using a cutoff of at least 5 targeting transcriptional regulators.

[00177] Gene knockdown using silicon nanowires: 4 x 4 mm silicon nanowire (NW) substrates were prepared and coated with 3 of a 50 μΜ pool of four siGENOME siRNAs (Dharmcon) in 96 well tissue culture plates, as previously described (Shalek, A. K. et al. Vertical silicon nanowires as a universal platform for delivering biomolecules into living cells. Proceedings of the National Academy of Sciences of the United States of America 107, 1870-1875, doi: 10.1073/pnas.0909350107 (2010)). Briefly, 150,000 naive T cells were seeded on siRNA- laced NWs in 10 of complete media and placed in a cell culture incubator (37°C, 5% C0₂) to settle for 45 minutes before full media addition. These samples were left undisturbed for 24 hours to allow target transcript knockdown. Afterward, siRNA-transfected T cells were activated with aCd3/Cd28 dynabeads (Invitrogen), according to the manufacturer's recommendations, under Thl7 polarization conditions (TGF-βΙ & IL-6, as above). 10 or 48hr post-activation, culture media was removed from each well and samples were gently washed with 100 μΐ, of PBS before being lysed in 20 μΐ, of buffer TCL (Qiagen) supplemented with 2-mercaptoethanol (1 : 100 by volume). After mRNA was harvested in Turbocapture plates (Qiagen) and converted to cDNA using Sensiscript RT enzyme (Qiagen), qRT-PCR was used to validate both knockdown levels and phenotypic changes relative to 8-12 non-targeting siRNA control samples, as previously described (Chevrier, N. et al. Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell 147, 853-867, doi: 10.1016/j.cell.2011.10.022 (2011)). A 60% reduction in target mRNA was used as the knockdown threshold. In each knockdown experiment, each individual siR A pool was run in quadruplicate; each siRNA was tested in at least three separate experiments (Fig. 11).

[00178] mRNA measurements in perturbation assays: the nCounter system, presented in full in Geiss et al. (Geiss, G. K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. SI. Nature Biotechnology 26, 317-325, doi: 10.1038/nbtl385 (2008)), was used to measure a custom CodeSet constructed to detect a total of 293 genes, selected as described above. The Fluidigm BioMark HD system was also used to measure a smaller set of 96 genes. Finally, RNA-Seq was used to follow up and validate 12 of the perturbations.

[00179] A custom CodeSet constructed to detect a total of 293 genes, selected as described above, including 18 control genes whose expression remain unaffected during the time course was used. Given the scarcity of input mRNA derived from each NW

knockdown, a Nanostring-CodeSet specific, 14 cycle Specific Target Amplification (STA) protocol was performed according to the manufacturer's recommendations by adding 5 of TaqMan PreAmp Master Mix (Invitrogen) and 1 of pooled mixed primers (500 nM each, see Table S6.1 for primer sequences) to 5 μΙ_, of cDNA from a validated knockdown. After amplification, 5 of the amplified cDNA product was melted at 95°C for 2 minutes, snap cooled on ice, and then hybridized with the CodeSet at 65°C for 16 hours. Finally, the hybridized samples were loaded into the nCounter prep station and product counts were quantified using the nCounter Digital Analyzer following the manufacturer's instructions. Samples that were too concentrated after amplification were diluted and rerun. Serial dilutions (1 : 1, 1 :4, 1 : 16, & 1 :64, pre-STA) ofwhole spleen and Thl7 polarized cDNAs were used to both control for the effects of different amounts of starting input material and check for biases in sample amplification.

[00180] Nanostring nCounter data analysis: For each sample, the count values were divided by the sum of counts that were assigned to a set of control genes that showed no change (in time or between treatments) in the microarray data (18 genes altogether). For each condition, a change fold ratio was computed, comparing to at least three different control samples treated with non-targeting (NT) siRNAs. The results of all pairwise comparisons (i.e. AxB pairs for A repeats of the condition and B control (NT) samples) were then pooled together: a substantial fold change (above a threshold value t) in the same direction (up/ down regulation) in more than half of the pairwise comparisons was required. The threshold t was determined as max {dl, d2}, where dl is the mean+std in the absolute log fold change between all pairs of matching NT samples (i.e., form the same batch and the same time point; dl=1.66), and where d2 is the mean + 1.645 times the standard deviation in the absolute log fold change shown by the 18 control genes (determined separately for every comparison by taking all the 18xAxB values; corresponding to p=0.05, under assumption of normality). All pairwise comparisons in which both NT and knockdown samples had low counts before normalization (<100) were ignored.

[00181] A permutation test was used to evaluate the overlap between the predicted network model (Fig. 2) and the knockdown effects measured in the Nanostring nCounter (Fig. 4, Fig. 10). Two indices were computed for every TF for which predicted target were available: (i) specificity - the percentage of predicted targets that are affected by the respective knockdown (considering only genes measured by nCounter), and (ii) sensitivity - the percentage of genes affected by a given TF knockdown that are also its predicted targets in the model. To avoid circularity, target genes predicted in the original network based on knockout alone were excluded from this analysis. The resulting values (on average, 13.5% and 24.8%, respectively) were combined into an F-score (the harmonic mean of specificity and sensitivity). The calculation of F-score was then repeated in 500 randomized datasets, where the target gene labels in the knockdown result matrix were shuffled. The reported empirical p- value is:

P=(l+ ^randomized datasets with equal of better F-score) # randomized datasets)

[00182] mRNA measurements on the Fluidigm BioMark HD: cDNA from validated knockdowns was prepared for quantification on the Fluidigm BioMark HD. Briefiy, 5 of TaqMan PreAmp Master Mix (Invitrogen), 1 μΐ_^ of pooled mixed primers (500 nM each, see Table S6.1 for primers), and 1.5 μΐ, of water were added to 2.5 μΐ, of knockdown validated cDNA and 14 cycles of STA were performed according to the manufacturer's recommendations. After the STA, an Exonuclease I digestion (New England Biosystems) was performed to remove unincorporated primers by adding 0.8 μΐ, Exonuclease I, 0.4 μΕ Exonuclease I Reaction Buffer and 2.8 μΐ_^ water to each sample, followed by vortexing, centrifuging and heating the sample to 37°C for 30 minutes. After a 15 minute 80°C heat inactivation, the amplified sample was diluted 1 :5 in Buffer TE. Amplified validated knockdowns and whole spleen and Thl7 serial dilution controls (1 : 1, 1 :4, 1 : 16, & 1 :64, pre- STA) were then analyzed using EvaGreen and 96x96 gene expression chips (Fluidigm BioMark HD) (Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat Biotechnol 29, 1120-1127, doi: 10.1038/nbt.2038 (2011)).

[00183] Fluidigm data analysis: For each sample, the Ct values were subtracted from the geometric mean of the Ct values assigned to a set of four housekeeping genes. For each condition, a fold change ratio was computed, comparing to at least three different control samples treated with non-targeting (NT) siRNAs. The results of all pairwise comparisons (i.e. AxB pairs for A repeats of the condition and B control (NT) samples) were then pooled together: a substantial difference between the normalized Ct values (above a threshold value) in the same direction (up/ down regulation) in more than half of the pairwise comparisons was required. The threshold t was determined as max (log2(1.5), dl(b), d2}, where dl(b) is the mean+std in the delta between all pairs of matching NT samples (i.e., from the same batch and the same time point), over all genes in expression quantile b (l<=b <=10). d2 is the mean + 1.645 times the standard deviation in the deltas shown by 10 control genes (the 4 housekeeping genes plus 6 control genes from the Nanostring signature); d2 is determined separately for each comparison by taking all the ΙΟχΑχΒ values; corresponding to p=0.05, under assumption of normality). All pairwise comparisons in which both NT and knockdown samples had low counts before normalization (Ct<21 (taking into account the amplification, this cutoff corresponds to a conventional Ct cutoff of 35)) were ignored.

[00184] mRNA measurements using RNA-Seq: Validated single stranded cDNAs from the NW-mediated knockdowns were converted to double stranded DNA using the NEBNext mRNA Second Strand Synthesis Module (New England BioLabs) according to the manufacturer's recommendations. The samples were then cleaned using 0.9x SPRI beads (Beckman Coulter). Libraries were prepared using the Nextera XT DNA Sample Prep Kit (Illumina), quantified, pooled, and then sequenced on the HiSeq 2500 (Illumnia) to an average depth 20M reads.

[00185] RNA-seq data analysis: a Bowtie index based on the UCSC known Gene transcriptome (Fujita, P. A. et al. The UCSC Genome Browser database: update 2011. Nucleic Acids Res. 39, D876-882, doi: 10.1093/nar/gkq963 (2011)) was created, and paired- end reads were aligned directly to this index using Bowtie (Langmead, B., Trapnell, C, Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10, R25, doi: 10.1186/gb-2009-10-3-r25 (2009)). Next, RSEM vl . l 1 (Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from R A-Seq data with or without a reference genome. BMC Bioinformatics 12, 323, doi: 10.1186/1471-2105-12-323 (2011)) was ran with default parameters on these alignments to estimate expression levels. RSEM's gene level expression estimates (tau) were multiplied by 1,000,000 to obtain transcript per million (TPM) estimates for each gene. Quantile normalization was used to further normalize the TPM values within each batch of samples. For each condition, a fold change ratio was computed, comparing to at least two different control samples treated with nontargeting (NT) siRNAs. The results of all pairwise comparisons (i.e. AxB pairs for A repeats of the condition and B control (NT) samples) were then pooled together: a significant difference between the TPM values in the same direction (up/ down regulation) in more than half of the pairwise comparisons was required. The significance cutoff t was determined as max (log2(1.5), dl(b)}, where dl(b) is the mean+1.645*std in the log fold ratio between all pairs of matching NT samples (i.e., from the same batch and the same time point), over all genes in expression quantile b (l<=b <=20). All pairwise comparisons in which both NT and knockdown samples had low counts (TPM<10) were ignored. To avoid spurious fold levels due to low expression values a small constant, set to the value of the 1st quantile (out of 10) of all TPM values in the respective batch, was add to the expression values.

[00186] A hypergeometric test was used to evaluate the overlap between the predicted network model (Fig. 2) and the knockdown effects measured by RNA-seq (Fig. 4d). As background, all of the genes that appeared in the microarray data (and hence 20 have the potential to be included in the network) were used. As an additional test, the Wilcoxon-Mann- Whitney rank-sum test was used, comparing the absolute log fold-changes of genes in the annotated set to the entire set of genes (using the same background as before). The rank-sum test does not require setting a significance threshold; instead, it considers the fold change values of all the genes. The p-values produced by the rank-sum test were lower (i.e., more significant) than in the hypergeometric test, and therefore, in Fig. 4c, only the more stringent (hypergeometric) p-values were reported.

[00187] Profiling Tsc22d3 DNA binding using ChlP-seq ChlP-seq for Tsc22d3 was performed as previously described (Ram, O. et al. Combinatorial Patterning of Chromatin Regulators Uncovered by Genome-wide Location Analysis in Human Cells. Cell 147, 1628- 1639 (2011)) using an antibody from Abeam. The analysis of this data was performed as previously described (Ram, O. et al. Combinatorial Patterning of Chromatin Regulators Uncovered by Genome-wide Location Analysis in Human Cells. Cell 147, 1628-1639 (2011)) and is detailed in the Methods described herein.

[00188] Analysis ofTsc22d3 ChlP-seq data: ChlP-seq reads were aligned to the

NCBI Build 37 (UCSC mm9) of the mouse genome using Bowtie (Langmead, B., Trapnell, C, Pop, M. & Salzberg, S. L. in Genome Biol Vol. 10 R25 (2009)). Enriched binding regions (peaks) were detected using MACS (Zhang, Y. et al. in Genome Biol Vol. 9 R137 (2008)) with a pvalue cutoff of 10^"8. A peak was associated with a gene if it falls in proximity to its 5' end (lOkb upstream and lkb downstream from transcription start site) or within the gene's body. The RefSeq transcript annotations for gene's coordinates were used.

[00189] The overlap of ChlP-seq peaks with annotated genomic regions was assessed. It was determined that a region A overlap with a peak B if A is within a distance of 50bp from B's summit (as determined by MACS). The regions used included: (i) regulatory features annotations from the Ensemble database (Flicek, P. et al. Ensembl 2011. Nucleic Acids Res. 39, D800-806, doi: 10.1093/nar/gkql064 (2011)); (ii) regulatory 21 features found by the Oregano algorithm (Smith, R. L. et al. Polymorphisms in the IL- 12beta and IL-23R genes are associated with psoriasis of early onset in a UK cohort. J Invest Dermatol 128, 1325-1327, doi:5701140 [pii] 10.1038/sj.jid.5701140 (2008)); (iii) conserved regions annotated by the multiz30way algorithm (here regions with multiz30way score>0.7 were considered); (iv) repeat regions annotated by RepeatMasker; (v) putative promoter regions - taking lOkb upstream and lkb downstream of transcripts annotated in RefSeq (Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61-65, doi: 10.1093/nar/gkl842 (2007)); (vi) gene body annotations in RefSeq; (vii) 3' proximal regions (taking lkb upstream and 5kb downstream to 3' end); (viii) regions enriched in histone marks H3K4me3 and H3K27me3 in Thl7 cells (Wei, G. et al. in Immunity Vol. 30 155-167 (2009)); (ix) regions enriched in binding of Stat3 and Stat5 (Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nat. Immunol. 12, 247-254, doi: 10.1038/ni. l995 (2011)), Irf4 and Batf (Glasmacher, E. et al. A Genomic Regulatory Element That Directs Assembly and Function of Immune-Specific AP-l-IRF Complexes. Science,

doi: 10.1126/science.1228309 (2012)), and RORyt (Xiao et al unpublished) in Thl7 cells, and Foxp3 in iTreg (Xiao et al, unpublished). [00190] For each set of peaks "x" and each set of genomic regions "y", a binomial pvalue was used to assess their overlap in the genome as described in Mclean, C. Y. et al. in Nature biotechnology Vol. 28 nbt.1630-1639 (2010). The number of hits is defined as the number of x peaks that overlap with y. The background probability in sets (i)— (vii) is set to the overall length of the region (in bp) divided by the overall length of the genome. The background probability in sets (viii)— (ix) is set to the overall length of the region divided by the overall length of annotated genomic regions: this includes annotated regulatory regions (as defined in sets i, and ii), regions annotated as proximal to genes (using the definitions from set v-vii), carry a histone mark in Thl7 cells (using the definition from set viii), or bound by transcription regulators in Thl7 cells (using the definitions from set ix).

[00191] For the transcription regulators (set ix), an additional "gene-level" test was also included: here the overlap between the set of bound genes using a hypergeometric p- value was evaluated. A similar test was used to evaluate the overlap between the bound genes and genes that are differentially expressed in Tsc22d3 knockdown.

[00192] The analysis was repeated with a second peak-calling software (Scripture)

(Guttman, M. et al. in Nature biotechnology Vol. 28 503-510 (2010); Garber, M. et al. A High-Throughput Chromatin Immunoprecipitation Approach Reveals Principles of

Dynamic Gene Regulation in Mammals. Molecular cell, doi: 10.1016/j.molcel.2012.07.030 (2012)), and obtained consistent results in all the above tests. Specifically, similar levels of overlap with the Thl7 factors tested, both in terms of co-occupied binding sites and in terms of common target genes, was seen.

[00193] Estimating statistical significance of monochromatic interactions between modules: The functional network in Fig. 4b consists of two modules: positive and negative. Two indices were computed: (1) within-module index: the percentage of positive edges between members of the same module {i.e., down-regulation in knockdown/knockout); and, (2) between-module index: the percentage of negative edges between members of the same module that are negative. The network was shuffled 1,000 times, while maintaining the nodes' out degrees {i.e., number of outgoing edges) and edges' signs (positive/ negative), and re-computed the two indices. The reported p-values were computed using a t-test.

[00194] Using literature microarray data for deriving a Thl 7 signature and for identifying genes responsive to Thl 7 -related perturbations: To define the Thl 7 signatures genes, the gene expression data from Wei et al, in Immunity, vol. 30 155-167 (2009) was downloaded and analyzed, and the data was preprocessed using the RMA algorithm, followed by quantile normalization using the default parameters in the

ExpressionFileCreator module of the 23 GenePattern suite (Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500-501, doi: 10.1038/ng0506-500 (2006)). This data includes replicate microarray measurements from Thl 7, Thl, Th2, iTreg, nTreg, and Na^'ive CD4+ T cells. For each gene, it was evaluated whether it is over-expressed in Thl 7 cells compared to all other cell subsets using a one-sided t-test. All cases that had a p-value < 0.05 were retained. As an additional filtering step, it was required that the expression level of a gene in Thl 7 cells be at least 1.25 fold higher than its expression in all other cell subsets. To avoid spurious fold levels due to low expression values, a small constant (c=50) was added to the expression values.

[00195] To define genes responsive to published Thl7-related perturbations, gene expression data from several sources that provided transcriptional profiles of Thl7 cells under various conditions (listed above) were downloaded and analyzed. These datasets were preprocessed as above. To find genes that were differentially expressed in a given condition (compared to their respective control), the fold change between the expression levels of each probeset in the case and control conditions was computed. To avoid spurious fold levels due to low expression values, a small constant as above was added to the expression values. Only cases where more than 50% of all of the possible case-control comparisons were above a cutoff of 1.5 fold change were reported. As an additional filter, when duplicates are available, a Z-score was computed as above and only cases with a corresponding p-value < 0.05 were reported.

[00196] Genes: The abbreviations set forth below in Table 11 are used herein to identify the genes used throughout the disclosure, including but not limited to those shown in Tables 1-9 of the specification.

Table 11. Gene Abbreviations, Entrez ID Numbers and Brief Description

Symbol Entrez ID Description

AAK1 22848 AP2 associated kinase 1

ABCG2 9429 ATP-binding cassette, sub-family G (WHITE), member 2

ACP5 54 acid phosphatase 5, tartrate resistant

ACVR1B 91 activin A receptor, type IB

ACVR2A 92 activin receptor MA

ADAM 10 102 a disintegrin and metallopeptidase domain 10

ADAM 17 6868 a disintegrin and metallopeptidase domain 17

ADRBK1 156 adrenergic receptor kinase, beta 1 AES 166 amino-terminal enhancer of split

AHR 196 aryl-hydrocarbon receptor

AIM1 202 absent in melanoma 1

AKT1 207 thymoma viral proto-oncogene 1

ALPK2 115701 alpha-kinase 2

ANKHD1 54882 ankyrin repeat and KH domain containing 1

acidic (leucine-rich) nuclear phosphoprotein 32 family,

ANP32A 8125

member A

ANXA4 307 annexin A4

AQ.P3 360 aquaporin 3

ARHGEF3 50650 Rho guanine nucleotide exchange factor (GEF) 3

ARID3A 1820 AT rich interactive domain 3A (BRIGHT-like)

ARID5A 10865 AT rich interactive domain 5A (MRFl-like)

ARL5A 26225 ADP-ribosylation factor-like 5A

ARMCX2 9823 armadillo repeat containing, X-linked 2

ARNTL 406 aryl hydrocarbon receptor nuclear translocator-like

ASXL1 171023 additional sex combs like 1 (Drosophila)

ATF2 1386 activating transcription factor 2

ATF3 467 activating transcription factor 3

ATF4 468 activating transcription factor 4

AURKB 9212 aurora kinase B

AXL 558 AXL receptor tyrosine kinase

UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase,

B4GALT1 2683

polypeptide 1

BATF 10538 basic leucine zipper transcription factor, ATF-like

BATF3 55509 basic leucine zipper transcription factor, ATF-like 3

BAZ2B 29994 bromodomain adjacent to zinc finger domain, 2B

BCL11B 64919 B-cell leukemia/lymphoma 11B

BCL2L11 10018 BCL2-like 11 (apoptosis facilitator)

BCL3 602 B-cell leukemia/lymphoma 3

BCL6 604 B-cell leukemia/lymphoma 6

BHLH40 8553 Basic Helix-Loop-Helix Family, Member E40

biogenesis of lysosome-related organelles complex-1,

BL0C1S1 2647

subunit 1

BMP2K 55589 BMP2 inducible kinase

BMPR1A 657 bone morphogenetic protein receptor, type 1A

BPGM 669 2,3-bisphosphoglycerate mutase

BSG 682 basigin

BTG1 694 B-cell translocation gene 1, anti-proliferative

BTG2 7832 B-cell translocation gene 2, anti-proliferative

budding uninhibited by benzimidazoles 1 homolog (S.

BUB1 699

cerevisiae) C140RF83 161145 RIKEN cDNA 6330442E10 gene

C16ORF80 29105 gene trap locus 3

C210RF66 94104 RIKEN cDNA 1810007M 14 gene

CAMK4 814 calcium/calmodulin-dependent protein kinase IV

CARM1 10498 coactivator-associated arginine methyltransferase 1

CAS PI 834 caspase 1

CASP3 836 caspase 3

CASP4 837 caspase 4, apoptosis-related cysteine peptidase

CASP6 839 caspase 6

CASP8AP2 9994 caspase 8 associated protein 2

CBFB 865 core binding factor beta

CBX4 8535 chromobox homolog 4 (Drosophila Pc class)

CCL1 6346 chemokine (C-C motif) ligand 1

CCL20 6364 chemokine (C-C motif) ligand 20

CCL4 6351 chemokine (C-C motif) ligand 4

CCND2 894 cyclin D2

CCR4 1233 chemokine (C-C motif) receptor 4

CCR5 1234 chemokine (C-C motif) receptor 5

CCR6 1235 chemokine (C-C motif) receptor 6

CCR8 1237 chemokine (C-C motif) receptor 8

CCRN4L 25819 CCR4 carbon catabolite repression 4-like (S. cerevisiae)

CD14 929 CD14 antigen

CD2 914 CD2 antigen

CD200 4345 CD200 antigen

CD226 10666 CD226 antigen

CD24 934 CD24a antigen

CD 247 919 CD247 antigen

CD27 939 CD27 antigen

CD274 29126 CD274 antigen

CD28 940 CD28 antigen

CD3D 915 CD3 antigen, delta polypeptide

CD3G 917 CD3 antigen, gamma polypeptide

CD4 920 CD4 antigen

CD40LG 959 CD40 ligand

CD44 960 CD44 antigen

CD53 963 CD53 antigen

CD5L 922 CD5 antigen-like

CD63 967 CD63 antigen

CD68 968 CD68 antigen

CD70 970 CD70 antigen

CD74 antigen (invariant polypeptide of major

CD74 972

histocompatibility complex, cl CD80 941 CD80 antigen

CD83 9308 CD83 antigen

CD84 8832 CD84 antigen

CD86 942 CD86 antigen

CD9 928 CD9 antigen

CD96 10225 CD96 antigen

CDC25B 994 cell division cycle 25 homolog B (S. pombe)

CDC42BPA 8476 CDC42 binding protein kinase alpha

CDC5L 988 cell division cycle 5-like (S. pombe)

CDK5 1020 cyclin-dependent kinase 5

CDK6 1021 cyclin-dependent kinase 6

CDKN3 1033 cyclin-dependent kinase inhibitor 3

CDYL 9425 chromodomain protein, Y chromosome-like

CEBPB 1051 CCAAT/enhancer binding protein (C/EBP), beta

CENPT 80152 centromere protein T

CHD7 55636 chromodomain helicase DNA binding protein 7

CHMP1B 57132 chromatin modifying protein IB

CHMP2A 27243 charged multivesicular body protein 2A

CHRAC1 54108 chromatin accessibility complex 1

CIC 23152 capicua homolog (Drosophila)

Cbp/p300-interacting transactivator, with Glu/Asp-rich

CITED2 10370

carboxy-terminal dom

CLCF1 23529 cardiotrophin-like cytokine factor 1

CLK1 1195 CDC-like kinase 1

CLK3 1198 CDC-like kinase 3

CMTM6 54918 CKLF-like MARVEL transmembrane domain containing 6

CN0T2 4848 CCR4-NOT transcription complex, subunit 2

CREB1 1385 cAMP responsive element binding protein 1

CREB3L2 64764 cAMP responsive element binding protein 3-like 2

CREG1 8804 cellular repressor of ElA-stimulated genes 1

CREM 1390 cAMP responsive element modulator

CSDA 8531 cold shock domain protein A

CSF1R 1436 colony stimulating factor 1 receptor

CSF2 1437 colony stimulating factor 2 (granulocyte-macrophage)

CTLA4 1493 cytotoxic T-lymphocyte-associated protein 4

CTSD 1509 cathepsin D

CTSW 1521 cathepsin W

CXCL10 3627 chemokine (C-X-C motif) ligand 10

CXCR3 2833 chemokine (C-X-C motif) receptor 3

CXCR4 7852 chemokine (C-X-C motif) receptor 4

CXCR5 643 chemochine (C-X-C motif) receptor 5 dual adaptor for phosphotyrosine and 3-

DAPP1 27071

phosphoinositides 1

DAXX 1616 Fas death domain-associated protein

DCK 1633 deoxycytidine kinase

DCLK1 9201 doublecortin-like kinase 1

DDIT3 1649 DNA-damage inducible transcript 3

DDR1 780 discoidin domain receptor family, member 1

DGKA 1606 diacylglycerol kinase, alpha

DGUOK 1716 deoxyguanosine kinase

DNAJC2 27000 DnaJ (Hsp40) homolog, subfamily C, member 2

DNTT 1791 deoxynucleotidyltransferase, terminal

DPP4 1803 dipeptidylpeptidase 4

DUSP1 1843 dual specificity phosphatase 1

DUSP10 11221 dual specificity phosphatase 10

DUSP14 11072 dual specificity phosphatase 14

DUSP16 80824 dual specificity phosphatase 16

DUSP2 1844 dual specificity phosphatase 2

DUSP22 56940 dual specificity phosphatase 22

DUSP6 1848 dual specificity phosphatase 6

E2F1 1869 E2F transcription factor 1

E2F4 1874 E2F transcription factor 4

E2F8 79733 E2F transcription factor 8

ECE2 9718 endothelin converting enzyme 2

EGR1 1958 early growth response 1

EGR2 1959 early growth response 2

EIF2AK2 5610 eukaryotic translation initiation factor 2-alpha kinase 2

ELK3 2004 ELK3, member of ETS oncogene family

ELL2 22936 elongation factor RNA polymerase II 2

EMP1 2012 epithelial membrane protein 1

ENTPD1 953 ectonucleoside triphosphate diphosphohydrolase 1 excision repair cross-complementing rodent repair

ERCC5 2073

deficiency, complementati

ERRFI1 54206 ERBB receptor feedback inhibitor 1

ETS1 2113 E26 avian leukemia oncogene 1, 5' domain

ETS2 2114 E26 avian leukemia oncogene 2, 3' domain

ETV6 2120 ets variant gene 6 (TEL oncogene)

EZH1 2145 enhancer of zeste homolog 1 (Drosophila)

FAS 355 Fas (TNF receptor superfamily member 6)

FASLG 356 Fas ligand (TNF superfamily, member 6)

FCER1G 2207 Fc receptor, IgE, high affinity 1, gamma polypeptide

FCGR2B 2213 Fc receptor, IgG, low affinity Mb

FES 2242 feline sarcoma oncogene FLU 2313 Friend leukemia integration 1

FLNA 2316 filamin, alpha

F0SL2 2355 fos-like antigen 2

F0XJ2 55810 forkhead box J2

F0XM1 2305 forkhead box Ml

FOXN3 1112 forkhead box N3

FOXOl 2308 forkhead box 01

FOXP1 27086 forkhead box PI

FOXP3 50943 forkhead box P3

FRMD4B 23150 FERM domain containing 4B

fusion, derived from t(12;16) malignant liposarcoma

FUS 2521

(human)

FZD7 8324 frizzled homolog 7 (Drosophila)

GAP43 2596 growth associated protein 43

GATA3 2625 GATA binding protein 3

GATAD1 57798 GATA zinc finger domain containing 1

GATAD2B 57459 GATA zinc finger domain containing 2B

GTP binding protein (gene overexpressed in skeletal

GEM 2669

muscle)

GFI1 2672 growth factor independent 1

GJA1 2697 gap junction protein, alpha 1

GK 2710 glycerol kinase

GLIPRl 11010 GLI pathogenesis-related 1 (glioma)

GMFB 2764 glia maturation factor, beta

GMFG 9535 glia maturation factor, gamma

GRN 2896 granulin

GUSB 2990 glucuronidase, beta

HCLS1 3059 hematopoietic cell specific Lyn substrate 1

HDAC8 55869 histone deacetylase 8

HIF1A 3091 hypoxia inducible factor 1, alpha subunit

HINT3 135114 histidine triad nucleotide binding protein 3

HIP1R 9026 huntingtin interacting protein 1 related

HIPK1 204851 homeodomain interacting protein kinase 1

HIPK2 28996 homeodomain interacting protein kinase 2

HK1 3098 hexokinase 1

HK2 3099 hexokinase 2

HLA-A 3105 major histocompatibility complex, class 1, A

HLA-DQA1 3117 histocompatibility 2, class II antigen A, alpha

HMGA1 3159 high mobility group AT-hook 1

HMGB2 3148 high mobility group box 2

HMGN1 3150 high mobility group nucleosomal binding domain 1

ICOS 29851 inducible T-cell co-stimulator ID1 3397 inhibitor of DNA binding 1

ID2 3398 inhibitor of DNA binding 2

ID3 3399 inhibitor of DNA binding 3

IER3 8870 immediate early response 3

IFI35 3430 interferon-induced protein 35

IFIH1 64135 interferon induced with helicase C domain 1 interferon-induced protein with tetratricopeptide

IFIT1 3434

repeats 1

IFITM2 10581 interferon induced transmembrane protein 2

IFNG 3458 interferon gamma

IFNGR1 3459 interferon gamma receptor 1

IFNGR2 3460 interferon gamma receptor 2

IKZF1 10320 IKAROS family zinc finger 1

IKZF3 22806 IKAROS family zinc finger 3

IKZF4 64375 IKAROS family zinc finger 4

IL10 3586 interleukin 10

IL10RA 3587 interleukin 10 receptor, alpha

IL12RB1 3594 interleukin 12 receptor, beta 1

IL12RB2 3595 interleukin 12 receptor, beta 2

IL15RA 3601 interleukin 15 receptor, alpha chain

IL17A 3605 interleukin 17A

IL17F 112744 interleukin 17F

IL17RA 23765 interleukin 17 receptor A

IL18R1 8809 interleukin 18 receptor 1

IL1R1 3554 interleukin 1 receptor, type 1

IL1RN 3557 interleukin 1 receptor antagonist

IL2 3558 interleukin 2

IL21 59067 interleukin 21

IL21R 50615 interleukin 21 receptor

IL22 50616 interleukin 22

IL23R 149233 interleukin 23 receptor

IL24 11009 interleukin 24

IL27RA 9466 interleukin 27 receptor, alpha

IL2RA 3559 interleukin 2 receptor, alpha chain

IL2RB 3560 interleukin 2 receptor, beta chain

IL2RG 3561 interleukin 2 receptor, gamma chain

IL3 3562 interleukin 3

IL4 3565 interleukin 4

IL4R 3566 interleukin 4 receptor, alpha

IL6ST 3572 interleukin 6 signal transducer

IL7R 3575 interleukin 7 receptor

IL9 3578 interleukin 9 INHBA 3624 inhibin beta-A

INPP1 3628 inositol polyphosphate-l-phosphatase

interleukin-1 receptor-associated kinase 1 binding

IRAK1BP1 134728

protein 1

IRF1 3659 interferon regulatory factor 1

IRF2 3660 interferon regulatory factor 2

IRF3 3661 interferon regulatory factor 3

IRF4 3662 interferon regulatory factor 4

IRF7 3665 interferon regulatory factor 7

IRF8 3394 interferon regulatory factor 8

IRF9 10379 interferon regulatory factor 9

ISG20 3669 interferon-stimulated protein

ITGA3 3675 integrin alpha 3

ITGAL 3683 integrin alpha L

ITGAV 3685 integrin alpha V

ITGB1 3688 integrin beta 1 (fibronectin receptor beta)

ITK 3702 IL2-inducible T-cell kinase

JAK2 3717 Janus kinase 2

JAK3 3718 Janus kinase 3

JARID2 3720 jumonji, AT rich interactive domain 2

JMJD1C 221037 jumonji domain containing 1C

JUN 3725 Jun oncogene

JUNB 3726 Jun-B oncogene

KAT2B 8850 K(lysine) acetyltransferase 2B

KATNA1 11104 katanin p60 (ATPase-containing) subunit Al

KDM6B 23135 lysine (K)-specific demethylase 6B

KLF10 7071 Kruppel-like factor 10

KLF13 51621 Kruppel-like factor 13

KLF6 1316 Kruppel-like factor 6

KLF7 8609 Kruppel-like factor 7 (ubiquitous)

KLF9 687 Kruppel-like factor 9

KLRD1 3824 killer cell lectin-like receptor, subfamily D, member 1

LAD1 3898 ladinin

LAMP2 3920 lysosomal-associated membrane protein 2

LASS4 79603 LAG1 homolog, ceramide synthase 4

LASS6 253782 LAG1 homolog, ceramide synthase 6

LEF1 51176 lymphoid enhancer binding factor 1

LGALS3BP 3959 lectin, galactoside-binding, soluble, 3 binding protein

LGTN 1939 ligatin

LIF 3976 leukemia inhibitory factor LILRBl,

10859, 10288,

LILRB2, leukocyte immunoglobulin-like receptor, subfamily B

11025, 11006,

LILRB3, (with TM and ITIM domains),members 1-5

10990

LILRB4, LILRB5

LIMK2 3985 LIM motif-containing protein kinase 2

LITAF 9516 LPS-induced TN factor

LMNB1 4001 lamin Bl

LRRFIP1 9208 leucine rich repeat (in FLII) interacting protein 1

LSP1 4046 lymphocyte specific 1

LTA 4049 lymphotoxin A

avian musculoaponeurotic fibrosarcoma (v-maf) AS42

MAF 4094

oncogene homolog

v-maf musculoaponeurotic fibrosarcoma oncogene

MAFF 23764

family, protein F (avian)

v-maf musculoaponeurotic fibrosarcoma oncogene

MAFG 4097

family, protein G (avian)

MAML2 84441 mastermind like 2 (Drosophila)

MAP3K5 4217 mitogen-activated protein kinase kinase kinase 5

MAP3K8 1326 mitogen-activated protein kinase kinase kinase 8

MAP4K2 5871 mitogen-activated protein kinase kinase kinase kinase 2

MAP4K3 8491 mitogen-activated protein kinase kinase kinase kinase 3

MAPKAPK2 9261 MAP kinase-activated protein kinase 2

MATR3 9782 matrin 3

MAX 4149 Max protein

MYC-associated zinc finger protein (purine-binding

MAZ 4150

transcription factor)

MBNL1 4154 muscleblind-like 1 (Drosophila)

MBNL3 55796 muscleblind-like 3 (Drosophila)

MDM4 4194 transformed mouse 3T3 cell double minute 4

MEN1 4221 multiple endocrine neoplasia 1

MFHAS1 9258 malignant fibrous histiocytoma amplified sequence 1

MGLL 11343 monoglyceride lipase

mesoderm induction early response 1 homolog

MIER1 57708

(Xenopus laevis

MINA 84864 myc induced nuclear antigen

MKNK2 2872 MAP kinase-interacting serine/threonine kinase 2

M0RF4L1 10933 mortality factor 4 like 1

MORF4L2 9643 mortality factor 4 like 2

membrane-spanning 4-domains, subfamily A, member

MS4A6A 64231

6B

MST4 51765 serine/threonine protein kinase MST4

MT1A 4489 metallothionein 1

MT2A 4502 metallothionein 2 MTA3 57504 metastasis associated 3

MXD3 83463 Max dimerization protein 3

MXI1 4601 Max interacting protein 1

MYC 4609 myelocytomatosis oncogene

MYD88 4615 myeloid differentiation primary response gene 88

MYST4 23522 MYST histone acetyltransferase monocytic leukemia 4

NAGK 55577 N-acetylglucosamine kinase

NAMPT 10135 nicotinamide phosphoribosyltransferase

NASP 4678 nuclear autoantigenic sperm protein (histone-binding)

NCF1C 654817 neutrophil cytosolic factor 1

NC0A1 8648 nuclear receptor coactivator 1

NCOA3 8202 nuclear receptor coactivator 3

NIMA (never in mitosis gene a)-related expressed kinase

NEK4 6787

4

NIMA (never in mitosis gene a)-related expressed kinase

NEK6 10783

6

nuclear factor of activated T-cells, cytoplasmic,

NFATC1 4772

calcineurin-dependent 1

nuclear factor of activated T-cells, cytoplasmic,

NFATC2 4773

calcineurin-dependent 2

NFE2L2 4780 nuclear factor, erythroid derived 2, like 2

NFIL3 4783 nuclear factor, interleukin 3, regulated

nuclear factor of kappa light polypeptide gene enhancer

NFKB1 4790

in B-cells 1, pl05

nuclear factor of kappa light polypeptide gene enhancer

NFKBIA 4792

in B-cells inhibito

nuclear factor of kappa light polypeptide gene enhancer

NFKBIB 4793

in B-cells inhibito

nuclear factor of kappa light polypeptide gene enhancer

NFKBIE 4794

in B-cells inhibito

nuclear factor of kappa light polypeptide gene enhancer

NFKBIZ 64332

in B-cells inhibito

NFYC 4802 nuclear transcription factor-Y gamma

NKG7 4818 natural killer cell group 7 sequence

NMI 9111 N-myc (and STAT) interactor

NOC4L 79050 nucleolar complex associated 4 homolog (S. cerevisiae)

NOTCH1 4851 Notch gene homolog 1 (Drosophila)

NOTCH2 4853 Notch gene homolog 2 (Drosophila)

NR3C1 2908 nuclear receptor subfamily 3, group C, member 1

NR4A2 4929 nuclear receptor subfamily 4, group A, member 2

NR4A3 8013 nuclear receptor subfamily 4, group A, member 3

nudix (nucleoside diphosphate linked moiety X)-type

NUDT4 11163

motif 4 OAS2 4939 2'-5' oligoadenylate synthetase 2

protein kinase C and casein kinase substrate in neurons

PACSIN1 29993

1

PAXBP1 94104 PAX3 and PAX7 binding protein 1

PCTK1 5127 PCTAIRE-motif protein kinase 1

PDCD1 5133 programmed cell death 1

PDCD1LG2 80380 programmed cell death 1 ligand 2

PDK3 5165 pyruvate dehydrogenase kinase, isoenzyme 3

PDPK1 5170 3-phosphoinositide dependent protein kinase-1

PDXK 8566 pyridoxal (pyridoxine, vitamin B6) kinase

peroxisomal delta3, delta2-enoyl-Coenzyme A

PECI 10455

isomerase

PELI2 57161 pellino 2

PGK1 5230 phosphoglycerate kinase 1

PHACTR2 9749 phosphatase and actin regulator 2

PHF13 148479 PHD finger protein 13

PHF21A 51317 PHD finger protein 21A

PHF6 84295 PHD finger protein 6

PHLDA1 22822 pleckstrin homology-like domain, family A, member 1

PH domain and leucine rich repeat protein phosphatase

PHLPP1 23239

1

phosphatidylinositol 4-kinase, catalytic, alpha

PI4KA 5297

polypeptide

PIM1 5292 proviral integration site 1

PIM2 11040 proviral integration site 2

phosphatidylinositol-5-phosphate 4-kinase, type II,

PIP4K2A 5305

alpha

PKM2 5315 pyruvate kinase, muscle

PLAC8 51316 placenta-specific 8

P LAG LI 5325 pleiomorphic adenoma gene-like 1

PLAUR 5329 plasminogen activator, urokinase receptor

PLEK 5341 pleckstrin

pleckstrin homology domain containing, family F (with

PLEKHF2 79666

FYVE domain) member 2

PLK2 10769 polo-like kinase 2 (Drosophila)

PMEPA1 56937 prostate transmembrane protein, androgen induced 1

PML 5371 promyelocytic leukemia

PNKP 11284 polynucleotide kinase 3'- phosphatase

P0U2AF1 5450 POU domain, class 2, associating factor 1

POU2F2 5452 POU domain, class 2, transcription factor 2

PPME1 51400 protein phosphatase methylesterase 1

protein phosphatase 2, regulatory subunit B (B56),

PPP2R5A 5525

alpha isoform PPP3CA 5530 protein phosphatase 3, catalytic subunit, alpha isoform

PRC1 9055 protein regulator of cytokinesis 1

PRDM1 639 PR domain containing 1, with ZNF domain

PRF1 5551 perforin 1 (pore forming protein)

PRICKLEl 144165 prickle like 1 (Drosophila)

PRKCA 5578 protein kinase C, alpha

PRKCD 5580 protein kinase C, delta

PRKCH 5583 protein kinase C, eta

PRKCQ 5588 protein kinase C, theta

PRKD3 23683 protein kinase D3

PRNP 5621 prion protein

PROCR 10544 protein C receptor, endothelial

PRPF4B 8899 PRP4 pre-mRNA processing factor 4 homolog B (yeast)

PRPS1 5631 phosphoribosyl pyrophosphate synthetase 1

proteasome (prosome, macropain) subunit, beta type 9

PSMB9 5698

(large multifunctional

proline-serine-threonine phosphatase-interacting

PSTPIP1 9051

protein 1

PTEN 5728 phosphatase and tensin homolog

PTK2B 2185 PTK2 protein tyrosine kinase 2 beta

PTP4A1 7803 protein tyrosine phosphatase 4al

protein tyrosine phosphatase-like (proline instead of

PTPLA 9200

catalytic arginine),

PTPN1 5770 protein tyrosine phosphatase, non-receptor type 1

PTPN18 26469 protein tyrosine phosphatase, non-receptor type 18

PTPN6 5777 protein tyrosine phosphatase, non-receptor type 6

PTPRC 5788 protein tyrosine phosphatase, receptor type, C

protein tyrosine phosphatase, receptor type, C

PTPRCAP 5790

polypeptide-associated prote

PTPRE 5791 protein tyrosine phosphatase, receptor type, E

PTPRF 5792 protein tyrosine phosphatase, receptor type, F

PTPRJ 5795 protein tyrosine phosphatase, receptor type, J

PTPRS 5802 protein tyrosine phosphatase, receptor type, S

PVR 5817 poliovirus receptor

PYCR1 5831 pyrroline-5-carboxylate reductase 1

RAB33A 9363 RAB33A, member of RAS oncogene family

RAD51AP1 10635 RAD51 associated protein 1

RARA 5914 retinoic acid receptor, alpha

RASGRP1 10125 RAS guanyl releasing protein 1

recombination signal binding protein for

RBPJ 3516

immunoglobulin kappa J region

REL 5966 reticuloendotheliosis oncogene v-rel reticuloendotheliosis viral oncogene homolog A

RELA 5970

(avian)

RFK 55312 riboflavin kinase

RIPK1 8737 receptor (TNFRSF)-interacting serine-threonine kinase 1

RIPK2 8767 receptor (TNFRSF)-interacting serine-threonine kinase 2

RIPK3 11035 receptor-interacting serine-threonine kinase 3

ribonuclease L (2', 5'-oligoisoadenylate synthetase-

RNASEL 6041

dependent)

RNF11 26994 ring finger protein 11

RNF5 6048 ring finger protein 5

RORA 6095 RAR-related orphan receptor alpha

RORC 6097 RAR-related orphan receptor gamma

RPP14 11102 ribonuclease P 14 subunit (human)

RPS6KB1 6198 ribosomal protein S6 kinase, polypeptide 1

RUNX1 861 runt related transcription factor 1

RUNX2 860 runt related transcription factor 2

RUNX3 864 runt related transcription factor 3

RXRA 6256 retinoid X receptor alpha

SAP18 10284 Sin3-associated polypeptide 18

SAP30 8819 sin3 associated polypeptide

SATB1 6304 special AT-rich sequence binding protein 1

sema domain, immunoglobulin domain (Ig),

SEMA4D 10507

transmembrane domain (TM) and shor

sema domain, immunoglobulin domain (Ig), and GPI

SEMA7A 8482

membrane anchor, (semaphor

serine (or cysteine) peptidase inhibitor, clade B,

SERPINB1 1992

member la

serine (or cysteine) peptidase inhibitor, clade E,

SERPINE2 5270

member 2

SERTAD1 29950 SERTA domain containing 1

SGK1 6446 serum/glucocorticoid regulated kinase 1

SH2D1A 4068 SH2 domain protein 1A

SIK1 150094 salt-inducible kinase 1

sirtuin 2 (silent mating type information regulation 2,

SIRT2 22933

homolog) 2 (S. cere

SKAP2 8935 src family associated phosphoprotein 2

SKI 6497 ski sarcoma viral oncogene homolog (avian)

SKIL 6498 SKI-like

SLAMF7 57823 SLAM family member 7

solute carrier family 2 (facilitated glucose transporter),

SLC2A1 6513

member 1 solute carrier family 3 (activators of dibasic and neutral

SLC3A2 6520

amino acid trans

SLK 9748 STE20-like kinase (yeast)

SMAD2 4087 MAD homolog 2 (Drosophila)

SMAD3 4088 MAD homolog 3 (Drosophila)

SMAD4 4089 MAD homolog 4 (Drosophila)

SMAD7 4092 MAD homolog 7 (Drosophila)

SWI/SNF related, matrix associated, actin dependent

SMARCA4 6597

regulator of chromatin,

SMOX 54498 spermine oxidase

SOCS3 9021 suppressor of cytokine signaling 3

SP1 6667 trans-acting transcription factor 1

SP100 6672 nuclear antigen SplOO

SP4 6671 trans-acting transcription factor 4

SPHK1 8877 sphingosine kinase 1

SPOP 8405 speckle-type POZ protein

SPP1 6696 secreted phosphoprotein 1

SPRY1 10252 sprouty homolog 1 (Drosophila)

SRPK2 6733 serine/arginine-rich protein specific kinase 2

SS18 6760 synovial sarcoma translocation, Chromosome 18

STARD10 10809 START domain containing 10

STAT1 6772 signal transducer and activator of transcription 1

STAT2 6773 signal transducer and activator of transcription 2

STAT3 6774 signal transducer and activator of transcription 3

STAT4 6775 signal transducer and activator of transcription 4

STAT5A 6776 signal transducer and activator of transcription 5A

STAT5B 6777 signal transducer and activator of transcription 5B

STAT6 6778 signal transducer and activator of transcription 6

STK17B 9262 serine/threonine kinase 17b (apoptosis-inducing)

STK19 8859 serine/threonine kinase 19

STK38 11329 serine/threonine kinase 38

STK38L 23012 serine/threonine kinase 38 like

serine/threonine kinase 39, STE20/SPS1 homolog

STK39 27347

(yeast)

STK4 6789 serine/threonine kinase 4

SULT2B1 6820 sulfotransferase family, cytosolic, 2B, member 1

SUZ12 23512 suppressor of zeste 12 homolog (Drosophila)

TATA box binding protein (Tbp)-associated factor, RNA

TAF1B 9014

polymerase 1, B

TAL2 6887 T-cell acute lymphocytic leukemia 2

transporter 1, ATP-binding cassette, sub-family B

TAP1 6890

(MDR/TAP) TBPL1 9519 TATA box binding protein-like 1

TBX21 30009 T-box 21

TCERG1 10915 transcription elongation regulator 1 (CA150)

cytoplasmic tyrosine kinase, Dscr28C related

TEC 7006

(Drosophila)

TFDP1 7027 transcription factor Dp 1

TFEB 7942 transcription factor EB

TGFB1 7040 transforming growth factor, beta 1

TGFB3 7043 transforming growth factor, beta 3

TGFBR1 7046 transforming growth factor, beta receptor 1

TGFBR3 7049 transforming growth factor, beta receptor III

TGIF1 7050 TGFB-induced factor homeobox 1

TGM2 7052 transglutaminase 2, C polypeptide

THRAP3 9967 thyroid hormone receptor associated protein 3

TIMP2 7077 tissue inhibitor of metalloproteinase 2

TK1 7083 thymidine kinase 1

TK2 7084 thymidine kinase 2, mitochondrial

transducin-like enhancer of split 1, homolog of

TLE1 7088

Drosophila E(spl)

TLR1 7096 toll-like receptor 1

TMEM126A 84233 transmembrane protein 126A

tumor necrosis factor receptor superfamily, member

TNFRSF12A 51330

12a

tumor necrosis factor receptor superfamily, member

TNFRSF13B 23495

13b

TNFRSF1B 7133 tumor necrosis factor receptor superfamily, member lb

TNFRSF25 8718 tumor necrosis factor receptor superfamily, member 25

TNFRSF4 7293 tumor necrosis factor receptor superfamily, member 4

TNFRSF9 3604 tumor necrosis factor receptor superfamily, member 9

TNFSF11 8600 tumor necrosis factor (ligand) superfamily, member 11

TNFSF8 944 tumor necrosis factor (ligand) superfamily, member 8

TNFSF9 8744 tumor necrosis factor (ligand) superfamily, member 9

TNK2 10188 tyrosine kinase, non-receptor, 2

T0X4 9878 TOX high mobility group box family member 4

TP53 7157 transformation related protein 53

TRAF3 7187 Tnf receptor-associated factor 3

TRAT1 50852 T cell receptor associated transmembrane adaptor 1

TRIM24 8805 tripartite motif-containing 24

TRIM25 7706 tripartite motif-containing 25

TRIM28 10155 tripartite motif-containing 28

TRIM5 85363 tripartite motif containing 5

TRIP12 9320 thyroid hormone receptor interactor 12

TRPS1 7227 trichorhinophalangeal syndrome 1 (human) TRRAP 8295 transformation/transcription domain-associated protein

TSC22D3 1831 TSC22 domain family, member 3

TSC22D4 81628 TSC22 domain family, member 4

TWF1 5756 twinfilin, actin-binding protein, homolog 1 (Drosophila)

TXK 7294 TXK tyrosine kinase

ubiquitin-conjugating enzyme E2B, RAD6 homology (S.

UBE2B 7320

cerevisiae)

UBIAD1 29914 UbiA prenyltransferase domain containing 1

ULK2 9706 Unc-51 like kinase 2 (C. elegans)

VAV1 7409 vav 1 oncogene

VAV3 10451 vav 3 oncogene

VAX2 25806 ventral anterior homeobox containing gene 2

VRK1 7443 vaccinia related kinase 1

VRK2 7444 vaccinia related kinase 2

WDHD1 11169 WD repeat and HMG-box DNA binding protein 1

WHSC1L1 54904 Wolf-Hirschhorn syndrome candidate 1-like 1 (human)

WNK1 65125 WNK lysine deficient protein kinase 1

XAB2 56949 XPA binding protein 2

XBP1 7494 X-box binding protein 1

X-ray repair complementing defective repair in Chinese

XRCC5 7520

hamster cells 5

YBX1 4904 Y box protein 1

ZAK 51776 RIKEN cDNA B230120H23 gene

ZAP70 7535 zeta-chain (TCR) associated protein kinase

ZBTB32 27033 zinc finger and BTB domain containing 32

ZEB1 6935 zinc finger E-box binding homeobox 1

ZEB2 9839 zinc finger E-box binding homeobox 2

ZFP161 7541 zinc finger protein 161

ZFP36L1 677 zinc finger protein 36, C3H type-like 1

ZFP36L2 678 zinc finger protein 36, C3H type-like 2

ZFP62 92379 zinc finger protein 62

ZNF238 10472 zinc finger protein 238

ZNF281 23528 zinc finger protein 281

ZNF326 284695 zinc finger protein 326

ZNF703 80139 zinc finger protein 703

ZNRF1 84937 zinc and ring finger 1

ZNRF2 223082 zinc and ring finger 2

[00197] Primers for Nanostring STA and qRT-PCR/Fluidigm and siRNA sequences:

Table S6.1 presents the sequences for each forward and reverse primer used in the

Fluidigm/qRT-PCR experiments and Nanostring nCounter gene expression profiling. Table S6.2 presents the sequences for RNAi used for knockdown analysis.

TABLE S6.1. Primer Sequences

SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string Dntt CCC AGA AGC CAC TTC CAG CCC TTT

41 42

STA AGA GGA CCT TCC

Nano string Ercc5 GTG CCA TTT GAC CTG GCC TAC CCT

43 44

STA ACA GCG CCA CCT

Nano string Foxml CAA GCC AGG CTG TGG GTC GTT TCT

45 46

STA GAA GAA GCT GTG

Nano string Gem GAC ACG CTT CGG CAA CTG TGA TGA

47 48

STA GTT CAC GGC CAG C

Nano string 6330442 CCC AGC ATT AAG AGG AGC AAC AGG

49 50

STA ElORik GCT CCA GGA CCT

Nano string Api5 CAG CTT TGA ACA AGC TGA CTG AAA

51 52

STA CAG GGT CTT TTC CTC CCT

Nano string B4galtl TCA CAG TGG ACA CAC TCA CCC TGG

53 54

STA TCG GGA GCA TCT

Nano string Candl CTA CTG CAG GGA GGG TCC CTC TTT

55 56

STA GGA GCG AGG GCA

Nano string Ccr4 GTC CGT GCA GTT GGT TTG GGG ACA

57 58

STA TGG CTT GGC TTT

Nano string Cd28 CCT TTG CAG TGA CGT TTT GAA AAT

59 60

STA GTT GGG A CTG CAG AGA A

Nano string Cd9 GCG GGA AAC ACT TGC TGA AGA TCA

61 62

STA CAA AGC TGC CGA

Nano string Ctsw GCC ACT GGA GCT TGA CCT CTC CTG

63 64

STA GAA GGA CCC GTA

Nano string Dpp4 CCC TGC TCC TGC AAA TCT TCC GAC

65 66

STA ATC TGT CCA GCC

Nano string Errfil TCC TGC TTT TCC CCA GCA ACA CAA

67 68

STA CAT CCA GAC CAG C

Nano string Foxol TCC AGT CTG GGC GGC AGC AGA GGG

69 70

STA AAG AGG TGG ATA

Nano string Gfil ATG TCT TCC CTG AAG CCC AAA GCA

71 72

STA CCT CCC CAG ACG

Nano string Abcg2 GGA ACA TCG GCC CAT TCC AGC GGC

73 74

STA TTC AAA ATC ATA

Nano string Aqp3 CGG CAC AGC TGG GGT TGA CGG CAT

75 76

STA AAT CTT AGC CAG

Nano string Batf CTA CCC AGA GGC AAC TAT CCA CCC

77 78

STA CCA GTG CCT GCC

Nano string Cas l TCC TGA GGG CAA GAT TTG GCT TGC

79 80

STA AGA GGA CTG GG

Nano string Ccr5 AAC TGA ATG GGG TTA CAG CCG CCT

81 82

STA AGG TTG G TTC AGG

Nano string Cd4 CCA GCC CTG GAT GCC ACT TTC ATC

83 84

STA CTC CTT ACC ACC A

Nano string Cebpb TGC ACC GAG GGG AAC CCC GCA GGA

85 86

STA ACA C ACA TCT SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string CxcllO TGC CGT CAT TTT CGT GGC AAT GAT

87 88

STA CTG CCT CTC AAC A

Nano string Egr2 AGG ACC TTG ATG CTG GCA TCC AGG

89 90

STA GAG CCC GTC AAC

Nano string Etv6 CAT GAG GGA GGA AAA TCC CTG CTA

91 92

STA TGC TGG TCA AAA ATC C

Nano string Fox l GCT CTC TGT CTC ACT CAC AAC CCA

93 94

STA CAA GGG C GAC CGC

Nano string Gjal GGC CTG ATG ACC TCC CTA CTT TTG

95 96

STA TGG AGA CCG CCT

Nano string Acly GAG GGC TGG GAC GCA GCT GCC CAG

97 98

STA CAT TG AAT CTT

Nano string ArhgeO GCA GCA GGC TGT TTC CTC CCC ACT

99 100

STA TTC TTA CC CAT CCA

Nano string BC02161 AAG GAG GGC AAG GAG CTT GGG TCG

101 102

STA 4 GAC CAG GGA TTT

Nano string Casp3 GGA GAT GGC TTG ACT CGA ATT CCG

103 104

STA CCA GAA TTG CCA

Nano string Ccr6 GCC AGA TCC ATG TTT GGT TGC CTG

105 106

STA ACT GAC G GAC GAT

Nano string Cd44 CAG GGA ACA TCC TAG CAT CAC CCT

107 108

STA ACC AGC TTG GGG

Nano string Chd7 CAT TGT CAG TGG GAA TCA CAG GCT

109 110

STA GCG TCA CGC CC

Nano string Cxcr3 CCA GAT CTA CCG CAT GAC CAG AAG

111 112

STA CAG GGA GGG CAG

Nano string EiOe GTC AAC CAG GGA CAG TTT TCC CCA

113 114

STA TGG CAG GAG CGA

Nano string Fas GCT GTG GAT CTG CCC CCA TTC ATT

115 116

STA GGC TGT TTG CAG

Nano string Foxp3 TGG AAA CAC CCA GGC AAG ACT CCT

117 118

STA GCC ACT GGG GAT

Nano string Gliprl TGG ATG GCT TCG TGC AGC TGT GGG

119 120

STA TCT GTG TTG TGT

Nano string Acvrlb GTG CCG ACA TCT GCA CTC CCG CAT

121 122

STA ATG CCC CAT CTT

Nano string Arid5a GGC CTC GGG TCT CTA GGC AGC TGG

123 124

STA TTC AGT GCT CAC

Nano string Bell lb GGA GGG GTG GCT AAG ATT CTC GGG

125 126

STA TTC AA GTC CCA

Nano string Casp4 GGA ACA GCT GGG GCC TGG GTC CAC

127 128

STA CAA AGA ACT GAA

Nano string Ccr8 GTG GGT GTT TGG ATC AAG GGG ATG

129 130

STA GAC TGC GTG GCT

Nano string Cd51 TGG GGG TAC CAC GGG CGT GTA GCC

131 132

STA GAC TGT TTG AGA SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string Clcfl AAT CCT CCT CGA TGA CAC CTG CAA

133 134

STA CTG GGG TGC TGC

Nano string Cxcr4 CCG ATA GCC TGT GTC GAT GCT GAT

135 136

STA GGA TGG CCC CAC

Nano string EiOh AGC CTT CGC CAT CGC CTT CAG CGA

137 138

STA GTC AAC GAG AGA

Nano string Fasl GCA AAT AGC CAA GTT GCA AGA CTG

139 140

STA CCC CAG ACC CCG

Nano string Frmd4b GGA GTC CCA GTC TGG ACC TTC TTC

141 142

STA CCA CCT TCC CCC

Nano string Golga3 TCC AAC CAG GTG TCA TCT CAG AGT

143 144

STA GAG CAC CCA GCC G

Nano string Acvr2a ATG GCA AAC TTG CAA GAT CTG TGC

145 146

STA GAC CCC AGG GCA

Nano string Arl5a CGG ATT TGA GCG AGT CAC TGG TGG

147 148

STA CTT CTG GTG GGA

Nano string Bcl2111 TGG CAA GCC CTC AAA CAC ACA CAA

149 150

STA TCA CTT CCA CGC A

Nano string Casp6 TGC TCA AAA TTC CAC GGG TAC GTC

151 152

STA ACG AGG TG ATG CTG

Nano string Cd2 CAC CCT GGT CGC GGT TGT GTT GGG

153 154

STA AGA GTT GCA TTC

Nano string Cd70 CTG GCT GTG GGC GGA GTT GTG GTC

155 156

STA ATC TG AAG GGC

Nano string Cmtm6 TGC TGG TGT AGG TCT CAG CAA TCA

157 158

STA CGT CTT T CAG TGC AA

Nano string Cxcr5 TGG CCT TAA TGT TGC TGG CTT GCC

159 160

STA GCC TGT C CTT TAC

Nano string EiOm TGG CTT GTT ACA CCG ATG TGT GCT

161 162

STA TGA GCA AAA GTG ACT G

Nano string Fi lll GGA TAC GAA TGG CCA ACG CTT GAA

163 164

STA GAC TGG AA CTG GCT

Nano string Fzd7 TTC CCT GCA ATA TGA AGT AAT CTG

165 166

STA GAA GTC TGG TCC TCC CGA

Nano string Grn CCG GCC TAC TCA AAC TTT ATT GGA

167 168

STA TCC TGA GCA ACA CAC G

Nano string Ahr GTT GTG ATG CCA CAA GCG TGC ATT

169 170

STA AAG GGC GGA CTG

Nano string Armcx2 TCC AAT CTT GCC TTC CAG CAC TTT

171 172

STA ACC ACC GGG AGC

Nano string Bcl3 CCA GGT TTT GCA CCT CCC AGA CCC

173 174

STA CCA AGG CTC TGT

Nano string Cell CAC TGA TGT GCC TGA GGC GCA GCT

175 176

STA TGC TGC TTC TCT

Nano string Cd247 TAC CAT CCC AGG GCA GGT TGG CAG

177 178

STA GAA GCA CAG TCT SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string Cd74 GCT TCC GAA ATC CGC CAT CCA TGG

179 180

STA TGC CAA AGT TCT

Nano string Csf2 GGC CAT CAA AGA GCT GTC ATG TTC

181 182

STA AGC CCT AAG GCG

Nano string Daxx GTT GAC CCC GCA ATT CCG AGG AGG

183 184

STA CTG TCT CTT TGG

Nano string Elk3 CCT GTG GAC CCA GAC GGA GTT CAG

185 186

STA GAT GCT CTC CCA

Nano string Flil GAT TCT GAG AAA GCC AGT GTT CCA

187 188

STA GGA GTA CGC A GTT GCC

Nano string Gap43 GCG AGA GAG CGA CCA CGG AAG CTA

189 190

STA GTG AGC GCC TGA

Nano string Gusb ATG GAG CAG ACG AAA GGC CGA AGT

191 192

STA CAA TCC TTT GGG

Nano string H2-Q10 GTG GGC ATC TGT TGG AGC GGG AGC

193 194

STA GGT GGT ATA GTC

Nano string Ifi35 CAG AGT CCC ACT AGG CAC AAC TGT

195 196

STA GGA CCG CAG GGC

Nano string I112rb2 GCA GCC AAC TCA GTG ATG CTC CCT

197 198

STA AAA GGC GGT TGG

Nano string 1122 TCA GAC AGG TTC TCT TCT CGC TCA

199 200

STA CAG CCC GAC GCA

Nano string I14ra CCT TCA GCC CCA AGC TCA GCC TGG

201 202

STA GTG GTA GTT CCT

Nano string Irffi AAG GGA CAC TTC TTT CCT GCA GTT

203 204

STA CCG GAG CCC CAG

Nano string Katnal CGG TGC GGG AAC CAT TTG GTC AAG

205 206

STA TAT CC AAC TCC CTG

Nano string Ladl GAA GGA GCT GTC GCA TCC AGG GAT

207 208

STA AGG CCA GTG GAC

Nano string Ly6c2 GTC CTT CCA ATG CCT CCA GGG CCA

209 210

STA ACC CCC AGA ATA G

Nano string Mina GTC TGC CGG AGC TAA TGT GGA GGG

211 212

STA ATC AGT AGG CCC

Nano string Nampt CAA GGA GAT GGC TGG GAT CAG CAA

213 214

STA GTG GAT CTG GGT

Nano string Nkg7 TGG CCC TCT GGT TTT CAT ACT CAG

215 216

STA CTC AAC CCC GAC G

Nano string Hifla AAG AAC TTT TGG GCA CTG TGG CTG

217 218

STA GCC GCT GGA GTT

Nano string Ifihl GCT GAA AAC CCA ACT TCA CTG CTG

219 220

STA AAA TAC GA TGC CCC

Nano string 1117a ATC AGG ACG CGC GAC GTG GAA CGG

221 222

STA AAA CAT TTG AGG

Nano string I123r CAC TGC AAG GCA CGT TTG GTT TGT

223 224

STA GCA GG TGT TGT TTT G SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string I16st TCG GAC GGC AAT GTT GCT GGA GAT

225 226

STA TTC ACT GCT GGG

Nano string Ir© ACT GAT CGT CGC TTG GTC TGT CTT

227 228

STA GTC TCC CCA AGT GCT

Nano string Kcmfl CTG ACC ACC CGA TCC AGG TAA CGC

229 230

STA TGC AGT TGC ACA

Nano string Lamp2 GGC TGC AGC TGA AAG CTG AGC CAT

231 232

STA ACA TCA TAG CCA AA

Nano string Maf AGG CAG GAG GAT TCA TGG GGG TGG

233 234

STA GGC TTC AGG AC

Nano string Mklnl GGT TTG CCC ATC GGA TCC ATT TGG

235 236

STA AAC TCG GCC TTT

Nano string Ncfl GCA AAG GAC AGG TTT GAC ACC CTC

237 238

STA ACT GGG CCC AAA

Nano string Notchl GCA GGC AAA TGC GTG GCC ATT GTG

239 240

STA CTC AAC CAG ACA

Nano string Hi lr CTC GAG CAG CTG CCA GCA GGG ACC

241 242

STA GGA CC CTC TTT

Nano string Ifitl TCA TTC GCT ATG GGC CTG TTG TGC

243 244

STA CAG CCA CAA TTC

Nano string I117f AAG AAC CCC AAA CAG CGA TCT CTG

245 246

STA GCA GGG AGG GGA

Nano string 1124 TCT CCA CTC TGG CTG CAT CCA GGT

247 248

STA CCA ACA CAG GAG A

Nano string Wr TGG CCT AGT CTC CGA GCG GTT TGC

249 250

STA CCC GAT ACT GT

Nano string Isg20 CTG TGG AAG ATG GTG GTT GGT GGC

251 252

STA CCA GGG AGT GGT

Nano string Khdrbsl GTT CGT GGA ACC TCC CCT TGA CTC

253 254

STA CCA GTG TGG CTG

Nano string Lgals3bp GGC CAC AGA GCT CCA GCT CAC TCT

255 256

STA TCA GGA TGG GGA

Nano string Maff TCT GAC TCT TGC TGG CAC AAT CCA

257 258

STA AGG CCC AAG CCT

Nano string Mtl ACT ATG CGT GGG GCA GGA GCT GGT

259 260

STA CTG GAG GCA AGT

Nano string Ncoal GCC TCC AGC CCA TGA GGG ATT TAT

261 262

STA TCC TAT TCG GGG A

Nano string Notch2 TAC GAG TGC ACC GCA GCG TCC TGG

263 264

STA TGC CAA AAT GTC

Nano string Hsbpl ATC ACG TGA CCA CTC TGA TAC CCT

265 266

STA CAG CCC GCC GGA

Nano string Ifhg TCT GGG CTT CTC TCC TTT TGC CAG

267 268

STA CTC CTG TTC CTC C

Nano string I117ra GGG GCT GAG CTG TGG TGT TCA GCT

269 270

STA CAG AGT GCA GGA SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string I127ra AAG GCT GGC CTC GGG CAG GGA ACC

271 272

STA GAA CTT AAA CTT

Nano string 119 TGG TGA CAT ACA TGT GTG GCA TTG

273 274

STA TCC TTG CC GTC AGC

Nano string Itga3 GCT TCA CCC AGA CCC ATA TGT TGG

275 276

STA ACA CCG TGC CGT

Nano string Kif2a TGC CGA ATA CAC TCC GCC GGT TCT

277 278

STA CAA GCA TTA CAA

Nano string Lif GGG GCA GGT AGT TCG GGA TCA AGG

279 280

STA TGC TCA ACA CAG A

Nano string Map3k5 CCA TCT TGG AGT GCT CAG TCA GGC

281 282

STA GCG AGA A CCT TCA

Nano string Mt2 TGT GCT GGC CAT AGG CAC AGG AGC

283 284

STA ATC CCT AGT TGG

Nano string Nfatc2 AGC TCC ACG GCT CGT TTC GGA GCT

285 286

STA ACA TGG TCA GGA

Nano string Nr3cl CAA GTG ATT GCC CAT TGG TCA TAC

287 288

STA GCA GTG ATG CAG GG

Nano string Icos CGG CCG ATC ATA TTC CCT GGG AGC

289 290

STA GGA TGT TGT CTG

Nano string Ifhgr2 CGA AAC AAC AGC CGG TGA ACC GTC

291 292

STA AAA TGC C CTT GTC

Nano string Illrl ACC CGA GGT CCA TCT CAT TCC GAG

293 294

STA GTG GTA GGC TCA

Nano string I12ra TGC AAG AGA GGT GTT CCC AAG GAG

295 296

STA TTC CGA GTG GCT

Nano string Inhba AGC AGA AGC ACC TCC TGG CAC TGC

297 298

STA CAC AGG TCA CAA

Nano string Itgbl TGG AAA ATT CTG TTG GCC CTT GAA

299 300

STA CGA GTG TG ACT TGG

Nano string KlflO CCC TCC AAA AGG GGC AAA AAC AAA

301 302

STA GCC TAA GTC CCC A

Nano string Litaf AGT GCA CAG AAG CCA GCA AAT GGA

303 304

STA GGC TGC GAA ATG G

Nano string Max AGG ACG CCT GCT GCT GCA AAT CTG

305 306

STA CTA CCA TCC CCA

Nano string Mta3 CGG AGA AGC AGA ACT TTG GGC CCA

307 308

STA AGC ACC CTC TGA

Nano string Nfe212 GCC GCT TAG AGG TGC TCC AGC TCG

309 310

STA CTC ATC ACA ATG

Nano string Nudt4 TGG GGT GCC ATC ATT CCA CAT GGC

311 312

STA CAG TAT TTT GGC

Nano string Id2 TCA GCC ATT TCA TAA CGT TTT CGC

313 314

STA CCA GGA G TCC CCA

Nano string Ikzf4 GGG GTC TAG CCC GCC GGG GAG AGA

315 316

STA AAT TCC GGT TAG SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string Illrn TGG TAA GCT TTC TCA TCA CAT CAG

317 318

STA CTT CTT TCC GAA GGG C

Nano string I12rb GCA CCC CAT CCT CAA GTC CAG CTC

319 320

STA CAG CTA GGT GGT

Nano string Irfl TAA GCA CGG CTG CAG CAG AGC TGC

321 322

STA GGA CAT CCT TGT

Nano string Jak3 CTC CCC AGC GAT CAG CCC AAA CCA

323 324

STA TGT CAT GTC AGG

Nano string Klf6 GAG CGG GAA CTC GGG AAA ATG ACC

325 326

STA AGG ACC ACT GCG

Nano string Lmnbl TGC CCT AGG GGA CAA GCG GGT CTC

327 328

STA CAA AAA ATG CTT

Nano string MbnB TGG AGC ATG AAT TGA GGG TCC CAT

329 330

STA CCA CAC C GAG TGG

Nano string Mxil CTC AGG AGA TGG CCT CGT CAC TCC

331 332

STA AGC GGA CGA CAC

Nano string Nfil3 CAC GGT GGT GAA GAA AGG AGG GAG

333 334

STA GGT TCC GGA GGA

Nano string Oas2 TGC CTG TGC TTG GAA GAA GGG CCA

335 336

STA CTC TGA GAA GGG

Nano string Id3 CCG AGG AGC CTC GTC TGG ATC GGG

337 338

STA TTA GCC AGA TGC

Nano string 1110 ACT GCC TTC AGC CAG CTT CTC ACC

339 340

STA CAG GTG CAG GGA

Nano string 1121 CCT GGA GTG GTA TGC GTT GGT TCT

341 342

STA TCA TCG C GAT TGT G

Nano string 113 CAC ACC ATG CTG CTC CTT GGC TTT

343 344

STA CTC CTG CCA CGA

Nano string Irf4 CAG AGA AAC GCA AGT CCA CCA GCT

345 346

STA TTC CTG G GGC TTT T

Nano string Jun TAT TGG CCG GCA GCC TGG CAC TTA

347 348

STA GAC TTT CAA GCC

Nano string Klf AGG GAA GGA AGA TGG CCA TGT AAA

349 350

STA CGC CAC AGC CAA A

Nano string Lrrfi l GTC TCC AAC GCC ATC TCT TCC CTT

351 352

STA CAG CTA TGC CGC

Nano string Med24 ACT GCT AGG GGT TGA GCC ATA GGT

353 354

STA CCT GGG CTG GGC

Nano string Myd88 GAA GCT GTT TGG TCA TTC CTC CCC

355 356

STA CTT CGC CAG ACA

Nano string Nfkbie TCG AGG CGC TCA CGG ACA ACA TCT

357 358

STA CAT ACA GGC TGA

Nano string Pcbp2 CTC AAC TGA GCG AGG GTT GAG GCA

359 360

STA GGC AAT CAT GGA

Nano string Ier3 CCT TCT CCA GCT CCT CTT GGC AAT

361 362

STA CCC TCC GTT GGG SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string IllOra GTA AAG GCC GGC TTT CCA GTG GAG

363 364

STA TCC AGT GAT GTG C

Nano string I121r AGG TCT GGC CAC GGC CAC AGT CAC

365 366

STA AAC ACC GTT CAA

Nano string 114 AGG GCT TCC AAG TGC TCT TTA GGC

367 368

STA GTG CTT TTT CCA GG

Nano string Irf7 GAG GCT GAG GCT ATC CTG GGG ACA

369 370

STA GCT GAG CAC CCT

Nano string Kat2b GGT GCT TTG AGC GCC CTG CAC AAG

371 372

STA AGT TCT GA CAA AGT

Nano string Klrdl GCC TGG CTA TGG CCG TGG ACC TTC

373 374

STA GAG GAT CTT GTC

Nano string Ls l CCT GAG CCC TAC GGG CAG CTC TAT

375 376

STA CAC CAA GGA GGG

Nano string Mgll CGC GCA GTA GTC AAG ATG AGG GCC

377 378

STA TGG CTC TTG GGT

Nano string Myst4 CAA CAA AGG GCA TTC AAC ACA AGG

379 380

STA GCA AGC GCA GAG G

Nano string Nfkbiz TTA GCT GGA TGA ATG TTG CTG CTG

381 382

STA GCC CCA TGG TGG

Nano string Peli2 GCC AGA CGG TAG CGT GCT GTG TAT

383 384

STA TGG TGG GGC TCG

Nano string Phldal GAT GAC GGA GGG GGG GTT GAG GCT

385 386

STA CAA AGA GGA TCT

Nano string Prdml ACC CTG GCT ATG GGG AAG CTG GAT

387 388

STA CAC CTG TGA GCA

Nano string Pstpi l GAG AGC GAG GAC CCT TCC ACA TCA

389 390

STA CGA GTG CAG CCC

Nano string Rela TGC GAC AAG GTG GAG CTC GCG ATC

391 392

STA CAG AAA AGA AGG

Nano string Runx3 GCC CCT TCC CAC CTC CCC CTG CTG

393 394

STA CAT TTA CTA CAA

Nano string Sgkl GGC TAG GCA CAA AGC GCT CCC TCT

395 396

STA GGC AGA GGA GAT

Nano string Smox ACA GCC TCG TGT GGC CAT TGG CTT

397 398

STA GGT GGT CTG CTA

Nano string Stat4 GCC TCT ATG GCC ACT TCC AGG AGT

399 400

STA TCA CCA TGG CCC

Nano string Tbx21 TGG GAA GCT GAG GCC TTC TGC CTT

401 402

STA AGT CGC TCC ACA

Nano string Tmed7 TGG TTA GCG TAG CCC ATG GGG ATA

403 404

STA GGC AGG TGC ACT

Nano string TraB ATC TGT GGG CGC GGA CTG TCA AGA

405 406

STA TCT GAC TGG GGC

Nano string Vav3 TTC TGG CAG GGA TTT GGT CCT GTG

407 408

STA CGA AAC CCT TAC AA SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string Plac8 TGC TCC CCA AAA AGG AAT GCC GTA

409 410

STA TTC CAA TCG GGT

Nano string Prfl ACC AAC CAG GAC CCC TGT GGA CAG

411 412

STA TGC TGC GAG CAC

Nano string Ptprj TCA CCT GGA GCA TGG TAC CAT TGG

413 414

STA ATG CAA CAT CCG

Nano string Rfk TTT CCC TCT TGG TCC CTC CCC ACA

415 416

STA TGG CCT CCA CTA

Nano string Rxra TTG TTG GGC GAC TGG AGA GTT GAG

417 418

STA TTT TGC GGA CGA A

Nano string Skap2 TGG GTG AAC ATT AAA CAG CAA CCC

419 420

STA CCT GCC TCA CCG

Nano string Socs3 TGC AGG AGA GCG GAA CTG GCT GCG

421 422

STA GAT TCT TGC TTC

Nano string Stat5a CCT CCG CTA GAA GCT CTT ACA CGA

423 424

STA GCT CCC GAG GCC C

Nano string Tgfbl CGC CTG AGT GGC ATG TCA TGG ATG

425 426

STA TGT CTT GTG CCC

Nano string Tmeml2 CTG CTT GAA TAT CCA ACT AGT GCA

427 428

STA 6a GGA TCA GCA CCC CGT

Nano string Tratl CAA TGG ATG CCA CCT TGC CAG TCC

429 430

STA ACG TTT C CTG TGT

Nano string Vax2 GGC CCC CGT GGA CAC ACA CAC ACG

431 432

STA CTA TAC CAC ACG

Nano string Plagll TTG AGA CTG TAT GCA GGG TCT TCA

433 434

STA CCC CCA GC AAG GTC AG

Nano string Prickle 1 TGG GTT TCC AGT GCC TTT ATT AAA

435 436

STA TGC AGT T CAC CTC CCT G

Nano string Pycrl CCC TGG GTG TGT AAG GGG TTG AAA

437 438

STA GCA GTC GGG GTG

Nano string Rngtt CCC AAA AGA CTG TCC ACA GGG TAA

439 440

STA CAT CGG GGC TGA A

Nano string Savl CGA CCC CCA ATG TAG CCC ACC CTG

441 442

STA TAA GGA ATG GAA

Nano string Ski GGT CCC CTG CAG CTT CCG TTT TCG

443 444

STA TGT CTG TGG CTG

Nano string Sppl CCA TGA CCA CAT CCA AGC TAT CAC

445 446

STA GGA CGA CTC GGC

Nano string Stat5b ACT CAG CGC CCA GCT CTG CAA AGG

447 448

STA CTT CAG CGT TGT

Nano string Tgfb3 GCC AAA GTC CCC AAG GAA GGC AGG

449 450

STA TGG AAT AGG AGG

Nano string Tnfrsfl2 GGG AGC CTT CCA GGC ATT ATA GCC

451 452

STA a AGG TGT CCT CCG

Nano string Trim24 CGG TGG TCC TTC TGC AGA GCC ATT

453 454

STA GCC CAA CAC A SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string Xb l GGA CCT CAT CAG GCA GGT TTG AGA

455 456

STA CCA AGC TGC CCA

Nano string Plekhf2 CGG CAA TAT TGT GGG CGT CTT CCC

457 458

STA TAT CCA GAA ACT TTT

Nano string Prkca TGC TGT CCC AGG CAA ATA GCC CAG

459 460

STA GAT GAT GAT ACC CA

Nano string Rab33a GCT GGC TTG GCA TTG ATC TTC TCG

461 462

STA TCC TT CCC TCG

Nano string Rora GAT GTG GCA GCT TTG AAG ACA TCG

463 464

STA GTG TGC GGG CTC

Nano string Sema4d TTC TTG GGC AGT TCG CGG GAT CAT

465 466

STA GAA CCC CAA CTT

Nano string Slamf7 CTC CAT GAA GCT TTG ATT ACG CAG

467 468

STA CAG CCA A GTG CCA

Nano string Spryl AGG ACT TCC CTT AGC CAG GAT TCA

469 470

STA CAC GCC ACT TTG TGA

Nano string Stat6 TGC TTT TGC CAG ACG CCC AGG GAG

471 472

STA TGT GAC C TTT ACA

Nano string Tgfbrl TGA TGT CAG CTC TCT GCA GCG AGA

473 474

STA TGG GCA ACC AAA

Nano string Tnfrsfl3 GGA AGG CAC CAG CTC GTC GCA AGC

475 476

STA b GGA TCT CTC TGT

Nano string Trim25 TCT GCC TTG TGC ACG GGT GCA TCA

477 478

STA CTG ACA GCC TAA

Nano string Xrcc5 AGG GGA CCT GGA GAC AAG TTG GGG

479 480

STA CTC TGG CCA ATG

Nano string Pmepal GTG ACC GCT TGA GCT GTG TCG GCT

481 482

STA TGG GG GAT GAA

Nano string Prkd3 CCT GGC CTC TCA AGA GGC CTT TCA

483 484

STA GTT CCA GCA GGC

Nano string Rac lap AGC AGC CAA GTG TGC CAC AAG GAG

485 486

STA 1 CGG TAG AGG TCC

Nano string Rorc CCT CTG ACC CGT GCT TCC AGA AGC

487 488

STA CTC CCT CAG GGT

Nano string Sema7a ATG AAA GGC TAT GTG CAC AAT GGT

489 490

STA GCC CCC GGC CTT

Nano string Slc2al GAC CCT GCA CCT GAA GCC AGC CAC

491 492

STA CAT TGG AGC AAT

Nano string StardlO AGG ACC CAG GAG ATC TCC ACA GCC

493 494

STA AGT CGG TGC ACC

Nano string Sufii ATG GGG AGT CCT TAG GCC CTG CAT

495 496

STA TCT GCC CAG CTC

Nano string Tgfbr3 TCT GGG ATT TGC GTG CAG GAA GAG

497 498

STA CAT CCA CAG GGA

Nano string Tnfrsf25 CGA GCC ATG TGG GAG GCT GAG AGA

499 500

STA GAA AAG TGG GCA SEQ SEQ

Gene

Assay ID Forward Sequence ID Reverse Sequence

Name

NO: NO:

Nano string Trpsl TTG TAA CGC ACT CGT GCC TTT TTG

501 502

STA TTG AGA TCC GTA GCC

Nano string Zebl AAG CGC TGT GTC GTG AGA TGC CCC

503 504

STA CCT TTG AGT GCT

Nano string Pml AAT TTG GGT CCT GCT CGA GAT GCC

505 506

STA CTC GGC AGT GCT

Nano string Prnp CCT CCC ACC TGG CCG TCA CAG GAG

507 508

STA GAT AGC GAC CAA

Nano string Rasgr l CAA GCA TGC AAA CGT TAT GAG CGG

509 510

STA GTC TGA GC GGT TTG

Nano string Rp l4 GCA GCA GTG GTC TGT CAC CAA CAG

511 512

STA TGG TCA GGG CTT

Nano string Serpinbl CAA GGT GCT GGA GCG GCC CAG GTT

513 514

STA a GAT GCC AGA GTT

Nano string Slc6a6 GGT GCG TTC CTC AGG CCA GGA TGA

515 516

STA ATA CCG CGA TGT

Nano string Statl GAG GTA GAG GCC TTT AAG CTC TGC

517 518

STA TGG GGA CGC CTC

Nano string Sult2bl CGA TGT CGT GGT GTC CTG CTG CAG

519 520

STA CTC CCT CTC CTC

Nano string Tgifl GGA CCC AGT CCA CGG CAA TCA GGA

521 522

STA AAC CCT CCG TAT

Nano string Tnfsfl 1 AAC AAG CCT TTC AGA GAT CTT GGC

523 524

STA AGG GGG CCA GCC

Nano string Tsc22d3 TGC CAG TGT GCT CTG TGC ACA AAG

525 526

STA CCA GAA CCA TGC

Nano string Zfpl61 CGC CAA GAT TTC TCC CCG ATT TCT

527 528

STA CGT GA TCC ACA

Nano string Pou2afl GCC CAC TGG CCT TGG GAT ATC AAA

529 530

STA TCA TTT GAA ACT GTC A

Nano string Procr GCC AAA ACG TCA ACG GCC ACA TCG

531 532

STA CCA TCC AAG AAG

Nano string Rbpj TCC CTT AAA ACA CTT CCC CTT GAC

533 534

STA GGA GCC A AAG CCA

Nano string Runxl GCC TGA GAA AAC CAT GTG CCT GAT

535 536

STA GGT AGG G GGA TTT TT

Nano string Serpine2 TGA GCC ATC AAA GCT TGT TCA CCT

537 538

STA GGC AAA GGC CC

Nano string Smad3 ACG TGC CCC TGT GAG TGG TGG GAC

539 540

STA CTG AAG AGG GC

Nano string Stat2 GCA ACC AGG AAC TCT TCG GCA AGA

541 542

STA GCA GAC ACC TGG

Nano string Tal2 GGT GGA GGC AGC CAT CCT CAT CTG

543 544

STA AGA GTG GCA GGC

Nano string Tgm2 CAG TCT CAG TGC ATG TCC TCC CGG

545 546

STA GAG CCA TCA TCA

TABLE S6.2. RNAi sequences

Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-044066-03 Ahr 1 1622 NM_013464 1 161 CCGCAAGAUGUUAUUAAUA

D-044066-04 Ahr 1 1622 NM_013464 1 162 CCAGUUCUCUUAUGAGUGC

D-054696-01 Arid5a 214855 NM_145996 1 163 GGAAGAACGUGUAUGAUGA

D-054696-02 Arid5a 214855 NM_145996 1 164 GAAGAGGGAUUCGCUCAUG

D-054696-03 Arid5a 214855 NM_145996 1 165 CCUCUAAACUUCACCGGUA

D-054696-04 Arid5a 214855 NM_145996 1 166 GGUCAUCCCUGCUUUCCCA

D-040483-02 ARNTL 1 1865 NM_007489 1 167 GCAUCGAUAUGAUAGAUAA

D-040483-03 ARNTL 1 1865 NM_007489 1 168 CAG U AAAGG UGG AAGAU AA

D-040483-04 ARNTL 1 1865 NM_007489 1 169 GAAAUACGGGUGAAAUCUA

D-040483-17 ARNTL 1 1865 NM_007489 1 170 UGUCGUAGGAUGUGACCGA

D-049093-01 Batf 53314 NM_016767 1 171 GAACGCAGCUCUCCGCAAA

D-049093-02 Batf 53314 NM_016767 1 172 UCAAACAGCUCACCGAGGA

D-049093-03 Batf 53314 NM_016767 1 173 GAGGAAAGUUCAGAGGAGA

D-049093-04 Batf 53314 NM_016767 1 174 UCAAGUACUUCACAUCAGU

D-058452-01 CCR5 12774 NM_009917 1 175 GGAGUUAUCUCUCAGUGUU

D-058452-02 CCR5 12774 NM_009917 1 176 UGAAGUUUCUACUGGUUUA

D-058452-03 CCR5 12774 NM_009917 1 177 GCUAUGACAUCGAUUAUGG

D-058452-04 CCR5 12774 NM_009917 1 178 UGAAACAAAUUGCGGCUCA

D-062489-01 CCR6 12458 NM_009835 1 179 GCACAUAUGCGGUCAACUU

D-062489-02 CCR6 12458 NM_009835 1 180 CCAAUUGCCUACUCCUUAA

D-062489-03 CCR6 12458 NM_009835 1 181 GAACGGAUGAUUAUGACAA

D-062489-04 CCR6 12458 NM_009835 1 182 U G U AU G AG AAGG AAG AAU A

D-040286-04 EGR1 13653 NM_007913 1 183 CGACAGCAGUCCCAUCUAC

D-040286-01 EGR1 13653 NM_007913 1 184 UGACAUCGCUCUGAAUAAU

D-040286-02 EGR1 13653 NM_007913 1 185 ACUCCACUAUCCACUAUUA

D-040286-03 EGR1 13653 NM_007913 1 186 AUGCGUAACUUCAGUCGUA

D-040303-01 Egr2 13654 NM_0101 18 1 187 GAAGGUAUCAUCAAUAUUG

D-040303-02 Egr2 13654 NM_0101 18 1 188 GAUCUCCCGUAUCCGAGUA

D-040303-03 Egr2 13654 NM_0101 18 1 189 UCUCUACCAUCCGUAAUUU Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-040303-04 Egr2 13654 NM_0101 18 1 190 UGACAUGACUGGAGAGAAG

D-058294-01 ELK3 13713 NM_013508 1 191 GUAGAGAUCAGCCGGGAGA

D-058294-02 ELK3 13713 NM_013508 1 192 GAUCAGGUUUGUGACCAAU

D-058294-03 ELK3 13713 NM_013508 1 193 UCUUUAAUGUUGCCAAAUG

D-058294-04 ELK3 13713 NM_013508 1 194 UGAGAUACUAUUACGACAA

D-050997-21 Ets1 23871 NM_001038642 1 195 GCUUAGAGAUGUAGCGAUG

D-050997-22 Ets1 23871 NM_001038642 1 196 CCUGUUACACCUCGGAUUA

D-050997-23 Ets1 23871 NM_001038642 1 197 CAGCUACGGUAUCGAGCAU

D-050997-24 Ets1 23871 NM_001038642 1 198 U CAAG U AU G AG AACG ACU A

D-040983-01 ETS2 23872 NM_01 1809 1 199 G AU CAACAG CAAU ACAU U A

D-040983-02 ETS2 23872 NM_01 1809 1200 UGAAUUUGCUCAACAACAA

D-040983-03 ETS2 23872 NM_01 1809 1201 U AG AG CAG AUG AU CAAAG A

D-040983-04 ETS2 23872 NM_01 1809 1202 GAAUGACUUUGGAAUCAAG

D-058395-01 Etv6 1401 1 NM_007961 1203 GAACAAACAUGACCUAUGA

D-058395-02 Etv6 1401 1 NM_007961 1204 CAAAGAGGAUUUCCGCUAC

D-058395-03 Etv6 1401 1 NM_007961 1205 G CAU U AAG CAGG AACG AAU

D-058395-04 Etv6 1401 1 NM_007961 1206 CGCCACUACUACAAACUAA

D-045283-04 Fas 14102 NM_007987 1207 GAGUAAAUACAUCCCGAGA

D-045283-03 Fas 14102 NM_007987 1208 GGAGGCGGGUUCAUGAAAC

D-045283-02 Fas 14102 NM_007987 1209 CGCAGAACCUUAGAUAAAU

D-045283-01 Fas 14102 NM_007987 1210 GUACCAAUCUCAUGGGAAG

D-041 127-01 Foxo1 56458 NM_019739 121 1 G AAG ACACCU U UACAAG U G

D-041 127-02 Foxo1 56458 NM_019739 1212 G G ACAACAACAG U AAAU U U

D-041 127-03 Foxo1 56458 NM_019739 1213 GGAGAUACCUUGGAUUUUA

D-041 127-04 Foxo1 56458 NM_019739 1214 G AAAU CAG CAAU CCAG AAA

D-040670-01 GATA3 14462 NM_008091 1215 G AAG AU G U CU AG CAAAU CG

D-040670-02 GATA3 14462 NM_008091 1216 CGGAAGAUGUCUAGCAAAU

D-040670-03 GATA3 14462 NM_008091 1217 GUACAUGGAAGCUCAGUAU

D-040670-04 GATA3 14462 NM_008091 1218 AG AAAG AG U G CCU CAAG U A Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-060495-01 Id2 15902 NM_010496 1219 CAUCUGAAUUCCCUUCUGA

D-060495-02 Id2 15902 NM_010496 1220 G AACACG G ACAU CAGCAU C

D-060495-03 Id2 15902 NM_010496 1221 GUCGAAUGAUAGCAAAGUA

D-060495-04 Id2 15902 NM_010496 1222 CGGUGAGGUCCGUUAGGAA

D-051517-01 Ikzf4 22781 NM_01 1772 1223 GAUGGUGCCUGACUCAAUG

D-051517-02 Ikzf4 22781 NM_01 1772 1224 CGACUGAACGGCCAACUUU

D-051517-03 Ikzf4 22781 NM_01 1772 1225 GUGAAGGCCUUUAAGUGUG

D-051517-04 Ikzf4 22781 NM_01 1772 1226 GAACUCACACCUGUCAUCA

D-040810-01 IL17RA 16172 NM_008359 1227 GGACAGAUUUGAGGAGGUU

D-040810-02 IL17RA 16172 NM_008359 1228 GAAUAGUACUUGUCUGGAU

D-040810-03 IL17RA 16172 NM_008359 1229 UCUGGGAGCUCGAGAAGAA

D-040810-04 IL17RA 16172 NM_008359 1230 GAGAGCAACUCCAAAAUCA

D-040007-04 IL6ST 16195 NM_010560 1231 GUCCAGAGAUUUCACAUUU

D-040007-03 IL6ST 16195 NM_010560 1232 AG ACU U ACCU UG AAACAAA

D-040007-02 IL6ST 16195 NM_010560 1233 GAACUUCACUGCCAUUUGU

D-040007-01 IL6ST 16195 NM_010560 1234 GCACAGAGCUGACCGUGAA

D-057981-04 IL7R 16197 NM_008372 1235 GGAUUAAACCUGUCGUAUG

D-057981-03 IL7R 16197 NM_008372 1236 UAAGAUGCCUGGCUAGAAA

D-057981-02 IL7R 16197 NM_008372 1237 GCAAACCGCUCGCCUGAGA

D-057981-01 IL7R 16197 NM_008372 1238 GAAAGUCGUUUAUCGCAAA

D-043796-04 IRF4 16364 NM_013674 1239 CCAUAUCAAUGUCCUGUGA

D-043796-03 IRF4 16364 NM_013674 1240 CG AG U UACCUG AACACG U U

D-043796-02 IRF4 16364 NM_013674 1241 UAUCAGAGCUGCAAGUGUU

D-043796-01 IRF4 16364 NM_013674 1242 GGACACACCUAUGAUGUUA

D-040737-01 I rf 8 15900 NM_008320 1243 GGACAUUUCUGAGCCAUAU

D-040737-02 I rf 8 15900 NM_008320 1244 GAGCGAAGUUCCUGAGAUG

D-040737-03 I if 8 15900 NM_008320 1245 GCAAGGGCGUGUUCGUGAA

D-040737-04 I if 8 15900 NM_008320 1246 GCAACGCGGUGGUGUGCAA

D-042246-04 ITGA3 16400 NM_013565 1247 GCGAUGACUGGCAGACAUA Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-042246-03 ITGA3 16400 NM_013565 1248 GAGUGGCCCUAUGAAGUUA

D-042246-02 ITGA3 16400 NM_013565 1249 G G ACAAU G U U CG CG AU AAA

D-042246-01 ITGA3 16400 NM_013565 1250 CCAGACACCUCCAACAUUA

D-043776-01 Jun 16476 NM_010591 1251 GAACAGGUGGCACAGCUUA

D-043776-02 Jun 16476 NM_010591 1252 GAAACGACCUUCUACGACG

D-043776-03 Jun 16476 NM_010591 1253 CCAAG AACG U G ACCG ACG A

D-043776-04 Jun 16476 NM_010591 1254 GCCAAGAACUCGGACCUUC

D-041 158-04 JUNB 16477 NM_008416 1255 CAACCUGGCGGAUCCCUAU

D-041 158-03 JUNB 16477 NM_008416 1256 CAACAG CAACGG CG UG AU C

D-041 158-02 JUNB 16477 NM_008416 1257 UGGAACAGCCUUUCUAUCA

D-041 158-01 JUNB 16477 NM_008416 1258 ACACCAACCUCAGCAGUUA

D-049885-01 Kat2b 18519 NM_020005 1259 GCAGUAACCUCAAAUGAAC

D-049885-02 Kat2b 18519 NM_020005 1260 U CACAU AU G CAG AU GAG U A

D-049885-03 Kat2b 18519 NM_020005 1261 GAAGAACCAUCCAAAUGCU

D-049885-04 Kat2b 18519 NM_020005 1262 AAACAAG CCCAG AU U CG AA

D-047145-02 LRRFIP1 16978 NM_001 1 1 1312 1263 GAAGGGCUCCCGUAACAUG

D-047145-17 LRRFIP1 16978 NM_001 1 1 1312 1264 AAAGAGGCCCUGCGGCAAA

D-047145-18 LRRFIP1 16978 NM_001 1 1 1312 1265 GCUCGAGAGAUCCGGAUGA

D-047145-19 LRRFIP1 16978 NM_001 1 1 1312 1266 AG ACACAG U AAAUG ACG U U

D-063455-01 Mina 67014 NM_025910 1267 G U AAACAG U U G CCAAGG U U

D-063455-02 Mina 67014 NM_025910 1268 GCACCUACCAGAACAAUUC

D-063455-03 Mina 67014 NM_025910 1269 GAAAUGGAACGGAGACGAU

D-063455-04 Mina 67014 NM_025910 1270 GGUCACCAAUUCGUGUUAA

D-040813-01 MYC 17869 NM_010849 1271 G ACG AG ACCU U CAU CAAG A

D-040813-02 MYC 17869 NM_010849 1272 G ACAG CAG CU CG CCCAAAU

D-040813-03 MYC 17869 NM_010849 1273 GAAUUUCUAUCACCAGCAA

D-040813-04 MYC 17869 NM_010849 1274 GUACAGCCCUAUUUCAUCU

D-063057-04 MYD88 17874 NM_010851 1275 G AU G AU CCG G CAACU AG AA

D-063057-03 MYD88 17874 NM_010851 1276 G U U AG ACCG U G AGG AU AU A Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-063057-02 MYD88 17874 NM_010851 1277 CGACUGAUUCCUAUUAAAU

D-063057-01 MYD88 17874 NM_010851 1278 GCCUAUCGCUGUUCUUGAA

D-041 128-01 NCOA1 17977 NM_010881 1279 GAACAUGAAUCCAAUGAUG

D-041 128-02 NCOA1 17977 NM_010881 1280 GAACAUGGGAGGACAGUUU

D-041 128-03 NCOA1 17977 NM_010881 1281 UCAAGAAUCUGCUACCAAA

D-041 128-04 NCOA1 17977 NM_010881 1282 CCAAGAAGAUGGUGAAGAU

D-047764-01 Nfkbl 18033 NM_008689 1283 GACAUGGGAUUUCAGGAUA

D-047764-02 Nfkbl 18033 NM_008689 1284 GGAUUUCGAUUCCGCUAUG

D-047764-03 Nfkbl 18033 NM_008689 1285 CUACGGAACUGGGCAAAUG

D-047764-04 Nfkbl 18033 NM_008689 1286 G G AAACG CCAG AAG CU U AU

D-041 1 10-01 NOTCH 1 18128 NM_008714 1287 GAACAACUCCUUCCACUUU

D-041 1 10-02 NOTCH 1 18128 NM_008714 1288 GGAAACAACUGCAAGAAUG

D-041 1 10-03 NOTCH 1 18128 NM_008714 1289 G AACCAG G CU ACACAGG AA

D-041 1 10-04 NOTCH 1 18128 NM_008714 1290 GAAGGUGUAUACUGUGAAA

D-045970-01 Nr3c1 14815 NM_008173 1291 GAUCGAGCCUGAGGUGUUA

D-045970-02 Nr3c1 14815 NM_008173 1292 U U ACAAAG AU UG CAG G U AU

D-045970-03 Nr3c1 14815 NM_008173 1293 G CCAAG AG UUAUUUGAU G A

D-045970-04 Nr3c1 14815 NM_008173 1294 GCAUGUAUGACCAAUGUAA

D-048514-04 PML 18854 NM_008884 1295 GCGCAAGUCCAAUAUCUUC

D-048514-03 PML 18854 NM_008884 1296 AGUGGUACCUCAAGCAUGA

D-048514-02 PML 18854 NM_008884 1297 G CG CAG ACAU UG AG AAG CA

D-048514-01 PML 18854 NM_008884 1298 CAGCAUAUCUACUCCUUUA

D-048879-01 POU2AF1 18985 NM_01 1 136 1299 GAAGAAAGCGUGGCCAUAC

D-048879-02 POU2AF1 18985 NM_01 1 136 1300 CGGAGUAUGUGUCCCAUGA

D-048879-03 POU2AF1 18985 NM_01 1 136 1301 UCACUAAUGUCACGCCAAG

D-048879-04 POU2AF1 18985 NM_01 1 136 1302 G CAACACG U ACG AG CU CAA

D-043069-09 Prdm l 12142 NM_007548 1303 GGAGAGACCCACCUACAUA

D-043069-10 Prdm l 12142 NM_007548 1304 G CAAU ACAG U AG U GAG AAA

D-043069-1 1 Prdm l 12142 NM_007548 1305 GGAAGGACAUCUACCGUUC Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-043069-21 Prdm l 12142 NM_007548 1306 G U ACAU ACAU AG U GAACGA

D-042664-04 PROCR 19124 NM_01 1 171 1307 UAUCUGACCCAGUUCGAAA

D-042664-03 PROCR 19124 NM_01 1 171 1308 UAACUCCGAUGGCUCCCAA

D-042664-02 PROCR 19124 NM_01 1 171 1309 G U AAG U U U CCG G CCAAAG A

D-042664-01 PROCR 19124 NM_01 1 171 1310 CCAAACAGGUCGCUCUUAC

D-042742-01 Rbpj 19664 NM_001080928 131 1 CCAAACGACUCACUAGGGA

D-042742-02 Rbpj 19664 NM_001080928 1312 UCUCAACCCUGUGCGUUUA

D-042742-03 Rbpj 19664 NM_001080928 1313 GCAGACGGCAUUACUGGAU

D-042742-04 Rbpj 19664 NM_001080928 1314 G U AG AAG CCG AAACAAU G U

D-040776-01 Rela 19697 NM_009045 1315 GGAGUACCCUGAAGCUAUA

D-040776-02 Rela 19697 NM_009045 1316 G AAG AAG AG UCCU U UCAAU

D-040776-03 Rela 19697 NM_009045 1317 UAUGAGACCUUCAAGAGUA

D-040776-04 Rela 19697 NM_009045 1318 G AAU CCAG ACCAACAAU AA

D-042209-01 Rorc 19885 NM_01 1281 1319 U GAG U AU AG U CCAG AACG A

D-042209-02 Rorc 19885 NM_01 1281 1320 CAAUGGAAGUCGUCCUAGU

D-042209-03 Rorc 19885 NM_01 1281 1321 GAGUGGAACAUCUGCAAUA

D-042209-04 Rorc 19885 NM_01 1281 1322 GCUCAUCAGCUCCAUAUUU

D-048982-01 RUNX1 12394 NM_001 1 1 1022 1323 UGACCACCCUGGCGAGCUA

D-048982-02 RUNX1 12394 NM_001 1 1 1022 1324 GCAACUCGCCCACCAACAU

D-048982-03 RUNX1 12394 NM_001 1 1 1022 1325 GAGCUUCACUCUGACCAUC

D-048982-04 RUNX1 12394 NM_001 1 1 1022 1326 ACAAAU CCG CCACAAG U UG

D-045547-01 Satbl 20230 NM_009122 1327 CAAAGGAUAUGAUGGUUGA

D-045547-02 Satbl 20230 NM_009122 1328 GAAACGAGCCGGAAUCUCA

D-045547-03 Satbl 20230 NM_009122 1329 G AAG GG AG CACAG ACG U U A

D-045547-04 Satbl 20230 NM_009122 1330 GCACGCGGAAUUUGUAUUG

D-042265-01 SKI 20481 NM_01 1385 1331 GACCAUCUCUUGUUUCGUG

D-042265-02 SKI 20481 NM_01 1385 1332 GGAAAGAGAUUGAGCGGCU

D-042265-03 SKI 20481 NM_01 1385 1333 GCUGGUUCCUCCAAUAAGA

D-042265-04 SKI 20481 NM_01 1385 1334 U G AAG GAG AAG U U CGACU A Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-040687-04 SMAD4 17128 NM_008540 1335 GAAGGACUGUUGCAGAUAG

D-040687-03 SMAD4 17128 NM_008540 1336 GCAAAGGAGUGCAGUUGGA

D-040687-02 SMAD4 17128 NM_008540 1337 GAAGUAGGACUGCACCAUA

D-040687-01 SMAD4 17128 NM_008540 1338 AAAG AG CAAU UG AG AG U U U

D-041 135-01 Smarca4 20586 NM_01 1417 1339 GGUCAACGGUGUCCUCAAA

D-041 135-02 Smarca4 20586 NM_01 1417 1340 GAUAAUGGCCUACAAGAUG

D-041 135-03 Smarca4 20586 NM_01 1417 1341 GAGCGAAUGCGGAGGCUUA

D-041 135-04 Smarca4 20586 NM_01 1417 1342 CAACGGGCCUUUCCUCAUC

D-051590-01 SMOX 228608 NM_145533 1343 GCACAGAGAUGCUUCGACA

D-051590-02 SMOX 228608 NM_145533 1344 CCACGGGAAUCCUAUCUAU

D-051590-03 SMOX 228608 NM_145533 1345 AGAAUGGCGUGGCCUGCUA

D-051590-04 SMOX 228608 NM_145533 1346 UGAGGAAUUCAGCGAUUUA

D-043282-01 Sp4 20688 NM_009239 1347 G G ACAACAG CAG AU U AU U A

D-043282-02 Sp4 20688 NM_009239 1348 GACAAUAGGUGCUGUUAGU

D-043282-03 Sp4 20688 NM_009239 1349 AAUUAGACCUGGCGUUUCA

D-043282-04 Sp4 20688 NM_009239 1350 G GAG U U CCAG U AACAAU CA

D-061490-01 Tgifl 21815 NM_009372 1351 G CAAAU AG CACCCAGCAAC

D-061490-02 Tgifl 21815 NM_009372 1352 CAAACG AG CGG CAG AG AU G

D-061490-03 Tgifl 21815 NM_009372 1353 UCAGUGAUCUGCCAUACCA

D-061490-04 Tgifl 21815 NM_009372 1354 GCCAAGAUUUCAGAAGCUA

D-047483-04 TRIM24 21848 NM_145076 1355 AAACUGACCUGUCGAGACU

D-047483-03 TRIM24 21848 NM_145076 1356 CCAAUACGUUCACCUAGUG

D-047483-02 TRIM24 21848 NM_145076 1357 GAUCAGCCUAGCUCAGUUA

D-047483-01 TRIM24 21848 NM_145076 1358 GCAAGCGGCUGAUUACAUA

D-065500-01 TRPS1 83925 NM_032000 1359 GCAAAUGGCGGAUAUGUAU

D-065500-02 TRPS1 83925 NM_032000 1360 G CG AG CAG AU U AU U AG AAG

D-065500-03 TRPS1 83925 NM_032000 1361 CUACGGUUCUGGAGUAAAU

D-065500-04 TRPS1 83925 NM_032000 1362 G AAG U U CG AG AG U CAAACA

D-055209-02 Tsc22d3 14605 NM_010286 1363 GUGAGCUGCUUGAGAAGAA Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-055209-17 Tsc22d3 14605 NM_010286 1364 CUGUACGACUCCAGGAUUU

D-055209-18 Tsc22d3 14605 NM_010286 1365 CUAUAUAGCCAUAAUGCGU

D-055209-19 Tsc22d3 14605 NM_010286 1366 CAGUGAGCCUGUCGUGUCA

D-060426-04 UBE2B 22210 NM_009458 1367 CAGAAUCGAUGGAGUCCCA

D-060426-03 UBE2B 22210 NM_009458 1368 GAUGGUAGCAUAUGUUUAG

D-060426-02 UBE2B 22210 NM_009458 1369 GGAAUGCAGUUAUAUUUGG

D-060426-01 UBE2B 22210 NM_009458 1370 G AAG AG AG U U U CGG CCAU U

D-047149-02 VAX2 241 13 NM_01 1912 1371 GGACUUGCCUGCUGGCUAC

D-047149-03 VAX2 241 13 NM_01 1912 1372 U G ACACAG G U AG CG CG AG U

D-047149-04 VAX2 241 13 NM_01 1912 1373 CUACAGCAGACUAGAACAA

D-047149-17 VAX2 241 13 NM_01 1912 1374 GCACUGAGUUGGCCCGACA

D-040825-04 XBP1 22433 NM_013842 1375 UCUCAAACCUGCUUUCAUC

D-040825-03 XBP1 22433 NM_013842 1376 G AG UCAAACU AACG UGG U A

D-040825-02 XBP1 22433 NM_013842 1377 GGAUCACCCUGAAUUCAUU

D-040825-01 XBP1 22433 NM_013842 1378 UGACAUGUCUUCUCCACUU

D-051513-01 Zeb1 21417 NM_01 1546 1379 G AACCCAG CU U G AACG U CA

D-051513-02 Zeb1 21417 NM_01 1546 1380 G AAAG AGCACU U ACGGAU U

D-051513-03 Zeb1 21417 NM_01 1546 1381 GGUUUGGUAUCUCCCAUAA

D-051513-04 Zeb1 21417 NM_01 1546 1382 GAAGUGUAUUAGCUUGAUG

D-058937-01 ZFP161 22666 NM_009547 1383 CCUCCGCUCUGACAUAUUU

D-058937-02 ZFP161 22666 NM_009547 1384 GAUUCUCGGUAUCCGGUUU

D-058937-03 ZFP161 22666 NM_009547 1385 CCGCCAAGAUUUCCGUGAA

D-058937-04 ZFP161 22666 NM_009547 1386 AAAGACCAUUUGCGUGUCA

D-057818-01 ZFP281 226442 NM_177643 1387 GCACCACCGCGAUGUAUUA

D-057818-02 ZFP281 226442 NM_177643 1388 G AACAACG UACCAGAU U G A

D-057818-03 ZFP281 226442 NM_177643 1389 AAGCAAGGCCCGAUAAGUA

D-057818-04 ZFP281 226442 NM_177643 1390 GAUCAGUACUCUGGCAAAU

D-041703-01 ZFP36L1 12192 NM_007564 1391 UCAAGACGCCUGCCCAUUU

D-041703-02 ZFP36L1 12192 NM_007564 1392 UCAGCAGCCUUAAGGGUGA Duplex SEQ

Gene GENE Gene

Catalog ID Sequence

Symbol ID Accession

Number NO:

D-041703-03 ZFP36L1 12192 NM_007564 1393 GGAGCUGGCGAGCCUCUUU

D-041703-04 ZFP36L1 12192 NM_007564 1394 CGAAUCCCCUCACAUGUUU

EXAMPLE 2: A transcriptional time course of Thl7 differentiation

[00198] The differentiation of na^'ive CD4+ T-cells into Thl7 cells was induced using

TGF-βΙ and IL-6, and measured transcriptional profiles using microarrays at eighteen time points along a 72hr time course during the differentiation of na^'ive CD4+ T-cells into Thl7 cells, induced by a combination of the anti-inflammatory cytokine TGF-βΙ and the proinflammatory cytokine IL-6 (Fig. 1, Fig. 6A, Fig. 6B and Fig. 6C, see Methods in Example 1). As controls, mR A profiles were measured for cells that were activated without the addition of differentiating cytokines (ThO). 1,291 genes that were differentially expressed specifically during Thl7 differentiation were identified by comparing the Thl7 differentiating cells to the control cells (see Methods in Example 1) and partitioned into 20 co-expression clusters (k-means clustering, see Methods in Example 1, Fig. lb and Fig. 7) that displayed distinct temporal profiles. These clusters were used to characterize the response and reconstruct a regulatory network model, as described below (Fig. 2a).

[00199] Three main waves of transcription and differentiation: There are three transcriptional phases as the cells transition from a na^'ive-like state (t=0.5hr) to Thl7 (t=72hr; Fig. lc and Fig. 6c): early (up to 4hr), intermediate (4-20hr), and late (20-72hr). Each corresponds, respectively, to a differentiation phase (Korn et al, Annu Rev Immunol 2009): (1) induction, (2) onset of phenotype and amplification, and (3) stabilization and IL- 23 signaling.

[00200] The early phase is characterized by transient induction {e.g., Cluster C5, Fig. lb) of immune response pathways {e.g., IL-6 and TGF-β signaling; Fig. Id). The first transition point (t=4hr) is marked by a significant increase in the expression level of

ROR-γί, which is not detectable at earlier time points. The second transition (t=20hr) is accompanied by significant changes in cytokine expression, with induction of Thl7 signature cytokines {e.g., IL-17) that strengthen the Thl7 phenotype and a concomitant decrease in other cytokines {e.g., IFN-γ) that belong to other T cell lineages.

[00201] Some early induced genes display sustained expression {e.g., Cluster CIO,

Fig. lb); these are enriched for transcription regulators (TRs) also referred to herein as transcription factors (TFs), including the key Thl7 factors Stat3, Irf4 and Batf, and the cytokine and receptor molecules IL-21, Lif, and I12ra.

[00202] The transition to the intermediate phase (t=4hr) is marked by induction of

ROR-γί (master TF; Fig. 6d) and another 12 TFs (Cluster C20, Fig. lb), both known (e.g., Ahr) and novel (e.g., Trpsl) to Thl7 differentiation. At the 4hr time point, the expression of ROR-γί, the master TF of Thl7 differentiation, significantly increases (Fig. 6d) - marking the beginning of the accumulation of differentiation phenotypes ('intermediate phase') - and remains elevated throughout the rest of the time course. Another 12 factors show a similar pattern (Cluster 8 C20, Fig. lb). These include Ahr and Rbpj, as well as a number of factors (e.g., Etv6 and Trpsl) not described previously as having roles in Thl7

differentiation. Overall, the 585 genes that are induced between 4 and 20hrs are

differentially expressed and substantially distinct from the early response genes (Fig. lb; e.g., clusters C20, C14, and CI).

[00203] During the transition to the late phase (t=20hr), mRNAs of Thl7 signature cytokines are induced (e.g., IL-17a, IL-9; cluster CI 9) whereas mRNAs of cytokines that signal other T cell lineages are repressed (e.g., IFN-γ and IL-4). Regulatory cytokines from the IL-10 family are also induced (IL- 10, IL-24), possibly as a self- limiting mechanism related to the emergence of 'pathogenic' or 'non-pathogenic' Thl7 cells (Lee et al, Induction and Molecular Signature of Pathogenic Thl7 Cells, Nature Immunol 13, 991-999; doi: 10.1038/ni.2416). Around 48hr, the cells induce IL23r (data not shown), which plays an important role in the late phase (Fig. 8A, 8B).

[00204] Between 20 and 42hrs post activation (i.e., starting 16hrs after the induction of ROR-γί expression), there is a substantial change compared to ThO in the expression of 821 genes, including many major cytokines (e.g., cluster CI 9, Fig. lb). The expression of Thl7-associated inflammatory cytokines, including IL-17a, IL-24, IL-9 and lymphotoxin alpha LTA (Elyaman, W. et al. Notch receptors and Smad3 signaling cooperate in the induction of interleukin-9-producing T cells. Immunity 36, 623-634,

doi: 10.1016/j.immuni.2012.01.020 (2012)), is strongly induced (Fig. Id), whereas other cytokines and chemokines are repressed or remain at their low basal level (Clusters C8 and C15, Fig. lb and Fig. 7). These include cytokines that characterize other T-helper cell types, such as IL-2 (Thl7 differentiation inhibitor), IL-4 (Th2), and IFN-γ (Thl), and others (Csfl, Tnfsf9/4 and Ccl3). Finally, regulatory cytokines from the IL-10 family are also induced (IL-10, IL-24), possibly as a self- limiting mechanism. Thus, the 20hr time point might be crucial for the emergence of the proposed 'pathogenic' versus 'nonpathogenic'/ regulatory Thl7 cells (Lee et al, Nature Immunol 2012).

[00205] Most expression changes in the 1,055 genes differentially expressed in the remainder of the time course (>48hr) are mild, occur in genes that responded during the 20- 42hr period (Fig. 1, e.g., clusters CI 8, CI 9, and C20), and typically continue on the same trajectory (up or down). Among the most strongly late-induced genes is the TF Hifla, previously shown to enhance Thl7 development via interaction with ROR- yt (Dang, E. V. et al. Control of T(H)17/T(reg) balance by hypoxia-inducible factor 1. Cell 146, 772-784, doi: 10.1016/j.cell.2011.07.033 (2011)). The genes over-expressed at the latest time point (72hr) are enriched for apoptotic functions (p<10^~6), consistent with the limited survival of Thl7 cells in primary cultures, and include the Th2 cytokine IL-4 (Fig. 8a), suggesting that under TGF-pi+IL-6 treatment, the cells might have a less stable phenotype.

[00206] The peak of induction of IL-23r mRNA expression occurs at 48hr and, at this time point one begins to see IL-23r protein on the cell surface (data not shown). The late phase response depends in part on IL-23, as observed when comparing temporal transcriptional profiles between cells stimulated with TGF-pi+IL-6 versus TGF-pi+IL- 6+IL-23, or between WT and IL-23r-/- cells treated with TGF-pi+IL-6+IL-23 (Fig. 8). For instance, in IL-23r-deficient Thl7 cells, the expression of IL-17ra, IL-lrl, IL-21r, ROR-yt, and Hifl a is decreased, and IL-4 expression is increased. The up-regulated genes in the IL- 23r-/- cells are enriched for other CD4+ T cell subsets, suggesting that, in the absence of IL- 23 signaling, the cells start to dedifferentiate, thus further supporting the hypothesis that IL- 23 may have a role in stabilizing the phenotype of differentiating Thl7 cells.

EXAMPLE 3: Inference of dynamic regulatory interactions

[00207] It was hypothesized that each of the clusters (Fig. lb) encompasses genes that share regulators active in the relevant time points. To predict these regulators, a general network of regulator-target associations from published genomics profiles was assembled (Linhart, C, Halperin, Y. & Shamir, R. Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome research 18, 1180-1189, doi: 10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integrated platform of mammalian transcription factors. Bio informatics 24, 2416-2417, doi: 10.1093/bioinformatics/btn439 (2008); Wilson, N. K. et al. Combinatorial

transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532-544, doi: 10.1016/j.stem.2010.07.016 (2010); Lachmann, A. et al. in Bio informatics Vol. 26 2438-2444 (2010); Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740,

doi: 10.1093/bioinformatics/btr260 (2011); Jiang, C, Xuan, Z., Zhao, F. & Zhang, M.

TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35, D137-140 (2007); Elkon, R., Linhart, C, Sharan, R., Shamir, R. & Shiloh, Y. in Genome Research Vol. 13 773-780 (2003); Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nat.

Immunol. 9, 1091-1094, doi: 10.1038/nil008-1091 (2008)) (Fig. 2a, see Methods in

Example 1).

[00208] The general network of regulator-target associations from published genomics profiles was assembled as follows: in vivo protein-DNA binding profiles for 298 regulators (Linhart, C, Halperin, Y. & Shamir, R. Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome research 18, 1180-1189, doi: 10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integrated platform of mammalian transcription factors. Bioinformatics 24, 2416-2417, doi: 10.1093/bioinformatics/btn439 (2008); Wilson, N. K. et al. Combinatorial

transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532-544, doi: 10.1016/j.stem.2010.07.016 (2010); Lachmann, A. et al. in Bioinformatics Vol. 26 2438-2444 (2010); Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740,

TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35, D137-140 (2007), 825 DNA cis-regulatory elements scored in each gene's promoter (Elkon, R., Linhart, C, Sharan, R., Shamir, R. & Shiloh, Y. Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome research 13, 773-780, doi: 10.1101/gr.947203 (2003)), transcriptional responses to the knockout of 11 regulatory proteins, and regulatory relations inferred from co-expression patterns across 159 immune cell types (Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091- 1094, doi: 10.1038/nil008-1091 (2008)) (see Methods in Example 1). While most protein- DNA binding profiles were not measured in Thl7 cells, DNA-binding profiles in Thl7 cells of a number of key TFs, including Irf4 and Batf (Glasmacher, E. et al. A Genomic Regulatory Element That Directs Assembly and Function of Immune-Specific AP-l-IRF Complexes. Science, doi: 10.1126/science.1228309 (2012)), Stat3 and Stat5 (Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nat. Immunol. 12, 247-254, doi: 10.1038/ni. l995 (2011)), and Rorc (Xiao et al, unpublished) has been included.

[00209] A regulator was then connected to a gene from its set of putative targets only if there was also a significant overlap between the regulator's putative targets and that gene's cluster (see Methods in Example 1). Since different regulators act at different times, the connection between a regulator and its target may be active only within a certain time window. To determine this window, each edge was labeled with a time stamp denoting when both the target gene is regulated (based on its expression profile) and the regulator node is expressed at sufficient levels (based on its mRNA levels and inferred protein levels (Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342, doi: 10.1038/naturel0098 (2011)); see Methods in Example 1). For the target gene, the time points in which it is either differentially expressed compared to the ThO condition or is being induced or repressed compared with preceding time points in the Thl7 time course were considered. For the regulator node, only time points where the regulator is sufficiently expressed and not repressed relative to the ThO condition were included. To this end, the regulator's predicted protein expression level was inferred from its mRNA level using a recently proposed model (Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342,

doi: 10.1038/naturel0098 (2011)) (see Methods in Example 1). In this way, a network 'snapshot' was derived for each of the 18 time points (Fig. 2b-d). Overall, 9,159 interactions between 71 regulators and 1,266 genes were inferred in at least one network.

[00210] Substantial regulatory re-wiring during differentiation: The active factors and interactions change from one network to the next. The vast majority of interactions are active only at some time window (Fig. 2c), even for regulators (e.g., Batf) that participate in all networks. Based on similarity in active interactions, three network classes were identified (Fig. 2c), corresponding to the three differentiation phases (Fig. 2d). All networks in each phase were collapsed into one model, resulting in three consecutive network models (Fig. 9A, 9B). Among the regulators, 33 are active in all of the networks (e.g. many known master regulators such as Batfl, Irf4, and Stat3), whereas 18 are active in only one (e.g. Statl and Irfl in the early network; ROR-γΐ in the late network). Indeed, while ROR-γί mR A levels are induced at ~4h, ROR-γΐ protein levels increase at approximately 20h and further rise over time, consistent with the model (Fig. 9).

[00211] Densely interconnected transcriptional circuits in each network: At the heart of each network is its 'transcriptional circuit', connecting active TFs to target genes that themselves encode TFs. For example, the transcriptional circuit in the early response network connects 48 factors that are predicted to act as regulators to 72 factors whose own transcript is up- or down-regulated during the first four hours (a subset of this model is shown in Fig. 2e). The circuit automatically highlights many TFs that were previously implicated in immune signaling and Thl7 differentiation, either as positive or negative regulators, including Stat family members, both negative (Statl, Stat5) and positive (Stat3), the pioneering factor Batf, TFs targeted by TGF-β signaling (Smad2, Runxl, and Irf7), several TFs targeted by TCR signaling (Rel, Nfkbl, and Jun), and several interferon regulatory factors (Irf4 and Irfl), positioned both as regulators and as target genes that are strongly induced. In addition, 34 regulators that were not previously described to have a role in Thl7 differentiation were identified {e.g., Sp4, Egr2, and Smarca4). Overall, the circuit is densely intraconnected (Novershtern et al, Cell 2011), with 16 of the 48 regulators themselves transcriptionally controlled {e.g., Statl, Irfl, Irf4, Batf). This suggests feedback circuitry, some of which may be auto -regulatory {e.g., for Irf4, Stat3 and Statl).

[00212] As in the early network, there is substantial cross-regulation between the 64

TFs in the intermediate and late transcriptional circuits, which include major Thl7 regulators such as ROR-γί, Irf4, Batf, Stat3, and Hifla (Fig. 2e).

[00213] Ranking novel regulators for systematic perturbation: In addition to known

Thl7 regulators, the network includes dozens of novel factors as predicted regulators (Fig. 2d), induced target genes, or both (Fig. 2E). It also contains receptor genes as induced targets, both previously known in Thl7 cells {e.g., IL-1R1, IL-17RA) and novel {e.g., Fas, Itga3). This suggests substantial additional complexity compared to current knowledge, but must be systematically tested to validate the role and characterize the function of each candidate.

[00214] Candidate regulators were ranked for perturbation (Fig. 2a, 3a, see Methods in Example 1), guided by features that reflect a regulatory role (Fig. 3a, "Network

Information") and a role as target (Fig. 3a, "Gene Expression Information").

[00215] To this end, a scoring scheme was devised to rank candidate regulators for perturbation (Fig. 2a, Fig. 3a, Fig. 10, Methods), guided by protein activity (participation as a regulator node, Fig. 3a, "Network Information") and mR A level (changes in expression as a target, Fig. 3a, "Gene Expression Information"; Methods). Under each criterion, several features were considered for selecting genes to perturb (see Methods in Example 1). In "Network Information", it was considered whether the gene acts as regulator in the network, the type of experimental support for this predicted role, and whether it is predicted to target key Thl7 genes. In "Gene Expression Information", it was considered changes in mRNA levels of the encoding gene in the time course data (preferring induced genes), under IL23R knockout, or in published data of perturbation in Thl7 cells (e.g., Batf knockout (Schraml, B. U. et al. in Nature Vol. 460 405-409 (2009)); See Methods for the complete list); and whether a gene is more highly expressed in Thl7 cells as compared to other CD4+ subsets, based on genome wide expression profiles (Wei, G. et al. in Immunity Vol. 30 155-167 (2009)).

[00216] The genes were computationally ordered to emphasize certain features (e.g., a predicted regulator of key Thl7 genes) over others (e.g., differential expression in the time course data). A similar scheme was used to rank receptor proteins (see Methods in Example 1). Supporting their quality, the top-ranked factors are enriched (p<10^~3) for manually curated Thl7 regulators (Fig. 10), and correlate well (Spearman r>0.86) with a ranking learned by a supervised method (see Methods in Example 1). 65 genes were chose for perturbation: 52 regulators and 13 receptors. These included most of the top 44 regulators and top 9 receptors (excluding a few well-known Thl7 genes and/or those for which knockout data already existed), as well as additional representative lower ranking factors.

EXAMPLE 4: Nanowire-based perturbation of primary T cells

[00217] While testing the response of na^'ive CD4+ T cells from knock-out mice deleted for key factors is a powerful strategy, it is limited by the availability of mouse strains or the ability to generate new ones. In unstimulated primary mouse T cells, viral- or transfection-based siRNA delivery has been nearly impossible because it either alters differentiation or cell viability (Dardalhon, V. et al. Lentivirus-mediated gene transfer in primary T cells is enhanced by a central DNA flap. Gene therapy 8, 190-198 (2001);

McManus, M. et al. Small interfering RNA-mediated gene silencing in T lymphocytes. The Journal of Immunology 169, 5754 (2002)). a new delivery technology based on silicon nano wires (NWs) (Shalek et al, Proc Natl Acad Sci U.S.A. 2010; Shalek, A. K. et al.

Nanowire-Mediated Delivery Enables Functional Interrogation of Primary Immune Cells: Application to the Analysis of Chronic Lymphocytic Leukemia. Nano Lett. 12, 6498-6504, doi: 10.1021/nl3042917 (2012)) was, therefore, used, which was optimized to effectively (>95%) deliver siRNA into na^'ive T cells without activating them (Figs. 3b and c) (Shalek et al., Nano Lett 2012).

[00218] Recently, it was demonstrated that NWs are able to effectively penetrate the membranes of mammalian cells and deliver a broad range of exogenous molecules in a minimally invasive, non-activating fashion (Shalek et al, Proc. Natl. Acad. Sci. U.S.A. 2010; Shalek, et al, Nano Lett. 2012). In particular, the NW-T cell interface (Fig. 3b) was optimized to effectively (>95%) deliver siRNAs into na^'ive murine T cells. This delivery neither activates nor induces differentiation of na^'ive T cells and does not affect their response to conventional TCR stimulation with anti-CD3/CD28 (Fig. 3c) (Shalek, et al, Nano Lett. 2012)). Importantly, NW-delivered siRNAs yielded substantial target transcript knockdowns, prior to and even up to 48h after anti- CD3/CD28 activation, despite rapid cellular proliferation (Fig. 3d).

[00219] It was then attempted to perturb 60 genes with NW-mediated siRNA delivery and efficient knockdown (<60% transcript remaining at 48hr post activation) was achieved for 34 genes (Fig. 3d and Fig. 11, Table S6.2). Knockout mice were obtained for seven other genes, two of which (Irf8 and I117ra) were also in the knockdown set. Altogether, 39 of the 65 selected genes were successfully perturbed - 29 regulators and 10 receptors - including 21 genes not previously associated with Thl7 differentiation.

[00220] Nanowire-based screen validates 39 regulators in the Thl 7 network: the effects of the perturbation on gene expression were profiled at two time points. 28 of the perturbations were profiled at lOhr after the beginning of differentiation, soon after the induction of ROR-γί (Fig. 6), and all of the perturbations were profiled at 48hr, when the Thl 7 phenotype becomes more established (Fig. lb). Two of the perturbations (I117ra and 112 lr knockouts) were also profiled at 60hr.

[00221] In particular, the effects of perturbations at 48hr post-activation on the expression of 275 signature genes were measured using the Nanostring nCounter system (I117ra and 112 lr knockouts were also measured at 60hr).

[00222] The signature genes were computationally chosen to cover as many aspects of the differentiation process as possible (see Methods in Example 1): they include most differentially expressed cytokines, TFs, and cell surface molecules, as well as

representatives from each cluster (Fig. lb), enriched function, and predicted targets in each network. For validation, a signature of 85 genes was profiled using the Fluidigm BioMark system, obtaining highly reproducible results (Fig. 12).

[00223] The signature genes for expression analysis were computationally chosen to cover as many aspects of the differentiation process as possible (see Methods in Example

1) . They include the majority of the differentially expressed cytokines, TFs, and cell surface genes, as well as representative genes from each expression cluster (Fig. lb), enriched biological function, and predicted targets of the regulators in each network. Importantly, since the signature includes most of the genes encoding the perturbed regulators, the connections between them (Fig. 4a, 'perturbed'), including feedback and feed- forward loops, could be determined.

[00224] The statistical significance of a perturbation's effect on a signature gene was scored by comparing to non-targeting siRNAs and to 18 control genes that were not differentially expressed (see Methods in Example 1, Fig. 4a, all non-grey entries are significant). Perturbation of 26 of the tested regulators had a significant effect on the expression of at least 25 signature genes at the 48hr time point (10% of signature genes that had any response). On average, a perturbation affected 40 genes, and 80%> of the signature genes were affected by at least one regulator. Supporting the original network model (Fig.

2) , there is a significant overlap between the genes affected by a regulator's knockdown and its predicted targets (p < 0.01, permutation test; see Methods in Example 1).

[00225] To study the network's dynamics, the effect of 28 of the perturbations at lOhr (shortly after the induction of ROR-γί) was measured using the Fluidigm Biomark system. It was found that 30% of the functional interactions are present with the same activation/repression logic at both lOhr and 48hr, whereas the rest are present only in one time point (Fig. 13). This is consistent with the extent of rewiring in the original model (Fig. 2b).

[00226] Whenever possible, the function of each regulator was classified as either positive or negative for Thl7 differentiation. Specifically, at the 48hr time point, perturbation of 22 of the regulators significantly attenuated IL-17A or IL-17F expression ('Thl7 positive regulators', Fig. 4b, blue) and perturbation of another five, significantly increased IL-17 levels ('Thl7 negative regulators', Fig. 4b, red). 12 of these strongly positive or negative regulators were not previously associated with Thl7 cells (Fig. 4b, light grey halos around blue and red nodes). A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/naturel 1981. Next, the role of these strong positive and negative regulators in the development of the Thl7 phenotype was focused on.

[00227] Two coupled antagonistic circuits in the Thl 7 network: Characterizing each regulator by its effect on Thl 7 signature genes {e.g. IL17A, IL17F, Fig. 4b, grey nodes, bottom), it was found that at 48hr the network is organized into two antagonistic modules: a module of 22 'Thl 7 positive factors' (Fig. 4b, blue nodes: 9 novel) whose perturbation decreased the expression of Thl 7 signature genes (Fig. 4b, grey nodes, bottom), and a module of 5 'Thl 7 negative factors' (Fig. 4b, red nodes: 3 novel) whose perturbation did the opposite. A color version of these figures can be found in Yosef et al, "Dynamic regulatory network controlling Thl7 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi:

10.1038/naturel 1981. Each of the modules is tightly intra- connected through positive, self- reinforcing interactions between its members (70% of the intra-module edges), whereas most (88%) inter-module interactions are negative. This organization, which is statistically significant (empirical p-value<10^{~ 3}; see Methods in Example 1, Fig. 14), is reminiscent to that observed previously in genetic circuits in yeast (Segre, D., Deluna, A., Church, G. M. & Kishony, R. Modular epistasis in yeast metabolism. Nat. Genet. 37, 77-83,

doi: 10.1038/ngl489 (2005); Peleg, T., Yosef, N., Ruppin, E. & Sharan, R. Network-free inference of knockout effects in yeast. PLoS Comput Biol 6, el000635,

doi: 10.1371/journal.pcbi.1000635 (2010)). At lOhrs, the same regulators do not yield this clear pattern (p>0.5), suggesting that at that point, the network is still malleable.

[00228] The two antagonistic modules may play a key role in maintaining the balance between Thl 7 and other T cell subsets and in self- limiting the pro -inflammatory status of Thl 7 cells. Indeed, perturbing Thl 7 positive factors also induces signature genes of other T cell subsets {e.g., Gata3, Fig. 4b, grey nodes, top), whereas perturbing Thl 7 negative factors suppresses them {e.g., Foxp3, Gata3, Stat4, and Tbx21).

EXAMPLE 5: Validation and characterization of novel factors

[00229] The studies presented herein focused on the role of 12 of the positive or negative factors (including 11 of the 12 novel factors that have not been associated with Thl 7 cells; Fig. 4b, light grey halos). RNA-Seq was used after perturbing each factor to test whether its predicted targets (Fig. 2) were affected by perturbation (Fig. 4c, Venn diagram, top). Highly significant overlaps (p<10^{~ 5}) for three of the factors (Egr2, Irf8, and Sp4) that exist in both datasets were found, and a border-line significant overlap for the fourth (Smarca4) was found, validating the quality of the edges in the network.

[00230] Next, the designation of each of the 12 factors as 'Thl7 positive' or hl7 negative' was assessed by comparing the set of genes that respond to that factor's knockdown (in RNA-Seq) to each of the 20 clusters (Fig. lb). Consistent with the original definitions, knockdown of a hl7 positive' regulator down-regulated genes in otherwise induced clusters, and up-regulated genes in otherwise repressed or un- induced clusters (and vice versa for hl7 negative' regulators; Fig. 4d and Fig. 15a,b). The genes affected by either positive or negative regulators also significantly overlap with those bound by key CD4+ transcription regulators (e.g., Foxp3 (Marson, A. et al. Foxp3 occupancy and regulation of key target genes during T cell stimulation. Nature 445, 931-935,

doi: 10.1038/nature05478 (2007); Zheng, Y. et al. Genome-wide analysis of Foxp3 target genes in developing and mature regulatory T cells. Nature 445, 936-940,

doi: 10.1038/nature05563 (2007)), Batf, Irf4, and ROR-γί (Glasmacher, E. et al. A Genomic Regulatory Element That Directs Assembly and Function of Immune-Specific AP-l-IRF Complexes. Science (New York, NY), doi: 10.1126/science.1228309 (2012); Ciofani, M. et al. A Validated Regulatory Network for Thl7 Cell Specification. Cell,

doi: 10.1016/j.cell.2012.09.016 (2012)), Xiao et al, unpublished data). For instance, genes that are down-regulated following knockdown of the 'Thl7-positive' regulator Mina are highly enriched (p<10^~6) in the late induced clusters (e.g., C19, C20). Conversely, genes in the same late induced clusters become even more up-regulated following knockdown of the 'Thl7 negative' regulator Sp4.

[00231] Mina promotes the Thl 7 program and inhibits the Foxp3 program:

Knockdown of Mina, a chromatin regulator from the Jumonji C (JmjC) family, represses the expression of signature Thl 7 cytokines and TFs (e.g. ROR-γί, Batf, Irf4) and of late- induced genes (clusters C9, C19; p<10^{" 5}), while increasing the expression of Foxp3, the master TF of Treg cells. Mina is strongly induced during Thl 7 differentiation (cluster C7), is down-regulated in IL23r-/- Thl7 cells, and is a predicted target of Batf (Glasmacher, E. et al. A Genomic Regulatory Element That Directs Assembly and Function of Immune- Specific AP-l-IRF Complexes. Science, doi: 10.1126/science.1228309 (2012)), ROR-γΐ (Glasmacher et al, Science 2012), and Myc in the model (Fig. 5a). Mina was shown to suppress Th2 bias by interacting with the TF NFAT and repressing the IL-4 promoter (Okamoto, M. et al. Mina, an 114 repressor, controls T helper type 2 bias. Nat. Immunol. 10, 872-879, doi: 10.1038/ni. l747 (2009)). However, in the cells, Mina knockdown did not induce Th2 genes, suggesting an alternative mode of action via positive feedback loops between Mina, Batf and ROR-yt (Fig. 5a, left). Consistent with this model, Mina expression is reduced in Thl7 cells from ROR-yt-knockout mice, and the Mina promoter was found to be bound by ROR-yt by ChlP-Seq (data not shown). Finally, the genes induced by Mina knockdown significantly overlap with those bound by Foxp3 in Treg cells (Marson et al, Nature 2007; Zheng et al, Nature 2007) (P<10^~25) and with a cluster previously linked to Foxp3 activity in Treg cells (Hill, J. A. et al. Foxp3 transcription- factor-dependent and - independent regulation of the regulatory T cell transcriptional signature. Immunity 27, 786- 800, doi:S1074-7613(07)00492-X [pii] 10.1016/j.immuni.2007.09.010 (2007)) (Fig. 15c). When comparing to previously defined transcriptional signatures of Treg cells (compared to conventional T cells, (Hill, J. A. et al. Foxp3 transcription-factor-dependent and - independent regulation of the regulatory T cell transcriptional signature. Immunity 27, 786- 800, doi: 10.1016/j.immuni.2007.09.010 (2007))), genes that are induced in the Mina knockdown are enriched in a cluster tightly linked to functional activity of FoxP3.

Conversely, genes down-regulated in the Mina knockdown are more directly responsive to TCR and IL-2 and less responsive to Foxp3 in Treg cells (Fig. 15c).

[00232] To further analyze the role of Mina, IL-17a and Foxp3 expression was measured following differentiation of na^'ive T cells from Mina-/- mice. Mina-/- cells had decreased IL-17a and increased Foxp3 compared to wild-type (WT) cells, as detected by intracellular staining (Fig. 5a). Cytokine analysis of the corresponding supernatants confirmed a decrease in IL-17a production and an increase in IFN-y (Fig. 5a) and TNF-a (Fig. 16a). Under Thl7 differentiation conditions, loss of Mina resulted in a decrease in IL- 17 expression and increase in FoxP3, as detected by intracellular staining (Fig. 5a).

Cytokine analysis of the supernatants from these differentiating cultures confirmed a decrease in IL-17 production with a commensurate increase in IFNy (Fig. 5a) and TNFa (Fig. 16a).

[00233] The reciprocal relationship between Tregs/Thl7 cells has been well described (Korn, T. et al. IL-21 initiates an alternative pathway to induce proinflammatory T(H)17 cells. Nature 448, 484-487, doi: 10.1038/nature05970 (2007)), and it was assumed that this is achieved by direct binding of the ROR-yt/Foxp3 TFs. However, the analysis suggests a critical role for the regulator Mina in mediating this process. This suggests a model where Mina, induced by ROR-yt and Batf, promotes transcription of ROR-yt, while suppressing induction of Foxp3, thus affecting the reciprocal Tregs/Thl7 balance (Korn, et al, Nature 2007)) by favoring rapid Thl7 differentiation.

[00234] Fas promotes the Thl 7 program and suppresses IFN-y expression: Fas, the

TNF receptor superfamily member 6, is another Thl 7 positive regulator (Fig. 5b). Fas is induced early, and is a target of Stat3 and Batf in the model. Fas knockdown represses the expression of key Thl 7 genes {e.g., IL-17a, IL-17f, Hifla, Irf4, and Rbpj) and of the induced cluster CI 4, and promotes the expression of Thl -related genes, including IFN-γ receptor 1 and Klrdl (Cd94; by RNA-Seq, Fig. 4, Fig. 5b, and Fig. 15). Fas and Fas-ligand deficient mice are resistant to the induction of autoimmune encephalomyelitis (EAE) (Waldner, H., Sobel, R. A., Howard, E. & Kuchroo, V. K. Fas- and FasL-deficient mice are resistant to induction of autoimmune encephalomyelitis. J Immunol 159, 3100-3103 (1997)), but have no defect in IFN-γ or Thl responses. The mechanism underlying this phenomenon was never studied.

[00235] To explore this, T cells from Fas-/- mice (Fig. 5b, Fig. 16c) were

differentiated. Consistent with the knockdown analysis, expression of IL-17a was strongly repressed and IFN-γ production was strongly increased under both Thl 7 and ThO polarizing conditions (Fig. 5b). These results suggest that besides being a death receptor, Fas may play an important role in controlling the Thl/Thl7 balance, and Fas-/- mice may be resistant to EAE due to lack of Thl 7 cells.

[00236] Pou2afl promotes the Thl 7 program and suppresses IL-2 expression:

Knockdown of Pou2afl (OBF1) strongly decreases the expression of Thl 7 signature genes (Fig. 5c) and of intermediate- and late-induced genes (clusters C19 and C20, p<10^{" 7}), while increasing the expression of regulators of other CD4+ subsets {e.g., Foxp3, Stat4, Gata3) and of genes in non-induced clusters (clusters C2 and C16 p<10^{" 9}). Pou2afl 's role in T cell differentiation has not been explored (Teitell, M. A. OCA-B regulation of B-cell development and function. Trends Immunol 24, 546-553 (2003)). To investigate its effects, T cells from Pou2afl-/- mice were differentiated (Fig. 5c, Fig. 16b). Compared to WT cells, IL-17a production was strongly repressed. Interestingly, IL-2 production was strongly increased in Pou2afl-/- T cells under non-polarizing (ThO) conditions. Thus, Pou2afl may promote Thl 7 differentiation by blocking production of IL-2, a known endogenous repressor of Thl 7 cells (Laurence, A. et al. Interleukin-2 signaling via STAT5 constrains T helper 17 cell generation. Immunity 26, 371-381, doi:S1074-7613(07)00176-8

[pii] 10.1016/j.immuni.2007.02.009 (2007)). Pou2afl acts as a transcriptional co-activator of the TFs 0CT1 or OCT2 (Teitell, Trends Immunol 2003). IL-17a production was also strongly repressed in Oct 1 -deficient cells (Fig. 16d), suggesting that Pou2afl may exert some of its effects through this co-factor.

[00237] TSC22d3 may limit Thl 7 differentiation and pro-inflammatory function:

Knockdown of the TSC22 domain family protein 3 (Tsc22d3) increases the expression of Thl7 cytokines (IL-17a, IL-21) and TFs (ROR-γί, Rbpj, Batf), and reduces Foxp3 expression. Previous studies in macrophages have shown that Tsc22d3 expression is stimulated by glucocorticoids and IL-10, and it plays a key role in their anti- inflammatory and immunosuppressive effects (Choi, S.-J. et al. Tsc-22 enhances TGF-beta signaling by associating with Smad4 and induces erythroid cell differentiation. Mol. Cell. Biochem. 271, 23-28 (2005)). Tsc22d3 knockdown in Thl7 cells increased the expression of IL-10 and other key genes that enhance its production (Fig. 5d). Although IL-10 production has been shown (Korn et al, Nature 2007; Peters, A., Lee, Y. & Kuchroo, V. K. The many faces of Thl7 cells. Curr. Opin. Immunol. 23, 702-706, doi: 10.1016/j.coi.2011.08.007 (2011);

Chaudhry, A. et al. Interleukin-10 signaling in regulatory T cells is required for suppression of Thl7 cell-mediated inflammation. Immunity 34, 566-578,

doi: 10.1016/j.immuni.2011.03.018 (2011)) to render Thl 7 cells less pathogenic in autoimmunity, co-production of IL-10 and IL-17a may be the indicated response for clearing certain infections like Staphylococcus aureus at mucosal sites (Zielinski, C. E. et al. Pathogen- induced human TH17 cells produce IFN-γ or IL-10 and are regulated by IL-Ιβ. Nature 484, 514-518, doi: 10.1038/naturel0957 (2012)). This suggests a model where Tsc22d3 is part of a negative feedback loop for the induction of a Thl 7 cell subtype that coproduce IL-17 and IL-10 and limits their pro-inflammatory capacity. Tsc22d3 is induced in other cells in response to the steroid Dexamethasone (Jing, Y. et al. A mechanistic study on the effect of dexamethasone in moderating cell death in Chinese Hamster Ovary cell cultures. Biotechnol Prog 28, 490-496, doi: 10.1002/btpr.747 (2012)), which represses Thl7 differentiation and ROR-γί expression (Hu, S. M., Luo, Y. L., Lai, W. Y. & Chen, P. F. [Effects of dexamethasone on intracellular expression of Thl 7 cytokine interleukin 17 in asthmatic mice]. Nan Fang Yi Ke Da Xue Xue Bao 29, 1185-1188 (2009)). Thus, Tsc22d3 may mediate this effect of steroids.

[00238] To further characterize Tsc22d3's role, ChlP-Seq was used to measure its

DNA-binding profile in Thl 7 cells and RNA-Seq following its knockdown to measure its functional effects. There is a significant overlap between Tsc22d3's functional and physical targets (P<0.01, e.g., IL-21, Irf4; see Methods in Example 1). For example, Tsc22d3 binds in proximity to IL-21 and Irf4, which also become up regulated in the Tsc22d3 knockdown. Furthermore, the Tsc22d3 binding sites significantly overlap those of major Thl7 factors, including Batf, Stat3, Irf4, and ROR-γί (>5 fold enrichment; Fig. 5d, and see Methods in Example 1). This suggests a model where Tsc22d3 exerts its Thl7-negative function as a transcriptional repressor that competes with Thl7 positive regulators over binding sites, analogous to previous findings in CD4+ regulation (Ciofani et al, Cell 2012; Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nat. Immunol. 12, 247-254, doi: 10.1038/ni. l995 (2011)).

Example 6. Protein C receptor (PROCR) regulates pathogenic phenotype of Thl7 cells

[00239] Thl7 cells, a recently identified T cell subset, have been implicated in driving inflammatory autoimmune responses as well as mediating protective responses against certain extracellular pathogens. Based on factors such as molecular signature, Thl7 cells are classified as pathogenic or non-pathogenic. (See e.g., Lee et al, "Induction and molecular signature of pathogenic Thl7 cells," Nature Immunology, vol. 13(10): 991-999 and online methods).

[00240] It should be noted that the terms "pathogenic" or "non-pathogenic" as used herein are not to be construed as implying that one Thl7 cell phenotype is more desirable than the other. As will be described herein, there are instances in which inhibiting the induction of pathogenic Thl7 cells or modulating the Thl7 phenotype towards the nonpathogenic Thl7 phenotype or towards another T cell phenotype is desirable. Likewise, there are instances where inhibiting the induction of non-pathogenic Thl7 cells or modulating the Thl7 phenotype towards the pathogenic Thl7 phenotype or towards another T cell phenotype is desirable. For example, pathogenic Thl7 cells are believed to be involved in immune responses such as autoimmunity and/or inflammation. Thus, inhibition of pathogenic Thl7 cell differentiation or otherwise decreasing the balance of Thl7 T cells towards non-pathogenic Thl7 cells or towards another T cell phenotype is desirable in therapeutic strategies for treating or otherwise ameliorating a symptom of an immune- related disorder such as an autoimmune disease or an inflammatory disorder. In another example, depending on the infection, non-pathogenic or pathogenic Thl7 cells are believed to be desirable in building a protective immune response in infectious diseases and other pathogen-based disorders. Thus, inhibition of non-pathogenic Thl7 cell differentiation or otherwise decreasing the balance of Thl7 T cells towards pathogenic Thl7 cells or towards another T cell phenotype or vice versa is desirable in therapeutic strategies for treating or otherwise ameliorating a symptom of an immune-related disorder such as infectious disease.

[00241] Thl7 cells are considered to be pathogenic when they exhibit a distinct pathogenic signature where one or more of the following genes or products of these genes is upregulated in TGF-P3-induced Thl7 cells as compared to TGF-βΙ -induced Thl7 cells: Cxcl3, 1122, 113, Ccl4, Gzmb, Lrmp, Ccl5, Caspl, Csf2, Ccl3, Tbx21, Icos, 117 r, Stat4, Lgals3 or Lag3. Thl7 cells are considered to be non-pathogenic when they exhibit a distinct non-pathogenic signature where one or more of the following genes or products of these genes is down-regulated in TGF-P3-induced Thl7 cells as compared to TGF-βΙ -induced Thl7 cells: IWst, Illrn, lkzf3, Maf, Ahr, 119 or 1110.

[00242] A temporal microarray analysis of developing Thl 7 cells was performed to identify cell surface molecules, which are differentially expressed in Thl 7 cells and regulate the development of Thl 7 cells. PROCR was identified as a receptor that is differentially expressed in Thl 7 cells and found its expression to be regulated by Thl7-specific transcription regulators.

[00243] Protein C receptor (PROCR; also called EPCR or CD201) is primarily expressed on endothelial cells, CD8⁺ dendritic cells and was also reported to be expressed to lower levels on other hematopoietic and stromal cells. It binds to activated protein C as well as factor VH/VIIa and factor Xa and was shown to have diverse biological functions, including anticoagulant, cytoprotective, anti-apoptotic and anti-inflammatory activity.

However, prior to these studies, the function of PROCR in T cells had not been explored.

[00244] The biological function of PROCR and its ligand activated protein C in Thl7 cells was analyzed, and it was found that it decreased the expression of some of the genes identified as a part of the pathogenic signature of Thl 7 cells. Furthermore, PROCR expression in Thl 7 cells reduced the pathogenicity of Thl 7 cells and ameliorated disease in a mouse model for human multiple sclerosis.

[00245] These results imply that PROCR functions as a regulatory gene for the pathogenicity of Thl7 cells through the binding of its ligand(s). It is therefore conceivable that the regulation of this pathway might be exploited for therapeutic approaches to inflammatory and autoimmune diseases.

[00246] These studies are the first to describe the Thl7-specific expression of PROCR and its role in reducing autoimmune Thl 7 pathogenicity. Thus, activation of PROCR through antibodies or other agonists are useful as a therapeutic strategy in an immune response such as inflammatory autoimmune disorders. In addition, blocking of PROCR through antibodies or other inhibitors could be exploited to augment protective Thl7 responses against certain infectious agents and pathogens.

[00247] PROCR is expressed in Th 17 cells: The membrane receptor PROCR (Protein

C receptor; also called EPCR or CD201) is present on epithelial cells, monocytes, macrophages, neutrophils, eosinophils, and natural killer cells but its expression had not previously been reported on T cells (Griffin JH, Zlokovic BV, Mosnier LO. 2012. Protein C anticoagulant and cytoprotective pathways. Int J Hematol 95: 333-45). However, the detailed transcriptomic analysis of Thl7 cells described herein has identified PROCR as an important node for Thl7 cell differentiation (Yosef N, Shalek AK, Gaublomme JT, Jin H, Lee Y, Awasthi A, Wu C, Karwacz K, Xiao S, Jorgolli M, Gennert D, Satija R, Shakya A, Lu DY, Trombetta JJ, Pillai MR, Ratcliffe PJ, Coleman ML, Bix M, Tantin D, Park H, Kuchroo VK, Regev A. 2013. Dynamic regulatory network controlling TH17 cell differentiation. Nature 496: 461-8). PROCR shares structural homologies with the

CD1/MHC molecules and binds activated protein C (aPC) as well as blood coagulation factor VII and the Vy4V55 TCR of γδ T cells. Due to its short cytoplasmic tail PROCR does not signal directly, but rather signals by associating with the G-protein-coupled receptor PARI (Fig. 30a; (Griffin et al, Int J Hematol 95: 333-45 (2012))). To analyze PROCR expression on Th subsets, CD4+ T cells were differentiated in vitro under polarizing conditions and determined PROCR expression. As indicated by the network analysis of Thl7 cells, high levels of PROCR could be detected in cells differentiated under Thl7 conditions (Fig. 31b). To study expression of PROCR on Thl7 cells during an immune response, mice were immunized with MOG/CFA to induce EAE. PROCR was not expressed on T cells in spleen and lymph nodes. In contrast, it could be detected on Thl7 cells infiltrating the CNS (Fig. 31c). These data indicate that PROCR is expressed on Thl7 cells in vitro and in vivo, where it is largely restricted to T cells infiltrating the target organ. To investigate the functions of PROCR in Thl7 cells, studies were designed to test how loss of PROCR would affect IL-17 production using T cells from a PROCR hypomorphic mutant (PROCRd/d). PROCR deficiency causes early embryonic lethality (embryonic day 10.5) (Gu JM, Crawley JT, Ferrell G, Zhang F, Li W, Esmon NL, Esmon CT. 2002.

Disruption of the endothelial cell protein C receptor gene in mice causes placental thrombosis and early embryonic lethality. J Biol Chem 277: 43335-43), whereas hypomorphic expression of PROCR, which retain only small amounts (<10% of wild-type) of PROCR, is sufficient to completely abolish lethality and mice develop normally under steady state conditions (Castellino FJ, Liang Z, Volkir SP, Haalboom E, Martin JA, Sandoval-Cooper MJ, Rosen ED. 2002. Mice with a severe deficiency of the endothelial protein C receptor gene develop, survive, and reproduce normally, and do not present with enhanced arterial thrombosis after challenge. Thromb Haemost 88: 462-72). When challenged in a model for septic shock, PROCRd/d mice show compromised survival compared to WT mice (Iwaki T, Cruz DT, Martin JA, Castellino FJ. 2005. A

cardioprotective role for the endothelial protein C receptor in lipopolysaccharide-induced endotoxemia in the mouse. Blood 105: 2364-71). Na^'ive CD4+ PROCRd/d T cells differentiated under Thl7 conditions produced less IL-17 compared to WT na^'ive CD4+ T cells (Fig. 3 Id). Effector memory PROCRd/d T cells cultured with IL-23 produced more IL-17 than WT memory T cells. Therefore PROCR, similar to PD-1, promotes generation of Thl7 cells from na^'ive CD4 T cells, but inhibits the function of Thl7 effector T cells.

[00248] Knockdown Analysis of PROCR in Tumor Model: Figure 34 is a graph depicting B16 tumor inoculation of PROCR mutant mice. 7 week old wild type or PROCR mutant (EPCR delta) C57BL/6 mice were inoculated with 5xl0⁵ B16F10 melanoma cells. As shown in Figure 34, inhibition of PROCR slowed tumor growth. Thus, inhibition of PROCR is useful for impeding tumor growth and in other therapeutic applications for treatment of cancer.

[00249] PD-1 and PROCR affect Thl 7 pathogenicity: Thl 7 cells are very

heterogeneous and the pathogenicity of Thl7 subsets differs depending on the cytokine environment during their differentiation (Zielinski CE, Mele F, Aschenbrenner D, Jarrossay D, Ronchi F, Gattorno M, Monticelli S, Lanzavecchia A, Sallusto F. 2012. Pathogen- induced human TH17 cells produce IFN-gamma or IL-10 and are regulated by IL-lbeta. Nature 484: 514-8; Lee Y, Awasthi A, YosefN, Quintana FJ, Peters A, Xiao S,

Kleinewietfeld M, Kunder S, Sobel RA, Regev A, Kuchroo V. 2012. Induction and molecular signature of pathogenic Thl 7 cells. Nat Immunol In press; and Ghoreschi K, Laurence A, Yang XP, Tato CM, McGeachy MJ, Konkel JE, Ramos HL, Wei L, Davidson TS, Bouladoux N, Grainger JR, Chen Q, Kanno Y, Watford WT, Sun HW, Eberl G, Shevach EM, Belkaid Y, Cua DJ, Chen W, O'Shea JJ. 2010. Generation of pathogenic T(H)17 cells in the absence of TGF-beta signalling. Nature 467: 967-71). In addition to the cytokine milieu, several costimulatory pathways have been implicated in regulating differentiation and function of T helper subsets, including Thl7 cells. CTLA-4-B7 interactions inhibit Thl7 differentiation (Ying H, Yang L, Qiao G, Li Z, Zhang L, Yin F, Xie D, Zhang J. 2010. Cutting edge: CTLA-4--B7 interaction suppresses Thl7 cell differentiation. J Immunol 185: 1375-8). Furthermore, the work described herein revealed that ICOS plays a critical role in the maintenance of Thl7 cells (Bauquet AT, Jin H, Paterson AM, Mitsdoerffer M, Ho IC, Sharpe AH, Kuchroo VK. 2009. The costimulatory molecule ICOS regulates the expression of c-Maf and IL-21 in the development of follicular T helper cells and TH-17 cells. Nat Immunol 10: 167-75).

[00250] Based on the detailed genomic analysis of pathogenic vs. non-pathogenic

Thl7 cells herein, it has been determined that the molecular signatures that define pathogenic vs. non-pathogenic effector Thl7 cells in autoimmune disease (Lee Y, Awasthi A, Yosef N, Quintana FJ, Peters A, Xiao S, Kleinewietfeld M, Kunder S, Sobel RA, Regev A, Kuchroo V. 2012. Induction and molecular signature of pathogenic Thl7 cells. Nat Immunol In press). Interestingly, PROCR is part of the signature for non-pathogenic Thl7 cells and its expression is highly increased in non-pathogenic subsets (Fig. 32a).

Furthermore, PROCR seems to play a functional role in regulating Thl7 pathogenicity as engagement of PROCR by its ligand aPC induces some non-pathogenic signature genes, while Thl7 cells from PROCRd/d mice show decreased expression of these genes (Fig. 32b). To study whether PROCR could also affect pathogenicity of Thl7 cells in an in vivo model of autoimmunity, an adoptive transfer model for EAE was used. To induce disease, MOG-specific 2D2 TCR transgenic T cells were differentiated under Thl7 conditions and then transferred into na^'ive recipients. As shown in Figure 32c, forced overexpression of PROCR on Thl7 cells ameliorated disease, confirming that PROCR drives conversion of pathogenic towards non-pathogenic Thl7 cells. In addition, it was found that PD-1 :PD-L1 interactions limit the pathogenicity of effector Thl7 cells in vivo. When MOG35-55- specific (2D2) Thl7 effector cells were transferred into WT vs. PD-L1-/- mice, PD-L1-/- recipients rapidly developed signs of EAE (as early as day 5 post transfer), and EAE severity was markedly increased with most experiments needed to be terminated due to rapid onset of morbidity in PD-L1-/- recipients (Fig. 32d). The number of CNS-infiltrating cells was significantly increased in PD-L1-/- recipients with a greater percentage of 2D2+ IL-17+ in PD-L1-/- recipients compared to WT mice. Therefore both PD-1 and PROCR seem to control pathogenicity of effector Thl7 cells. [00251] Several co-inhibitory molecules have been implicated in T cell dysfunction during antigen persistence. PD-1 and Tim-3, in particular, have wide implications in cancer and chronic viral infections such as HIV, HCV in human and LCMV in mice. Autoreactive T cell responses in mice and human are characterized with reduced expression of inhibitory molecules. The ability to induce T cell dysfunction in autoimmune settings could be clinically beneficial. MS patients that respond to Copaxone treatment show significantly elevated levels of expression of PROCR and PD-L1. It has been previously demonstrated that increasing Tim-3 expression and promoting T cell exhaustion provides the ability to limit encephalitogenecity of T cells and reduce EAE severity (Rangachari M, Zhu C, Sakuishi K, Xiao S, Karman J, Chen A, Angin M, Wakeham A, Greenfield EA, Sobel RA, Okada H, McKinnon PJ, Mak TW, Addo MM, Anderson AC, Kuchroo VK. 2012. Bat3 promotes T cell responses and autoimmunity by repressing Tim-3 -mediated cell death and exhaustion. Nat Med 18: 1394-400). Studies were, therefore, designed to determine whether the novel inhibitory molecule PROCR, which is selectively enriched in Thl7 cells, could also play a role in T cell exhaustion. It was found that PROCR is expressed in exhausted tumor infiltrating lymphocytes that express both PD-1 and Tim-3 (Fig. 33a). Consistent with this observation, it was found that PROCR was most enriched in antigen-specific exhausted CD8 T cells (Fig. 33b) during chronic LCMV infection. While T cell exhaustion is detrimental in chronic viral infection and tumor immunity, induction of exhaustion may play a beneficial role in controlling potentially pathogenic effector cells that cause autoimmune diseases. Regulating the expression and /or function of PD-1 and PROCR might provide the avenues to accomplish this task in controlling autoimmunity.

EXAMPLE 7. Fas in Th cell differentiation

[00252] Fas, also known as FasR, CD95, APO-1, TNFRSF6, is a member of the TNF receptor superfamily. Binding of FasL leads to FAS trimers that bind FADD (death domains), which activates caspase-8 and leads to apoptosis. Fas also exhibits apoptosis independent effects such as interaction with Akt, STAT3, and NF-κΒ in liver cells and interaction with NF-κΒ and MAPK pathways in cancer cells.

[00253] Lpr mice are dominant negative for Fas (transposon intron 1), creating a functional knockout (KO). These mice exhibit lymphoproliferative disease (lpr); age dependent >25-fold size increase of LN, Spleen; expansion of Thyl+B220+CD4-CD8- TCRa/b+ T cells. These mice produce spontaneous anti-dsDNA Ab, systemic autoimmunity, which makes them a model of systemic lupus erythematosus (SLE), but these mice are resistant to experimental autoimmune encephalomyelitis (EAE). Gld mice are dominant negative for FasL.

[00254] Fas fiox mice that are CD4Cre- / CD 19Cre- / CD4Cre-CD 19Cre- / LckCre-

Fasflox exhibit no lymphoproliferation and no expansion of Thyl+B220+CD4-CD8- TCRa/b+ T cells. These mice do exhibit progressive lymphopenia, inflammatory lung fibrosis, and wasting syndrome. Fas fiox mice that are MxCre+poly(IC)-Fasflox exhibit an lpr phenotype. Fas fiox mice that are MOGCre-Fasflox are resistant to EAE. Fas fiox mice that are LysMCre-Fasflox exhibit lymphoproliferation and glomerulonephritis.

[00255] Although Fas (CD95) has been identified as a receptor mediating apoptosis, the data herein clearly show that Fas is important for Thl7 differentiation and development of EAE. The data herein demonstrates that Fas-deficient mice have a defect in Thl7 cell differentiation and preferentially differentiate into Thl and Treg cells. The expansion of Treg cells and inhibition of Thl 7 cells in Fas-deficient mice might be responsible for disease resistance in EAE.

[00256] Fas-deficient cells are impaired in their ability to differentiate into Thl 7 cells, and they produce significantly lower levels of IL-17 when cultured in vitro under Thl7 conditions (IL-Ιβ + IL-6 + IL-23). Furthermore, they display reduced levels of IL- 23R, which is crucial for Thl 7 cells as IL-23 is required for Thl 7 stability and

pathogenicity. In contrast, Fas inhibits IFN-γ production and Thl differentiation, as cells derived from Fas-deficient mice secrete significantly higher levels of IFN-γ. Similarly, Fas- deficient cells more readily differentiate into Foxp3+ Tregs and secrete higher levels of the Treg effector cytokine IL-10. It therefore seems as if Fas suppresses the differentiation into Tregs and IFN-y-producing Thl cells while promoting Thl 7 differentiation. In

inflammatory autoimmune disorders, such as EAE, Fas therefore seems to promote disease progression by shifting the balance in T helper cells away from the protective Tregs and from IFN-y-producing Thl cells towards pathogenic Thl 7 cells.

[00257] The invention having now been described by way of written description and example, those of skill in the art will recognize that the invention can be practiced in a variety of embodiments and that the description and examples above are for purposes of illustration and not limitation of the following claims.

Claims

What is claimed is:

1. 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 Thl7 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.

2. The method of claim 1, wherein the T cell modulating agent is an agent that modulates the expression, activity and/or function of one or more target genes or one or more products of one or more target genes selected from those listed in Tables 3 - 9.

3. The method of claim 2, wherein a desired gene or combination of target genes is selected and identified as a positive regulator of Thl7 differentiation, maintenance and/or function or a negative regulator of Thl7 differentiation, maintenance and/or function.

4. The method of claim 3, wherein the gene or combination of target genes is a positive regulator of Thl7 differentiation, maintenance and/or function, and wherein the T cell modulating agent is an antagonist in an amount sufficient to shift differentiation, maintenance and/or function away from the Thl7 phenotype.

5. The method of claim 3, wherein the target gene or combination of target genes is a positive regulator of Thl7 differentiation, maintenance and/or function, and wherein the T cell modulating agent is an agonist in an amount sufficient to shift differentiation, maintenance and/or function toward the Thl7 phenotype.

6. The method of claim 3, wherein the target gene or combination of target genes is a negative regulator of Thl7 differentiation, maintenance and/or function, and wherein the T cell modulating agent is an antagonist in an amount sufficient to shift differentiation, maintenance and/or function toward the Thl7 phenotype.

7. The method of claim 3, wherein the target gene or combination of target genes is a negative regulator of Thl7 differentiation, maintenance and/or function, and wherein the T cell modulating agent is an agonist in an amount sufficient to shift differentiation away from the Thl7 phenotype and/or maintenance.

8. The method of claim 3, wherein the positive regulator of Thl7 differentiation, maintenance and/or function is selected from MINA, 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

9. The method of claim 3, wherein the positive regulator of Thl7 differentiation, maintenance and/or function is selected from MINA, PML, POU2AF1, PROCR,

SMARCA4, ZEB1, EGR2, CCR6, FAS and combinations thereof

10. The method of claim 3, wherein the negative regulator of Thl7 differentiation, maintenance and/or function is selected from SP4, ETS2, IKZF4, TSC22D3, IRFl and combinations thereof.

11. The method of claim 3, wherein the negative regulator of Thl7 differentiation, maintenance and/or function is selected from SP4, IKZF4, TSC22D3 and combinations thereof.

12. The method of claim 1, wherein the T cell modulating agent alters the balance between Thl7 cells and other T cell subtypes.

13. The method of claim 12, wherein the other T cell subtype is regulatory T cell (Treg).

14. The method of claim 1, wherein the T cell modulating agent is a soluble Fas polypeptide or a polypeptide derived from FAS in an amount sufficient to induce T cell differentiation toward Thl7 cells or an agonist that enhances or increases the expression, activity and/or function of FAS in Thl7 cells in an amount sufficient to induce T cell differentiation toward Thl7 cells.

15. The method of claim 1, wherein the T cell modulating agent is an antagonist that inhibits the expression, activity and/or function of FAS in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Thl cells, or a combination of Tregs and Thl cells.

16. The method of claim 1, wherein the T cell modulating agent alters the balance between pathogenic Thl 7 cells and non-pathogenic Thl 7 cells.

17. The method of claim 16, wherein the T cell modulating agent is a soluble Protein C Receptor (PROCR) polypeptide or a polypeptide derived from PROCR in an amount sufficient to switch Thl 7 cells from a pathogenic to non-pathogenic signature or an agonist that enhances or increases the expression, activity and/or function of PROCR in Thl 7 cells in an amount sufficient to switch Thl 7 cells from a pathogenic to non-pathogenic signature.

18. The method of claim 16, wherein the T cell modulating agent is an antagonist of PROCR in Thl 7 cells in an amount sufficient to switch Thl 7 cells from a non-pathogenic to a pathogenic signature.

19. The method according to any one of claims 1 to 18, 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.

20. The method according to any one of claims 1 to 19, wherein the T cell modulating agent is one or more agents selected from those listed in Table 10.

21. The method according to any one of claims 1 to 20, wherein the T cells are na^'ive T cells, partially differentiated T cells, differentiated T cells, a combination of na^'ive T cells and partially differentiated T cells, a combination of na^'ive T cells and differentiated T cells, a combination of partially differentiated T cells and differentiated T cells, or a combination of na^'ive T cells, partially differentiated T cells and differentiated T cells.

22. A method of inhibiting tumor growth in a subject in need thereof, the method comprising administering to said subject a therapeutically effective amount of an inhibitor of Protein C Receptor (PROCR).

23. The method of claim 22, wherein 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.

24. The method of claim 22, wherein the inhibitor of PROCR is one or more agents selected from the group consisting of lipopolysaccharide; cisp latin; fibrinogen; 1, 10- phenanthroline; 5-N-ethylcarboxamido adenosine; cystathionine; hirudin; phospholipid; Drotrecogin alfa; VEGF; Phosphatidylethanolamme; serine; gamma-carboxyglutamic acid; calcium; warfarin; endotoxin; curcumin; lipid; and nitric oxide.

25. A method of inhibiting Thl7 differentiation in a cell population and/or increasing expression, activity and/or function of one or more non-Thl7-associated cytokines or non- Thl7-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GAT A3, STAT4, BCL6 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.

26. The method of claim 25, wherein 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.

27. The method of claim 25 or claim 26, wherein 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.

28. A method of inhibiting Thl7 differentiation in a cell population and/or increasing expression, activity and/or function of one or more non-Thl7-associated cytokines or non- Thl7-associated transcription factor selected from FOXP3, interferon gamma (IFN-γ), GAT A3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRFl or combinations thereof.

29. The method of claim 28, wherein the agent enhances expression, activity and/or function of at least one of SP4, IKZF4, TSC22D3 or combinations thereof.

30. The method of claim 28 or claim 29, 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.

31. A method of enhancing Thl7 differentiation in a cell population increasing expression, activity and/or function of one or more Thl7-associated cytokines or one or more Thl7-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-Thl7- associated cytokines or non-Thl7-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GAT A3, STAT4 and TBX21, comprising contacting a T cell with an agent that inhibits expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRFl or combinations thereof.

32. The method of claim 31 , wherein the agent inhibits expression, activity and/or function of at least one of SP4, IKZF4 or TSC22D3.

33. The method of claim 31 or claim 32, wherein 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.

34. A method of enhancing Thl7 differentiation in a cell population, increasing expression, activity and/or function of one or more Thl7-associated cytokines or one or more Thl7-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-Thl7- associated cytokines or non-Thl7-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GAT A3, 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.

35. The method of claim 34, wherein the agent enhances expression, activity and/or function of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEBl, EGR2, CCR6 or FAS.

36. The method of claim 34 or claim 35, 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.

37. The method of claim 27, wherein the agent is one or more agents selected from those listed in Table 10.

38. The method of claim 30, wherein the agent is one or more agents selected from those listed in Table 10.

39. The method of claim 33, wherein the agent is one or more agents selected from those listed in Table 10.

40. The method of claim 36, wherein the agent is one or more agents selected from those listed in Table 10.

41. The method of claim 27, wherein the agent is an antibody.

42. The method of claim 30, wherein the agent is an antibody.

43. The method of claim 33, wherein the agent is an antibody.

44. The method of claim 36, wherein the agent is an antibody.

45. The method of claim 41, wherein the antibody is a monoclonal antibody.

46. The method of claim 42, wherein the antibody is a monoclonal antibody.

47. The method of claim 43, wherein the antibody is a monoclonal antibody.

48. The method of claim 44, wherein the antibody is a monoclonal antibody.

49. The method of claim 41, wherein the antibody is a chimeric, humanized or fully human monoclonal antibody.

50. The method of claim 42, wherein the antibody is a chimeric, humanized or fully human monoclonal antibody.

51. The method of claim 43, wherein the antibody is a chimeric, humanized or fully human monoclonal antibody.

52. The method of claim 44, wherein the antibody is a chimeric, humanized or fully human monoclonal antibody.

53. The method of claim 25, wherein the T cell is a na^'ive T cell, a combination of na^'ive T cells and partially differentiated T cells, a combination of na^'ive T cells and differentiated T cells, or a combination of na^'ive T cells, partially differentiated T cells and differentiated T cells.

54. The method of claim 28, wherein the T cell is a na^'ive T cell, a combination of na^'ive T cells and partially differentiated T cells, a combination of na^'ive T cells and differentiated T cells, or a combination of na^'ive T cells, partially differentiated T cells and differentiated T cells.

55. The method of claim 31 , wherein the T cell is a na^'ive T cell, a combination of na^'ive T cells and partially differentiated T cells, a combination of na^'ive T cells and differentiated T cells, or a combination of na^'ive T cells, partially differentiated T cells and differentiated T cells.

56. The method of claim 34, wherein the T cell is a na^'ive T cell, a combination of na^'ive T cells and partially differentiated T cells, a combination of na^'ive T cells and differentiated T cells, or a combination of na^'ive T cells, partially differentiated T cells and differentiated T cells.

57. The method of claim 25, wherein the T cell is a partially differentiated T cell, a differentiated T cell, or a combination of partially differentiated T cells and differentiated T cells.

58. The method of claim 28, wherein the T cell is a partially differentiated T cell, a differentiated T cell, or a combination of partially differentiated T cells and differentiated T cells.

59. The method of claim 31 , wherein the T cell is a partially differentiated T cell, a differentiated T cell, or a combination of partially differentiated T cells and differentiated T cells.

60. The method of claim 34, wherein the T cell is a partially differentiated T cell, a differentiated T cell, or a combination of partially differentiated T cells and differentiated T cells.

61. The method of claim 25 or claim 28, wherein the T cell is a Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Thl7 T cell to produce a CD4+ T cell phenotype other than a Thl7 T cell phenotype.

62. The method of claim 31 or claim 34, wherein the T cell is a CD4+ T cell other than a Thl7 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the non-Thl7 T cell to produce a Thl7 T cell phenotype.

63. A method of identifying a signature gene, a gene signature or other genetic element associated with Thl7 differentiation, maintenance and/or function comprising:

a) contacting a T cell with an inhibitor of Thl7 differentiation or an agent that enhances Thl7 differentiation; and b) identifying a signature gene, a gene signature or other genetic element whose expression is modulated by step (a).

64. The method of claim 63, further comprising

c) perturbing expression of the signature gene, gene signature or genetic

element identified in step (b) in a T cell that has been contact with an inhibitor of Thl7 differentiation or an agent that enhances Thl7 differentiation; and

d) identifying a target gene whose expression is modulated by step (c).

65. The method of claim 63 or claim 64, wherein the inhibitor of Thl7 differentiation is an agent that inhibits the expression, activity and/or function of a target gene or one or more products of one or more target genes selected from those listed in Tables 3 - 9.

66. The method of claim 63 or claim 64, wherein the inhibitor of Thl7 differentiation is an agent that inhibits the expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEBl, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or

combinations thereof.

67. The method of claim 66, wherein the agent inhibits expression, activity and/or function of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEBl, EGR2, CCR6 or FAS.

68. The method of claim 66, wherein 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.

69. The method of claim 67, wherein 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.

70. The method of claim 66, wherein the agent is one or more agents selected from those listed in Table 10.

71. The method of claim 67, wherein the agent is one or more agents selected from those listed in Table 10.

72. The method of claim 63 or claim 64, wherein the inhibitor of Thl7 differentiation is an agent that enhances expression, activity and/or function of SP4, ETS2, IKZF4,

TSC22D3, IRFl or combinations thereof.

73. The method of claim 72, wherein the agent enhances expression, activity and/or function of at least one of SP4, IKZF4, TSC22D3 or combinations thereof.

74. The method of claim 72, 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.

75. The method of claim 73, 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.

76. The method of claim 72, wherein the agent is one or more agents selected from those listed in Table 10.

77. The method of claim 73, wherein the agent is one or more agents selected from those listed in Table 10.

78. A method of modulating induction of Thl7 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 IRFl, IRF8, IRF9, STAT2, STAT3, IRF7, STAT1, ZFP281, IFI35, REL, TBX21, FLU, BATF, IRF4, AES, AHR, ARID5A, BCLl lB, BCL3, CBFB, CBX4, CHD7, CITED2, CREBl, E2F4, EGRl, EGR2, ELL2, ETS1, ETS2, ETV6, EZH1, FOXOl, GAT A3, GATAD2B, HIF1A, ID2, IKZF4, IRF2, IRF3, JMJDIC, JUN, LEF1, LR FIP1, MAX, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF21A, PML, PRDM1, RELA, RUNX1, SAP18, SATB1, SMAD2, SMARCA4, SP100, SP4, STAT4, STAT5B, STAT6, TFEB, TP53, TRIM24, ZFP161, and any combination thereof.

79. A method of modulating onset of Thl7 phenotype and amplification of Thl7 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 IRF8, STAT2, STAT3, IRF7, JUN, STAT5B, ZPF2981, CHD7, TBX21, FLU, SATB1, RUNX1, BATF, RORC, SP4, AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, BATF, BCL11B, BCL3, BCL6, CBFB, CBX4, CDC5L, CEBPB, CREBl, CREB3L2, CREM, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, ETV6, EZH1, FOSL2, FOXJ2, FOXOl, FUS, HIF1A, HMGB2, ID1, ID2, IFI35, IKZF4, IRF3, IRF4, IRF9, JUNB, KAT2B, KLF10, KLF6, KLF9, LEF1, LRRFIP1, MAFF, MAX, MAZ, MINA, MTA3, MYC, MYST4, NCOA1, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, RELA, RORA, SAP 18, SKI, SKIL, SMAD2, SMAD7, SMARCA4, SMOX, SP1, SSI 8, STAT1, STAT5A, STAT6, SUZ12, TFEB, TLE1, TP53, TRIM24, TRIM28, TRPS1, VAV1, ZEB1, ZEB2, ZFP161, ZFP62, ZNF238, ZNF281, ZNF703, and any combination thereof.

80. A method of modulating stabilization of Thl7 cells and/or modulating Thl7- 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 STAT2, STAT3, JUN, STAT5B, CHD7, SATB1, RUNX1, BATF, RORC, SP4, IRF4, AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, ATF3, ATF4, BATF3, BCL11B, BCL3, BCL6, C210RF66, CBFB, CBX4, CDC5L, CDYL, CEBPB, CHMP1B, CIC, CITED2, CREBl, CREB3L2, CREM, CSDA, DDIT3, E2F1, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, EZH1, FLU, FOSL2, FOXJ2, FOXOl, FUS, GAT A3, GATAD2B, HCLS1, HIF1A, ID1, ID2, IFI35, IKZF4, IRF3, IRF7, IRF8, IRF9, JARID2, JMJDIC, JUNB, KAT2B, KLF10, KLF6, KLF7, KLF9, LASS4, LEF1, LRRFIP1, MAFF, MAX, MEN1, MINA, MTA3, MXI1, MYC, MYST4, NCOA1, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF13, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, REL, RELA, RNFl l, ROPvA, RUNX2, SAP 18, SAP30, SERTAD1, SIRT2, SKI, SKIL, SMAD2, SMAD4, SMAD7, SMARCA4, SMOX, SPl, SPl 00, SSI 8, STATl, STAT4, STAT5A, STAT6, SUZ12, TBX21, TFEB, TGIF1, TLE1, TP53, TRIM24, TRPS1, TSC22D3, UBE2B, VAVl, VAX2, XBPl, ZEBl, ZEB2, ZFP161, ZFP36L1, ZFP36L2, ZNF238, ZNF281, ZNF703, ZNRF1, ZNRF2, and any combination thereof.

81. A method of modulating one or more of target genes associated with the early stage of Thl7 differentiation, maintenance and/or function, wherein the target gene is selected from:

(a) one or more of the target genes listed in Table 5 as being associated with the early stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of AES, AHR, ARID5A, BATF, BCL1 IB, BCL3, CBFB, CBX4, CHD7, CITED2, CREBl, E2F4, EGR1, EGR2, ELL2, ETS1, ETS2, ETV6, EZH1, FLU, FOXOl, GAT A3, 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, SPlOO, SP4, STATl, STAT2, STAT3, STAT4, STAT5B, STAT6, TFEB, TP53, TRIM24, ZFP161, and any combination thereof;

(b) one or more of the target genes listed in Table 6 as being associated with the early stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of FAS, CCR5, IL6ST, IL17RA, IL2RA, MYD88, CXCR5, PVR, IL15RA, IL12RB1, and any combination thereof;

(c) one or more of the target genes listed in Table 7 as being associated with the early stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of EIF2AK2, DUSP22, HK2, RIPK1, RNASEL, TEC, MAP3K8, SGK1, PRKCQ, DUSP16, BMP2K, PIM2, and any combination thereof;

(d) one or more of the target genes listed in Table 8 as being associated with the early stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of HK2, CDKNIA, DUT, DUSP1, NADK, LIMK2, DUSP11, TAOK3, PRPS1, PPP2R4, MKNK2, SGK1, BPGM, TEC, MAPK6, PTP4A2, PRPF4B, ACPI, CCRN4L, and any combination thereof; and (e) one or more of the target genes listed in Table 9 as being associated with the early stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of CD200, CD40LG, CD24, CCND2, ADAM 17, BSG, ITGAL, FAS, GPR65, SIGMAR1, CAP1, PLAUR, SRPRB, TRPV2, IL2RA, KDELR2,

TNFRSF9, and any combination thereof.

82. A method of modulating one or more of target genes associated with the

intermediate stage of Thl7 differentiation, maintenance and/or function, wherein the target gene is selected from:

(a) one or more of the target genes listed in Table 5 as being associated with the

intermediate stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, BATF, BCL11B, BCL3, BCL6, CBFB, CBX4, CDC5L, CEBPB, CHD7, CREB1, CREB3L2, CREM, E2F4, E2F8, EGRl, EGR2, ELK3, ELL2, ETSl, ETS2, ETV6, EZH1, FLU, FOSL2, FOXJ2, FOXOl, FUS, HIF1A, HMGB2, ID1, ID2, IFI35, IKZF4, IRF3, IRF4, IRF7, IRF8, IRF9, JUN, JUNB, KAT2B, KLF10, KLF6, KLF9, LEF1, LRRFIP1, MAFF, MAX, MAZ, MINA, MTA3, MYC, MYST4, NCOA1, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, RELA, RORA, RUNX1, SAP18, SATB1, SKI, SKIL, SMAD2, SMAD7, SMARCA4, SMOX, SP1, SP4, SSI 8, STAT1, STAT2, STAT3, STAT5A, STAT5B, STAT6, SUZ12, TBX21, TFEB, TLE1, TP53, TRIM24, TRIM28, TRPS1, VAV1, ZEB1, ZEB2, ZFP161, ZFP62, ZNF238, ZNF281, ZNF703, and any combination thereof;

(b) one or more of the target genes listed in Table 6 as being associated with the

intermediate stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of IL7R, ITGA3, IL1R1, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, CCR8, DDR1, PROCR, IL2RA, IL12RB2, MYD88, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRDl, IRAKI BP 1, PVR, IL12RB1, IL18R1, TRAF3, and any combination thereof;

(c) one or more of the target genes listed in Table 7 as being associated with the

intermediate stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of PSTPIP1, PTPN1, ACP5, TXK, RIPK3, PTPRF, NEK4, PPME1, PHACTR2, HK2, GMFG, DAPP1, TEC, GMFB, PIM1, NEK6, ACVR2A, FES, CDK6, ZAK, DUSP14, SGK1, JAK3, ULK2, PTPRJ, SPHK1, TNK2, PCTK1, MAP4K3, TGFBR1, HK1, DDR1 , BMP2K, DUSP10, ALPK2, and any combination thereof;

one or more of the target genes listed in Table 8 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of HK2, ZAP70, NEK6, DUSP14, SH2D1A, ITK, DUT, PPPlRl l, DUSP1, PMVK, TK1, TAOK3, GMFG, PRPS1, SGK1, TXK, WNK1, DUSP19, TEC, RPS6KA1, PKM2, PRPF4B, ADRBK1, CKB, ULK2, PLK1, PPP2R5A, PLK2, and any combination thereof; and

one or more of the target genes listed in Table 9 as being associated with the intermediate stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of CTLA4, CD200, CD24, CD5L, CD9, IL2RB, CD53, CD74, CAST, CCR6, IL2RG, ITGAV, FAS, IL4R, PROCR, GPR65, TNFRSF18, RORA, IL1RN, RORC, CYSLTR1, PNRC2, LOC390243, ADAM 10, TNFSF9, CD96, CD82, SLAMF7, CD27, PGRMC1, TRPV2, ADRBK1, TRAF6, IL2RA, THY1, IL12RB2, TNFRSF9, and any combination thereof

83. A method of modulating one or more of target genes associated with the late stage of Thl7 differentiation, maintenance and/or function, wherein the target gene is selected from: (a) one or more of the target genes listed in Table 5 as being associated with the late stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, ATF3, ATF4, BATF, BATF3, BCL11B, BCL3, BCL6, C210RF66, CBFB, CBX4, CDC5L, CDYL, CEBPB, CHD7, CHMP1B, CIC, CITED2, CREBl, CREB3L2, CREM, CSDA, DDIT3, E2F1, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, EZHl, FLU, FOSL2, FOXJ2, FOXOl, FUS, GAT A3, GATAD2B, HCLSl, HIFIA, ID1, ID2, IFI35, IKZF4, IRF3, IRF4, IRF7, IRF8, IRF9, JARID2, JMJD1C, JUN, JUNB, KAT2B, KLF10, KLF6, KLF7, KLF9, LASS4, LEF1, LRRFIP1, MAFF, MAX, MEN1, MINA, MTA3, MXI1, MYC, MYST4, NCOA1, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF13, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, REL, RELA, RNF11, RORA, RORC, RUNX1, RUNX2, SAP 18, SAP30, SATB1, SERTAD1, SIRT2, SKI, SKIL, SMAD2, SMAD4, SMAD7, SMARCA4, SMOX, SP1, SP100, SP4, SS18, STAT1, STAT3, STAT4, STAT5A, STAT5B, STAT6, SUZ12, TBX21, TFEB, TGIFI, TLEl, TP53, TRIM24, TRPS1, TSC22D3, UBE2B, VAV1, VAX2, XBP1, ZEB1, ZEB2, ZFP161, ZFP36L1, ZFP36L2, ZNF238, ZNF281, ZNF703, ZNRF1, ZNRF2, and any combination thereof;

one or more of the target genes listed in Table 6 as being associated with the late stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of IL7R, ITGA3, IL1R1, FAS, CCR5, CCR6, ACVR2A, IL6ST,

IL17RA, DDR1, PROCR, IL2RA, IL12RB2, MYD88, BMPR1A, PTPRJ,

TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRDl, IRAKI BP 1, PVR, IL15RA, TLRl, ACVRIB, IL12RB1, IL18R1, TRAF3, IFNGR1, PLAUR, IL21R, IL23R, and any combination thereof;

one or more of the target genes listed in Table 7 as being associated with the late stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of PTPLA, PSTPIP1, TK1, PTEN, BPGM, DCK, PTPRS, PTPN18, MKNK2, PTPN1, PTPRE, SH2D1A, PLK2, DUSP6, CDC25B, SLK, MAP3K5, BMPR1A, ACP5, TXK, RIPK3, PPP3CA, PTPRF, PACSIN1, NEK4, PIP4K2A, PPME1, SRPK2, DUSP2, PHACTR2, DCLK1, PPP2R5A, RIPK1, GK, RNASEL, GMFG, STK4, HINT3, DAPP1, TEC, GMFB, PTPN6, RIPK2, PIM1, NEK6, ACVR2A, AURKB, FES, ACVRIB, CDK6, ZAK, VRK2, MAP3K8, DUSP14, SGK1, PRKCQ, JAK3, ULK2, HIPK2, PTPRJ, INPP1, TNK2, PCTK1, DUSP1, NUDT4, TGFBR1, PTP4A1, HK1, DUSP16, ANP32A, DDR1, ITK, WNK1, NAGK, STK38, BMP2K, BUBl, AAKl, SIK1, DUSPIO, PRKCA, PIM2, STK17B, TK2, STK39, ALPK2, MST4, PHLPP1, and any combination thereof;

is one or more of the target genes listed in Table 8 as being associated with the late stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of ZAP70, PFKP, NEK6, DUSP14, SH2D1A, INPP5B, ITK, PFKL, PGK1, CDKNIA, DUT, PPP1R11, DUSP1, PMVK, PTPN22, PSPH, TK1,

PGAM1, LIMK2, CLK1, DUSP11, TAOK3, RIOK2, GMFG, UCKLl, PRPS1, PPP2R4, MKNK2, DGKA, SGK1, TXK, WNK1, DUSP19, CHP, BPGM,

PIP5K1A, TEC, MAP2K1, MAPK6, RPS6KA1, PTP4A2, PKM2, PRPF4B, ADRBK1, CKB, ACPI, ULK2, CCRN4L, PRKCH, PLK1, PPP2R5A, PLK2, and any combination thereof; (e) one or more of the target genes listed in Table 9 as being associated with the late stage of Thl7 differentiation, maintenance and/or function selected from the group consisting of CTLA4, TNFRSF4, CD44, PDCD1, CD200, CD247, CD24, CD5L, CCND2, CD9, IL2RB, CD53, CD74, ADAM 17, BSG, CAST, CCR6, IL2RG, CD81, CD6, CD48, ITGAV, TFRC, ICAM2, ATP1B3, FAS, IL4R, CCR7, CD52, PROCR, GPR65, TNFRSF18, FCRL1, RORA, IL1RN, RORC, P2RX4, SSR2, PTPN22, SIGMAR1, CYSLTR1, LOC390243, ADAM 10, TNFSF9, CD96, CAP1, CD82, SLAMF7, PLAUR, CD27, SIVA1, PGRMC1, SRPRB, TRPV2, NR1H2, ADRBK1, GABARAPL1, TRAF6, IL2RA, THY1, KDELR2, IL12RB2, TNFRSF9, SCARBl, IFNGR1, and any combination thereof.

84. 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 and those listed in Table 2 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.

85. The method of claim 84, wherein the immune response is an autoimmune response.

86. The method of claim 84, wherein the immune response is an inflammatory response.

87. A method of monitoring an immune response in a subject, comprising 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 Table 2 at a first time point, detecting a second level of expression, activity and/or function of the 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 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.

88. The method of claim 87, wherein the immune response is an autoimmune response.

89. The method of claim 87, wherein the immune response is an inflammatory response.

90. 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 the 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 the 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.

91. The method of claim 90, wherein the first ratio and the second ratio comprise a comparison of the level of Thl7 cells to non-Thl7 cells in the first and second T cell populations.

92. The method of claim 90, wherein the non-Thl7 cell is a regulatory T cell (Treg).

93. The method of claim 90, wherein the first ratio and the second ratio comprise a comparison of the level of pathogenic Thl7 cells to non-pathogenic Thl7 cells in the first and second T cell populations.

94. The method of claim 90, wherein the immune response is an autoimmune response.

95. The method of claim 90, wherein the immune response is an inflammatory response.