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

This application is a continuation-in-part application of international patent application Serial No. PCT/US2014/019127 filed Feb. 27, 2014, which published as PCT Publication No. WO 2014/134351 on Sep. 4, 2014, which claims benefit of and priority to U.S. Provisional Application No. 61/770,036, filed Feb. 27, 2013.

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

This invention was made with government support under Grant Nos. OD003958, HG006193, NS030843, NS045937, AI073748, AI045757, AI056299, OD003893 and HG005062 awarded by the National Institutes of Health. The government has certain rights in the invention.

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

Despite their importance, the molecular circuits that control the balance of T cells, including the differentiation of naïve T cells, remain largely unknown. Recent studies that reconstructed regulatory networks in mammalian cells have focused on short-term responses and relied on perturbation-based approaches that cannot be readily applied to primary T cells. Accordingly, there exists a need for a better understanding of the dynamic regulatory network that modulates, controls, or otherwise influences T cell balance, including Th17 cell differentiation, maintenance and function, and means for exploiting this network in a variety of therapeutic and diagnostic methods.

Citation or identification of any document in this application is not an admission that such document is available as prior art to the present invention.

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

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

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

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

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

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

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

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

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

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

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

In some embodiments, the target gene is one or more Th17-associated cytokine(s) or receptor molecule(s) selected from those listed in Table 3 of the specification. In some embodiments, the target gene is one or more Th17-associated transcription regulator(s) selected from those shown in Table 4 of the specification.

In some embodiments, the target gene is one or more Th17-associated transcription regulator(s) selected from those shown in Table 5 of the specification. In some embodiments, the target gene is one or more Th17-associated receptor molecule(s) selected from those listed in Table 6 of the specification. In some embodiments, the target gene is one or more Th17-associated kinase(s) selected from those listed in Table 7 of the specification. In some embodiments, the target gene is one or more Th17-associated signaling molecule(s) selected from those listed in Table 8 of the specification. In some embodiments, the target gene is one or more Th17-associated receptor molecule(s) selected from those listed in Table 9 of the specification.

In some embodiments, the target gene is one or more target genes involved in induction of Th17 differentiation such as, for example, IRF1, IRF8, IRF9, STAT2, STAT3, IRF7, STAT1, ZFP281, IFI35, REL, TBX21, FLI1, BATF, IRF4, one or more of the target genes listed in Table 5 as being associated with the early stage of Th17 differentiation, maintenance and/or function, e.g., AES, AHR, ARID5A, BATF, BCL11B, BCL3, CBFB, CBX4, CHD7, CITED2, CREB1, E2F4, EGR1, EGR2, ELL2, ETS1, ETS2, ETV6, EZH1, FLI1, FOXO1, GATA3, GATAD2B, HIF1A, ID2, IFI35, IKZF4, IRF1, IRF2, IRF3, IRF4, IRF7, IRF9, JMJD1C, JUN, LEF1, LRRFIP1, MAX, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF21A, PML, PRDM1, REL, RELA, RUNX1, SAP18, SATB1, SMAD2, SMARCA4, SP100, SP4, STAT1, STAT2, STAT3, STAT4, STATSB, STAT6, TFEB, TP53, TRIM24, and/or ZFP161, or any combination thereof.

In some embodiments, the target gene is one or more target genes involved in onset of Th17 phenotype and amplification of Th17 T cells such as, for example, IRF8, STAT2, STAT3, IRF7, JUN, STATSB, ZPF2981, CHD7, TBX21, FLI1, SATB1, RUNX1, BATF, RORC, SP4, one or more of the target genes listed in Table 5 as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function, e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, BATF, BCL11B, BCL3, BCL6, CBFB, CBX4, CDC5L, CEBPB, CHD7, CREB1, CREB3L2, CREM, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, ETV6, EZH1, FLI1, FOSL2, FOXJ2, FOXO1, 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, SS18, STAT1, STAT2, STAT3, STAT5A, STATSB, STAT6, SUZ12, TBX21, TFEB, TLE1, TP53, TRIM24, TRIM28, TRPS1, VAV1, ZEB1, ZEB2, ZFP161, ZFP62, ZNF238, ZNF281, and/or ZNF703, or any combination thereof.

In some embodiments, the target gene is one or more target genes involved in stabilization of Th17 cells and/or modulating Th17-associated interleukin 23 (IL-23) signaling such as, for example, STAT2, STAT3, JUN, STATSB, 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 Th17 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, CREB1, CREB3L2, CREM, CSDA, DDIT3, E2F1, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, EZH1, FLI1, FOSL2, FOXJ2, FOXO1, FUS, GATA3, GATAD2B, HCLS1, HIF1A, 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, SAP18, SAP30, SATB1, SERTAD1, SIRT2, SKI, SKIL, SMAD2, SMAD4, SMAD7, SMARCA4, SMOX, SP1, SP100, SP4, SS18, STAT1, STAT3, STAT4, STAT5A, STATSB, 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.

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 Th17 differentiation, maintenance and/or function, e.g., FAS, CCR5, IL6ST, IL17RA, IL2RA, MYD88, CXCR5, PVR, IL15RA, IL12RB1, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., IL7R, ITGA3, IL1R1, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, CCR5, DDR1, PROCR, IL2RA, IL12RB2, MYD88, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRD1, IRAK1BP1, PVR, IL12RB1, IL18R1, TRAF3, or any combination thereof.

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 Th17 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, IRAK1BP1, PVR, IL15RA, TLR1, ACVR1B, IL12RB1, IL18R1, TRAF3, IFNGR1, PLAUR, IL21R, IL23R, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., EIF2AK2, DUSP22, HK2, RIPK1, RNASEL, TEC, MAP3K8, SGK1, PRKCQ, DUSP16, BMP2K, PIM2, or any combination thereof.

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 Th17 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.

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 Th17 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, SGK1, PRKCQ, JAK3, ULK2, HIPK2, PTPRJ, INPP1, TNK2, PCTK1, DUSP1, NUDT4, TGFBR1, PTP4A1, HK1, DUSP16, ANP32A, DDR1, ITK, WNK1, NAGK, STK38, BMP2K, BUB1, AAK1, SIK1, DUSP10, PRKCA, PIM2, STK17B, TK2, STK39, ALPK2, MST4, PHLPP1, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., HK2, CDKN1A, DUT, DUSP1, NADK, LIMK2, DUSP11, TAOK3, PRPS1, PPP2R4, MKNK2, SGK1, BPGM, TEC, MAPK6, PTP4A2, PRPF4B, ACP1, CCRN4L, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., HK2, ZAP70, NEK6, DUSP14, SH2D1A, ITK, DUT, PPP1R11, DUSP1, PMVK, TK1, TAOK3, GMFG, PRPS1, SGK1, TXK, WNK1, DUSP19, TEC, RPS6KA1, PKM2, PRPF4B, ADRBK1, CKB, ULK2, PLK1, PPP2R5A, PLK2, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., ZAP70, PFKP, NEK6, DUSP14, SH2D1A, INPP5B, ITK, PFKL, PGK1, CDKN1A, DUT, PPP1R11, DUSP1, PMVK, PTPN22, PSPH, TK1, PGAM1, LIMK2, CLK1, DUSP11, TAOK3, RIOK2, GMFG, UCKL1, PRPS1, PPP2R4, MKNK2, DGKA, SGK1, TXK, WNK1, DUSP19, CHP, BPGM, PIP5K1A, TEC, MAP2K1, MAPK6, RPS6KA1, PTP4A2, PKM2, PRPF4B, ADRBK1, CKB, ACP1, ULK2, CCRN4L, PRKCH, PLK1, PPP2R5A, PLK2, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., CD200, CD40LG, CD24, CCND2, ADAM17, BSG, ITGAL, FAS, GPR65, SIGMAR1, CAP1, PLAUR, SRPRB, TRPV2, IL2RA, KDELR2, TNFRSF9, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., CTLA4, CD200, CD24, CD6L, CD9, IL2RB, CD53, CD74, CAST, CCR6, IL2RG, ITGAV, FAS, IL4R, PROCR, GPR65, TNFRSF18, RORA, IL1RN, RORC, CYSLTR1, PNRC2, LOC390243, ADAM10, TNFSF9, CD96, CD82, SLAMF7, CD27, PGRMC1, TRPV2, ADRBK1, TRAF6, IL2RA, THY1, IL12RB2, TNFRSF9, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., CTLA4, TNFRSF4, CD44, PDCD1, CD200, CD247, CD24, CD6L, CCND2, CD9, IL2RB, CD53, CD74, ADAM17, 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, ADAM10, 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.

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

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

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

In some embodiments, the T cell modulating agent is a soluble Fas polypeptide or a polypeptide derived from FAS. In some embodiments, the T cell modulating agent is an agent that enhances or otherwise increases the expression, activity, and/or function of FAS in Th17 cells. As shown herein, expression of FAS in T cell populations induced or otherwise influenced differentiation toward Th17 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, these T cell modulating agents are useful in the treatment of an infectious disease or other pathogen-based disorders. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.

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

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

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

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

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

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

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

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

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

In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of the Protein C Receptor (PROCR, also called EPCR or CD201). Inhibition of PROCR expression, activity and/or function in Th17 cells switches non-pathogenic Th17 cells to pathogenic Th17 cells. Thus, these PROCR antagonists are useful in the treatment of a variety of indications, for example, infectious disease and/or other pathogen-based disorders. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cell modulating agent is a soluble Protein C Receptor (PROCR, also called EPCR or CD201) polypeptide or a polypeptide derived from PROCR.

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

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

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

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

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

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

In some embodiments, the invention provides a method of modulating onset of Th17 phenotype and amplification of Th17 T cells which may comprise 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, STATSB, ZPF2981, CHD7, TBX21, FLI1, SATB1, RUNX1, BATF, RORC, SP4, one or more of the target genes listed in Table 5 as being associated with the intermediate stage of Th17 differentiation, maintenance and/or function, e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, BATF, BCL11B, BCL3, BCL6, CBFB, CBX4, CDC5L, CEBPB, CHD7, CREB1, CREB3L2, CREM, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, ETV6, EZH1, FLI1, FOSL2, FOXJ2, FOXO1, 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, SS18, STAT1, STAT2, STAT3, STAT5A, STATSB, STAT6, SUZ12, TBX21, TFEB, TLE1, TP53, TRIM24, TRIM28, TRPS1, VAV1, ZEB1, ZEB2, ZFP161, ZFP62, ZNF238, ZNF281, and/or ZNF703, or any combination thereof.

In some embodiments, the invention provides a method of modulating stabilization of Th17 cells and/or modulating Th17-associated interleukin 23 (IL-23) signaling which may comprise 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, STATSB, 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 Th17 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, CREB1, CREB3L2, CREM, CSDA, DDIT3, E2F1, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, EZH1, FLI1, FOSL2, FOXJ2, FOXO1, FUS, GATA3, GATAD2B, HCLS1, HIF1A, 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, SAP18, SAP30, SATB1, SERTAD1, SIRT2, SKI, SKIL, SMAD2, SMAD4, SMAD7, SMARCA4, SMOX, SP1, SP100, SP4, SS18, STAT1, STAT3, STAT4, STAT5A, STATSB, 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.

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 Th17 differentiation, maintenance and/or function, e.g., FAS, CCR5, IL6ST, IL17RA, IL2RA, MYD88, CXCR5, PVR, IL15RA, IL12RB1, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., IL7R, ITGA3, IL1R1, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, CCR5, DDR1, PROCR, IL2RA, IL12RB2, MYD88, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRD1, IRAK1BP1, PVR, IL12RB1, IL18R1, TRAF3, or any combination thereof.

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 Th17 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, IRAK1BP1, PVR, IL15RA, TLR1, ACVR1B, IL12RB1, IL18R1, TRAF3, IFNGR1, PLAUR, IL21R, IL23R, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., EIF2AK2, DUSP22, HK2, RIPK1, RNASEL, TEC, MAP3K8, SGK1, PRKCQ, DUSP16, BMP2K, PIM2, or any combination thereof.

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 Th17 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.

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 Th17 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, SGK1, PRKCQ, JAK3, ULK2, HIPK2, PTPRJ, INPP1, TNK2, PCTK1, DUSP1, NUDT4, TGFBR1, PTP4A1, HK1, DUSP16, ANP32A, DDR1, ITK, WNK1, NAGK, STK38, BMP2K, BUB1, AAK1, SIK1, DUSP10, PRKCA, PIM2, STK17B, TK2, STK39, ALPK2, MST4, PHLPP1, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., HK2, CDKN1A, DUT, DUSP1, NADK, LIMK2, DUSP11, TAOK3, PRPS1, PPP2R4, MKNK2, SGK1, BPGM, TEC, MAPK6, PTP4A2, PRPF4B, ACP1, CCRN4L, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., HK2, ZAP70, NEK6, DUSP14, SH2D1A, ITK, DUT, PPP1R11, DUSP1, PMVK, TK1, TAOK3, GMFG, PRPS1, SGK1, TXK, WNK1, DUSP19, TEC, RPS6KA1, PKM2, PRPF4B, ADRBK1, CKB, ULK2, PLK1, PPP2R5A, PLK2, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., ZAP70, PFKP, NEK6, DUSP14, SH2D1A, INPP5B, ITK, PFKL, PGK1, CDKN1A, DUT, PPP1R11, DUSP1, PMVK, PTPN22, PSPH, TK1, PGAM1, LIMK2, CLK1, DUSP11, TAOK3, RIOK2, GMFG, UCKL1, PRPS1, PPP2R4, MKNK2, DGKA, SGK1, TXK, WNK1, DUSP19, CHP, BPGM, PIP5K1A, TEC, MAP2K1, MAPK6, RPS6KA1, PTP4A2, PKM2, PRPF4B, ADRBK1, CKB, ACP1, ULK2, CCRN4L, PRKCH, PLK1, PPP2R5A, PLK2, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., CD200, CD40LG, CD24, CCND2, ADAM17, BSG, ITGAL, FAS, GPR65, SIGMAR1, CAP1, PLAUR, SRPRB, TRPV2, IL2RA, KDELR2, TNFRSF9, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., CTLA4, CD200, CD24, CD6L, CD9, IL2RB, CD53, CD74, CAST, CCR6, IL2RG, ITGAV, FAS, IL4R, PROCR, GPR65, TNFRSF18, RORA, IL1RN, RORC, CYSLTR1, PNRC2, LOC390243, ADAM10, TNFSF9, CD96, CD82, SLAMF7, CD27, PGRMC1, TRPV2, ADRBK1, TRAF6, IL2RA, THY1, IL12RB2, TNFRSF9, or any combination thereof.

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 Th17 differentiation, maintenance and/or function, e.g., CTLA4, TNFRSF4, CD44, PDCD1, CD200, CD247, CD24, CD6L, CCND2, CD9, IL2RB, CD53, CD74, ADAM17, 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, ADAM10, 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.

In some embodiments, the invention provides a method of inhibiting tumor growth in a subject in need thereof by administering to the subject a therapeutically effective amount of an inhibitor of Protein C Receptor (PROCR). In some embodiments, the inhibitor of PROCR is an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent. In some embodiments, the inhibitor of PROCR is one or more agents selected from the group consisting of lipopolysaccharide; cisplatin; fibrinogen; 1, 10-phenanthroline; 5-N-ethylcarboxamido adenosine; cystathionine; hirudin; phospholipid; Drotrecogin alfa; VEGF; Phosphatidylethanolamine; serine; gamma-carboxyglutamic acid; calcium; warfarin; endotoxin; curcumin; lipid; and nitric oxide.

In some embodiments, the invention provides a method of diagnosing an immune response in a subject, which may comprise 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.

In some embodiments, the invention provides a method of monitoring an immune response in a subject, which may comprise 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.

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

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

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

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

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

Accordingly, it is an object of the invention not to encompass within the invention any previously known product, process of making the product, or method of using the product such that Applicants reserve the right and hereby disclose a disclaimer of any previously known product, process, or method. It is further noted that the invention does not intend to encompass within the scope of the invention any product, process, or making of the product or method of using the product, which does not meet the written description and enablement requirements of the USPTO (35 U.S.C. § 112, first paragraph) or the EPO (Article 83 of the EPC), such that Applicants reserve the right and hereby disclose a disclaimer of any previously described product, process of making the product, or method of using the product. It may be advantageous in the practice of the invention to be in compliance with Art. 53(c) EPC and Rule 28(b) and (c) EPC. Nothing herein is to be construed as a promise.

It is noted that in this disclosure and particularly in the claims and/or paragraphs, terms such as “comprises”, “comprised”, “comprising” and the like can have the meaning attributed to it in U.S. Patent law; e.g., they can mean “includes”, “included”, “including”, and the like; and that terms such as “consisting essentially of” and “consists essentially of” have the meaning ascribed to them in U.S. Patent law, e.g., they allow for elements not explicitly recited, but exclude elements that are found in the prior art or that affect a basic or novel characteristic of the invention.

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

The following detailed description, given by way of example, but not intended to limit the invention solely to the specific embodiments described, may best be understood in conjunction with the accompanying drawings.

FIGS. 1A, 1B-1, 1B-2, 1C and 1D are a series of graphs and illustrations depicting genome wide temporal expression profiles of Th17 differentiation. FIG. 1A depicts an overview of approach. FIGS. 1B-1 and 1B-2 depict gene expression profiles during Th17 differentiation. Shown are the differential expression levels for genes (rows) at 18 time points (columns) in Th17 polarizing conditions (TGF-β1 and IL-6; left panel, Z-normalized per row) or Th17 polarizing conditions relative to control activated Th0 cells (right panel, log 2(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. FIG. 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. FIG. 1D depicts transcriptional profiles of key cytokines and receptor molecules. Shown are the differential expression levels (log 2(ratio)) for each gene (column) at each of 18 time points (rows) in Th17 polarizing conditions (TGF-β1 and IL-6; left panel, Z-normalized per row) vs. control activated Th0 cells.

FIGS. 2A, 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 Th17 differentiation. FIG. 2A depicts an overview of computational analysis. FIG. 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). FIG. 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. FIG. 1D 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 Th17 regulators (grey), and the maximal number of target genes across the three canonical networks (green, ranging from 0 to 250 targets). FIGS. 1E-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 Th0) during the respective time segment. Edge color reflects the data type supporting the regulatory interaction (legend).

FIGS. 3A, 3B, 3C and 3D are a series of graphs and illustrations depicting knockdown screen in Th17 differentiation using silicon nanowires. FIG. 3A 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 Th17 function (this information was not used to rank the genes; FIGS. 10A, 10B ). FIG. 3B depicts scanning electron micrograph of primary T cells (false colored purple) cultured on vertical silicon nanowires. FIG. 3C depicts delivery by silicon nanowire neither activates nor induces differentiation of naïve T cells and does not affect their response to conventional TCR stimulation with anti-CD3/CD28. FIG. 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 48 hrs after introduction of polarizing cytokines. In FIG. 3A and FIG. 2D , the candidate regulators shown are those listed in Table 5. In FIG. 3A , 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, FLI1, REL, SP4, EGR2, NFKB1, ZFP281, STAT4, RELA, TBX21, STATSB, IRF7, STAT2, IRF3, XBP1, FOXO1, PRDM1, ATF4, IRF1, GATA3, EGR1, MYC, CREB1, IRF9, IRF2, FOXJ2, SMARCA4, TRP53, SUZ12, POU2AF1, CEBPB, ID2, CREM, MYST4, MXI1, RBPJ, CHD7, CREB3L2, VAX2, KLF10, SKI, ELK3, ZEB1, PML, SERTAD1, NOTCH1, LRRFIP1, AHR, 1810007M14RIK, SAP30, ID1, ZFP238, VAV1, MINA, BATF3, CDYL, IKZF4, NCOA1, 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.

FIGS. 4A, 4B, 4C and 4D are a series of graphs and illustrations depicting coupled and mutually-antagonistic modules in the Th17 network. A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. FIG. 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 48 hr (as well as IL-21r and IL-17ra KO at 60 hours). Blue (left side of Fold change (log 2) scale): decreased expression of target following perturbation of a regulator (compared to a non-targeting control); red (right side of Fold change (log 2) 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. FIG. 4B depicts two coupled and opposing modules. Shown is the perturbation network associating the ‘positive regulators’ (blue nodes, left side of x-axis) of Th17 signature genes, the ‘negative regulators’ (red nodes, right side of x-axis), Th17 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 Th17 differentiation. FIG. 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 −log 10(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. FIG. 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. 1B (purple circle). The knockdown of a ‘Th17 positive’ regulator was expected to repress genes in induced clusters, and induce genes in repressed clusters (and vice versa for ‘Th17 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.

FIGS. 5A, 5B, 5C, and 5D are a series of graphs and illustrations depicting that Mina, Fas, Pou2af1, and Tsc22d3 are key novel regulators affecting the Th17 differentiation programs. A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. FIGS. 5A-5D , left: Shown are regulatory network models centered on different pivotal regulators (square nodes): ( FIG. 5A ) Mina, ( FIG. 5B ) Fas, ( FIG. 5C ) Pou2af1, 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 72 h of in vitro differentiated cells from respective KO mice activated in vitro with anti-CD3+anti-CD28 with or without Th17 polarizing cytokines (TGF-β+IL-6). ( FIG. 5D , middle) ChIP-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 Th17 canonical factors (X axis). All results are statistically significant (P<10 −6 ; see Methods in Example 1).

FIGS. 6A, 6B, 6C, and 6D are a series of graphs and illustrations depicting treatment of Naïve CD4+ T-cells with TGF-β1 and IL-6 for three days induces the differentiation of Th17 cells. A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. FIG. 6A depicts an overview of the time course experiments. Naïve T cells were isolated from WT mice, and treated with IL-6 and TGF-β1. Microarrays were then used to measure global mRNA levels at 18 different time points (0.5 hr-72 hr, see Methods in Example 1). As a control, the same WT naïve T cells under Th0 conditions harvested at the same 18 time points were used. For the last four time points (48 hr-72 hr), cells treated with IL-6, TGF-β1, and IL-23 were also profiled. FIG. 6B depicts generation of Th17 cells by IL-6 and TGF-β1 polarizing conditions. FACS analysis of naïve T cells differentiated with TGF-β1 and IL-6 (right) shows enrichment for IL-17 producing Th17 T cells; these cells are not observed in the Th0 controls. FIG. 6C depicts comparison of the obtained microarray profiles to published data from naïve T-cells and differentiated Th17 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 naïve T cell profiles (blue) or the differentiated Th17 profiles (green). The expression profiles gradually transition from a naïve-like state (at t=0.5 hr, r2>0.8, p<10 −10 ) to a Th17 differentiated state (at t=72 hr, r2>0.65, p<10 −10 ). FIG. 6D depicts expression of key cytokines. Shown are the mRNA levels (Y axis) as measured at each of the 18 time points (X axis) in the Th17 polarizing (blue) and Th0 control (red) conditions for the key Th17 genes RORc (left) and IL-17a (middle), both induced, and for the cytokine IFN-γ, unchanged in the time course.

FIG. 7 is a series of graphs depicting clusters of differentially expressed genes in the Th17 time course data. A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. For each of the 20 clusters in FIG. 1B shown are the average expression levels (Y axis, ±standard deviation, error bars) at each time point (X axis) under Th17 polarizing (blue) and Th0 (red) conditions. The cluster size (“n”), enriched functional annotations (“F”), and representative member genes (“M”) are denoted on top.

FIGS. 8A and 8B are a series of graphs depicting transcriptional effects of IL-23. FIG. 8A depicts transcriptional profiles of key genes. A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. Shown are the expression levels (Y axis) of three key genes (IL-22, RORc, IL-4) at each time point (X axis) in Th17 polarizing conditions (blue), Th0 controls (red), and following the addition of IL-23 (beginning at 48 hr post differentiation) to the Th17 polarizing conditions (green). FIG. 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 Th17 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.

FIGS. 9A and 9B are a series of graphs depicting predicted and validated protein levels of ROR-γt during Th17 differentiation. A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. FIG. 9A shows RORγt mRNA levels along the original time course under Th17 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 Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342, doi:10.1038/nature10098 (2011)) that predicts the level of RORγt protein at each time point (red). FIG. 9B depicts distribution of measured ROR-γt protein levels (x axis) as determined by FACS analysis in Th17 polarizing conditions (blue) and Th0 conditions (red) at 4, 12, 24, and 48 hr post stimulation.

FIGS. 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 Th17 factors for different ( FIG. 10A ) network-based features and ( FIG. 10B ) expression-base features (as used in FIG. 3A ).

FIGS. 11A, 11B, and 11C are a series of graphs depicting Nanowire activation on T-cells, knockdown at 10 h, and consistency of NW-based knockdowns and resulting phenotypes. FIG. 11A 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 48 hr 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 Th17 polarizing cytokines (‘TGF-β1+IL6’). FIG. 11B 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. FIG. 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.

FIGS. 12A and 12B are a series of graphs depicting cross-validation of the Nanostring expression profiles for each nanowire-delivered knockdown using Fluidigm 96×96 gene expression chips. FIG. 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. FIG. 12B depicts how analysis of Fluidigm data recapitulates the partitioning of targeted factors into two modules of positive and negative Th17 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).

FIG. 13 is a graph depicting rewiring of the Th17 “functional” network between 10 hr to 48 hr post stimulation. For each regulator that was profiled at 10 hr and 48 hr, 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).

FIG. 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 Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. 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. Bioinformatics 22, 1152-1153, doi:10.1093/bioinformatics/bt1038 (2006)).

FIGS. 15A, 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 Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. FIG. 15A depicts a correlation matrix of knockdown profiles. Shown is the Spearman rank correlation coefficient between the RNA-Seq profiles (fold change relative to NT siRNA controls) of regulators perturbed by knockdowns. Genes that were not significantly differentially expressed in any of the samples were excluded from the profiles. FIG. 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 Th17-positive regulators (left) and Th17-negative regulators (right). FIG. 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 Th17-positive regulators strongly induces the expression of genes in the ‘Foxp3’ Cluster 1. The knockdown profiles are hierarchically clustered, forming distinct clusters for Th17-positive regulators (left) and Th17-negative 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.

FIGS. 16A, 16B, 16C, and 16D are a series of graphs depicting quantification of cytokine production in knockout cells at 72 h of in-vitro differentiation using Flow cytometry and Enzyme-linked immunosorbent assay (ELISA). All flow cytometry figures shown, except for Oct1, are representative of at least 3 repeats, and all ELISA data has at least 3 replicates. For Oct1, only a limited amount of cells were available from reconstituted mice, allowing for only 2 repeats of the Oct1 deficient mouse for flow cytometry and ELISA. ( FIG. 16A , left) Mina −/− T cells activated under Th0 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. FIG. 15B depicts intracellular cytokine staining of Pou2af1 −/− 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 Oct1 −/− and WT cells for IFN-γ and IL-17a, showing an increase in IFN-γ positive cells in the Th0 condition for the Oct1 deficient mouse. ( FIG. 15D , right) Il-17a, IFN-γ, IL-2 and TNF production by Oct1 −/− 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.

FIGS. 17A and 17B are a series of illustrations depicting that Zeb1, Smarca4, and Sp4 are key novel regulators affecting the Th17 differentiation programs. A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. Shown are regulatory network models centered on different pivotal regulators (square nodes): ( FIG. 17A ) Zeb1 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).

FIG. 18 is a graph depicting the overlap with ChIP-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 ChIP-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 ChIP-seq based network of Ciofani et al. (blue) and to their combined ChIP-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 ChIP-seq only or with the combined ChIP-seq/RNA-seq network) is below 10 −1 ° (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.

FIGS. 19A, 19B, 19C, and 19D are a series of graphs depicting that PROCR is specifically induced in Th17 cells induced by TGF-β1 with IL-6. FIG. 19A depicts how PROCR expression level was assessed by the microarray analysis under Th0 and Th17 conditions at 18 different time points. FIG. 19B depicts how kinetic expression of PROCR mRNA was measured by quantitative RT-PCR analysis in Th17 cells differentiated with TGF-β1 and IL-6. FIG. 19C depicts how PROCR mRNA expression was measured by quantitative RT-PCR analysis in different T cell subsets 72 hr after stimulation by each cytokine. FIG. 19D depicts how PROCR protein expression was examined by flow cytometry in different T cell subsets 72 hr after stimulation with each cytokine.

FIGS. 20A, 20B, 20C, and 20D are a series of graphs depicting that PROCR stimulation and expression is not essential for cytokine production from Th17 cells. FIG. 20A depicts how naïve CD4+ T cells were differentiated into Th17 cells by anti-CD3/anti-CD28 stimulation in the presence of activated protein C (aPC, 300 nM), the ligand of PROCR. On day 3, cells were stimulated with PMA and Ionomycin for 4 hr, stained intracellularly for IFN-γ and IL-17 and analyzed by flow cytometry. FIG. 20B depicts IL-17 production from Th17 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. FIG. 20C depicts how naïve CD4+ T cells were polarized under Th17 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. FIG. 20D depicts how naïve CD4+ T cells from EPCR δ/δmice and control mice were polarized under Th17 conditions with TGF-β1 and IL-6. Intracellular expression of IFN-γ and IL-17 were assessed by flow cytometry.

FIGS. 21A and 21B are a series of graphs depicting that PROCR expression only induces minor changes in the expression of co-stimulatory molecules on Th17 cells. FIG. 21A depicts how naïve CD4 + T cells were polarized under Th17 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. FIG. 21B depicts how naïve wild type (WT) or EPCR δ/δ CD4 + T cells were differentiated into Th17 cells by anti-CD3/anti-CD28 stimulation in the presence of TGF-β1 and IL-6. Expression of ICOS, CTLA-4, PD-1, Pdp and Tim-3 was assessed by flow cytometry.

FIGS. 22A, 22B, and 22C are a series of graphs depicting that PROCR is expressed in non-pathogenic Th17 cells. FIG. 22A depicts genes for Th17 cells differentiated with TGF-β3+IL-6 (pathogenic) or TGF-β1+IL-6 (non-pathogenic) and comparison of their expression levels in these two subsets. FIGS. 22B and 22C depict how naïve CD4 + T cells were differentiated with TGF-β1 and IL-6, TGF-β3 and IL-6 or IL-1β 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.

FIGS. 23A, 23B, and 23C are a series of graphs depicting that PROCR stimulation or expression impairs some pathogenic signature genes in Th17 cells. FIG. 23A depicts quantitative RT-PCR analysis of mRNA expression of several pathogenic signature genes in Th17 cells differentiated with TGFβ1 and IL-6 in the presence of activated protein C (aPC) for 3 days in vitro. FIG. 23B depicts quantitative RT-PCR analysis of mRNA expression of several pathogenic signature genes in naïve CD4 + T cells polarized under Th17 conditions, transduced with either GFP control retrovirus (Control RV) or PROCR-expressing retrovirus (PROCR RV) for 3 days. FIG. 23C depicts quantitative RT-PCR analysis of mRNA expression of several pathogenic signature genes in Th17 cells from EPCR δ/δmice and control mice differentiated with TGFβ1 and IL-6 for 3 days in vitro.

FIGS. 24A, 24B, 24C, and 24D are a series of graphs depicting that Rorγt induces PROCR expression under Th17 conditions polarized with TGF-β1 and IL-6. FIG. 24A depicts ChIP-Seq of Rorγt. The PROCR genomic region is depicted. FIG. 24B depicts how the binding of Rorγt to the Procr promoter in Th17 cells was assessed by chromatin immunoprecipitation (ChIP). ChIP was performed using digested chromatin from Th17 cells and anti-Rorγt antibody. DNA was analyzed by quantitative RT-PCR analysis.

FIG. 24C depicts how naïve CD4+ T cells from Roγt−/− mice and control mice were polarized under Th17 conditions with TGF-β1 and IL-6 and under Th0 conditions (no cytokines) and PROCR expression was analyzed on day 3 by flow cytometry. FIG. 24D depicts how naïve CD4+ T cells polarized under Th17 conditions were transduced with either GFP control retrovirus (Ctl RV) or Roγt-expressing retrovirus (Rorγt RV) for 3 days. PROCR mRNA expression was measured by quantitative RT-PCR analysis and PROCR protein expression was assessed by flow cytometry.

FIGS. 25A, 25B, and 25C are a series of graphs depicting that IRF4 and STAT3 bind to the Procr promoter and induce PROCR expression. FIG. 25A depicts how binding of IRF4 or STAT3 to the Procr promoter was assessed by chromatin immunoprecipitation (ChIP)-PCR. ChIP was performed using digested chromatin from Th17 cells and anti-IRF4 or anti-STAT3 antibody. DNA was analyzed by quantitative RT-PCR analysis. FIG. 25B depicts how naïve CD4+ T cells from Cd4 Cre STAT3 fl/fl mice (STAT3 KO) and control mice (WT) were polarized under Th17 conditions with TGF-β1 with IL-6 and under Th0 condition with no cytokines. On day 3, PROCR expression was determined by quantitative PCR. FIG. 25C depicts how naïve CD4+ T cells from Cd4 Cre IRF4 fl/fl mice and control mice were polarized under Th17 conditions with TGF-β1 and IL-6 and under Th0 condition with no cytokines. On day 3, PROCR expression was determined by flow cytometry.

FIGS. 26A, 26B, 26C, and 26D are a series of graphs and illustrations depicting that PROCR deficiency exacerbates EAE severity. FIG. 26A depicts frequency of CD4+ T cells expressing IL-17 and PROCR isolated from EAE mice 21d after immunization with MOG 35-55 . FIG. 26B depicts how EAE was induced by adoptive transfer of MOG 35-55 -specific 2D2 cells transduced with a control retrovirus (Ctl_GFP) or a PROCR-expression retrovirus (PROCR_RV) and differentiated into Th17 cells. Mean clinical scores and summaries for each group are shown. Results are representative of one of two experiments. FIG. 26C depicts how Rag1−/− mice were reconstituted with either PROCR-deficient (EPCR δ/δRag1−/−) or WT T cells (WT Rag1−/−) and immunized with MOG 35-55 to induce EAE. The mean clinical score of each group is shown. Results are representative of one of two experiments. FIG. 26D depicts a schematic representation of PROCR regulation. Rorγt, 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.

FIGS. 27A, 27B, and 27C are a series of graphs depicting that FAS promotes Th17 differentiation. Naïve CD4 + T cells from wild type (WT) or FAS-deficient (LPR) mice were differentiated into Th17 cells by anti-CD3/anti-CD28 stimulation in the presence of IL-1β, IL-6 and IL-23. On day 4, cells were ( FIG. 27A ) stimulated with PMA and Ionomycin for 4 hr, stained intracellularly for IFN-γ and IL-17 and analyzed by flow cytometry and ( FIG. 27B ) IL-17 production was assessed by ELISA. FIG. 27C depicts how RNA was extracted and expression of IL17a and Il23r mRNA was determined by quantitative PCR.

FIGS. 28A, 28B, and 28C are a series of graphs depicting that FAS inhibits Th1 differentiation. Naïve CD4 + T cells from wild type (WT) or FAS-deficient (LPR) mice were differentiated into Th1 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 4 hr, stained intracellularly for IFN-γ and IL-17 and analyzed by flow cytometry and ( FIG. 28B ) IFN-γ production was assessed by ELISA. FIG. 28C depicts how RNA was extracted and expression of Ifng mRNA was determined by quantitative PCR.

FIGS. 29A and 29B are a series of graphs depicting that FAS inhibits Treg differentiation. Naïve 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 4 hr, stained intracellularly for IL-17 and Foxp3 and analyzed by flow cytometry and ( FIG. 29B ) IL-10 production was assessed by ELISA.

FIGS. 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 100 μg MOG 35-55 in CFA s.c. and received pertussis toxin i.v. to induce EAE. FIG. 30A depicts mean clinical score±s.e.m. of each group as shown. FIG. 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.

FIGS. 31A, 31B, 31C and 31D are a series of graphs and illustrations depicting that PROCR is expressed on Th17 cells. FIG. 31A depicts a schematic representation of PROCR, its ligand activated protein C and the signaling adapter PAR1. FIG. 31B depicts how naïve 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. FIG. 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. FIG. 31D depicts how naïve and memory cells were isolated from WT and PROCRd/d mice and stimulated with anti-CD3/CD28. Naïve cells were cultured under Th17 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.

FIGS. 32A, 32B, 32C and 32D are a series of graphs depicting how PROCR and PD-1 expression affects Th17 pathogenicity. FIG. 32A depicts signature genes of pathogenic and non-pathogenic Th17 cells. Naïve CD4+ T cells were differentiated into non-pathogenic (TGFβ+IL-6) or pathogenic (TGFβ3+IL-6 or IL-β+IL-6) Th17 cells and PROCR expression was determined by quantitative PCR. FIG. 32B depicts how naïve WT or PROCRd/d CD4+ T cells were stimulated under Th17 polarizing conditions (TGFβ+IL-6) in the presence or absence of aPC. Quantitative expression of three pathogenic signature genes was determined on day 3. FIG. 32C depicts how naïve 2D2 T cells were transduced with a retrovirus encoding for PROCR or a control (GFP), differentiated into Th17 cells in vitro, and transferred into naïve recipients. Mice were monitored for EAE. FIG. 32D depicts how naïve 2D2 T cells were differentiated into Th17 cells in vitro with TGFβ1+IL-6+IL-23 and transferred into WT or PD-L1−/− recipients. Mice were monitored for EAE.

FIGS. 33A and 33B are a series of graphs depicting that PROCR expression is enriched in exhausted T cells. FIG. 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 naïve mice. FIG. 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.

FIG. 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 5×10 5 B16F10 melanoma cells.

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

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

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

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

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

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

The methods provided herein combine transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing perturbations in primary T cells to systematically derive and experimentally validate a model of the dynamic regulatory network that controls Th17 differentiation. See e.g., Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981, the contents of which are hereby incorporated by reference in their entirety. The network consists of two self-reinforcing, but mutually antagonistic, modules, with novel regulators, whose coupled action may be essential for maintaining the level and/or balance between Th17 and other CD4+ T cell subsets. Overall, 9,159 interactions between 71 regulators and 1,266 genes were active in at least one network; 46 of the 71 are novel. The examples provided herein identify and validate 39 regulatory factors, embedding them within a comprehensive temporal network and reveals its organizational principles, and highlights novel drug targets for controlling Th17 differentiation.

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

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 Th17 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. Th17 differentiation from naïve T-cells can be triggered in vitro by the cytokines TGF-β1 and IL-6. While TGF-β1 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 Th17 differentiation (Bettelli et al., Nat Immunol 2007).

While TGF-β1 is required for the induction of Foxp3+ induced Tregs (iTregs), the presence of IL-6 inhibits the generation of iTregs and initiates the Th17 differentiation program. This led to the hypothesis that a reciprocal relationship between pathogenic Th17 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-γt (in Th17 cells) and Foxp3 (in Treg cells) (Zhou et al., Nature 2008). Other cytokine combinations have also been shown to induce ROR-γt and differentiation into Th17 cells, in particular TGF-β1 and IL-21 or IL-1β, TGF-β3+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 Th17 cells, exposure to IL-23 is critical for both stabilizing the Th17 phenotype and the induction of pathogenic effector functions in Th17 cells.

Much remains unknown about the regulatory network that controls Th17 cells (O'Shea, J. et al. Signal transduction and Th17 cell differentiation. Microbes Infect 11, 599-611 (2009); Zhou, L. & Littman, D. Transcriptional regulatory networks in Th17 cell differentiation. Curr Opin Immunol 21, 146-152 (2009)). Developmentally, as TGF-β is required for both Th17 and iTreg differentiation, it is not understood how balance is achieved between them or how IL-6 biases toward Th17 differentiation (Bettelli et al., Nat Immunol 2007). Functionally, it is unclear how the pro-inflammatory status of Th17 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 Th17 remain unknown (Korn, T., Bettelli, E., Oukka, M. & Kuchroo, V. K. IL-17 and Th17 Cells. Annu Rev Immunol 27, 485-517, doi:10.1146/annurev.immuno1.021908.13271010.1146/annurev.immuno1.021908. 132710 [pii] (2009)).

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.1721 (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. Th17 differentiation, however, spans several days, during which the components and wiring of the regulatory network likely change. Furthermore, naïve T cells and Th17 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.

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. 1A ) to construct and validate the transcriptional network of Th17 differentiation. Using genome-wide profiles of mRNA expression levels during differentiation, a model of the dynamic regulatory circuit that controls Th17 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 naïve 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 Th17 development. The reconstructed model is organized into two coupled, antagonistic, and densely intra-connected modules, one promoting and the other suppressing the Th17 program. The model highlights 12 novel regulators, whose function was further characterized by their effects on global gene expression, DNA binding profiles, or Th17 differentiation in knockout mice. The studies provided herein demonstrate an unbiased systematic and functional approach to understanding the development of the Th17 T cell subset.

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 Th17 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/nature11582 (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.

It was found that the Th17 regulators are organized into two tightly coupled, self-reinforcing but mutually antagonistic modules, whose coordinated action may explain how the balance between Th17, Treg, and other effector T cell subsets is maintained, and how progressive directional differentiation of Th17 cells is achieved. Within the two modules are 12 novel factors ( FIGS. 4 and 5 ), which were further characterized, highlighting four of the factors (others are in FIGS. 17A, 17B ).

This validated model highlights at least 12 novel regulators that either positively or negatively impact the Th17 program ( FIGS. 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 Th17, Treg, and other effector T cells is maintained, and how progressive directional differentiation of Th17 cells is achieved while repressing differentiation of other T cell subsets. The function of four of the 12 regulators—Mina, Fas, Pou2af1, and Tsc22d3—was further validated and characterized by undertaking Th17 differentiation of T cells from corresponding knockout mice or with ChIP-Seq binding profiles.

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

In some embodiments, the target gene is one or more Th17-associated cytokine(s) or receptor molecule(s) selected from those listed in Table 3. In some embodiments, the target gene is one or more Th17-associated transcription regulator(s) selected from those shown in Table 4.

In some embodiments, the target gene is one or more Th17-associated transcription regulator(s) selected from those shown in Table 5. In some embodiments, the target gene is one or more Th17-associated receptor molecule(s) selected from those listed in Table 6. In some embodiments, the target gene is one or more Th17-associated kinase(s) selected from those listed in Table 7. In some embodiments, the target gene is one or more Th17-associated signaling molecule(s) selected from those listed in Table 8. In some embodiments, the target gene is one or more Th17-associated receptor molecule(s) selected from those listed in Table 9.

Among the novel ‘Th17 positive’ factors is the zinc finger E-box binding homeobox 1 Zeb1, which is early-induced and sustained in the Th17 time course ( FIG. 17A ), analogous to the expression of many known key Th17 factors. Zeb1 knockdown decreases the expression of Th17 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 Th17 cells by ROR-γt, Batf and Stat3, and is down-regulated in cells from Stat3 knockout mice ( FIG. 17A ). Interestingly, Zeb1 is known to interact with the chromatin factor Smarca4/Brg1 to repress the E-cadherin promoter in epithelial cells and induce an epithelial-mesenchymal transition (Sánchez-Tilló, 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 ( FIGS. 2 d,e ) and a member of the ‘positive module’ ( FIG. 4B ). Although it is not differentially expressed in the Th17 time course, it is bound by Batf, Irf4 and Stat3 (positive regulators of Th17), 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 Th1 cells (Zhang, F. & Boothby, M. T helper type 1-specific Brg1 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 Zeb1, positive regulates Th17 cells and is essential for IL-17 expression.

Conversely, among the novel ‘Th17 negative’ factors is Sp4, an early-induced gene, predicted in the model as a regulator of ROR-γt and as a target of ROR-γt, Batf, Irf4, Stat3 and Smarca4 ( FIG. 17B ). Sp4 knockdown results in an increase in ROR-γt expression at 48 h, and an overall stronger and “cleaner” Th17 differentiation as reflected by an increase in the expression of Th17 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.

These novel and known regulatory factors act coordinately to orchestrate intra- and intermodules interactions and to promote progressive differentiation of Th17 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 Zeb1 leads to decrease in Mina (due to all-positive interactions between Th17 ‘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 Th17 transcriptional program: e.g. knock-down of Mina has substantial effects on the progression of the Th17 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 Th17 cells from entering into the late phase of differentiation by inhibiting the cytokine signaling (C19; p<10 −7 ) and haematopoiesis (C20; p<10 −3 ) clusters, which include Ahr, Batf, ROR-γt, etc. These findings emphasize the power of large-scale functional perturbation studies in understanding the action of complex molecular circuits that govern Th17 differentiation.

In a recent work, Ciofani et al. (Ciofani, M. et al. A Validated Regulatory Network for Th17 Cell Specification. Cell, doi:10.1016/j.cell.2012.09.016 (2012)) systematically ranked Th17 regulators based on ChIPSeq data for known key factors and transcriptional profiles in wild type and knockout cells. While their network centered on known core Th17 TFs, the complementary approach presented herein perturbed many genes in a physiologically meaningful setting. Reassuringly, their core Th17 network significantly overlaps with the computationally inferred model ( FIG. 18 ).

The wiring of the positive and negative modules ( FIGS. 4 and 5 ) uncovers some of the functional logic of the Th17 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-1-IRF Complexes. Science, doi:10.1126/science.1228309 (2012)) or protein-protein interactions (Wu, C., Yosef, N. & Thalhamer, T. SGK1 kinase regulates Th17 cells maintenance through IL-23 signaling pathway. (Submitted)). The regulators identified are compelling new targets for regulating the Th17/Tregs balance and for switching pathogenic Th17 into non-pathogenic ones.

Automated Procedure for Selection of Signature Genes

The invention also provides methods of determining gene signatures that are useful in various therapeutic and/or diagnostic indications. The goal of these methods is to select a small signature of genes that will be informative with respect to a process of interest. The basic concept is that different types of information can entail different partitions of the “space” of the entire genome (>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.

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

An exemplary embodiment of this procedure is the selection of the 275-gene signature (Table 1), which combined several criteria to reflect as many aspect of the differentiation program as was possible. The following requirements were defined: (1) the signature must include all of the TFs that belong to a Th17 microarray signature (comparing to other CD4+ T cells, see e.g., Wei et al., in Immunity vol. 30 155-167 (2009)), see Methods 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

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

Exemplary signatures are shown in Tables 1 and 2 and are collectively referred to herein as, inter alia, “Th17-associated genes,” “Th17-associated nucleic acids,” “signature genes,” or “signature nucleic acids.”

These signatures are useful in methods of diagnosing, prognosing and/or staging an immune response in a subject by detecting a first level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes selected from those listed in Table 1 or 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.

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.

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.

The present invention also may comprise 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, radiolabels, among others. Instructions (e.g., written, tape, VCR, CD-ROM, etc.) for carrying out the assay may be included in the kit. The assay may for example be in the form of a Northern hybridization or DNA chips or a sandwich ELISA or any other method as known in the art. Alternatively, the kit contains a nucleic acid substrate array which may comprise one or more nucleic acid sequences.

Use of T Cell Modulating Agents

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined in the appended claims.

The present invention will be further illustrated in the following Examples which are given for illustration purposes only and are not intended to limit the invention in any way.