US20200081017A1

US20200081017A1 - T cell balance gene expression, compositions of matters and methods of use thereof

Info

Publication number: US20200081017A1
Application number: US16/675,398
Authority: US
Inventors: Vijay K. Kuchroo; Aviv Regev; Jellert Gaublomme; Youjin LEE; Alexander K. Shalek; Chao Wang; Nir Yosef; Hongkun Park
Original assignee: Harvard College; Brigham and Womens Hospital Inc; Massachusetts Institute of Technology; Broad Institute Inc
Current assignee: Harvard College; Brigham and Womens Hospital Inc; Massachusetts Institute of Technology; Broad Institute Inc
Priority date: 2014-02-27
Filing date: 2019-11-06
Publication date: 2020-03-12
Also published as: WO2015130968A3; EP3110971A2; EP3110971B1; JP2017508457A; CA2940653A1; US20160377631A1; EP3514246B1; AU2015222944A1; US11209440B2; WO2015130968A2; CN106817904A; EP3514246A1

Abstract

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

Description

RELATED APPLICATIONS AND INCORPORATION BY REFERENCE

This application is a is a continuation of prior U.S. patent application Ser. No. 15/245,748 filed Aug. 24, 2016, which is a continuation-in-part of International Application Number PCT/US15/17826 filed on Feb. 26, 2015, which published as PCT Publication Number WO2015/130968 on Sep. 3, 2015 and, which claims priority from U.S. Provisional Patent Application 61/945,641, filed Feb. 27, 2014, incorporated herein by reference. Reference is made to WO/2012/048265; WO/2014/145631; WO/2014/134351. The foregoing applications, and all documents cited therein or during prosecution (“appln cited documents”) and all documents cited or referenced in the appln cited documents, and all documents cited or referenced herein (“herein cited documents”), and all documents cited or referenced in herein cited documents, together with any manufacturer's instructions, descriptions, product specifications, and product sheets for any products mentioned herein or in any document incorporated by reference herein, are hereby incorporated herein by reference, and may be employed in the practice of the invention. Appln cited documents, herein cited documents, all documents herein referenced or cited, and all documents indicated to be incorporated herein by reference, are incorporated by reference to the same extent as if each individual document was specifically and individually set forth herein in full and indicated to be incorporated by reference when or where cited or referenced.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

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

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by references in its entirety. Said ASCII copy, created on Aug. 23, 2016 is named 46783992100_SL.txt and is 324,708 bytes in size.

FIELD OF THE INVENTION

BACKGROUND OF THE INVENTION

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

SUMMARY OF THE INVENTION

The invention has many utilities. The invention pertains to and includes methods and compositions therefrom of Drug Discovery, as well as for detecting patients or subjects who may or may not respond or be responding to a particular treatment, therapy, compound, drug or combination of drugs or compounds; and accordingly ascertaining which drug or combination of drugs may provide a particular treatment or therapy as to a condition or disease or infection or infectious state, as well as methods and compositions for selecting patient populations (e.g., by detecting those who may or may not respond or be responding), or methods and compositions involving personalized treatment—a combination of Drug Discovery and detecting patients or subjects who may not respond or be responding to a particular treatment, therapy, compound, drug or combination of drugs or compounds (e.g., by as to individual(s), so detecting response, nor responding, potential to respond or not, and adjusting particular treatment, therapy, compound, drug or combination of drugs or compounds to be administered or administering a treatment, therapy, compound, drug or combination of drugs or compounds indicated from the detecting).
The invention provides compositions and methods for modulating T cell balance, e.g., Th17 cell differentiation, maintenance and function, and means for exploiting this network in a variety of therapeutic and diagnostic methods. 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, 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, STAT5B, 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, STAT5B, 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, STAT5B, 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, STAT5B, CHD7, SATB1, RUNX1, BATF, RORC, SP4 IRF4, one or more of the target genes listed in Table 5 as being associated with the late stage of Th17 differentiation, maintenance and/or function, e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, ATF3, ATF4, BATF, BATF3, BCL11B, BCL3, BCL6, C21ORF66, 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, STAT5B, STATE, 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, CCR8, 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, DU5P14, 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, CD5L, 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, CD5L, 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 target gene is one or more target genes that is a promoter of Th17 cell differentiation. In some embodiments, the target gene is GPR65. In some embodiments, the target gene is also a promoter of pathogenic Th17 cell differentiation and is selected from the group consisting of CD5L, DEC1, PLZP and TCF4.
In some embodiments, the target gene is one or more target genes that is a promoter of pathogenic Th17 cell differentiation. In some embodiments, the target gene is selected from the group consisting of CD5L, DEC1, PLZP and TCF4.
The desired gene or combination of target genes is selected, and after determining whether the selected target gene(s) is overexpressed or under-expressed during Th17 differentiation and/or Th17 maintenance, a suitable antagonist or agonist is used depending on the desired differentiation, maintenance and/or function outcome. For example, for target genes that are identified as positive regulators of Th17 differentiation, use of an antagonist that interacts with those target genes will shift differentiation away from the Th17 phenotype, while use of an agonist that interacts with those target genes will shift differentiation toward the Th17 phenotype. For target genes that are identified as negative regulators of Th17 differentiation, use of an antagonist that interacts with those target genes will shift differentiation toward from the Th17 phenotype, while use of an agonist that interacts with those target genes will shift differentiation away the Th17 phenotype. For example, for target genes that are identified as positive regulators of Th17 maintenance, use of an antagonist that interacts with those target genes will reduce the number of cells with the Th17 phenotype, while use of an agonist that interacts with those target genes will increase the number of cells with the Th17 phenotype. For target genes that are identified as negative regulators of Th17 differentiation, use of an antagonist that interacts with those target genes will increase the number of cells with the Th17 phenotype, while use of an agonist that interacts with those target genes will reduce the number of cells with the Th17 phenotype. Suitable T cell modulating agents include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent.
In some embodiments, the positive regulator of Th17 differentiation is a target gene selected from MINA, TRPS1, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3, and combinations thereof. In some embodiments, the positive regulator of Th17 differentiation is a target gene selected from MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS and combinations thereof.
In some embodiments, the negative regulator of Th17 differentiation is a target gene selected from SP4, ETS2, IKZF4, TSC22D3, IRF1 and combinations thereof. In some embodiments, the negative regulator of Th17 differentiation is a target gene selected from SP4, IKZF4, TSC22D3 and combinations thereof.
In some embodiments, the T cell modulating agent is a soluble Fas polypeptide or a polypeptide derived from FAS. In some embodiments, the T cell modulating agent is an agent that enhances or otherwise increases the expression, activity, and/or function of FAS in Th17 cells. As shown herein, expression of FAS in T cell populations induced or otherwise influenced differentiation toward Th17 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, these T cell modulating agents are useful in the treatment of an infectious disease or other pathogen-based disorders. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.
In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of FAS. Inhibition of FAS expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, these T cell modulating agents are useful in the treatment of autoimmune diseases such as psoriasis, inflammatory bowel disease (IBD), ankylosing spondylitis, multiple sclerosis, Sjögren's syndrome, uveitis, and rheumatoid arthritis, asthma, systemic lupus erythematosus, transplant rejection including allograft rejection, and combinations thereof. In addition, enhancement of Th17 cells is also useful for clearing fungal infections and extracellular pathogens. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells that express additional cytokines. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.
In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of CCR5. Inhibition of CCR5 expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, the T cell modulating agent is an inhibitor or neutralizing agent. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.
In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of CCR6. Inhibition of CCR6 expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.
In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of EGR1. Inhibition of EGR1 expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.
In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of EGR2. Inhibition of EGR2 expression, activity and/or function in T cell populations repressed or otherwise influenced differentiation away from Th17 cells and/or induced or otherwise influenced differentiation toward regulatory T cells (Tregs) and towards Th1 cells. In some embodiments, these T cell modulating agents are useful in the treatment of an immune response, for example, an autoimmune response or an inflammatory response. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cells are naïve T cells. In some embodiments, the T cells are differentiated T cells. In some embodiments, the T cells are partially differentiated T cells. In some embodiments, the T cells are a mixture of naïve T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells and partially differentiated T cells. In some embodiments, the T cells are mixture of partially differentiated T cells and differentiated T cells. In some embodiments, the T cells are mixture of naïve T cells, partially differentiated T cells, and differentiated T cells.
For example, in some embodiments, the invention provides T cell modulating agents and methods of using these T cell modulating agents to regulate, influence or otherwise impact the phenotype of a Th17 cell or population of cells, for example, by influencing a naïve T cell or population of cells to differentiate to a pathogenic or non-pathogenic Th17 cell or population of cells, by causing a pathogenic Th17 cell or population of cells to switch to a non-pathogenic Th17 cell or population of T cells (e.g., populations of naïve T cells, partially differentiated T cells, differentiated T cells and combinations thereof), or by causing a non-pathogenic Th17 cell or population of T cells (e.g., populations of naïve T cells, partially differentiated T cells, differentiated T cells and combinations thereof) to switch to a pathogenic Th17 cell or population of cells.
In some embodiments, the invention comprises a method of drug discovery for the treatment of a disease or condition involving an immune response involving T cell balance in a population of cells or tissue of a target gene comprising the steps of providing a compound or plurality of compounds to be screened for their efficacy in the treatment of said disease or condition, contacting said compound or plurality of compounds with said population of cells or tissue, detecting a first level of expression, activity and/or function of a target gene, comparing the detected level to a control of level of a target gene, and evaluating the difference between the detected level and the control level to determine the immune response elicited by said compound or plurality of compounds. For example, the method contemplates comparing tissue samples which can be inter alia infected tissue, inflamed tissue, healthy tissue, or combinations of tissue samples thereof.
In one embodiment of the invention, the reductase null animals of the present invention may advantageously be used to modulate T cell balance in a tissue or cell specific manner. Such animals may be used for the applications hereinbefore described, where the role of T cell balance in product/drug metabolism, detoxification, normal homeostasis or in disease etiology is to be studied. It is envisaged that this embodiment will also allow other effects, such as drug transporter-mediated effects, to be studied in those tissues or cells in the absence of metabolism, e.g., carbon metabolism. Accordingly the animals of the present invention, in a further aspect of the invention may be used to modulate the functions and antibodies in any of the above cell types to generate a disease model or a model for product/drug discovery or a model to verify or assess functions of T cell balance
In another embodiment, the method contemplates use of animal tissues and/or a population of cells derived therefrom of the present invention as an in vitro assay for the study of any one or more of the following events/parameters: (i) role of transporters in product uptake and efflux; (ii) identification of product metabolites produced by T cells; (iii) evaluate whether candidate products are T cells; or (iv) assess drug/drug interactions due to T cell balance.
The terms “pathogenic” or “non-pathogenic” as used herein are not to be construed as implying that one Th17 cell phenotype is more desirable than the other. As described herein, there are instances in which inhibiting the induction of pathogenic Th17 cells or modulating the Th17 phenotype towards the non-pathogenic Th17 phenotype is desirable. Likewise, there are instances where inhibiting the induction of non-pathogenic Th17 cells or modulating the Th17 phenotype towards the pathogenic Th17 phenotype is desirable.
As used herein, terms such as “pathogenic Th17 cell” and/or “pathogenic Th17 phenotype” and all grammatical variations thereof refer to Th17 cells that, when induced in the presence of TGF-β3, express an elevated level of one or more genes selected from Cxcl3, IL22, IL3, Ccl4, Gzmb, Lrmp, Ccl5, Casp1, Csf2, Ccl3, Tbx21, Icos, IL17r, Stat4, Lgals3 and Lag, as compared to the level of expression in a TGF-β3-induced Th17 cells. As used herein, terms such as “non-pathogenic Th17 cell” and/or “non-pathogenic Th17 phenotype” and all grammatical variations thereof refer to Th17 cells that, when induced in the presence of TGF-β3, express a decreased level of one or more genes selected from IL6st, IL1rn, Ikzf3, Maf, Ahr, IL9 and IL10, as compared to the level of expression in a TGF-β3-induced Th17 cells.
In some embodiments, the T cell modulating agent is an agent that enhances or otherwise increases the expression, activity and/or function of Protein C Receptor (PROCR, also called EPCR or CD201) in Th17 cells. As shown herein, expression of PROCR in Th17 cells reduced the pathogenicity of the Th17 cells, for example, by switching Th17 cells from a pathogenic to non-pathogenic signature. Thus, PROCR and/or these agonists of PROCR are useful in the treatment of a variety of indications, particularly in the treatment of aberrant immune response, for example in autoimmune diseases and/or inflammatory disorders. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist.
In some embodiments, the T cell modulating agent is an agent that inhibits the expression, activity and/or function of the Protein C Receptor (PROCR, also called EPCR or CD201). Inhibition of PROCR expression, activity and/or function in Th17 cells switches non-pathogenic Th17 cells to pathogenic Th17 cells. Thus, these PROCR antagonists are useful in the treatment of a variety of indications, for example, infectious disease and/or other pathogen-based disorders. In some embodiments, the T cell modulating agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the T cell modulating agent is a soluble Protein C Receptor (PROCR, also called EPCR or CD201) polypeptide or a polypeptide derived from PROCR.
In some embodiments, the invention provides a method of inhibiting Th17 differentiation, maintenance and/or function in a cell population and/or increasing expression, activity and/or function of one or more non-Th17-associated cytokines, one or more non-Th17 associated receptor molecules, or non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that inhibits expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments, the agent inhibits expression, activity and/or function of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the antibody is a chimeric, humanized or fully human monoclonal antibody. In some embodiments, the T cell is a naïve T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the T cell to become and/or produce a desired non-Th17 T cell phenotype, for example, a regulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. In some embodiments, the T cell is a partially differentiated T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the partially differentiated T cell to become and/or produce a desired non-Th17 T cell phenotype, for example, a regulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a CD4+ T cell phenotype other than a Th17 T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a shift in the Th17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17 cell phenotype.
In some embodiments, the invention provides a method of inhibiting Th17 differentiation in a cell population and/or increasing expression, activity and/or function of one or more non-Th17-associated cytokines, one or more non-Th17-associated receptor molecules, or non-Th17-associated transcription factor selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments, the agent enhances expression, activity and/or function of at least one of SP4, IKZF4, TSC22D3 or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the T cell is a naïve T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the T cell to become and/or produce a desired non-Th17 T cell phenotype, for example, a regulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. In some embodiments, the T cell is a partially differentiated T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the partially differentiated T cell to become and/or produce a desired non-Th17 T cell phenotype, for example, a regulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a CD4+ T cell phenotype other than a Th17 T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a shift in the Th17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17 cell phenotype.
In some embodiments, the invention provides a method of enhancing Th17 differentiation in a cell population increasing expression, activity and/or function of one or more Th17-associated cytokines, one or more Th17-associated receptor molecules, or one or more Th17-associated transcription regulators selected from interleukin 17F (IL-17F), interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-related orphan receptor C (RORC), and/or decreasing expression, activity and/or function of one or more non-Th17-associated cytokines, one or more Th17-associated receptor molecules, or one or more non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that inhibits expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments, the agent inhibits expression, activity and/or function of at least one of SP4, IKZF4, TSC22D3 or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the antibody is a chimeric, humanized or fully human monoclonal antibody. In some embodiments, the T cell is a naïve T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the T cell to become and/or produce a desired Th17 T cell phenotype. In some embodiments, the T cell is a partially differentiated T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the partially differentiated T cell to become and/or produce a desired Th17 T cell phenotype. In some embodiments, the T cell is a CD4+ T cell other than a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the non-Th17 T cell to become and/or produce a Th17 T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a shift in the Th17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17 cell phenotype.
In some embodiments, the invention provides a method of enhancing Th17 differentiation in a cell population, increasing expression, activity and/or function of one or more Th17-associated cytokines, one or more Th17-associated receptor molecules, and/or one or more Th17-associated transcription regulators selected from interleukin 17F (IL-17F), interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-related orphan receptor C (RORC), and/or decreasing expression, activity and/or function of one or more non-Th17-associated cytokines, one or more Th17-associated receptor molecules, or one or more non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments, the agent enhances expression, activity and/or function of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the antibody is a chimeric, humanized or fully human monoclonal antibody. In some embodiments, the agent is administered in an amount sufficient to inhibit Foxp3, IFN-γ, GATA3, STAT4 and/or TBX21 expression, activity and/or function. In some embodiments, the T cell is a naïve T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the T cell to become and/or produce a desired Th17 T cell phenotype. In some embodiments, the T cell is a partially differentiated T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the partially differentiated T cell to become and/or produce a desired Th17 T cell phenotype. In some embodiments, the T cell is a CD4+ T cell other than a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the non-Th17 T cell to become and/or produce a Th17 T cell phenotype. In some embodiments, the T cell is a Th17 T cell, and wherein the agent is administered in an amount that is sufficient to modulate the phenotype of the Th17 T cell to become and/or produce a shift in the Th17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17 cell phenotype.
In some embodiments, the invention provides a method of identifying genes or genetic elements associated with Th17 differentiation comprising: a) contacting a T cell with an inhibitor of Th17 differentiation or an agent that enhances Th17 differentiation; and b) identifying a gene or genetic element whose expression is modulated by step (a). In some embodiments, the method also comprises c) perturbing expression of the gene or genetic element identified in step b) in a T cell that has been in contact with an inhibitor of Th17 differentiation or an agent that enhances Th17 differentiation; and d) identifying a gene whose expression is modulated by step c). In some embodiments, the inhibitor of Th17 differentiation is an agent that inhibits the expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments, the agent inhibits expression, activity and/or function of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS or combinations thereof. In some embodiments, the inhibitor of Th17 differentiation is an agent that enhances expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments, the agent enhances expression, activity and/or function of at least one of SP4, IKZF4 or TSC22D3. In some embodiments, the agent that enhances Th17 differentiation is an agent that inhibits expression, activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments, wherein the agent that enhances Th17 differentiation is an agent that enhances expression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments, the agent is an antibody, a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, or a small molecule antagonist.
In some embodiments, the invention provides a method of modulating induction of Th17 differentiation comprising contacting a T cell with an agent that modulates expression, activity and/or function of one or more target genes or one or more products of one or more target genes selected from IRF1, IRF8, IRF9, STAT2, STAT3, IRF7, STAT1, ZFP281, IFI35, REL, TBX21, FLI1, BATF, IRF4, one or more of the target genes listed in Table 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, STAT5B, STAT6, TFEB, TP53, TRIM24, and/or ZFP161, or any combination thereof.
In some embodiments, the invention provides a method of modulating onset of Th17 phenotype and amplification of Th17 T cells comprising contacting a T cell with an agent that modulates expression, activity and/or function of one or more target genes or one or more products of one or more target genes selected from IRF8, STAT2, STAT3, IRF7, JUN, STAT5B, 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, STAT5B, 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 comprising contacting a T cell with an agent that modulates expression, activity and/or function of one or more target genes or one or more products of one or more target genes selected from STAT2, STAT3, JUN, STAT5B, CHD7, SATB1, RUNX1, BATF, RORC, SP4 IRF4, 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, C21ORF66, 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, STAT5B, STATE, 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, CCR8, 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, CD5L, 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, CD5L, 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, comprising detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes selected from those listed in Table 1 or Table 2 and comparing the detected level to a control of level of signature gene or gene product expression, activity and/or function, wherein a difference between the detected level and the control level indicates that the presence of an immune response in the subject. In some embodiments, the immune response is an autoimmune response. In some embodiments, the immune response is an inflammatory response, including inflammatory response(s) associated with an autoimmune response and/or inflammatory response(s) associated with an infectious disease or other pathogen-based disorder.
In some embodiments, the invention provides a method of monitoring an immune response in a subject, comprising detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes, e.g., one or more signature genes selected from those listed in Table 1 or Table 2 at a first time point, detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes, e.g., one or more signature genes selected from those listed in Table 1 or Table 2 at a second time point, and comparing the first detected level of expression, activity and/or function with the second detected level of expression, activity and/or function, wherein a change between the first and second detected levels indicates a change in the immune response in the subject. In some embodiments, the immune response is an autoimmune response. In some embodiments, the immune response is an inflammatory response.
In some embodiments, the invention provides a method of monitoring an immune response in a subject, comprising isolating a population of T cells from the subject at a first time point, determining a first ratio of T cell subtypes within the T cell population at a first time point, isolating a population of T cells from the subject at a second time point, determining a second ratio of T cell subtypes within the T cell population at a second time point, and comparing the first and second ratio of T cell subtypes, wherein a change in the first and second detected ratios indicates a change in the immune response in the subject. In some embodiments, the immune response is an autoimmune response. In some embodiments, the immune response is an inflammatory response.
In some embodiments, the invention provides a method of activating therapeutic immunity by exploiting the blockade of immune checkpoints. The progression of a productive immune response requires that a number of immunological checkpoints be passed. Immunity response is regulated by the counterbalancing of stimulatory and inhibitory signal. The immunoglobulin superfamily occupies a central importance in this coordination of immune responses, and the CD28/cytotoxic T-lymphocyte antigen-4 (CTLA-4):B7.1/B7.2 receptor/ligand grouping represents the archetypal example of these immune regulators (see e.g., Korman A J, Peggs K S, Allison J P, “Checkpoint blockade in cancer immunotherapy.” Adv Immunol. 2006; 90:297-339). In part the role of these checkpoints is to guard against the possibility of unwanted and harmful self-directed activities. While this is a necessary function, aiding in the prevention of autoimmunity, it may act as a barrier to successful immunotherapies aimed at targeting malignant self-cells that largely display the same array of surface molecules as the cells from which they derive. The expression of immune-checkpoint proteins can be dysregulated in a disease or disorder and can be an important immune resistance mechanism. Therapies aimed at overcoming these mechanisms of peripheral tolerance, in particular by blocking the inhibitory checkpoints, offer the potential to generate therapeutic activity, either as monotherapies or in synergism with other therapies.
Thus, the present invention relates to a method of engineering T-cells, especially for immunotherapy, comprising modulating T cell balance to inactivate or otherwise inhibit at least one gene or gene product involved in the immune check-point.
Suitable T cell modulating agent(s) for use in any of the compositions and methods provided herein include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent. By way of non-limiting example, suitable T cell modulating agents or agents for use in combination with one or more T cell modulating agents are shown in Table 10 of the specification.
One skilled in the art will appreciate that the T cell modulating agents have a variety of uses. For example, the T cell modulating agents are used as therapeutic agents as described herein. The T cell modulating agents can be used as reagents in screening assays, diagnostic kits or as diagnostic tools, or these T cell modulating agents can be used in competition assays to generate therapeutic reagents.
In one embodiment, the invention relates to a method of diagnosing, prognosing and/or staging an immune response involving T cell balance, which may comprise 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 the genes of 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.
In another embodiment, the invention relates to a method of monitoring an immune response in a subject comprising detecting a level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes of 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 of 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.
In yet another embodiment, the invention relates to a method of identifying a patient population at risk or suffering from an immune response 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 of Table 1 or Table 2 in the patient population and comparing the level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes of Table 1 or Table 2 in a patient population not at risk or suffering from an immune response, wherein a difference in the level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes of Table 1 or Table 2 in the patient populations identifies the patient population as at risk or suffering from an immune response.
In still another embodiment, the invention relates to a method for monitoring subjects undergoing a treatment or therapy for an aberrant immune response to determine whether the patient is responsive to the treatment or therapy 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 of Table 1 or Table 2 in the absence of the treatment or therapy and comparing the level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes of Table 1 or Table 2 in the presence of the treatment or therapy, wherein a difference in the level of expression, activity and/or function of one or more signature genes or one or more products of one or more signature genes of Table 1 or Table 2 in the presence of the treatment or therapy indicates whether the patient is responsive to the treatment or therapy.
The invention may also involve a method of modulating T cell balance, the method which may comprise contacting a T cell or a population of T cells with a T cell modulating agent in an amount sufficient to modify differentiation, maintenance and/or function of the T cell or population of T cells by altering balance between Th17 cells, regulatory T cells (Tregs) and other T cell subsets as compared to differentiation, maintenance and/or function of the T cell or population of T cells in the absence of the T cell modulating agent.
The immune response may be an autoimmune response or an inflammatory response. The inflammatory response may be associated with an autoimmune response, an infectious disease and/or a pathogen-based disorder.
The signature genes may be Th17-associated genes.
The treatment or therapy may be an antagonist for GPR65 in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells. The treatment or therapy may be an agonist that enhances or increases the expression of GPR65 in an amount sufficient to induce T cell differentiation toward Th17 cells. The treatment or therapy may be specific for a target gene selected from the group consisting of DEC1, PZLP, TCF4 and CD5L. The treatment or therapy may be an antagonist of a target gene selected from the group consisting of DEC1, PZLP, TCF4 and CD5L in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature. The treatment or therapy may be an agonist that enhances or increases the expression of a target gene selected from the group consisting of DEC1, PZLP, TCF4 and CD5L in an amount sufficient to switch Th17 cells from a non-pathogenic to a pathogenic signature.
The T cell modulating agent may be an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent.
The T cells may be naïve T cells, partially differentiated T cells, differentiated T cells, a combination of naïve T cells and partially differentiated T cells, a combination of naïve T cells and differentiated T cells, a combination of partially differentiated T cells and differentiated T cells, or a combination of naïve T cells, partially differentiated T cells and differentiated T cells.
The invention also involves a method of enhancing Th17 differentiation in a cell population, increasing expression, activity and/or function of one or more Th17-associated cytokines or one or more Th17-associated transcription regulators selected from interleukin 17F (IL-17F), interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-related orphan receptor C (RORC), and/or decreasing expression, activity and/or function of one or more non-Th17-associated cytokines or non-Th17-associated transcription regulators selected from FOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cell with an agent that enhances expression, activity and/or function of CD5L, DEC1, PLZP, TCF4 or combinations thereof. The agent may enhance expression, activity and/or function of at least one of CD5L, DEC1, PLZP, or TCF4. Thw agent may be an antibody, a soluble polypeptide, a polypeptide agonist, a peptide agonist, a nucleic acid agonist, a nucleic acid ligand, or a small molecule agonist. The antibody may be a monoclonal antibody or a chimeric, humanized or fully human monoclonal antibody.
The present invention also involves the use of an antagonist for GPR65 in an amount sufficient to induce differentiation toward regulatory T cells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells, use of an agonist that enhances or increases the expression of GPR65 in an amount sufficient to induce T cell differentiation toward Th17 cells, use of an antagonist of a target gene selected from the group consisting of DEC1, PZLP, TCF4 and CD5L in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature, use of an agonist that enhances or increases the expression of a target gene selected from the group consisting of DEC1, PZLP, TCF4 and CD5L in an amount sufficient to switch Th17 cells from a non-pathogenic to a pathogenic signature and Use of T cell modulating agent for treating an aberrant immune response in a patient.
Accordingly, it is an object of the invention to not encompass within the invention any previously known product, process of making the product, or method of using the product such that Applicants reserve the right and hereby disclose a disclaimer of any previously known product, process, or method. It is further noted that the invention does not intend to encompass within the scope of the invention any product, process, or making of the product or method of using the product, which does not meet the written description and enablement requirements of the USPTO (35 U.S.C. § 112, first paragraph) or the EPO (Article 83 of the EPC), such that Applicants reserve the right and hereby disclose a disclaimer of any such subject matter.
It is noted that in this disclosure and particularly in the claims and/or paragraphs, terms such as “comprises”, “comprised”, “comprising” and the like can have the meaning attributed to it in U.S. Patent law; e.g., they can mean “includes”, “included”, “including”, and the like; and that terms such as “consisting essentially of” and “consists essentially of” have the meaning ascribed to them in U.S. Patent law, e.g., they allow for elements not explicitly recited, but exclude elements that are found in the prior art or that affect a basic or novel characteristic of the invention. Nothing herein is to be construed as a promise.
These and other embodiments are disclosed or are obvious from and encompassed by, the following Detailed Description.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIGS. 1A-1E 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-2G 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. 2C 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. 2D 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. 2E-1, 2E-2, and 2E-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-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; FIG. 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. 3D, 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, STATE, TRIM24, HIF1A, IRF4, IRF8, ETS2, JUN, RUNX1, FLI1, REL, SP4, EGR2, NFKB1, ZFP281, STAT4, RELA, TBX21, STAT5B, 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-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-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-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-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-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-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-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 keygenes, 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-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-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-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. 16B depicts intracellular cytokine staining of Pou2af1^−/− and WT cells for IFN-γ and IL-17a as measured by flow cytometry. (FIG. 16C, left) Flow cytometric analysis of Fas^−/− and WT cells for Foxp3 and Il-17 expression. (FIG. 16C, right) IL-2 and Tnf secretion by Fas^−/− and WT cells, as measured by a cytokine bead assay ELISA. (FIG. 16D, 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. 16D, 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-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 10⁻¹⁰(using Fisher exact test), but the fold enrichment is particularly high in genes that are both bound by a factor and affected by its knockout, the most functional edges.

FIGS. 19A-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-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/δ mice 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-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-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-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/δ mice and control mice differentiated with TGFβ1 and IL-6 for 3 days in vitro.

FIGS. 24A-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 Rorγt−/− 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 Rorγt-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-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^CreSTAT3^fl/flmice (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^CreIRF4^fl/flmice 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-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/δ Rag1−/−) or WT T cells (WT→Rag1−/−) and immunized with MOG_35-55to 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-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-β3, 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-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-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-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 m MOG_35-55in 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-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-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 (TGFβ1+IL-6) or pathogenic (TGFβ3+IL-6 or IL-β1+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 (TGFβ1+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 naive 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-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.

FIGS. 34A-34C are a series of graphs demonstrating the expression of CD5L on Th17 cells.

FIGS. 35A-35C are a series of illustrations and graphs depicting how CD5L deficiency does not alter Th17 differentiation.

FIGS. 36A-36B are a series of illustrations and graphs depicting how CD5L deficiency alters Th17 memory by affecting survival or stability.

FIGS. 37A-37B are a series of graphs depicting how CD5L deficiency results in more severe and prolonged EAE with higher Th17 responses.

FIGS. 38A-38C are a series of illustrations and graphs depicting how loss of CD5L converts non-pathogenic Th17 cells into pathogenic effector Th17 cells.

FIGS. 39A-39B are a series of graphs depicting how CD5L-deficient Th17 cells (TGF-β+IL-6) develop a pathogenic phenotype.

FIGS. 40A-40B are a series of graphs depicting IL17A expression was reduced in GPR65 knock out cells exposed to various T cell conditions (Th0, T16, T36, B623 and T).

FIGS. 41A-41D are a series of graphs depicting that IL17A expression in DEC1 knock out cells exposed to various T cell conditions (Th0, T16, T36, B623 and T) was unchanged in the non-pathogenic condition (T16), but was reduced in the pathogenic conditions (T36, B623).

FIGS. 42A-42B are a series of graphs depicting that IL17A expression in PLZP knock out cells exposed to various T cell conditions (Th0, T16, T36, B623 and T) was unchanged in the non-pathogenic condition (T16), but was reduced in the pathogenic conditions (T36, B623).

FIG. 43 is a graph depicting IL17A expression in TCF4 knock out cells exposed to various T cell conditions (Th0, T16, T36, B623 and T) was reduced in the pathogenic condition B623.

FIG. 44 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 5×10⁵B16F10 melanoma cells.

FIG. 45A-4511 show Single-cell RNAseq identifies Cd5l as a novel regulator associated with Th17 cell pathogenicity and expressed only by non-pathogenic Th17 cells. Single-cells were sorted from in-vitro Th17 cells differentiated with TGFβ1+IL-6 (A,B), IL-1β+IL-6+IL-23 (C), TGFβ3+IL-6 (D) and in-vivo Th17 cells from CNS of mice at the peak of EAE (score=3) (D). IL-17A.GFP⁺ CD4⁺ T cells were sorted in all panels in D. (A) Correlation of CD5L expression in non-pathogenic Th17 cells with the pathogenic signature (Lee, Awasthi et al.). (B) Principal Component Analysis of CD5L expression where the direction of PC1 correlates with pathogenicity. (C, D) Histogram of CD5L expression in single-cell from conditions as indicated. CD5L expression in vitro is validated by qPCR (E, F) and flow cytometry (G). FIG. 45E, F, G shows validation of CD5L expression in vitro. Naïve T cells (CD4⁺CD62L⁺CD44⁻CD25⁻) were sorted and activated by plate-bound anti-CD3 and anti-CD28 antibodies in the presence of various differentiation cytokines as indicated. CD5L expression was measured by qPCR at 48 h (E) and 72 h (F) and intracellularly by flow cytometry at 48 h (G); (F) At 48 h, cells were lifted from plate, washed and replated in fresh media with IL-23 or PBS and cultured for additional 24 h. FIG. 45H shows validation of CD5L expression in vivo. IL-17A.GFP reporter mice were immunized by MOG/CFA (s.c., d1) with pertussis toxin (i.v., d1 and d3). Mice were sacrificed at the peak of disease (score=3) and CD4⁺GFP⁺ and CD4⁺GFP⁻ cells were sorted from CNS and spleen respectively. Cd5l and Il17a expression are measured by qPCR. Figure shown is representative data of technical replicates from two independent mouse experiments. I. IL-17⁺ (GFP⁺) and IL-17⁻ (GFP⁻) CD4⁺ cells were sorted from the gut of naïve mice and the number of RNA transcripts measured by nanostring and normalized based on four house-keeping genes. Figure is summary of two independent experiments.

FIG. 46A-46H shows CD5L regulates Th17 cell effector function. (A) WT and CD5L^−/− mice were immunized with 40 μg MOG/CFA with pertussis toxin injection (iv) on day 1 and day 3. EAE was scored as previously published (Jager, Dardalhon et al. 2009). Upper panel is pooled results from 3 independent mice experiments; Lower panel is representative FACS plot showing cytokine production from CD4 T cells isolated from CNS at day 15 post immunization after 4 hours of restimulation with PMA/ionomycin. Summary data is shown in FIG. 50B. FIG. 46B, C, D shows naïve T cells (CD4⁺CD62L⁺CD44⁻CD25⁻) were sorted, activated with plate-bound anti-CD3/anti-CD28 antibodies in the presence of TGFβ1 and IL-6 for 48 h. Cells were restimulated with PMA/ionomycine for 4 hours in the presence of Brefeldin A and cytokine production was measured using FACS (B); Supernatant were used for ELISA analysis of IL-17 and IL-10 (C); and RNA were purified from cells directly and subject to qPCR (D). FIG. 46E, F shows cells were sorted and cultured as in B, at 48 hours, cells were lifted, washed and resuspended in fresh media with no cytokines for an additional 72 h and restimulated. Cytokine production was measured by FACS (E) and mRNA was quantified by qPCR (F). FIG. 46G, H show effector memory T cells (CD4⁺CD62L⁻CD44⁺) (G) or Effector memory Th17 cells (CD4⁺CD62L⁻CD44⁺RorγtGFP⁺) (H) were sorted directly ex vivo and activated with plate-bound anti-CD3/anti-CD28 antibodies for 48 hours. Cells were harvested and cultured with PMA/ionomycine for 4 hours in the presence of Brefeldin A and subject to FACS. Data are representative of at least 3 independent mouse experiments.

FIG. 47A-47F shows CD5L is a major switch that regulates the pathogenicity of Th17 cells. Naïve WT or CD5L ^−/− 2 D2 T cells were sorted and differentiated with TGFβ1+IL-6 in the presence of irradiated APC (Jager, Dardalhon et al. 2009). Cells were rested and reactivated with plate-bound anti-CD3 and anti-CD28 antibodies for 48 h and intravenously injected into WT host. (A) Representative FACS plot are shown of cytokine profile of 2D2 T cells after differentiation and prior to in vivo transfer. (B) Weight and EAE score of recipient mice; (C) Representative histology of optic nerve (upper two panels) and CNS (lower panel). Panels are Luxol fast blue-hematoxylin and eosin stains. Demyelination is indicated by loss of normal blue staining of myelin in lower panels of CNS. (D) Representative cytokine profile of WT and CD5L ^−/− 2 D2 lymphocytes isolated from CNS at day 27 post transfer. Cells were gated on Va3.2⁺CD4⁺. All data are representative of 3 independent mouse experiments. (E) Naïve 2D2 WT or CD5L^−/− T cells were sorted and 100,000 cells were transferred into CD45.1 WT host. Recipients were than immunized with MOG/CFA the following day. T cells were isolated from the draining LN on day 10 following immunization and restimulated with PMA/ionomycin as described in FIG. 46. Representative FACS plots are gated on CD45.2⁺CD4⁺ cells and are of 2 independent experiments each with four mice. (F) Naïve T cells were differentiated with TGFβ1+IL-6 as in FIG. 46E and subject to RNA purification and qPCR. Data are summary of at least three independent mouse experiments.

FIG. 48A-48J shows CD5L shifts Th17 cell lipidome balance from saturated to unsaturated lipid, modulating Rorγt ligand availability and function. FIG. 48A, B shows. Lipidome analysis of Th17 cells. (A) WT and CD5L^−/− naïve T cells were differentiated as in FIG. 46B in the presence of cytokines as indicated. Cells and supernatant were harvested at 96 hours and subjected to MS/LC. Three independent mouse experiments were performed. Data shown are median expression of each metabolite identified that have at least 1.5 fold differences between and WT and CD5L^−/− under the TGFβ1+IL-6 condition. (B) Expression of representative metabolites including a cholesterol ester and a PUFA-containing TAG species. FIG. 48 C, D, E, F-J show as follows: (C) Metabolomic analysis of independent mouse experiments where T cells were differentiated under various cytokine conditions as indicated and harvested at 48 h and 96 h. Summary metabolomics analysis is shown in FIG. 52A. (D,E) Rorγt ChIP from Th17 cells differentiated as described in A. under various conditions as indicated. F-K. Dual luciferase reporter assay was performed in EL4 cells stably transfected with a control vector or Rorγt vector. (F, G) CD5L retroviral vector was cotransfected in F and G at 0, 25, 50 and 100 ng/well. (H-J) 10 μM of either arachidonic acid (PUFA) or 20 μM of palmitic acid (SFA) were used whenever a single dose was indicated and in titration experiments, 20 μM, 4 μM and 0.8 μM for PUFA/SFA and 5 μM, 0.5 μM and 0.05 μM of 7, 27-dihydroxycholesterol were used. All ChIP and luciferase assay are representative of at least 3 independent experiments.

FIG. 49 CD5L expression follows the pro-inflammatory/regulatory module dichotomy across single cells. Shown is a PCA plot (first two PCs) with the cells differentiated under the TGF-β1+IL-6 condition at 48 h, where each cell is colored by an expression ranking score of CD5L (red: high, blue: low) and the first PC is marked by the pro-inflammatory/regulatory module dichotomy.

FIG. 50A-50E PUFA and SFA can regulate Th17 cell function and contribute to CD5L-dependent regulation of Th17 cells. (A) Naïve T cells were sorted from either WT or IL-23RGFP reporter mice, activated with plate-bound anti-CD3/anti-CD28 and differentiated with TGFβ1+IL-6 for 48 hours. At 48 h, cells were cultured with IL-23 in fresh media in the presence of either 10 uM arachidonic acid (PUFA) or 20 uM of palmitic acid (SFA) for another 48 hours and harvested for PMA/ionomycin restimulation and FACS. The concentration of FFA was predetermined in titration experiments (data not shown). (B) Cells from WT and Rorc^−/− mice were sorted, differentiated and treated with FFA as in A. Cells were harvested for RNA purification and qPCR. (C) Naïve WT and CD5L^−/− T cells were differentiated as in A. Cells were then lifted, washed and replated in fresh media with no addition of cytokines and in the presence of control or 5 uM of arachidonic acid (PUFA). Cytokine profile of T cells were measured after PMA/ionomycin restimulation. Data are representative of at least 3 independent differentiation experiments. DE. naïve T cells were sorted and differentiated with TGFβ1+IL-6 as in A. At 48 h, cells were then lifted, washed and replated in fresh media with no addition of cytokines and in the presence of control or 5 uM arachidonic acid (PUFA) for CD5L−/− T cells; and control or 25 uM palmitic acid (SFA) for WT T cells. Another 48 hours later, cells were harvested for nanostring analysis (D) or qPCR (E).

FIG. 51A-51C Model for action of PUFA and CD5L. During differentiation (A) abundant Rorγt ligand are synthesized, limiting the specific impact of PUFA/SFA; once Th17 cells are differentiated (B,C), however, ligand synthesis is substantially reduced due to decreased glucose metabolism, allowing PUFA to have a more pronounced effect. The extent of this effect depends on whether CD5L is present (B) or absent (C), resulting in less or more pathogenic cells, respectively.

FIG. 52A-52D shows characterization of WT and CD5L−/− mice with EAE. Mice were immunized as in FIG. 46A. (A) 15 days post immunization, lymphocytes from CNS were isolated and directly stained and analyzed with flow cytometry for the expression of FoxP3. (B) Cells from CNS as in A were restimulated with PMA/ionomycin with Brefeldin A for 4 hours and profiled for cytokine production by flow cytometry. (C) Cells were isolated from Inguinal LN of mice 10 days after immunization. 3H Thymidine incorporation assays was used to determine T cell proliferation in response to MOG35-55 peptide; (D) Supernatant from C were harvested amount of IL-17 was determined by ELISA.

FIG. 53A-53D shows CD5L antagonizes pathogenicity of Th17 cells. Passive EAE is induced as described in FIG. 46. Briefly, naïve 2D2 cells were sorted from WT mice and differentiated with IL-1β+IL-6+IL-23. At 24 h, retroviral supernatant containing either CD5L-GFP overexpression- or control-GFP construct were used to infect the activated cells. The expression of CD5L was analyzed at day 3 post-infection. 50% of cells expressed GFP in both groups. (A) Representative flow cytometry analysis of cytokine profile prior to transfer; (B) Weight loss curve after transfer; (C) EAE score; (D) representative flow cytometry data of cytokine profile of CD4+ T cells from CNS at day 30 post transfer.

FIG. 54A-54D CD5L regulate lipid metabolism in Th17 cells and modulate Rorγt function. (A) Rorγt binding sites in the Il17, Il23r and Il10 regions as identified from Rorγt ChIP-seq (Xiao, Yosef et al. 2014). Top row is isotype control (red) and bottom role shows Rorγt ChIP-seq results from anti-Rorγt antibody (Experimental Procedures) (B) ChIP-PCR of Rorγt in the genomic region of Il23r as in FIG. 48E. (C,D) Rorγt transcriptional activity was measured with respect to Il123r (C) and Il10 (D) in the presence of retroviral vector expressing Cd5l as in FIG. 48G.

DETAILED DESCRIPTION

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- and Th17 Cells. Annu Rev Immunol 27, 485-517, doi:10.1146/annurev.immunol.021908.13271010.1146/annurev.immunol.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 FIG. 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.

TABLE 1

Signature Genes

IL17A	IL21R	CCL1	PSTPIP1
IL7R	BCL3	CD247	IER3
IRF4	DPP4	PROCR	FZD7
CXCL10	TGFBR1	RELA	GLIPR1
IL12RB1	CD83	HIF1A	AIM1
TBX21	RBPJ	PRNP	CD4
ZNF281	CXCR3	IL17RA	LMNB1
IL10RA	NOTCH2	STAT1	MGLL
CXCR4	CCL4	LRRFIP1	LSP1
TNFRSF13B	TAL2	KLRD1	GJA1
ACVR1B	IL9	RUNX1	LGALS3BP
TGIF1	FAS	ID2	ARHGEF3
ABCG2	SPRY1	STAT5A	BCL2L11
REL	PRF1	TNFRSF25	TGM2
ID3	FASLG	BATF	UBIAD1
ZEB1	MT2A	KAT2B	MAP3K5
MYD88	POU2AF1	NFATC2	RAB33A
EGR2	IFNG	CD70	CASP1
AES	PLAC8	LITAF	FOXP1
PML	IL17F	IL27RA	MTA3
TGFBR3	DDR1	IL22	IFIH1
CCR8	IL4	MINA	RASGRP1
ZFP161	CD28	XBP1	XRCC5
IRF1	TNFSF9	PRDM1	NCF1C
CCR6	SMARCA4	AHR	NUDT4
SMOX	YAX2	SLAMF7	PDCD1LG2
ITGB1	IL21	IL1RN	PYCR1
CASP6	SAP30	MBNL3	AQP3
NFKBIE	CD9	ARID5A	SEMA7A
LAMP2	IL24	TRIM24	PRC1
GATA3	STAT5B	CSF2	IFIT1
RORA	SKI	NFE2L2	DNTT
SGK1	BCL6	IL23R	PMEPA1
IL2RA	ELK3	KLF6	GAP43
MT1A	CD74	ACVR2A	PRICKLE1
JAK3	STAT6	NR3C1	OAS2
IL4R	TNFSF8	CCR4	ERRFI1
NAMPT	IL3	CXCR5	LAD1
ITGA3	TGFB1	SKAP2	TMEM126A
TGFB3	ETV6	PLEKHF2	LILRB1,
			LILRB2,
			LILRB3,
INHBA	CASP4	STAT2	KATNA1
KLF7	CEBPB	IRF7	B4GALT1
RUNX3	TRAF3	FLI1	ANXA4
NFKBIZ	TRPS1	IRF9	SULT2B1
SERPINE2	JUN	GFI1	PHLDA1
RXRA	STAT4	MXI1	PRKD3
SERTAD1	CMTM6	IFI35	TAP1
MAF	SOCS3	MAX	TRIM5
IL10	TSC22D3	ZNF238	FLNA
BMPR1A	LIF	CHD7	GUSB
PTPRJ	DAXX	FOXM1	C14ORF83
STAT3	KLF9	BCL11B	VAV3
CCR5	IL6ST	RUNX2	ARL5A
CCL20	CLCF1	EMP1	GRN
SPP1	NFIL3	PELI2	PRKCA
CD80	IKZF4	SEMA4D	PECI
RORC	ISG20	STARD10	ARMCX2
SERPINB1	CD86	TIMP2	SLC2A1
IL12RB2	IL2RB	KLF10	RPP14
IFNGR2	NCOA1	CTSW	PSMB9
SMAD3	NOTCH1	GEM	CASP3
FOXP3	TNFRSF12A	TRIM25	TRAT1
CD24	CD274	HLA-A	PLAGL1
CD5L	MAFF	MYST4	RAD51AP1
CD2	ATF4	FRMD4B	NKG7
TNFSF11	ARNTL	RFK	IFITM2
ICOS	IL1R1	CD44	HIP1R
IRF8	FOXO1	ERCC5

TABLE 2

Subset of Signature Genes

AHR	HIF1A	IRF4	REL
ARID5A	ICOS	IRF8	RORA
BATF	ID2	ITGA3	RORC
CASP4	ID3	KLF6	SERPINB1
CASP6	IFNG	KLRD1	SGK1
CCL20	IL10	LIF	SKAP2
CCL4	IL10RA	LTA	SKI
CCR5	IL17A	MAF	SMOX
CCR6	IL17F	MAFF	SOCS3
CD24	IL17RA	MINA	STAT1
CD5L	IL2	MYC	STAT3
CD80	IL21	NFATC2	STAT4
CEBPB	IL21R	NFE2L2	TBX21
CLCF1	IL22	NFIL3	TGFBR1
CSF2	IL23R	NOTCH1	TGIF1
CXCR3	IL24	NUDT4	TNFRSF25
EGR2	IL2RA	PML	TNFSF8
ELK3	IL7R	POU2AF1	TRIM24
ETV6	IL9	PROCR	TRPS1
FAS	INHBA	PSMB9	TSC22D3
FOXP3	IRF1	RBPJ	ZFP36L1
GATA3

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.

TABLE 3

Th17-Associated Receptor Molecules

ACVR1B	CXCR4	IL6ST	PROCR
ACVR2A	CXCR5	IL7R	PTPRJ
BMPR1A	DDR1	IRAK1BP1	PVR
CCR4	FAS	ITGA3	TLR1
CCR5	IL15RA	KLRD1	TNFRSF12A
CCR6	IL18R1	MYD88	TNFRSF13B
CCR8	IL1RN	PLAUR	TRAF3
CXCR3

TABLE 4

Th17-Associated Transcription Regulators

TRPS1	SMARCA4	CDYL	SIRT2
SMOX	ZFP161	IKZF4	MAFF
ARNTL	TP53	NCOA1	CHMP1B
UBE2B	SUZ12	SS18	GATAD2B
NR3C1	POU2AF1	PHF13	ZNF703
TRIM24	MYST4	MTA3	ZNRF1
FLI1	MXI1	ASXL1	JMJD1C
SP4	CHD7	LASS4	ZFP36L2
EGR2	CREB3L2	SKIL	TSC22D4
ZNF281	VAX2	FOSL2	NFE2L2
RELA	KLF10	RUNX2	RNF11
IRF7	SKI	TLE1	ARID3A
STAT2	ELK3	ELL2	MEN1
IRF3	ZEB1	BCL11B	CBX4
XBP1	LRRFIP1	KAT2B	ZFP62
PRDM1	PAXBP1	KLF6	CIC
ATF4	ID1	E2F8	HCLS1
CREB1	ZNF238	ZNRF2	ZFP36L1
IRF9	VAV1	TSC22D3	TGIF1
IRF2	MINA	HMGB2
FOXJ2	BATF3	FUS

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.

TABLE 5

Candidate Regulators

% Interactions OR differential expression (compared to Th0)

IL23R knockout

Symbol	Early	Intermediate	Late	(late)

IRF4	0.892473118	0.841397849	1	UNDER-EXPR
IFI35	1	0.952380952	0.904761905	UNDER-EXPR
ETS1	1	0.636363636	0.636363636	UNDER-EXPR
NMI	1	0.857142857	0	UNDER-EXPR
SAP18	0.785714286	0.928571429	1	OVER-EXPR
FLI1	1	0.971590909	0.869318182
SP4	1	0.710900474	0.63507109	UNDER-EXPR
SP100	1	0	0	UNDER-EXPR
TBX21	0	1	0	OVER-EXPR
POU2F2	0	1	0	OVER-EXPR
ZNF281	0	1	0	UNDER-EXPR
NFIL3	0.611111111	0.611111111	1
SMARCA4	0.805825243	0.757281553	1	OVER-EXPR
CSDA	0	0	1	OVER-EXPR
STAT3	0.855392157	0.970588235	1	UNDER-EXPR
FOXO1	0.875	1	0.875
NCOA3	0.875	1	0.9375
LEF1	0.380952381	0.904761905	1	UNDER-EXPR
SUZ12	0	1	0	OVER-EXPR
CDC5L	0	1	0	UNDER-EXPR
CHD7	1	0.860465116	0.686046512	UNDER-EXPR
HIF1A	0.733333333	0.666666667	1	UNDER-EXPR
RELA	0.928571429	1	0.880952381	UNDER-EXPR
STAT2	1	0.821428571	0
STAT5B	1	0.848484848	0.515151515	UNDER-EXPR
RORC	0	0	1	UNDER-EXPR
STAT1	1	0.635658915	0	UNDER-EXPR
MAZ	0	1	0
LRRFIP1	0.9	0.8	1
REL	1	0	0	OVER-EXPR
CITED2	1	0	0	UNDER-EXPR
RUNX1	0.925149701	0.925149701	1	UNDER-EXPR
ID2	0.736842105	0.789473684	1
SATB1	0.452380952	0.5	1	UNDER-EXPR
TRIM28	0	1	0
STAT6	0.54	0.64	1	OVER-EXPR
STAT5A	0	0.642241379	1	UNDER-EXPR
BATF	0.811732606	0.761255116	1	UNDER-EXPR
EGR1	0.857142857	1	0	OVER-EXPR
EGR2	0.896428571	0.839285714	1	OVER-EXPR
AES	0.888888889	1	0.777777778
IRF8	0	1	0.824786325	OVER-EXPR
SMAD2	0.806060606	0.781818182	1
NFKB1	0.266666667	0.706666667	1	UNDER-EXPR
PHF21A	1	0.533333333	0.933333333	UNDER-EXPR
CBFB	0.35	0.9	1
ZFP161	0.818181818	0.714876033	1	OVER-EXPR
ZEB2	0	0.411764706	1
SP1	0	0.740740741	1
FOXJ2	0	1	1
IRF1	1	0	0
MYC	0	0.595505618	1	UNDER-EXPR
IRF2	1	0	0
EZH1	1	0.8	0.44	UNDER-EXPR
RUNX2	0	0	1
JUN	0.647058824	0.647058824	1	OVER-EXPR
STAT4	1	0	0	UNDER-EXPR
MAX	0.947368421	0.789473684	1
TP53	0.292307692	0.615384615	1	UNDER-EXPR
IRF3	1	0.485294118	0.235294118	UNDER-EXPR
BCL11B	0.666666667	0.611111111	1
E2F1	0	0	1	OVER-EXPR
IRF9	1	0.440433213	0	UNDER-EXPR
GATA3	1	0	0	OVER-EXPR
TRIM24	0.965517241	1	0.965517241	UNDER-EXPR
E2F4	0.083333333	0.5	1
NR3C1	1	1	0	UNDER-EXPR
ETS2	1	0.925925926	0.864197531	OVER-EXPR
CREB1	0.802197802	0.706959707	1
IRF7	1	0.777777778	0	OVER-EXPR
TFEB	0.8	0.6	1
TRPS1		OVER-EXPR		UNDER-EXPR
SMOX		OVER-EXPR	OVER-EXPR	UNDER-EXPR
RORA		OVER-EXPR	OVER-EXPR	UNDER-EXPR
ARID5A	OVER-EXPR	OVER-EXPR	OVER-EXPR	OVER-EXPR
ETV6	OVER-EXPR	OVER-EXPR
ARNTL		OVER-EXPR		UNDER-EXPR
UBE2B			OVER-EXPR	UNDER-EXPR
XBP1			OVER-EXPR
PRDM1	OVER-EXPR	OVER-EXPR		UNDER-EXPR
ATF4			OVER-EXPR	OVER-EXPR
POU2AF1		OVER-EXPR		UNDER-EXPR
CEBPB		OVER-EXPR	OVER-EXPR	UNDER-EXPR
CREM		OVER-EXPR	OVER-EXPR	UNDER-EXPR
MYST4		OVER-EXPR	OVER-EXPR	UNDER-EXPR
MXI1			OVER-EXPR	UNDER-EXPR
RBPJ		OVER-EXPR	OVER-EXPR	OVER-EXPR
CREB3L2		OVER-EXPR	OVER-EXPR	UNDER-EXPR
VAX2			OVER-EXPR	OVER-EXPR
KLF10		OVER-EXPR	OVER-EXPR
SKI		OVER-EXPR	OVER-EXPR	UNDER-EXPR
ELK3		OVER-EXPR		OVER-EXPR
ZEB1		OVER-EXPR	OVER-EXPR	OVER-EXPR
PML	OVER-EXPR	OVER-EXPR		UNDER-EXPR
SERTAD1			OVER-EXPR	UNDER-EXPR
NOTCH1	OVER-EXPR	OVER-EXPR		OVER-EXPR
AHR	OVER-EXPR	OVER-EXPR	OVER-EXPR	UNDER-EXPR
C21ORF66			OVER-EXPR	UNDER-EXPR
SAP30			OVER-EXPR	OVER-EXPR
ID1		OVER-EXPR	OVER-EXPR	OVER-EXPR
ZNF238		OVER-EXPR	OVER-EXPR
VAV1		OVER-EXPR		UNDER-EXPR
MINA		OVER-EXPR	OVER-EXPR	UNDER-EXPR
BATF3			OVER-EXPR	OVER-EXPR
CDYL				UNDER-EXPR
IKZF4	OVER-EXPR	OVER-EXPR	OVER-EXPR	OVER-EXPR
NCOA1		OVER-EXPR		OVER-EXPR
BCL3	OVER-EXPR	OVER-EXPR	OVER-EXPR	UNDER-EXPR
JUNB		OVER-EXPR		UNDER-EXPR
SS18		OVER-EXPR		OVER-EXPR
PHF13				OVER-EXPR
MTA3		OVER-EXPR		UNDER-EXPR
ASXL1		OVER-EXPR		OVER-EXPR
LASS4			OVER-EXPR	UNDER-EXPR
SKIL		OVER-EXPR	OVER-EXPR	OVER-EXPR
DDIT3			OVER-EXPR	OVER-EXPR
FOSL2		OVER-EXPR	OVER-EXPR
TLE1		OVER-EXPR	OVER-EXPR
ATF3				OVER-EXPR
ELL2	OVER-EXPR	OVER-EXPR		OVER-EXPR
JARID2			OVER-EXPR	OVER-EXPR
KLF9		OVER-EXPR	OVER-EXPR	OVER-EXPR
KAT2B		OVER-EXPR		UNDER-EXPR
KLF6		OVER-EXPR	OVER-EXPR	UNDER-EXPR
E2F8		OVER-EXPR	OVER-EXPR	OVER-EXPR
BCL6		OVER-EXPR		UNDER-EXPR
ZNRF2				UNDER-EXPR
TSC22D3			OVER-EXPR	UNDER-EXPR
KLF7			OVER-EXPR
HMGB2		OVER-EXPR
FUS		OVER-EXPR		OVER-EXPR
SIRT2			OVER-EXPR
MAFF		OVER-EXPR	OVER-EXPR	OVER-EXPR
CHMP1B			OVER-EXPR	UNDER-EXPR
GATAD2B	OVER-EXPR			OVER-EXPR
SMAD7		OVER-EXPR		OVER-EXPR
ZNF703		OVER-EXPR		OVER-EXPR
ZNRF1			OVER-EXPR	OVER-EXPR
JMJD1C	OVER-EXPR			UNDER-EXPR
ZFP36L2			OVER-EXPR	UNDER-EXPR
TSC22D4
NFE2L2	OVER-EXPR	OVER-EXPR	OVER-EXPR	UNDER-EXPR
RNF11				OVER-EXPR
ARID3A		OVER-EXPR	OVER-EXPR	UNDER-EXPR
MEN1			OVER-EXPR	OVER-EXPR
RARA		OVER-EXPR	OVER-EXPR	UNDER-EXPR
CBX4	OVER-EXPR	OVER-EXPR		OVER-EXPR
ZFP62		OVER-EXPR
CIC			OVER-EXPR
HCLS1				UNDER-EXPR
ZFP36L1				UNDER-EXPR
TGIF1				UNDER-EXPR
SMAD4				OVER-EXPR
IL7R		OVER EXPR	OVER EXPR	UNDER EXPR
ITGA3		OVER EXPR	OVER EXPR
IL1R1		OVER EXPR	OVER EXPR	UNDER EXPR
FAS	OVER EXPR			UNDER EXPR
CCR5	OVER EXPR	OVER EXPR	OVER EXPR	UNDER EXPR
CCR6		OVER EXPR	OVER EXPR
ACVR2A		OVER EXPR	OVER EXPR	UNDER EXPR
IL6ST	OVER EXPR	OVER EXPR		UNDER EXPR
IL17RA	OVER EXPR	OVER EXPR		UNDER EXPR
CCR8		OVER EXPR
DDR1		OVER EXPR	OVER EXPR	UNDER EXPR
PROCR		OVER EXPR	OVER EXPR	OVER EXPR
IL2RA	OVER EXPR	OVER EXPR	OVER EXPR	OVER EXPR
IL12RB2		OVER EXPR	OVER EXPR	UNDER EXPR
MYD88	OVER EXPR	OVER EXPR		UNDER EXPR
BMPR1A			OVER EXPR	UNDER EXPR
PTPRJ		OVER EXPR	OVER EXPR	OVER EXPR
TNFRSF13		OVER EXPR	OVER EXPR	UNDER EXPR
CXCR3		OVER EXPR		UNDER EXPR
IL1RN		OVER EXPR	OVER EXPR	UNDER EXPR
CXCR5	OVER EXPR	OVER EXPR	OVER EXPR	UNDER EXPR
CCR4		OVER EXPR	OVER EXPR	UNDER EXPR
IL4R		OVER EXPR	OVER EXPR	UNDER EXPR
IL2RB		OVER EXPR	OVER EXPR
TNFRSF12		OVER EXPR	OVER EXPR	OVER EXPR
CXCR4		OVER EXPR	OVER EXPR	UNDER EXPR
KLRD1		OVER EXPR	OVER EXPR
IRAK1BP1		OVER EXPR		OVER EXPR
PVR	OVER EXPR	OVER EXPR	OVER EXPR	UNDER EXPR
IL15RA	OVER EXPR			OVER EXPR
TLR1				OVER EXPR
ACVR1B			OVER EXPR	OVER EXPR
IL12RB1	OVER EXPR	OVER EXPR		OVER EXPR
IL18R1		OVER EXPR		OVER EXPR
TRAF3		OVER EXPR		OVER EXPR
IFNGR1			OVER EXPR	UNDER EXPR
PLAUR			OVER EXPR	OVER EXPR
IL21R				UNDER EXPR
IL23R			OVER EXPR	UNDER EXPR

TABLE 6

Candidate Receptor Molecules

% Differential expression (compared to Th0)

IL23R knockout

Symbol	Early	Intermediate	Late	(late)

PTPLA				UNDER EXPR
PSTPIP1		OVER EXPR	OVER EXPR	UNDER EXPR
TK1				UNDER EXPR
EIF2AK2	OVER EXPR
PTEN				UNDER EXPR
BPGM				UNDER EXPR
DCK				OVER EXPR
PTPRS				OVER EXPR
PTPN18				OVER EXPR
MKNK2				OVER EXPR
PTPN1		OVER EXPR		UNDER EXPR
PTPRE				UNDER EXPR
SH2D1A				OVER EXPR
DUSP22	OVER EXPR
PLK2				OVER EXPR
DUSP6				UNDER EXPR
CDC25B				UNDER EXPR
SLK			OVER EXPR	UNDER EXPR
MAP3K5				UNDER EXPR
BMPR1A			OVER EXPR	UNDER EXPR
ACP5		OVER EXPR	OVER EXPR	UNDER EXPR
TXK		OVER EXPR	OVER EXPR	UNDER EXPR
RIPK3		OVER EXPR	OVER EXPR	UNDER EXPR
PPP3CA				OVER EXPR
PTPRF		OVER EXPR	OVER EXPR	OVER EXPR
PACSIN1				OVER EXPR
NEK4		OVER EXPR		UNDER EXPR
PIP4K2A				UNDER EXPR
PPME1		OVER EXPR	OVER EXPR	UNDER EXPR
SRPK2				UNDER EXPR
DUSP2				OVER EXPR
PHACTR2		OVER EXPR		OVER EXPR
HK2	OVER EXPR	OVER EXPR
DCLK1				OVER EXPR
PPP2R5A				UNDER EXPR
RIPK1	OVER EXPR			UNDER EXPR
GK				OVER EXPR
RNASEL	OVER EXPR			OVER EXPR
GMFG		OVER EXPR	OVER EXPR	OVER EXPR
STK4				UNDER EXPR
HINT3				OVER EXPR
DAPP1		OVER EXPR		UNDER EXPR
TEC	OVER EXPR	OVER EXPR	OVER EXPR	UNDER EXPR
GMFB		OVER EXPR		OVER EXPR
PTPN6				UNDER EXPR
RIPK2				UNDER EXPR
PIM1		OVER EXPR	OVER EXPR	OVER EXPR
NEK6		OVER EXPR	OVER EXPR	UNDER EXPR
ACVR2A		OVER EXPR	OVER EXPR	UNDER EXPR
AURKB				UNDER EXPR
FES		OVER EXPR	OVER EXPR
ACVR1B			OVER EXPR	OVER EXPR
CDK6		OVER EXPR	OVER EXPR	UNDER EXPR
ZAK		OVER EXPR	OVER EXPR	UNDER EXPR
VRK2				UNDER EXPR
MAP3K8	OVER EXPR			UNDER EXPR
DUSP14		OVER EXPR		UNDER EXPR
SGK1	OVER EXPR	OVER EXPR	OVER EXPR	UNDER EXPR
PRKCQ	OVER EXPR			UNDER EXPR
JAK3		OVER EXPR		UNDER EXPR
ULK2		OVER EXPR		UNDER EXPR
HIPK2			OVER EXPR	OVER EXPR
PTPRJ		OVER EXPR	OVER EXPR	OVER EXPR
SPHK1		OVER EXPR
INPP1				UNDER EXPR
TNK2		OVER EXPR	OVER EXPR	OVER EXPR
PCTK1		OVER EXPR	OVER EXPR	OVER EXPR
DUSP1				OVER EXPR
NUDT4				UNDER EXPR
MAP4K3		OVER EXPR
TGFBR1		OVER EXPR	OVER EXPR	OVER EXPR
PTP4A1				OVER EXPR
HK1		OVER EXPR		OVER EXPR
DUSP16	OVER EXPR			UNDER EXPR
AMP32A				OVER EXPR
DDR1		OVER EXPR	OVER EXPR	UNDER EXPR
ITK				UNDER EXPR
WNK1				UNDER EXPR
NAGK			OVER EXPR	UNDER EXPR
STK38			OVER EXPR
BMP2K	OVER EXPR	OVER EXPR	OVER EXPR	OVER EXPR
BUB1				UNDER EXPR
AAK1				OVER EXPR
SIK1				OVER EXPR
DUSP10		OVER EXPR		UNDER EXPR
PRKCA				OVER EXPR
PIM2	OVER EXPR			UNDER EXPR
STK17B			OVER EXPR	UNDER EXPR
TK2				UNDER EXPR
STK39				OVER EXPR
ALPK2		OVER EXPR	OVER EXPR	UNDER EXPR
MST4				OVER EXPR
PHLPP1				UNDER EXPR

TABLE 7

Candidate Kinases

% Differential expression (compared to Th)

IL23R knockout

Symbol	Early	Intermediate	Late	(late)

SGK1	OVER EXPR	OVER EXPR	OVER EXPR	UNDER EXPR
HK2	OVER EXPR	OVER EXPR	OVER EXPR
PRPS1				UNDER EXPR
CAMK4
ZAP70
TXK	OVER EXPR	OVER EXPR	OVER EXPR	UNDER EXPR
NEK6		OVER EXPR	OVER EXPR
MAPKAPK2			OVER EXPR
MFHAS1	UNDER EXPR			OVER EXPR
PDXK
PRKCH			OVER EXPR	UNDER EXPR
CDK6		OVER EXPR	OVER EXPR
ZAK		OVER EXPR	OVER EXPR	UNDER EXPR
PKM2		OVER EXPR
JAK2		OVER EXPR	UNDER EXPR	UNDER EXPR
STK38	UNDER EXPR	UNDER EXPR	OVER EXPR
ADRBK1
PTK2B	UNDER EXPR
DGUOK		UNDER EXPR	UNDER EXPR
DGKA				UNDER EXPR
RIPK3		OVER EXPR	OVER EXPR	UNDER EXPR
PIM1		OVER EXPR	OVER EXPR	OVER EXPR
CDK5
STK17B			OVER EXPR
CLK3
CLK1
ITK		UNDER EXPR
AKT1				UNDER EXPR
PGK1
TWF1
LIMK2
RFK				UNDER EXPR
WNK1		UNDER EXPR		OVER EXPR
HIPK1
AXL		OVER EXPR	UNDER EXPR	UNDER EXPR
RPS6KB1
CDC42BPA
STK38L
PRKCD
PDK3
PI4KA
PNKP
CDKN3
STK19
PRPF4B				UNDER EXPR
MAP4K2
PDPK1
VRK1
TRRAP

TABLE 8

Candidate Signaling Molecules From Single Cell Analysis

% Differential expression (compared to Th)

IL23R knockout

Symbol	Early	Intermediate	Late	(late)

CTLA4		OVER EXPR	OVER EXPR	UNDER EXPR
CD9		UNDER EXPR	UNDER EXPR	UNDER EXPR
IL2RA	OVER EXPR	OVER EXPR	OVER EXPR	OVER EXPR
CD5L		OVER EXPR	OVER EXPR	OVER EXPR
CD24		OVER EXPR	OVER EXPR	UNDER EXPR
CD200	OVER EXPR	UNDER EXPR	UNDER EXPR	OVER EXPR
CD53		UNDER EXPR	OVER EXPR	UNDER EXPR
TNFRSF9	UNDER EXPR	UNDER EXPR		OVER EXPR
CD44			UNDER EXPR
CD96		UNDER EXPR	UNDER EXPR
CD83		UNDER EXPR	UNDER EXPR
IL27RA
CXCR3		OVER EXPR	OVER EXPR
TNFRSF4			UNDER EXPR
IL4R		OVER EXPR	OVER EXPR
PROCR		OVER EXPR	OVER EXPR	OVER EXPR
LAMP2	OVER EXPR	OVER EXPR		UNDER EXPR
CD74		UNDER EXPR	UNDER EXPR	OVER EXPR
TNFRSF13		OVER EXPR	OVER EXPR	UNDER EXPR
PDCD1			UNDER EXPR
TNFRSF1B
IL21R		UNDER EXPR	UNDER EXPR
IFNGR1			OVER EXPR	UNDER EXPR
ICOS			UNDER EXPR	OVER EXPR
PTPRC
ADAM17
FCGR2B
TNFSF9		UNDER EXPR	UNDER EXPR	UNDER EXPR
MS4A6A		UNDER EXPR	UNDER EXPR	UNDER EXPR
CCR4		OVER EXPR	OVER EXPR
CD226
CD3G		UNDER EXPR	UNDER EXPR
ENTPD1
ADAM10		UNDER EXPR	UNDER EXPR	UNDER EXPR
CD27	UNDER EXPR	UNDER EXPR	UNDER EXPR	UNDER EXPR
CD84		UNDER EXPR	UNDER EXPR
ITGAL	UNDER EXPR
CCND2				UNDER EXPR
BSG				UNDER EXPR
CD40LG
PTPRCAP	UNDER EXPR		UNDER EXPR	UNDER EXPR
CD68
CD63
SLC3A2
HLA-DQA1		OVER EXPR
CTSD
CSF1R
CD3D		UNDER EXPR
CD247			UNDER EXPR	UNDER EXPR
CD14
ITGAV
FCER1G
IL2RG		OVER EXPR		UNDER EXPR

TABLE 9

Candidate Receptor Molecules From Single Cell Analysis

% Differential expression (compared to Th)

IL23R knockout

Symbol	Early	Intermediate	Late	(late)

PLEK		OVER EXPR
BHLH40	OVER EXPR	OVER EXPR
ARID5A	OVER EXPR	OVER EXPR	OVER EXPR	OVER EXPR
ETS1		OVER EXPR	OVER EXPR	UNDER EXPR
IRF4	OVER EXPR	OVER EXPR	OVER EXPR
IKZF3
RORC		OVER EXPR	OVER EXPR	UNDER EXPR
STAT4		UNDER EXPR	UNDER EXPR	UNDER EXPR
RORA		OVER EXPR	OVER EXPR	UNDER EXPR
PHF6
ID3	UNDER EXPR	UNDER EXPR	UNDER EXPR	OVER EXPR
ZBTB32		UNDER EXPR		OVER EXPR
IFI35	OVER EXPR
ID2	OVER EXPR	OVER EXPR	OVER EXPR	UNDER EXPR
MDM4
CHMP2A
ANKHD1
CHD7		OVER EXPR	OVER EXPR	UNDER EXPR
STAT5B		OVER EXPR	OVER EXPR
MAML2
ID1		OVER EXPR	OVER EXPR	OVER EXPR
SS18		OVER EXPR
MAF
ETV6	OVER EXPR	OVER EXPR
CCRN4L		OVER EXPR	OVER EXPR
NASP
BLOC1S1				OVER EXPR
XAB2
STAT5A		OVER EXPR		UNDER EXPR
IKZF1	UNDER EXPR
JUNB		OVER EXPR	OVER EXPR
THRAP3				OVER EXPR
SP100	OVER EXPR
PYCR1		OVER EXPR	OVER EXPR	OVER EXPR
HMGA1
TAF1B				UNDER EXPR
CNOT2
NOC4L	OVER EXPR
SKI	UNDER EXPR	OVER EXPR	OVER EXPR
VAV1		OVER EXPR	OVER EXPR
NR4A2		UNDER EXPR	UNDER EXPR	OVER EXPR
LGTN
NFKBIA				UNDER EXPR
KDM6B
MAZ
CDC5L				UNDER EXPR
HCLS1	UNDER EXPR		OVER EXPR
BAZ2B	OVER EXPR
MXD3
BATF	OVER EXPR	OVER EXPR
E2F4
NFKBIB
RBPJ		OVER EXPR	OVER EXPR	OVER EXPR
TOX4
CENPT
CASP8AP2
ECE2
MIER1
AHR	OVER EXPR	OVER EXPR	OVER EXPR
SPOP				UNDER EXPR
BTG1
MATR3				UNDER EXPR
JMJD1C	OVER EXPR	OVER EXPR
HMGB2		OVER EXPR
CREG1				OVER EXPR
NFATC1
NFE2L2	OVER EXPR	OVER EXPR	OVER EXPR
WHSC1L1
TBPL1
TRIP12
BTG2
HMGN1				UNDER EXPR
ATF2
NR4A3
C16ORF80
MBNL1		UNDER EXPR	UNDER EXPR
WDHD1
LASS6
CREM		OVER EXPR	OVER EXPR
CARM1
RNF5				UNDER EXPR
SMARCA4				OVER EXPR
GATAD1
TCERG1				UNDER EXPR
CHRAC1
NFYC
ATF3			OVER EXPR	OVER EXPR
ZNF326	OVER EXPR
KLF13
TFDP1
LRRFIP1		OVER EXPR	OVER EXPR
MORF4L2
FOXN3
HDAC8
MORF4L1
DNAJC2				OVER EXPR
MAFG
YBX1

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 (Sänchez-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 (FIG. 2d,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 heamatopoesis (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). 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 comprises a kit with a detection reagent that binds to one or more signature nucleic acids. Also provided by the invention is an array of detection reagents, e.g., oligonucleotides that can bind to one or more signature nucleic acids. Suitable detection reagents include nucleic acids that specifically identify one or more signature nucleic acids by having homologous nucleic acid sequences, such as oligonucleotide sequences, complementary to a portion of the signature nucleic acids packaged together in the form of a kit. The oligonucleotides can be fragments of the signature genes. For example the oligonucleotides can be 200, 150, 100, 50, 25, 10 or fewer nucleotides in length. The kit may contain in separate container or packaged separately with reagents for binding them to the matrix), control formulations (positive and/or negative), and/or a detectable label such as fluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase, radiolabels, among others. Instructions (e.g., written, tape, VCR, CD-ROM, etc.) for carrying out the assay may be included in the kit. The assay may for example be in the form of a Northern hybridization or DNA chips or a sandwich ELISA or any other method as known in the art. Alternatively, the kit contains a nucleic acid substrate array comprising one or more nucleic acid sequences.
Use of T Cell Modulating Agents
Suitable T cell modulating agent(s) for use in any of the compositions and methods provided herein include an antibody, a soluble polypeptide, a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a small molecule agent. By way of non-limiting example, suitable T cell modulating agents or agents for use in combination with one or more T cell modulating agents are shown below in Table 10.

TABLE 10

T cell Modulating Agents

Target	Agent

CCR6	prostaglandin E2, lipopolysaccharide, mip-3alpha, vegf, rantes, calcium,
	bortezomib, ccl4, larc, tarc, lipid, E. coli B5 lipopolysaccharide
CCR5	cholesterol, cyclosporin a, glutamine, methionine, guanine, simvastatin,
	threonine, indinavir, lipoxin A4, cysteine, prostaglandin E2, zinc, dapta,
	17-alpha-ethinylestradiol, polyacrylamide, progesterone, zidovudine,
	rapamycin, rantes, glutamate, alanine, valine, ccl4, quinine, NSC 651016,
	methadone, pyrrolidine dithiocarbamate, palmitate, nor-binaltorphimine,
	interferon beta-1a, vitamin-e, tak779, lipopolysaccharide, cisplatin,
	albuterol, fluvoxamine, vicriviroc, bevirimat, carbon tetrachloride,
	galactosylceramide, ATP-gamma-S, cytochalasin d, hemozoin, CP
	96345, tyrosine, etravirine, vitamin d, mip 1alpha, ammonium, tyrosine
	sulfate, isoleucine, isopentenyl diphosphate, Il 10, serine, N-acetyl-L-
	cysteine, histamine, cocaine, ritonavir, tipranavir, aspartate, atazanavir,
	tretinoin, ATP, ribavirin, butyrate, N-nitro-L-arginine methyl ester, larc,
	buthionine sulfoximine, DAPTA, aminooxypentane-rantes, triamcinolone
	acetonide, shikonin, actinomycin d, bucladesine, aplaviroc, nevirapine,
	N-formyl-Met-Leu-Phe, cyclosporin A, lipoarabinomannan, nucleoside,
	sirolimus, morphine, mannose, calcium, heparin, c-d4i, pge2, beta-
	estradiol, mdms, dextran sulfate, dexamethasone, arginine, ivig, mcp 2,
	cyclic amp, U 50488H, N-methyl-D-aspartate, hydrogen peroxide, 8-
	carboxamidocyclazocine, latex, groalpha, xanthine, ccl3, retinoic acid,
	Maraviroc, sdf 1, opiate, efavirenz, estrogen, bicyclam, enfuvirtide,
	filipin, bleomycin, polysaccharide, tarc, pentoxifylline, E. coli B5
	lipopolysaccharide, methylcellulose, maraviroc
ITGA3	SP600125, paclitaxel, decitabine, e7820, retinoid, U0126, serine, retinoic
	acid, tyrosine, forskolin, Ca2+
IRF4	prostaglandin E2, phorbol myristate acetate, lipopolysaccharide, A23187,
	tacrolimus, trichostatin A, stallimycin, imatinib, cyclosporin A, tretinoin,
	bromodeoxyuridine, ATP-gamma-S, ionomycin
BATF	Cyclic AMP, serine, tacrolimus, beta-estradiol, cyclosporin A, leucine
RBPJ	zinc, tretinoin
PROCR	lipopolysaccharide, 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,
	nitric oxide
ZEB1	resveratrol, zinc, sulforafan, sorafenib, progesterone, PD-0332991,
	dihydrotestosterone, silibinin, LY294002, 4-hydroxytamoxifen, valproic
	acid, beta-estradiol, forskolin, losartan potassium, fulvestrant, vitamin d
POU2AF1	terbutaline, phorbol myristate acetate, bucladesine, tyrosine, ionomycin,
	KT5720, H89
EGR1	ghrelin, ly294002, silicone, sodium, propofol, 1,25 dihydroxy vitamin
	d3, tetrodotoxin, threonine, cyclopiazonic acid, urea, quercetin,
	ionomycin, 12-o-tetradecanoylphorbol 13-acetate, fulvestrant,
	phenylephrine, formaldehyde, cysteine, leukotriene C4, prazosin,
	LY379196, vegf, rapamycin, leupeptin, pd 98, 059, ruboxistaurin, pCPT-
	cAMP, methamphetamine, nitroprusside, H-7, Ro31-8220,
	phosphoinositide, lysophosphatidylcholine, bufalin, calcitriol, leuprolide,
	isobutylmethylxanthine, potassium chloride, acetic acid, cyclothiazide,
	quinolinic acid, tyrosine, adenylate, resveratrol, topotecan, genistein,
	thymidine, D-glucose, mifepristone, lysophosphatidic acid, leukotriene
	D4, carbon monoxide, poly rI:rC-RNA, sp 600125, agar, cocaine, 4-
	nitroquinoline-1-oxide, tamoxifen, lead, fibrinogen, tretinoin, atropine,
	mithramycin, K+, epigallocatechin-gallate, ethylenediaminetetraacetic
	acid, h2o2, carbachol, sphingosine-1-phosphate, iron, 5-
	hydroxytryptamine, amphetamine, SP600125, actinomycin d, SB203580,
	cyclosporin A, norepinephrine, okadaic acid, ornithine, LY294002, pge2,
	beta-estradiol, glucose, erlotinib, arginine, 1-alpha, 25-dihydroxy vitamin
	D3, dexamethasone, pranlukast, phorbol myristate acetate, nimodipine,
	desipramine, cyclic amp, N-methyl-D-aspartate, atipamezole, acadesine,
	losartan, salvin, methylnitronitrosoguanidine, EGTA, gf 109203x,
	nitroarginine, 5-N-ethylcarboxamido adenosine, 15-deoxy-delta-12, 14-
	PGJ 2, dbc-amp, manganese superoxide, di(2-ethylhexyl) phthalate,
	egcg, mitomycin C, 6,7-dinitroquinoxaline-2, 3-dione, GnRH-A,
	estrogen, ribonucleic acid, imipramine, bapta, L-triiodothyronine,
	prostaglandin, forskolin, nogalamycin, losartan potassium, lipid,
	vincristine, 2-amino-3-phosphonopropionic acid, prostacyclin,
	methylnitrosourea, cyclosporin a, vitamin K3, thyroid hormone,
	diethylstilbestrol, D-tubocurarine, tunicamycin, caffeine, phorbol,
	guanine, bisindolylmaleimide, apomorphine, arachidonic acid, SU6656,
	prostaglandin E2, zinc, ptx1, progesterone, cyclosporin H,
	phosphatidylinositol, U0126, hydroxyapatite, epoprostenol, glutamate,
	5fluorouracil, indomethacin, 5-fluorouracil, RP 73401, Ca2+, superoxide,
	trifluoperazine, nitric oxide, lipopolysaccharide, cisplatin, diazoxide, tgf
	beta1, calmidazolium, anisomycin, paclitaxel, sulindac sulfide,
	ganciclovir, gemcitabine, testosterone, ag 1478, glutamyl-Se-
	methylselenocysteine, doxorubicin, tolbutamide, cytochalasin d,
	PD98059, leucine, SR 144528, cyclic AMP, matrigel, haloperidol, serine,
	sb 203580, triiodothyronine, reverse, N-acetyl-L-cysteine, ethanol, s-
	nitroso-n-acetylpenicillamine, curcumin, l-nmma, H89, tpck, calyculin a,
	chloramphenicol, A23187, dopamine, platelet activating factor, arsenite,
	selenomethylselenocysteine, ropinirole, saralasin, methylphenidate,
	gentamicin, reserpine, triamcinolone acetonide, methyl
	methanesulfonate, wortmannin, thapsigargin, deferoxamine, calyculin A,
	peptidoglycan, dihydrotestosterone, calcium, phorbol-12-myristate,
	ceramide, nmda, 6-cyano-7-nitroquinoxaline-2,3-dione, hydrogen
	peroxide, carrageenan, sch 23390, linsidomine, oxygen, clonidine,
	fluoxetine, retinoid, troglitazone, retinoic acid, epinephrine, n
	acetylcysteine, KN-62, carbamylcholine, 2-amino-5-phosphonovaleric
	acid, oligonucleotide, gnrh, rasagiline, 8-bromo-cAMP, muscarine,
	tacrolimus, kainic acid, chelerythrine, inositol 1,4,5 trisphosphate,
	yohimbine, acetylcholine, atp, 15-deoxy-delta-12, 14-prostaglandin j2,
	ryanodine, CpG oligonucleotide, cycloheximide, BAPTA-AM,
	phenylalanine
ETV6	lipopolysaccharide, retinoic acid, prednisolone, valproic acid, tyrosine,
	cerivastatin, vegf, agar, imatinib, tretinoin
IL17RA	rantes, lipopolysaccharide, 17-alpha-ethinylestradiol, camptothecin, E.
	coli B5 lipopolysaccharide
EGR2	phorbol myristate acetate, lipopolysaccharide, platelet activating factor,
	carrageenan, edratide, 5-N-ethylcarboxamido adenosine, potassium
	chloride, dbc-amp, tyrosine, PD98059, camptothecin, formaldehyde,
	prostaglandin E2, leukotriene C4, zinc, cyclic AMP, GnRH-A,
	bucladesine, thapsigargin, kainic acid, cyclosporin A, mifepristone,
	leukotriene D4, LY294002, L-triiodothyronine, calcium, beta-estradiol,
	H89, dexamethasone, cocaine
SP4	betulinic acid, zinc, phorbol myristate acetate, LY294002, methyl 2-
	cyano-3,12-dioxoolean-1, 9-dien-28-oate, beta-estradiol, Ca2+
IRF8	oligonucleotide, chloramphenicol, lipopolysaccharide, estrogen,
	wortmannin, pirinixic acid, carbon monoxide, retinoic acid, tyrosine
NFKB1	Bay 11-7085, Luteolin, Triflusal, Bay 11-7821, Thalidomide, Caffeic
	acid phenethyl ester, Pranlukast
TSC22D3	phorbol myristate acetate, prednisolone, sodium, dsip, tretinoin, 3-
	deazaneplanocin, gaba, PD98059, leucine, triamcinolone acetonide,
	prostaglandin E2, steroid, norepinephrine, U0126, acth, calcium, ethanol,
	beta-estradiol, lipid, chloropromazine, arginine, dexamethasone
PML	lipopolysaccharide, glutamine, thyroid hormone, cadmium, lysine,
	tretinoin, bromodeoxyuridine, etoposide, retinoid, pic 1, arsenite, arsenic
	trioxide, butyrate, retinoic acid, alpha-retinoic acid, h2o2, camptothecin,
	cysteine, leucine, zinc, actinomycin d, proline, stallimycin, U0126
IL12RB1	prostaglandin E2, phorbol myristate acetate, lipopolysaccharide,
	bucladesine, 8-bromo-cAMP, gp 130, AGN194204, galactosylceramide-
	alpha, tyrosine, ionomycin, dexamethasone, Il-12
IL21R	azathioprine, lipopolysaccharide, okadaic acid, E. coli B5
	lipopolysaccharide, calyculin A
NOTCH1	interferon beta-1a, lipopolysaccharide, cisplatin, tretinoin, oxygen,
	vitamin B12, epigallocatechin-gallate, isobutylmethylxanthine, threonine,
	apomorphine, matrigel, trichostatin A, vegf, 2-acetylaminofluorene,
	rapamycin, dihydrotestosterone, poly rI:rC-RNA, hesperetin, valproic
	acid, asparagine, lipid, curcumin, dexamethasone, glycogen, CpG
	oligonucleotide, nitric oxide
ETS2	oligonucleotide
MINA	phorbol myristate acetate, 4-hydroxytamoxifen
SMARCA4	cyclic amp, cadmium, lysine, tretinoin, latex, androstane, testosterone,
	sucrose, tyrosine, cysteine, zinc, oligonucleotide, estrogen, steroid,
	trichostatin A, tpmp, progesterone, histidine, atp, trypsinogen, glucose,
	agar, lipid, arginine, vancomycin, dihydrofolate
FAS	hoechst 33342, ly294002, 2-chlorodeoxyadenosine, glutamine, cd 437,
	tetrodotoxin, cyclopiazonic acid, arsenic trioxide, phosphatidylserine,
	niflumic acid, gliadin, ionomycin, safrole oxide, methotrexate, rubitecan,
	cysteine, propentofylline, vegf, boswellic acids, rapamycin, pd 98, 059,
	captopril, methamphetamine, vesnarinone, tetrapeptide, oridonin,
	raltitrexed, pirinixic acid, nitroprusside, H-7, beta-boswellic acid,
	adriamycin, concanamycin a, etoposide, trastuzumab, cyclophosphamide,
	ifn-alpha, tyrosine, rituximab, selenodiglutathione, chitosan, omega-N-
	methylarginine, creatinine, resveratrol, topotecan, genistein, trichostatin
	A, decitabine, thymidine, D-glucose, mifepristone, tetracycline, Sn50
	peptide, poly rI:rC-RNA, actinomycin D, sp 600125, doxifluridine, agar,
	ascorbic acid, acetaminophen, aspirin, tamoxifen, okt3, edelfosine,
	sulforafan, aspartate, antide, n, n-dimethylsphingosine, epigallocatechin-
	gallate, N-nitro-L-arginine methyl ester, h2o2, cerulenin, sphingosine-1-
	phosphate, SP600125, sodium nitroprusside, glycochenodeoxycholic
	acid, ceramides, actinomycin d, SB203580, cyclosporin A, morphine,
	LY294002, n(g)-nitro-l-arginine methyl ester, 4-hydroxynonenal,
	piceatannol, valproic acid, beta-estradiol, 1-alpha, 25-dihydroxy vitamin
	D3, arginine, dexamethasone, sulfadoxine, phorbol myristate acetate,
	beta-lapachone, nitrofurantoin, chlorambucil,
	methylnitronitrosoguanidine, CD 437, opiate, egcg, mitomycin C,
	estrogen, ribonucleic acid, fontolizumab, tanshinone iia, recombinant
	human endostatin, fluoride, L-triiodothyronine, bleomycin, forskolin,
	nonylphenol, zymosan A, vincristine, daunorubicin, prednisolone,
	cyclosporin a, vitamin K3, diethylstilbestrol, deoxyribonucleotide,
	suberoylanilide hydroxamic acid, orlistat, 3-(4,5-dimethylthiazol-2-yl)-2,5-
	diphenyltetrazolium bromide, rottlerin, arachidonic acid, ibuprofen,
	prostaglandin E2, toremifene, depsipeptide, ochratoxin A, (glc)4,
	phosphatidylinositol, mitomycin c, rantes, sphingosine, indomethacin,
	5fluorouracil, phosphatidylcholine, 5-fluorouracil, mg 132, thymidylate,
	trans-cinnamaldehyde, sterol, polyadenosine diphosphate ribose, nitric
	oxide, vitamin e succinate, lipopolysaccharide, cisplatin, herbimycin a, 5-
	aza-2′deoxycytidine, proteasome inhibitor PSI, 2,5-hexanedione,
	epothilone B, caffeic acid phenethyl ester, glycerol 3-phosphate, tgf
	beta1, anisomycin, paclitaxel, gemcitabine, medroxyprogesterone
	acetate, hymecromone, testosterone, ag 1478, doxombicin, S-nitroso-N-
	acetylpenicillamine, adpribose, sulforaphane, vitamin d, annexin-v,
	lactate, reactive oxygen species, sb 203580, serine, N-acetyl-L-cysteine,
	dutp, infliximab, ethanol, curcumin, cytarabine, tpck, calyculin a,
	dopamine, gp 130, bromocriptine, apicidin, fatty acid, citrate,
	glucocorticoid, arsenite, butyrate, peplomycin, oxaliplatin, camptothecin,
	benzyloxycarbonyl-Leu-Leu-Leu aldehyde, clofibrate, carbon,
	wortmannin, fludarabine, N-(3-(aminomethyl)benzyl)acetamidine,
	sirolimus, peptidoglycan, c2ceramide, dihydrotestosterone, 7-
	aminoactinomycin d, carmustine, heparin, ceramide, paraffin,
	mitoxantrone, docosahexaenoic acid, vitamin a, ivig, hydrogen peroxide,
	7-ethyl-10-hydroxy-camptomecin, oxygen, pydrin, bortezomib, retinoic
	acid, 1,4-phenylenebis(methylene)selenocyanate, teriflunomide,
	epinephrine, n acetylcysteine, noxa, irinotecan, oligonucleotide, d-api,
	rasagiline, 8-bromo-cAMP, atpo, agarose, fansidar, clobetasol
	propionate, teniposide, aurintricarboxylic acid, polysaccharide, CpG
	oligonucleotide, cycloheximide
IRF1	tamoxifen, chloramphenicol, polyinosinic-polycytidylic acid, inosine
	monophosphate, suberoylanilide hydroxamic acid, butyrate, iron, gliadin,
	zinc, actinomycin d, deferoxamine, phosphatidylinositol, adenine,
	ornthine, rantes, calcium, 2′,5′-oligoadenylate, pge2, poly(i-c),
	indoleamine, arginine, estradiol, nitric oxide, etoposide, adriamycin,
	oxygen, retinoid, guanylate, troglitazone, ifn-alpha, retinoic acid,
	tyrosine, adenylate, am 580, guanosine, oligonucleotide, estrogen,
	thymidine, tetracycline, serine, sb 203580, pdtc, lipid, cycloheximide
MYC	cd 437, 1,25 dihydroxy vitamin d3, phenethyl isothiocyanate, threonine,
	arsenic trioxide, salicylic acid, quercetin, prostaglandin E1, ionomycin,
	12-o-tetradecanoylphorbol 13-acetate, fulvestrant, phenylephrine, fisetin,
	4-coumaric acid, dihydroartemisinin, 3-deazaadenosine, nitroprusside,
	pregna-4, 17-diene-3, 16-dione, adriamycin, bromodeoxyuridine,
	AGN194204, STA-9090, isobutylmethylxanthine, potassium chloride,
	docetaxel, quinolinic acid, 5,6,7,8-tetrahydrobiopterin, propranolol,
	delta 7-pga1, topotecan, AVI-4126, trichostatin A, decitabine, thymidine,
	D-glucose, mifepristone, poly rI:rC-RNA, letrozole, L-threonine, 5-
	hydroxytryptamine, bucladesine, SB203580, 1′-acetoxychavicol acetate,
	cyclosporin A, okadaic acid, dfmo, LY294002, hmba, piceatannol, 2′,5′-
	oligoadenylate, 4-hydroxytamoxifen, butylbenzyl phthalate,
	dexamethasone, ec 109, phosphatidic acid, grape seed extract, phorbol
	myristate acetate, coumermycin, tosylphenylalanyl chloromethyl ketone,
	CD 437, di(2-ethylhexyl) phthalate, butyrine, cytidine, sodium arsenite,
	tanshinone iia, L-triiodothyronine, niacinamide, glycogen, daunorubicin,
	vincristine, carvedilol, bizelesin, 3-deazaneplanocin, phorbol, neplanocin
	a, panobinostat, [alcl], phosphatidylinositol, U0126,
	dichlororibofuranosylbenzimidazole, flavopiridol, 5-fluorouracil,
	verapamil, cyclopamine, nitric oxide, cisplatin, hrgbeta1, 5,6-dichloro-1-
	beta-d-ribofuranosylbenzimidazole, amsacrine, gemcitabine,
	aristeromycin, medroxyprogesterone acetate, gambogic acid, leucine,
	alpha-naphthyl acetate, cyclic AMP, reactive oxygen species, PD
	180970, curcumin, chloramphenicol, A23187, crocidolite asbestos, 6-
	hydroxydopamine, cb 33, arsenite, gentamicin, benzyloxycarbonyl-Leu-
	Leu-Leu aldehyde, clofibrate, wortmannin, sirolimus, ceramide,
	melphalan, 3M-001, linsidomine, CP-55940, hyaluronic acid, ethionine,
	clonidine, retinoid, bortezomib, oligonucleotide, methyl 2-cyano-3, 12-
	dioxoolean-1, 9-dien-28-oate, tacrolimus, embelin, methyl-beta-
	cyclodextrin, 3M-011, folate, ly294002, PP1, hydroxyurea, aclarubicin,
	phenylbutyrate, PD 0325901, methotrexate, Cd2+, prazosin, vegf,
	rapamycin, alanine, phenobarbital, pd 98, 059, trapoxin, 4-
	hydroperoxycyclophosphamide, methamphetamine, s-(1,2-
	dichlorovinyl)-l-cysteine, aphidicolin, vesnarinone, ADI PEG20,
	pirinixic acid, wp631, H-7, carbon tetrachloride, bufalin, 2,2-
	dimethylbutyric acid, etoposide, calcitriol, trastuzumab,
	cyclophosphamide, harringtonine, tyrosine, N(6)-(3-iodobenzyl)-5′-N-
	methylcarboxamidoadenosine, resveratrol, thioguanine, genistein, S-
	nitroso-N-acetyl-DL-penicillamine, zearalenone, lysophosphatidic acid,
	Sn50 peptide, roscovitine, actinomycin D, propanil, agar, tamoxifen,
	acetaminophen, imatinib, tretinoin, mithramycin, ATP, epigallocatechin-
	gallate, ferric ammonium citrate, acyclic retinoid, L-cysteine, nitroblue
	tetrazolium, actinomycin d, sodium nitroprusside, 1,2-
	dimethylhydrazine, dibutyl phthalate, ornithine, 4-hydroxynonenal, beta-
	estradiol, 1-alpha, 25-dihydroxy vitamin D3, cyproterone acetate,
	nimodipine, nitrofurantoin, temsirolimus,
	15-deoxy-delta-12, 14-PGJ 2, estrogen, ribonucleic acid, ciprofibrate,
	alpha-amanitin, SB 216763, bleomycin, forskolin, prednisolone,
	cyclosporin a, thyroid hormone, tunicamycin, phosphorothioate,
	suberoylanilide hydroxamic acid, pga2, 3-(4,5-dimethylthiazol-2-yl)-2,
	5-diphenyltetrazolium bromide, benzamide riboside,
	bisindolylmaleimide, SU6656, prostaglandin E2, depsipeptide,
	zidovudine, cerivastatin, progesterone, sethoxydim, indomethacin, mg
	132, mezerein, pyrrolidine dithiocarbamate, vitamin e succinate,
	herbimycin a, 5-aza-2′deoxycytidine, lipopolysaccharide, diazoxide,
	anisomycin, paclitaxel, sodium dodecylsulfate, nilotinib, oxysterol,
	doxombicin, lipofectamine, PD98059, steroid, delta-12-pgj2, serine, H-8,
	N-acetyl-L-cysteine, ethanol, n-(4-hydroxyphenyl)retinamide, tiazofurin,
	cytarabine, H89, 10-hydroxycamptothecin, everolimus, lactacystin, n(1),
	n(12)-bis(ethyl)spermine, silibinin, glucocorticoid, butyrate,
	camptothecin, triamcinolone acetonide, tocotrienol, n-ethylmaleimide,
	phorbol 12, 13-didecanoate, thapsigargin, deferoxamine, R59949,
	bryostatin 1, paraffin, romidepsin, vitamin a, docosahexaenoic acid,
	hydrogen peroxide, droloxifene, saikosaponin, fluoxetine, retinoic acid, n
	acetylcysteine, dithiothreitol, cordycepin, agarose, 8-bromo-cAMP, D-
	galactosamine, tachyplesin i, theophylline, metoprolol, SU6657, 15-
	deoxy-delta-12, 14-prostaglandin j2, dmso, 2-amino-5-azotoluene,
	cycloheximide

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 hepetiformis; chronic fatigue immune dysfunction syndrome (CFIDS), chronic inflammatory demyelinating polyneuropathy (CIPD), cicatricial pemphigold, 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, pernacious anemia, polyarteritis nodosa, polychondritis, polyglandular syndromes, polymyalgia rheumatica, polymyositis and dermatomyositis, primary agammaglobulinemia, primary biliary cirrhosis, psoriasis, psoriatic arthritis, Raynaud's phenomena, Reiter's syndrome, rheumatic fever, rheumatoid arthritis, sarcoidosis, scleroderma (progressive systemic sclerosis (PSS), also known as systemic sclerosis (SS)), Sjögren's syndrome, stiffman syndrome, systemic lupus erythematosus, Takayasu arteritis, temporal arteritis/giant cell arteritis, ulcerative colitis, uveitis, vitiligo and Wegener's granulomatosis.
In some embodiments, T cell modulating agents are useful in treating, delaying the progression of, or otherwise ameliorating a symptom of an autoimmune disease having an inflammatory component such as an aberrant inflammatory response in a subject. In some embodiments, T cell modulating agents are useful in treating an autoimmune disease that is known to be associated with an aberrant Th17 response, e.g., aberrant IL-17 production, such as, for example, multiple sclerosis (MS), psoriasis, inflammatory bowel disease, ulcerative colitis, Crohn's disease, uveitis, lupus, ankylosing spondylitis, and rheumatoid arthritis.
Inflammatory disorders include, for example, chronic and acute inflammatory disorders. Examples of inflammatory disorders include Alzheimer's disease, asthma, atopic allergy, allergy, atherosclerosis, bronchial asthma, eczema, glomerulonephritis, graft vs. host disease, hemolytic anemias, osteoarthritis, sepsis, stroke, transplantation of tissue and organs, vasculitis, diabetic retinopathy and ventilator induced lung injury.
Symptoms associated with these immune-related disorders include, for example, inflammation, fever, general malaise, fever, pain, often localized to the inflamed area, rapid pulse rate, joint pain or aches (arthralgia), rapid breathing or other abnormal breathing patterns, chills, confusion, disorientation, agitation, dizziness, cough, dyspnea, pulmonary infections, cardiac failure, respiratory failure, edema, weight gain, mucopurulent relapses, cachexia, wheezing, headache, and abdominal symptoms such as, for example, abdominal pain, diarrhea or constipation.
Efficaciousness of treatment is determined in association with any known method for diagnosing or treating the particular immune-related disorder. Alleviation of one or more symptoms of the immune-related disorder indicates that the T cell modulating agent confers a clinical benefit.
Administration of a T cell modulating agent to a patient suffering from an immune-related disorder or aberrant immune response is considered successful if any of a variety of laboratory or clinical objectives is achieved. For example, administration of a T cell modulating agent to a patient is considered successful if one or more of the symptoms associated with the immune-related disorder or aberrant immune response is alleviated, reduced, inhibited or does not progress to a further, i.e., worse, state. Administration of T cell modulating agent to a patient is considered successful if the immune-related disorder or aberrant immune response enters remission or does not progress to a further, i.e., worse, state.
A therapeutically effective amount of a T cell modulating agent relates generally to the amount needed to achieve a therapeutic objective. The amount required to be administered will furthermore depend on the specificity of the T cell modulating agent for its specific target, and will also depend on the rate at which an administered T cell modulating agent is depleted from the free volume other subject to which it is administered.
T cell modulating agents can be administered for the treatment of a variety of diseases and disorders in the form of pharmaceutical compositions. Principles and considerations involved in preparing such compositions, as well as guidance in the choice of components are provided, for example, in Remington: The Science And Practice Of Pharmacy 19th ed. (Alfonso R. Gennaro, et al., editors) Mack Pub. Co., Easton, Pa.: 1995; Drug Absorption Enhancement: Concepts, Possibilities, Limitations, And Trends, Harwood Academic Publishers, Langhorne, Pa., 1994; and Peptide And Protein Drug Delivery (Advances In Parenteral Sciences, Vol. 4), 1991, M. Dekker, New York.
Where polypeptide-based T cell modulating agents are used, the smallest fragment that specifically binds to the target and retains therapeutic function is preferred. Such fragments can be synthesized chemically and/or produced by recombinant DNA technology. (See, e.g., Marasco et al., Proc. Natl. Acad. Sci. USA, 90: 7889-7893 (1993)). The formulation can also contain more than one active compound as necessary for the particular indication being treated, preferably those with complementary activities that do not adversely affect each other. Alternatively, or in addition, the composition can comprise an agent that enhances its function, such as, for example, a cytotoxic agent, cytokine, chemotherapeutic agent, or growth-inhibitory agent. Such molecules are suitably present in combination in amounts that are effective for the purpose intended.
The invention comprehends a treatment method or Drug Discovery method or method of formulating or preparing a treatment comprising any one of the methods or uses herein discussed.
The present invention also relates to identifying molecules, advantageously small molecules or biologics, that may be involved in inhibiting one or more of the mutations in one or more genes selected from the group consisting of DEC1, PZLP, TCF4 and CD5L. The invention contemplates screening libraries of small molecules or biologics to identify compounds involved in suppressing or inhibiting expression of somatic mutations or alter the cells phenotypically so that the cells with mutations behave more normally in one or more of DEC1, PZLP, TCF4 and CD5L.
High-throughput screening (HTS) is contemplated for identifying small molecules or biologics involved in suppressing or inhibiting expression of somatic mutations in one or more of DEC1, PZLP, TCF4 and CD5L. The flexibility of the process has allowed numerous and disparate areas of biology to engage with an equally diverse palate of chemistry (see, e.g., Inglese et al., Nature Chemical Biology 3, 438-441 (2007)). Diverse sets of chemical libraries, containing more than 200,000 unique small molecules, as well as natural product libraries, can be screened. This includes, for example, the Prestwick library (1,120 chemicals) of off-patent compounds selected for structural diversity, collective coverage of multiple therapeutic areas, and known safety and bioavailability in humans, as well as the NINDS Custom Collection 2 consisting of a 1,040 compound-library of mostly FDA-approved drugs (see, e.g., U.S. Pat. No. 8,557,746) are also contemplated.
The NIH's Molecular Libraries Probe Production Centers Network (MLPCN) offers access to thousands of small molecules—chemical compounds that can be used as tools to probe basic biology and advance our understanding of disease. Small molecules can help researchers understand the intricacies of a biological pathway or be starting points for novel therapeutics. The Broad Institute's Probe Development Center (BIPDeC) is part of the MLPCN and offers access to a growing library of over 330,000 compounds for large scale screening and medicinal chemistry. Any of these compounds may be utilized for screening compounds involved in suppressing or inhibiting expression of somatic mutations in one or more of DEC1, PZLP, TCF4 and CD5L.
The phrase “therapeutically effective amount” as used herein refers to a nontoxic but sufficient amount of a drug, agent, or compound to provide a desired therapeutic effect.
As used herein “patient” refers to any human being receiving or who may receive medical treatment.
A “polymorphic site” refers to a polynucleotide that differs from another polynucleotide by one or more single nucleotide changes.
A “somatic mutation” refers to a change in the genetic structure that is not inherited from a parent, and also not passed to offspring.
Therapy or treatment according to the invention may be performed alone or in conjunction with another therapy, and may be provided at home, the doctor's office, a clinic, a hospital's outpatient department, or a hospital. Treatment generally begins at a hospital so that the doctor can observe the therapy's effects closely and make any adjustments that are needed. The duration of the therapy depends on the age and condition of the patient, the stage of the a cardiovascular disease, and how the patient responds to the treatment. Additionally, a person having a greater risk of developing a cardiovascular disease (e.g., a person who is genetically predisposed) may receive prophylactic treatment to inhibit or delay symptoms of the disease.
The medicaments of the invention are prepared in a manner known to those skilled in the art, for example, by means of conventional dissolving, lyophilizing, mixing, granulating or confectioning processes. Methods well known in the art for making formulations are found, for example, in Remington: The Science and Practice of Pharmacy, 20th ed., ed. A. R. Gennaro, 2000, Lippincott Williams & Wilkins, Philadelphia, and Encyclopedia of Pharmaceutical Technology, eds. J. Swarbrick and J. C. Boylan, 1988-1999, Marcel Dekker, New York.
Administration of medicaments of the invention may be by any suitable means that results in a compound concentration that is effective for treating or inhibiting (e.g., by delaying) the development of a cardiovascular disease. The compound is admixed with a suitable carrier substance, e.g., a pharmaceutically acceptable excipient that preserves the therapeutic properties of the compound with which it is administered. One exemplary pharmaceutically acceptable excipient is physiological saline. The suitable carrier substance is generally present in an amount of 1-95% by weight of the total weight of the medicament. The medicament may be provided in a dosage form that is suitable for oral, rectal, intravenous, intramuscular, subcutaneous, inhalation, nasal, topical or transdermal, vaginal, or ophthalmic administration. Thus, the medicament may be in form of, e.g., tablets, capsules, pills, powders, granulates, suspensions, emulsions, solutions, gels including hydrogels, pastes, ointments, creams, plasters, drenches, delivery devices, suppositories, enemas, injectables, implants, sprays, or aerosols.
In order to determine the genotype of a patient according to the methods of the present invention, it may be necessary to obtain a sample of genomic DNA from that patient. That sample of genomic DNA may be obtained from a sample of tissue or cells taken from that patient.
The tissue sample may comprise but is not limited to hair (including roots), skin, buccal swabs, blood, or saliva. The tissue sample may be marked with an identifying number or other indicia that relates the sample to the individual patient from which the sample was taken. The identity of the sample advantageously remains constant throughout the methods of the invention thereby guaranteeing the integrity and continuity of the sample during extraction and analysis. Alternatively, the indicia may be changed in a regular fashion that ensures that the data, and any other associated data, can be related back to the patient from whom the data was obtained. The amount/size of sample required is known to those skilled in the art.
Generally, the tissue sample may be placed in a container that is labeled using a numbering system bearing a code corresponding to the patient. Accordingly, the genotype of a particular patient is easily traceable.
In one embodiment of the invention, a sampling device and/or container may be supplied to the physician. The sampling device advantageously takes a consistent and reproducible sample from individual patients while simultaneously avoiding any cross-contamination of tissue. Accordingly, the size and volume of sample tissues derived from individual patients would be consistent.
According to the present invention, a sample of DNA is obtained from the tissue sample of the patient of interest. Whatever source of cells or tissue is used, a sufficient amount of cells must be obtained to provide a sufficient amount of DNA for analysis. This amount will be known or readily determinable by those skilled in the art.
DNA is isolated from the tissue/cells by techniques known to those skilled in the art (see, e.g., U.S. Pat. Nos. 6,548,256 and 5,989,431, Hirota et al., Jinrui Idengaku Zasshi. September 1989; 34(3):217-23 and John et al., Nucleic Acids Res. Jan. 25, 1991; 19(2):408; the disclosures of which are incorporated by reference in their entireties). For example, high molecular weight DNA may be purified from cells or tissue using proteinase K extraction and ethanol precipitation. DNA may be extracted from a patient specimen using any other suitable methods known in the art.
It is an object of the present invention to determine the genotype of a given patient of interest by analyzing the DNA from the patent, in order to identify a patient carrying specific somatic mutations of the invention that are associated with developing a cardiovascular disease. In particular, the kit may have primers or other DNA markers for identifying particular mutations such as, but not limited to, one or more genes selected from the group consisting of DEC1, PZLP, TCF4 and CD5L.
There are many methods known in the art for determining the genotype of a patient and for identifying or analyzing whether a given DNA sample contains a particular somatic mutation. Any method for determining genotype can be used for determining genotypes in the present invention. Such methods include, but are not limited to, amplimer sequencing, DNA sequencing, fluorescence spectroscopy, fluorescence resonance energy transfer (or “FRET”)-based hybridization analysis, high throughput screening, mass spectroscopy, nucleic acid hybridization, polymerase chain reaction (PCR), RFLP analysis and size chromatography (e.g., capillary or gel chromatography), all of which are well known to one of skill in the art.
The methods of the present invention, such as whole exome sequencing and targeted amplicon sequencing, have commercial applications in diagnostic kits for the detection of the somatic mutations in patients. A test kit according to the invention may comprise any of the materials necessary for whole exome sequencing and targeted amplicon sequencing, for example, according to the invention. In a particular advantageous embodiment, a diagnostic for the present invention may comprise testing for any of the genes in disclosed herein. The kit further comprises additional means, such as reagents, for detecting or measuring the sequences of the present invention, and also ideally a positive and negative control.
The present invention further encompasses probes according to the present invention that are immobilized on a solid or flexible support, such as paper, nylon or other type of membrane, filter, chip, glass slide, microchips, microbeads, or any other such matrix, all of which are within the scope of this invention. The probe of this form is now called a “DNA chip”. These DNA chips can be used for analyzing the somatic mutations of the present invention. The present invention further encompasses arrays or microarrays of nucleic acid molecules that are based on one or more of the sequences described herein. As used herein “arrays” or “microarrays” refers to an array of distinct polynucleotides or oligonucleotides synthesized on a solid or flexible support, such as paper, nylon or other type of membrane, filter, chip, glass slide, or any other suitable solid support. In one embodiment, the microarray is prepared and used according to the methods and devices described in U.S. Pat. Nos. 5,446,603; 5,545,531; 5,807,522; 5,837,832; 5,874,219; 6,114,122; 6,238,910; 6,365,418; 6,410,229; 6,420,114; 6,432,696; 6,475,808 and 6,489,159 and PCT Publication No. WO 01/45843 A2, the disclosures of which are incorporated by reference in their entireties.

EXAMPLES & TECHNOLOGIES AS TO THE INSTANT INVENTION

The following examples are given for the purpose of illustrating various embodiments of the invention and are not meant to limit the present invention in any fashion. The present examples, along with the methods described herein are presently representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the invention. Changes therein and other uses which are encompassed within the spirit of the invention as defined by the scope of the claims will occur to those skilled in the art.
In this regard, mention is made that mutations in cells and also mutated mice for use in or as to the invention can be by way of the CRISPR-Cas system or a Cas9-expressing eukaryotic cell or Cas-9 expressing eukaryote, such as a mouse. The Cas9-expressing eukaryotic cell or eukaryote, e.g., mouse, can have guide RNA delivered or administered thereto, whereby the RNA targets a loci and induces a desired mutation for use in or as to the invention. With respect to general information on CRISPR-Cas Systems, components thereof, and delivery of such components, including methods, materials, delivery vehicles, vectors, particles, and making and using thereof, including as to amounts and formulations, as well as Cas9-expressing eukaryotic cells, Cas-9 expressing eukaryotes, such as a mouse, all useful in or as to the instant invention, reference is made to: U.S. Pat. Nos. 8,697,359, 8,771,945, 8,795,965, 8,865,406, 8,871,445, 8,889,356, 8,889,418, 8,895,308, 8,932,814, 8,945,839, 8,906,616; US Patent Publications US 2014-0310830 (U.S. application Ser. No. 14/105,031), US 2014-0287938 A1 (U.S. application Ser. No. 14/213,991), US 2014-0273234 A1 (U.S. application Ser. No. 14/293,674), US2014-0273232 A1 (U.S. application Ser. No. 14/290,575), US 2014-0273231 (U.S. application Ser. No. 14/259,420), US 2014-0256046 A1 (U.S. application Ser. No. 14/226,274), US 2014-0248702 A1 (U.S. application Ser. No. 14/258,458), US 2014-0242700 A1 (U.S. application Ser. No. 14/222,930), US 2014-0242699 A1 (U.S. application Ser. No. 14/183,512), US 2014-0242664 A1 (U.S. application Ser. No. 14/104,990), US 2014-0234972 A1 (U.S. application Ser. No. 14/183,471), US 2014-0227787 A1 (U.S. application Ser. No. 14/256,912), US 2014-0189896 A1 (U.S. application Ser. No. 14/105,035), US 2014-0186958 (U.S. application Ser. No. 14/105,017), US 2014-0186919 A1 (U.S. application Ser. No. 14/104,977), US 2014-0186843 A1 (U.S. application Ser. No. 14/104,900), US 2014-0179770 A1 (U.S. application Ser. No. 14/104,837) and US 2014-0179006 A1 (U.S. application Ser. No. 14/183,486), US 2014-0170753 (U.S. application Ser. No. 14/183,429); European Patents/Patent Applications: EP 2 771 468 (EP13818570.7), EP 2 764 103 (EP13824232.6), and EP 2 784 162 (EP14170383.5); and PCT Patent Publications WO 2014/093661 (PCT/US2013/074743), WO 2014/093694 (PCT/US2013/074790), WO 2014/093595 (PCT/US2013/074611), WO 2014/093718 (PCT/US2013/074825), WO 2014/093709 (PCT/US2013/074812), WO 2014/093622 (PCT/US2013/074667), WO 2014/093635 (PCT/US2013/074691), WO 2014/093655 (PCT/US2013/074736), WO 2014/093712 (PCT/US2013/074819), WO2014/093701 (PCT/US2013/074800), WO2014/018423 (PCT/US2013/051418), WO 2014/204723 (PCT/US2014/041790), WO 2014/204724 (PCT/US2014/041800), WO 2014/204725 (PCT/US2014/041803), WO 2014/204726 (PCT/US2014/041804), WO 2014/204727 (PCT/US2014/041806), WO 2014/204728 (PCT/US2014/041808), WO 2014/204729 (PCT/US2014/041809), and:

Multiplex genome engineering using CRISPR/Cas systems. Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., & Zhang, F. Science Feb. 15; 339(6121):819-23 (2013);
RNA-guided editing of bacterial genomes using CRISPR-Cas systems. Jiang W., Bikard D., Cox D., Zhang F, Marraffini L A. Nat Biotechnol March; 31(3):233-9 (2013);
One-Step Generation of Mice Carrying Mutations in Multiple Genes by CRISPR/Cas-Mediated Genome Engineering. Wang H., Yang H., Shivalila C S., Dawlaty M M., Cheng A W., Zhang F., Jaenisch R. Cell May 9; 153(4):910-8 (2013);
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DNA targeting specificity of RNA-guided Cas9 nucleases. Hsu, P., Scott, D., Weinstein, J., Ran, F A., Konermann, S., Agarwala, V., Li, Y., Fine, E., Wu, X., Shalem, O., Cradick, T J., Marraffini, L A., Bao, G., & Zhang, F. Nat Biotechnol doi:10.1038/nbt.2647 (2013);
Genome engineering using the CRISPR-Cas9 system. Ran, F A., Hsu, P D., Wright, J., Agarwala, V., Scott, D A., Zhang, F. Nature Protocols November; 8(11):2281-308. (2013);
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each of which is incorporated herein by reference.

The invention involves a high-throughput single-cell RNA-Seq and/or targeted nucleic acid profiling (for example, sequencing, quantitative reverse transcription polymerase chain reaction, and the like) where the RNAs from different cells are tagged individually, allowing a single library to be created while retaining the cell identity of each read. In this regard, technology of U.S. provisional patent application Ser. No. 62/048,227 filed Sep. 9, 2014, the disclosure of which is incorporated by reference, may be used in or as to the invention. A combination of molecular barcoding and emulsion-based microfluidics to isolate, lyse, barcode, and prepare nucleic acids from individual cells in high-throughput is used. Microfluidic devices (for example, fabricated in polydimethylsiloxane), sub-nanoliter reverse emulsion droplets. These droplets are used to co-encapsulate nucleic acids with a barcoded capture bead. Each bead, for example, is uniquely barcoded so that each drop and its contents are distinguishable. The nucleic acids may come from any source known in the art, such as for example, those which come from a single cell, a pair of cells, a cellular lysate, or a solution. The cell is lysed as it is encapsulated in the droplet. To load single cells and barcoded beads into these droplets with Poisson statistics, 100,000 to 10 million such beads are needed to barcode 10,000-100,000 cells. In this regard there can be a single-cell sequencing library which may comprise: merging one uniquely barcoded mRNA capture microbead with a single-cell in an emulsion droplet having a diameter of 75-125 μm; lysing the cell to make its RNA accessible for capturing by hybridization onto RNA capture microbead; performing a reverse transcription either inside or outside the emulsion droplet to convert the cell's mRNA to a first strand cDNA that is covalently linked to the mRNA capture microbead; pooling the cDNA-attached microbeads from all cells; and preparing and sequencing a single composite RNA-Seq library. Accordingly, it is envisioned as to or in the practice of the invention provides that there can be a method for preparing uniquely barcoded mRNA capture microbeads, which has a unique barcode and diameter suitable for microfluidic devices which may comprise: 1) performing reverse phosphoramidite synthesis on the surface of the bead in a pool-and-split fashion, such that in each cycle of synthesis the beads are split into four reactions with one of the four canonical nucleotides (T, C, G, or A) or unique oligonucleotides of length two or more bases; 2) repeating this process a large number of times, at least six, and optimally more than twelve, such that, in the latter, there are more than 16 million unique barcodes on the surface of each bead in the pool. (See http://www.ncbi.nlm.nih.gov/pmc/articles/PMC206447). Likewise, in or as to the instant invention there can be an apparatus for creating a single-cell sequencing library via a microfluidic system, which may comprise: an oil-surfactant inlet which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel further may comprise a resistor; an inlet for an analyte which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel may further comprise a resistor; an inlet for mRNA capture microbeads and lysis reagent which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel may further comprise a resistor; said carrier fluid channels have a carrier fluid flowing therein at an adjustable or predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a mixer, which contains an outlet for drops. Similarly, as to or in the practice of the instant invention there can be a method for creating a single-cell sequencing library which may comprise: merging one uniquely barcoded RNA capture microbead with a single-cell in an emulsion droplet having a diameter of 125 μm lysing the cell thereby capturing the RNA on the RNA capture microbead; performing a reverse transcription either after breakage of the droplets and collection of the microbeads; or inside the emulsion droplet to convert the cell's RNA to a first strand cDNA that is covalently linked to the RNA capture microbead; pooling the cDNA-attached microbeads from all cells; and preparing and sequencing a single composite RNA-Seq library; and, the emulsion droplet can be between 50-210 μm. In a further embodiment, the method wherein the diameter of the mRNA capture microbeads is from 10 μm to 95 μm. Thus, the practice of the instant invention comprehends preparing uniquely barcoded mRNA capture microbeads, which has a unique barcode and diameter suitable for microfluidic devices which may comprise: 1) performing reverse phosphoramidite synthesis on the surface of the bead in a pool-and-split fashion, such that in each cycle of synthesis the beads are split into four reactions with one of the four canonical nucleotides (T, C, G, or A); 2) repeating this process a large number of times, at least six, and optimally more than twelve, such that, in the latter, there are more than 16 million unique barcodes on the surface of each bead in the pool. The covalent bond can be polyethylene glycol. The diameter of the mRNA capture microbeads can be from 10 μm to 95 μm. Accordingly, it is also envisioned as to or in the practice of the invention that there can be a method for preparing uniquely barcoded mRNA capture microbeads, which has a unique barcode and diameter suitable for microfluidic devices which may comprise: 1) performing reverse phosphoramidite synthesis on the surface of the bead in a pool-and-split fashion, such that in each cycle of synthesis the beads are split into four reactions with one of the four canonical nucleotides (T, C, G, or A); 2) repeating this process a large number of times, at least six, and optimally more than twelve, such that, in the latter, there are more than 16 million unique barcodes on the surface of each bead in the pool. And, the diameter of the mRNA capture microbeads can be from 10 μm to 95 μm. Further, as to in the practice of the invention there can be an apparatus for creating a composite single-cell sequencing library via a microfluidic system, which may comprise: an oil-surfactant inlet which may comprise a filter and two carrier fluid channels, wherein said carrier fluid channel further may comprise a resistor; an inlet for an analyte which may comprise a filter and two carrier fluid channels, wherein said carrier fluid channel further may comprise a resistor; an inlet for mRNA capture microbeads and lysis reagent which may comprise a carrier fluid channel; said carrier fluid channels have a carrier fluid flowing therein at an adjustable and predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a constriction for droplet pinch-off followed by a mixer, which connects to an outlet for drops. The analyte may comprise a chemical reagent, a genetically perturbed cell, a protein, a drug, an antibody, an enzyme, a nucleic acid, an organelle like the mitochondrion or nucleus, a cell or any combination thereof. In an embodiment of the apparatus the analyte is a cell. In a further embodiment the cell is a brain cell. In an embodiment of the apparatus the lysis reagent may comprise an anionic surfactant such as sodium lauroyl sarcosinate, or a chaotropic salt such as guanidinium thiocyanate. The filter can involve square PDMS posts; e.g., with the filter on the cell channel of such posts with sides ranging between 125-135 μm with a separation of 70-100 mm between the posts. The filter on the oil-surfactant inlet may comprise square posts of two sizes; one with sides ranging between 75-100 μm and a separation of 25-30 μm between them and the other with sides ranging between 40-50 μm and a separation of 10-15 μm. The apparatus can involve a resistor, e.g., a resistor that is serpentine having a length of 7000-9000 μm, width of 50-75 μm and depth of 100-150 mm. The apparatus can have channels having a length of 8000-12,000 μm for oil-surfactant inlet, 5000-7000 for analyte (cell) inlet, and 900-1200 μm for the inlet for microbead and lysis agent; and/or all channels having a width of 125-250 mm, and depth of 100-150 mm. The width of the cell channel can be 125-250 μm and the depth 100-150 μm. The apparatus can include a mixer having a length of 7000-9000 μm, and a width of 110-140 μm with 35-45o zig-zigs every 150 μm. The width of the mixer can be about 125 μm. The oil-surfactant can be a PEG Block Polymer, such as BIORAD™ QX200 Droplet Generation Oil. The carrier fluid can be a water-glycerol mixture. In the practice of the invention or as to the invention, a mixture may comprise a plurality of microbeads adorned with combinations of the following elements: bead-specific oligonucleotide barcodes; additional oligonucleotide barcode sequences which vary among the oligonucleotides on an individual bead and can therefore be used to differentiate or help identify those individual oligonucleotide molecules; additional oligonucleotide sequences that create substrates for downstream molecular-biological reactions, such as oligo-dT (for reverse transcription of mature mRNAs), specific sequences (for capturing specific portions of the transcriptome, or priming for DNA polymerases and similar enzymes), or random sequences (for priming throughout the transcriptome or genome). The individual oligonucleotide molecules on the surface of any individual microbead may contain all three of these elements, and the third element may include both oligo-dT and a primer sequence. A mixture may comprise a plurality of microbeads, wherein said microbeads may comprise the following elements: at least one bead-specific oligonucleotide barcode; at least one additional identifier oligonucleotide barcode sequence, which varies among the oligonucleotides on an individual bead, and thereby assisting in the identification and of the bead specific oligonucleotide molecules; optionally at least one additional oligonucleotide sequences, which provide substrates for downstream molecular-biological reactions. A mixture may comprise at least one oligonucleotide sequence(s), which provide for substrates for downstream molecular-biological reactions. In a further embodiment the downstream molecular biological reactions are for reverse transcription of mature mRNAs; capturing specific portions of the transcriptome, priming for DNA polymerases and/or similar enzymes; or priming throughout the transcriptome or genome. The mixture may involve additional oligonucleotide sequence(s) which may comprise a oligio-dT sequence. The mixture further may comprise the additional oligonucleotide sequence which may comprise a primer sequence. The mixture may further comprise the additional oligonucleotide sequence which may comprise a oligo-dT sequence and a primer sequence. Examples of the labeling substance which may be employed include labeling substances known to those skilled in the art, such as fluorescent dyes, enzymes, coenzymes, chemiluminescent substances, and radioactive substances. Specific examples include radioisotopes (e.g., 32P, 14C, 125I, 3H, and 131I), fluorescein, rhodamine, dansyl chloride, umbelliferone, luciferase, peroxidase, alkaline phosphatase, β-galactosidase, β-glucosidase, horseradish peroxidase, glucoamylase, lysozyme, saccharide oxidase, microperoxidase, biotin, and ruthenium. In the case where biotin is employed as a labeling substance, preferably, after addition of a biotin-labeled antibody, streptavidin bound to an enzyme (e.g., peroxidase) is further added. Advantageously, the label is a fluorescent label. Examples of fluorescent labels include, but are not limited to, Atto dyes, 4-acetamido-4′-isothiocyanatostilbene-2,2′disulfonic acid; acridine and derivatives: acridine, acridine isothiocyanate; 5-(2′-aminoethyl)aminonaphthalene-1-sulfonic acid (EDANS); 4-amino-N-[3-vinyl sulfonyl)phenyl]naphthalimide-3,5 disulfonate; N-(4-anilino-1-naphthyl)maleimide; anthranilamide; BODIPY; Brilliant Yellow; coumarin and derivatives; coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4-trifluoromethylcouluarin (Coumaran 151); cyanine dyes; cyanosine; 4′,6-diaminidino-2-phenylindole (DAPI); 5′5″-dibromopyrogallol-sulfonaphthalein (Bromopyrogallol Red); 7-diethylamino-3-(4′-isothiocyanatophenyl)-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4′-diisothiocyanatodihydro-stilbene-2,2′-disulfonic acid; 4,4′-diisothiocyanatostilbene-2,2′-disulfonic acid; 5-[dimethylamino]naphthalene-1-sulfonyl chloride (DNS, dansylchloride); 4-dimethylaminophenylazophenyl-4′-isothiocyanate (DABITC); eosin and derivatives; eosin, eosin isothiocyanate, erythrosin and derivatives; erythrosin B, erythrosin, isothiocyanate; ethidium; fluorescein and derivatives; 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazin-2-yl)aminofluorescein (DTAF), 2′,7′-dimethoxy-4′5′-dichloro-6-carboxyfluorescein, fluorescein, fluorescein isothiocyanate, QFITC, (XRITC); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4-methylumbelliferoneortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B-phycoerythrin; o-phthaldialdehyde; pyrene and derivatives: pyrene, pyrene butyrate, succinimidyl 1-pyrene; butyrate quantum dots; Reactive Red 4 (Cibacron™ Brilliant Red 3B-A) rhodamine and derivatives: 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulforhodamine B, sulforhodamine 101, sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N′,N′ tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid; terbium chelate derivatives; Cy3; Cy5; Cy5.5; Cy7; IRD 700; IRD 800; La Jolta Blue; phthalo cyanine; and naphthalo cyanine. A fluorescent label may be a fluorescent protein, such as blue fluorescent protein, cyan fluorescent protein, green fluorescent protein, red fluorescent protein, yellow fluorescent protein or any photoconvertible protein. Colormetric labeling, bioluminescent labeling and/or chemiluminescent labeling may further accomplish labeling. Labeling further may include energy transfer between molecules in the hybridization complex by perturbation analysis, quenching, or electron transport between donor and acceptor molecules, the latter of which may be facilitated by double stranded match hybridization complexes. The fluorescent label may be a perylene or a terrylen. In the alternative, the fluorescent label may be a fluorescent bar code. Advantageously, the label may be light sensitive, wherein the label is light-activated and/or light cleaves the one or more linkers to release the molecular cargo. The light-activated molecular cargo may be a major light-harvesting complex (LHCII). In another embodiment, the fluorescent label may induce free radical formation. Advantageously, agents may be uniquely labeled in a dynamic manner (see, e.g., U.S. provisional patent application Ser. No. 61/703,884 filed Sep. 21, 2012). The unique labels are, at least in part, nucleic acid in nature, and may be generated by sequentially attaching two or more detectable oligonucleotide tags to each other and each unique label may be associated with a separate agent. A detectable oligonucleotide tag may be an oligonucleotide that may be detected by sequencing of its nucleotide sequence and/or by detecting non-nucleic acid detectable moieties to which it may be attached. Oligonucleotide tags may be detectable by virtue of their nucleotide sequence, or by virtue of a non-nucleic acid detectable moiety that is attached to the oligonucleotide such as but not limited to a fluorophore, or by virtue of a combination of their nucleotide sequence and the nonnucleic acid detectable moiety. A detectable oligonucleotide tag may comprise one or more nonoligonucleotide detectable moieties. Examples of detectable moieties may include, but are not limited to, fluorophores, microparticles including quantum dots (Empodocles, et al., Nature 399:126-130, 1999), gold nanoparticles (Reichert et al., Anal. Chem. 72:6025-6029, 2000), microbeads (Lacoste et al., Proc. Natl. Acad. Sci. USA 97(17):9461-9466, 2000), biotin, DNP (dinitrophenyl), fucose, digoxigenin, haptens, and other detectable moieties known to those skilled in the art. In some embodiments, the detectable moieties may be quantum dots. Methods for detecting such moieties are described herein and/or are known in the art. Thus, detectable oligonucleotide tags may be, but are not limited to, oligonucleotides which may comprise unique nucleotide sequences, oligonucleotides which may comprise detectable moieties, and oligonucleotides which may comprise both unique nucleotide sequences and detectable moieties. A unique label may be produced by sequentially attaching two or more detectable oligonucleotide tags to each other. The detectable tags may be present or provided in a plurality of detectable tags. The same or a different plurality of tags may be used as the source of each detectable tag may be part of a unique label. In other words, a plurality of tags may be subdivided into subsets and single subsets may be used as the source for each tag. One or more other species may be associated with the tags. In particular, nucleic acids released by a lysed cell may be ligated to one or more tags. These may include, for example, chromosomal DNA, RNA transcripts, tRNA, mRNA, mitochondrial DNA, or the like. Such nucleic acids may be sequenced, in addition to sequencing the tags themselves, which may yield information about the nucleic acid profile of the cells, which can be associated with the tags, or the conditions that the corresponding droplet or cell was exposed to.
The invention accordingly may involve or be practiced as to high throughput and high resolution delivery of reagents to individual emulsion droplets that may contain cells, organelles, nucleic acids, proteins, etc. through the use of monodisperse aqueous droplets that are generated by a microfluidic device as a water-in-oil emulsion. The droplets are carried in a flowing oil phase and stabilized by a surfactant. In one aspect single cells or single organelles or single molecules (proteins, RNA, DNA) are encapsulated into uniform droplets from an aqueous solution/dispersion. In a related aspect, multiple cells or multiple molecules may take the place of single cells or single molecules. The aqueous droplets of volume ranging from 1 pL to 10 nL work as individual reactors. 104 to 105 single cells in droplets may be processed and analyzed in a single run. To utilize microdroplets for rapid large-scale chemical screening or complex biological library identification, different species of microdroplets, each containing the specific chemical compounds or biological probes cells or molecular barcodes of interest, have to be generated and combined at the preferred conditions, e.g., mixing ratio, concentration, and order of combination. Each species of droplet is introduced at a confluence point in a main microfluidic channel from separate inlet microfluidic channels. Preferably, droplet volumes are chosen by design such that one species is larger than others and moves at a different speed, usually slower than the other species, in the carrier fluid, as disclosed in U.S. Publication No. US 2007/0195127 and International Publication No. WO 2007/089541, each of which are incorporated herein by reference in their entirety. The channel width and length is selected such that faster species of droplets catch up to the slowest species. Size constraints of the channel prevent the faster moving droplets from passing the slower moving droplets resulting in a train of droplets entering a merge zone. Multi-step chemical reactions, biochemical reactions, or assay detection chemistries often require a fixed reaction time before species of different type are added to a reaction. Multi-step reactions are achieved by repeating the process multiple times with a second, third or more confluence points each with a separate merge point. Highly efficient and precise reactions and analysis of reactions are achieved when the frequencies of droplets from the inlet channels are matched to an optimized ratio and the volumes of the species are matched to provide optimized reaction conditions in the combined droplets. Fluidic droplets may be screened or sorted within a fluidic system of the invention by altering the flow of the liquid containing the droplets. For instance, in one set of embodiments, a fluidic droplet may be steered or sorted by directing the liquid surrounding the fluidic droplet into a first channel, a second channel, etc. In another set of embodiments, pressure within a fluidic system, for example, within different channels or within different portions of a channel, can be controlled to direct the flow of fluidic droplets. For example, a droplet can be directed toward a channel junction including multiple options for further direction of flow (e.g., directed toward a branch, or fork, in a channel defining optional downstream flow channels). Pressure within one or more of the optional downstream flow channels can be controlled to direct the droplet selectively into one of the channels, and changes in pressure can be effected on the order of the time required for successive droplets to reach the junction, such that the downstream flow path of each successive droplet can be independently controlled. In one arrangement, the expansion and/or contraction of liquid reservoirs may be used to steer or sort a fluidic droplet into a channel, e.g., by causing directed movement of the liquid containing the fluidic droplet. In another, the expansion and/or contraction of the liquid reservoir may be combined with other flow-controlling devices and methods, e.g., as described herein. Non-limiting examples of devices able to cause the expansion and/or contraction of a liquid reservoir include pistons. Key elements for using microfluidic channels to process droplets include: (1) producing droplet of the correct volume, (2) producing droplets at the correct frequency and (3) bringing together a first stream of sample droplets with a second stream of sample droplets in such a way that the frequency of the first stream of sample droplets matches the frequency of the second stream of sample droplets. Preferably, bringing together a stream of sample droplets with a stream of premade library droplets in such a way that the frequency of the library droplets matches the frequency of the sample droplets. Methods for producing droplets of a uniform volume at a regular frequency are well known in the art. One method is to generate droplets using hydrodynamic focusing of a dispersed phase fluid and immiscible carrier fluid, such as disclosed in U.S. Publication No. US 2005/0172476 and International Publication No. WO 2004/002627. It is desirable for one of the species introduced at the confluence to be a pre-made library of droplets where the library contains a plurality of reaction conditions, e.g., a library may contain plurality of different compounds at a range of concentrations encapsulated as separate library elements for screening their effect on cells or enzymes, alternatively a library could be composed of a plurality of different primer pairs encapsulated as different library elements for targeted amplification of a collection of loci, alternatively a library could contain a plurality of different antibody species encapsulated as different library elements to perform a plurality of binding assays. The introduction of a library of reaction conditions onto a substrate is achieved by pushing a premade collection of library droplets out of a vial with a drive fluid. The drive fluid is a continuous fluid. The drive fluid may comprise the same substance as the carrier fluid (e.g., a fluorocarbon oil). For example, if a library consists of ten pico-liter droplets is driven into an inlet channel on a microfluidic substrate with a drive fluid at a rate of 10,000 pico-liters per second, then nominally the frequency at which the droplets are expected to enter the confluence point is 1000 per second. However, in practice droplets pack with oil between them that slowly drains. Over time the carrier fluid drains from the library droplets and the number density of the droplets (number/mL) increases. Hence, a simple fixed rate of infusion for the drive fluid does not provide a uniform rate of introduction of the droplets into the microfluidic channel in the substrate. Moreover, library-to-library variations in the mean library droplet volume result in a shift in the frequency of droplet introduction at the confluence point. Thus, the lack of uniformity of droplets that results from sample variation and oil drainage provides another problem to be solved. For example if the nominal droplet volume is expected to be 10 pico-liters in the library, but varies from 9 to 11 pico-liters from library-to-library then a 10,000 pico-liter/second infusion rate will nominally produce a range in frequencies from 900 to 1,100 droplet per second. In short, sample to sample variation in the composition of dispersed phase for droplets made on chip, a tendency for the number density of library droplets to increase over time and library-to-library variations in mean droplet volume severely limit the extent to which frequencies of droplets may be reliably matched at a confluence by simply using fixed infusion rates. In addition, these limitations also have an impact on the extent to which volumes may be reproducibly combined. Combined with typical variations in pump flow rate precision and variations in channel dimensions, systems are severely limited without a means to compensate on a run-to-run basis. The foregoing facts not only illustrate a problem to be solved, but also demonstrate a need for a method of instantaneous regulation of microfluidic control over microdroplets within a microfluidic channel. Combinations of surfactant(s) and oils must be developed to facilitate generation, storage, and manipulation of droplets to maintain the unique chemical/biochemical/biological environment within each droplet of a diverse library. Therefore, the surfactant and oil combination must (1) stabilize droplets against uncontrolled coalescence during the drop forming process and subsequent collection and storage, (2) minimize transport of any droplet contents to the oil phase and/or between droplets, and (3) maintain chemical and biological inertness with contents of each droplet (e.g., no adsorption or reaction of encapsulated contents at the oil-water interface, and no adverse effects on biological or chemical constituents in the droplets). In addition to the requirements on the droplet library function and stability, the surfactant-in-oil solution must be coupled with the fluid physics and materials associated with the platform. Specifically, the oil solution must not swell, dissolve, or degrade the materials used to construct the microfluidic chip, and the physical properties of the oil (e.g., viscosity, boiling point, etc.) must be suited for the flow and operating conditions of the platform. Droplets formed in oil without surfactant are not stable to permit coalescence, so surfactants must be dissolved in the oil that is used as the continuous phase for the emulsion library. Surfactant molecules are amphiphilic—part of the molecule is oil soluble, and part of the molecule is water soluble. When a water-oil interface is formed at the nozzle of a microfluidic chip for example in the inlet module described herein, surfactant molecules that are dissolved in the oil phase adsorb to the interface. The hydrophilic portion of the molecule resides inside the droplet and the fluorophilic portion of the molecule decorates the exterior of the droplet. The surface tension of a droplet is reduced when the interface is populated with surfactant, so the stability of an emulsion is improved. In addition to stabilizing the droplets against coalescence, the surfactant should be inert to the contents of each droplet and the surfactant should not promote transport of encapsulated components to the oil or other droplets. A droplet library may be made up of a number of library elements that are pooled together in a single collection (see, e.g., US Patent Publication No. 2010002241). Libraries may vary in complexity from a single library element to 1015 library elements or more. Each library element may be one or more given components at a fixed concentration. The element may be, but is not limited to, cells, organelles, virus, bacteria, yeast, beads, amino acids, proteins, polypeptides, nucleic acids, polynucleotides or small molecule chemical compounds. The element may contain an identifier such as a label. The terms “droplet library” or “droplet libraries” are also referred to herein as an “emulsion library” or “emulsion libraries.” These terms are used interchangeably throughout the specification. A cell library element may include, but is not limited to, hybridomas, B-cells, primary cells, cultured cell lines, cancer cells, stem cells, cells obtained from tissue, or any other cell type. Cellular library elements are prepared by encapsulating a number of cells from one to hundreds of thousands in individual droplets. The number of cells encapsulated is usually given by Poisson statistics from the number density of cells and volume of the droplet. However, in some cases the number deviates from Poisson statistics as described in Edd et al., “Controlled encapsulation of single-cells into monodisperse picolitre drops.” Lab Chip, 8(8): 1262-1264, 2008. The discrete nature of cells allows for libraries to be prepared in mass with a plurality of cellular variants all present in a single starting media and then that media is broken up into individual droplet capsules that contain at most one cell. These individual droplets capsules are then combined or pooled to form a library consisting of unique library elements. Cell division subsequent to, or in some embodiments following, encapsulation produces a clonal library element. A bead based library element may contain one or more beads, of a given type and may also contain other reagents, such as antibodies, enzymes or other proteins. In the case where all library elements contain different types of beads, but the same surrounding media, the library elements may all be prepared from a single starting fluid or have a variety of starting fluids. In the case of cellular libraries prepared in mass from a collection of variants, such as genomically modified, yeast or bacteria cells, the library elements will be prepared from a variety of starting fluids. Often it is desirable to have exactly one cell per droplet with only a few droplets containing more than one cell when starting with a plurality of cells or yeast or bacteria, engineered to produce variants on a protein. In some cases, variations from Poisson statistics may be achieved to provide an enhanced loading of droplets such that there are more droplets with exactly one cell per droplet and few exceptions of empty droplets or droplets containing more than one cell. Examples of droplet libraries are collections of droplets that have different contents, ranging from beads, cells, small molecules, DNA, primers, antibodies. Smaller droplets may be in the order of femtoliter (fL) volume drops, which are especially contemplated with the droplet dispensors. The volume may range from about 5 to about 600 fL. The larger droplets range in size from roughly 0.5 micron to 500 micron in diameter, which corresponds to about 1 pico liter to 1 nano liter. However, droplets may be as small as 5 microns and as large as 500 microns. Preferably, the droplets are at less than 100 microns, about 1 micron to about 100 microns in diameter. The most preferred size is about 20 to 40 microns in diameter (10 to 100 picoliters). The preferred properties examined of droplet libraries include osmotic pressure balance, uniform size, and size ranges. The droplets within the emulsion libraries of the present invention may be contained within an immiscible oil which may comprise at least one fluorosurfactant. In some embodiments, the fluorosurfactant within the immiscible fluorocarbon oil may be a block copolymer consisting of one or more perfluorinated polyether (PFPE) blocks and one or more polyethylene glycol (PEG) blocks. In other embodiments, the fluorosurfactant is a triblock copolymer consisting of a PEG center block covalently bound to two PFPE blocks by amide linking groups. The presence of the fluorosurfactant (similar to uniform size of the droplets in the library) is critical to maintain the stability and integrity of the droplets and is also essential for the subsequent use of the droplets within the library for the various biological and chemical assays described herein. Fluids (e.g., aqueous fluids, immiscible oils, etc.) and other surfactants that may be utilized in the droplet libraries of the present invention are described in greater detail herein. The present invention can accordingly involve an emulsion library which may comprise a plurality of aqueous droplets within an immiscible oil (e.g., fluorocarbon oil) which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise the same aqueous fluid and may comprise a different library element. The present invention also provides a method for forming the emulsion library which may comprise providing a single aqueous fluid which may comprise different library elements, encapsulating each library element into an aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise the same aqueous fluid and may comprise a different library element, and pooling the aqueous droplets within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, thereby forming an emulsion library. For example, in one type of emulsion library, all different types of elements (e.g., cells or beads), may be pooled in a single source contained in the same medium. After the initial pooling, the cells or beads are then encapsulated in droplets to generate a library of droplets wherein each droplet with a different type of bead or cell is a different library element. The dilution of the initial solution enables the encapsulation process. In some embodiments, the droplets formed will either contain a single cell or bead or will not contain anything, i.e., be empty. In other embodiments, the droplets formed will contain multiple copies of a library element. The cells or beads being encapsulated are generally variants on the same type of cell or bead. In another example, the emulsion library may comprise a plurality of aqueous droplets within an immiscible fluorocarbon oil, wherein a single molecule may be encapsulated, such that there is a single molecule contained within a droplet for every 20-60 droplets produced (e.g., 20, 25, 30, 35, 40, 45, 50, 55, 60 droplets, or any integer in between). Single molecules may be encapsulated by diluting the solution containing the molecules to such a low concentration that the encapsulation of single molecules is enabled. In one specific example, a LacZ plasmid DNA was encapsulated at a concentration of 20 fM after two hours of incubation such that there was about one gene in 40 droplets, where 10 μm droplets were made at 10 kHz per second. Formation of these libraries rely on limiting dilutions.
The present invention also provides an emulsion library which may comprise at least a first aqueous droplet and at least a second aqueous droplet within a fluorocarbon oil which may comprise at least one fluorosurfactant, wherein the at least first and the at least second droplets are uniform in size and comprise a different aqueous fluid and a different library element. The present invention also provides a method for forming the emulsion library which may comprise providing at least a first aqueous fluid which may comprise at least a first library of elements, providing at least a second aqueous fluid which may comprise at least a second library of elements, encapsulating each element of said at least first library into at least a first aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, encapsulating each element of said at least second library into at least a second aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein the at least first and the at least second droplets are uniform in size and may comprise a different aqueous fluid and a different library element, and pooling the at least first aqueous droplet and the at least second aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant thereby forming an emulsion library. One of skill in the art will recognize that methods and systems of the invention are not preferably practiced as to cells, mutations, etc as herein disclosed, but that the invention need not be limited to any particular type of sample, and methods and systems of the invention may be used with any type of organic, inorganic, or biological molecule (see, e.g, US Patent Publication No. 20120122714). In particular embodiments the sample may include nucleic acid target molecules. Nucleic acid molecules may be synthetic or derived from naturally occurring sources. In one embodiment, nucleic acid molecules may be isolated from a biological sample containing a variety of other components, such as proteins, lipids and non-template nucleic acids. Nucleic acid target molecules may be obtained from any cellular material, obtained from an animal, plant, bacterium, fungus, or any other cellular organism. In certain embodiments, the nucleic acid target molecules may be obtained from a single cell. Biological samples for use in the present invention may include viral particles or preparations. Nucleic acid target molecules may be obtained directly from an organism or from a biological sample obtained from an organism, e.g., from blood, urine, cerebrospinal fluid, seminal fluid, saliva, sputum, stool and tissue. Any tissue or body fluid specimen may be used as a source for nucleic acid for use in the invention. Nucleic acid target molecules may also be isolated from cultured cells, such as a primary cell culture or a cell line. The cells or tissues from which target nucleic acids are obtained may be infected with a virus or other intracellular pathogen. A sample may also be total RNA extracted from a biological specimen, a cDNA library, viral, or genomic DNA. Generally, nucleic acid may be extracted from a biological sample by a variety of techniques such as those described by Maniatis, et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor, N.Y., pp. 280-281 (1982). Nucleic acid molecules may be single-stranded, double-stranded, or double-stranded with single-stranded regions (for example, stem- and loop-structures). Nucleic acid obtained from biological samples typically may be fragmented to produce suitable fragments for analysis. Target nucleic acids may be fragmented or sheared to desired length, using a variety of mechanical, chemical and/or enzymatic methods. DNA may be randomly sheared via sonication, e.g. Covaris method, brief exposure to a DNase, or using a mixture of one or more restriction enzymes, or a transposase or nicking enzyme. RNA may be fragmented by brief exposure to an RNase, heat plus magnesium, or by shearing. The RNA may be converted to cDNA. If fragmentation is employed, the RNA may be converted to cDNA before or after fragmentation. In one embodiment, nucleic acid from a biological sample is fragmented by sonication. In another embodiment, nucleic acid is fragmented by a hydroshear instrument. Generally, individual nucleic acid target molecules may be from about 40 bases to about 40 kb. Nucleic acid molecules may be single-stranded, double-stranded, or double-stranded with single-stranded regions (for example, stem- and loop-structures). A biological sample as described herein may be homogenized or fractionated in the presence of a detergent or surfactant. The concentration of the detergent in the buffer may be about 0.05% to about 10.0%. The concentration of the detergent may be up to an amount where the detergent remains soluble in the solution. In one embodiment, the concentration of the detergent is between 0.1% to about 2%. The detergent, particularly a mild one that is nondenaturing, may act to solubilize the sample. Detergents may be ionic or nonionic. Examples of nonionic detergents include triton, such as the Triton™ X series (Triton™ X-100 t-Oct-C6H4-(OCH2-CH2)xOH, x=9-10, Triton™ X-100R, Triton™ X-114 x=7-8), octyl glucoside, polyoxyethylene(9)dodecyl ether, digitonin, IGEPAL™ CA630 octylphenyl polyethylene glycol, n-octyl-beta-D-glucopyranoside (betaOG), n-dodecyl-beta, Tween™. 20 polyethylene glycol sorbitan monolaurate, Tween™ 80 polyethylene glycol sorbitan monooleate, polidocanol, n-dodecyl beta-D-maltoside (DDM), NP-40 nonylphenyl polyethylene glycol, C12E8 (octaethylene glycol n-dodecyl monoether), hexaethyleneglycol mono-n-tetradecyl ether (C14E06), octyl-beta-thioglucopyranoside (octyl thioglucoside, OTG), Emulgen, and polyoxyethylene 10 lauryl ether (C12E10). Examples of ionic detergents (anionic or cationic) include deoxycholate, sodium dodecyl sulfate (SDS), N-lauroylsarcosine, and cetyltrimethylammoniumbromide (CTAB). A zwitterionic reagent may also be used in the purification schemes of the present invention, such as Chaps, zwitterion 3-14, and 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulf-onate. It is contemplated also that urea may be added with or without another detergent or surfactant. Lysis or homogenization solutions may further contain other agents, such as reducing agents. Examples of such reducing agents include dithiothreitol (DTT), β-mercaptoethanol, DTE, GSH, cysteine, cysteamine, tricarboxyethyl phosphine (TCEP), or salts of sulfurous acid. Size selection of the nucleic acids may be performed to remove very short fragments or very long fragments. The nucleic acid fragments may be partitioned into fractions which may comprise a desired number of fragments using any suitable method known in the art. Suitable methods to limit the fragment size in each fragment are known in the art. In various embodiments of the invention, the fragment size is limited to between about 10 and about 100 Kb or longer. A sample in or as to the instant invention may include individual target proteins, protein complexes, proteins with translational modifications, and protein/nucleic acid complexes. Protein targets include peptides, and also include enzymes, hormones, structural components such as viral capsid proteins, and antibodies. Protein targets may be synthetic or derived from naturally-occurring sources. The invention protein targets may be isolated from biological samples containing a variety of other components including lipids, non-template nucleic acids, and nucleic acids. Protein targets may be obtained from an animal, bacterium, fungus, cellular organism, and single cells. Protein targets may be obtained directly from an organism or from a biological sample obtained from the organism, including bodily fluids such as blood, urine, cerebrospinal fluid, seminal fluid, saliva, sputum, stool and tissue. Protein targets may also be obtained from cell and tissue lysates and biochemical fractions. An individual protein is an isolated polypeptide chain. A protein complex includes two or polypeptide chains. Samples may include proteins with post translational modifications including but not limited to phosphorylation, methionine oxidation, deamidation, glycosylation, ubiquitination, carbamylation, s-carboxymethylation, acetylation, and methylation. Protein/nucleic acid complexes include cross-linked or stable protein-nucleic acid complexes. Extraction or isolation of individual proteins, protein complexes, proteins with translational modifications, and protein/nucleic acid complexes is performed using methods known in the art.
The invention can thus involve forming sample droplets. The droplets are aqueous droplets that are surrounded by an immiscible carrier fluid. Methods of forming such droplets are shown for example in Link et al. (U.S. patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163), Stone et al. (U.S. Pat. No. 7,708,949 and U.S. patent application number 2010/0172803), Anderson et al. (U.S. Pat. No. 7,041,481 and which reissued as RE41,780) and European publication number EP2047910 to Raindance Technologies Inc. The content of each of which is incorporated by reference herein in its entirety. The present invention may relates to systems and methods for manipulating droplets within a high throughput microfluidic system. A microfluid droplet encapsulates a differentiated cell The cell is lysed and its mRNA is hybridized onto a capture bead containing barcoded oligo dT primers on the surface, all inside the droplet. The barcode is covalently attached to the capture bead via a flexible multi-atom linker like PEG. In a preferred embodiment, the droplets are broken by addition of a fluorosurfactant (like perfluorooctanol), washed, and collected. A reverse transcription (RT) reaction is then performed to convert each cell's mRNA into a first strand cDNA that is both uniquely barcoded and covalently linked to the mRNA capture bead. Subsequently, a universal primer via a template switching reaction is amended using conventional library preparation protocols to prepare an RNA-Seq library. Since all of the mRNA from any given cell is uniquely barcoded, a single library is sequenced and then computationally resolved to determine which mRNAs came from which cells. In this way, through a single sequencing run, tens of thousands (or more) of distinguishable transcriptomes can be simultaneously obtained. The oligonucleotide sequence may be generated on the bead surface. During these cycles, beads were removed from the synthesis column, pooled, and aliquoted into four equal portions by mass; these bead aliquots were then placed in a separate synthesis column and reacted with either dG, dC, dT, or dA phosphoramidite. In other instances, dinucleotide, trinucleotides, or oligonucleotides that are greater in length are used, in other instances, the oligo-dT tail is replaced by gene specific oligonucleotides to prime specific targets (singular or plural), random sequences of any length for the capture of all or specific RNAs. This process was repeated 12 times for a total of 4¹²=16,777,216 unique barcode sequences. Upon completion of these cycles, 8 cycles of degenerate oligonucleotide synthesis were performed on all the beads, followed by 30 cycles of dT addition. In other embodiments, the degenerate synthesis is omitted, shortened (less than 8 cycles), or extended (more than 8 cycles); in others, the 30 cycles of dT addition are replaced with gene specific primers (single target or many targets) or a degenerate sequence. The aforementioned microfluidic system is regarded as the reagent delivery system microfluidic library printer or droplet library printing system of the present invention. Droplets are formed as sample fluid flows from droplet generator which contains lysis reagent and barcodes through microfluidic outlet channel which contains oil, towards junction. Defined volumes of loaded reagent emulsion, corresponding to defined numbers of droplets, are dispensed on-demand into the flow stream of carrier fluid. The sample fluid may typically comprise an aqueous buffer solution, such as ultrapure water (e.g., 18 mega-ohm resistivity, obtained, for example by column chromatography), 10 mM Tris HCl and 1 mM EDTA (TE) buffer, phosphate buffer saline (PBS) or acetate buffer. Any liquid or buffer that is physiologically compatible with nucleic acid molecules can be used. The carrier fluid may include one that is immiscible with the sample fluid. The carrier fluid can be a non-polar solvent, decane (e.g., tetradecane or hexadecane), fluorocarbon oil, silicone oil, an inert oil such as hydrocarbon, or another oil (for example, mineral oil). The carrier fluid may contain one or more additives, such as agents which reduce surface tensions (surfactants). Surfactants can include Tween, Span, fluorosurfactants, and other agents that are soluble in oil relative to water. In some applications, performance is improved by adding a second surfactant to the sample fluid. Surfactants can aid in controlling or optimizing droplet size, flow and uniformity, for example by reducing the shear force needed to extrude or inject droplets into an intersecting channel. This can affect droplet volume and periodicity, or the rate or frequency at which droplets break off into an intersecting channel. Furthermore, the surfactant can serve to stabilize aqueous emulsions in fluorinated oils from coalescing. Droplets may be surrounded by a surfactant which stabilizes the droplets by reducing the surface tension at the aqueous oil interface. Preferred surfactants that may be added to the carrier fluid include, but are not limited to, surfactants such as sorbitan-based carboxylic acid esters (e.g., the “Span” surfactants, Fluka Chemika), including sorbitan monolaurate (Span 20), sorbitan monopalmitate (Span 40), sorbitan monostearate (Span 60) and sorbitan monooleate (Span 80), and perfluorinated polyethers (e.g., DuPont Krytox 157 FSL, FSM, and/or FSH). Other non-limiting examples of non-ionic surfactants which may be used include polyoxyethylenated alkylphenols (for example, nonyl-, p-dodecyl-, and dinonylphenols), polyoxyethylenated straight chain alcohols, polyoxyethylenated polyoxypropylene glycols, polyoxyethylenated mercaptans, long chain carboxylic acid esters (for example, glyceryl and polyglyceryl esters of natural fatty acids, propylene glycol, sorbitol, polyoxyethylenated sorbitol esters, polyoxyethylene glycol esters, etc.) and alkanolamines (e.g., diethanolamine-fatty acid condensates and isopropanolamine-fatty acid condensates). In some cases, an apparatus for creating a single-cell sequencing library via a microfluidic system provides for volume-driven flow, wherein constant volumes are injected over time. The pressure in fluidic channels is a function of injection rate and channel dimensions. In one embodiment, the device provides an oil/surfactant inlet; an inlet for an analyte; a filter, an inlet for mRNA capture microbeads and lysis reagent; a carrier fluid channel which connects the inlets; a resistor; a constriction for droplet pinch-off; a mixer; and an outlet for drops. In an embodiment the invention provides apparatus for creating a single-cell sequencing library via a microfluidic system, which may comprise: an oil-surfactant inlet which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel may further comprise a resistor; an inlet for an analyte which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel may further comprise a resistor; an inlet for mRNA capture microbeads and lysis reagent which may comprise a filter and a carrier fluid channel, wherein said carrier fluid channel further may comprise a resistor; said carrier fluid channels have a carrier fluid flowing therein at an adjustable or predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a mixer, which contains an outlet for drops. Accordingly, an apparatus for creating a single-cell sequencing library via a microfluidic system icrofluidic flow scheme for single-cell RNA-seq is envisioned. Two channels, one carrying cell suspensions, and the other carrying uniquely barcoded mRNA capture bead, lysis buffer and library preparation reagents meet at a junction and is immediately co-encapsulated in an inert carrier oil, at the rate of one cell and one bead per drop. In each drop, using the bead's barcode tagged oligonucleotides as cDNA template, each mRNA is tagged with a unique, cell-specific identifier. The invention also encompasses use of a Drop-Seq library of a mixture of mouse and human cells. The carrier fluid may be caused to flow through the outlet channel so that the surfactant in the carrier fluid coats the channel walls. The fluorosurfactant can be prepared by reacting the perflourinated polyether DuPont Krytox 157 FSL, FSM, or FSH with aqueous ammonium hydroxide in a volatile fluorinated solvent. The solvent and residual water and ammonia can be removed with a rotary evaporator. The surfactant can then be dissolved (e.g., 2.5 wt %) in a fluorinated oil (e.g., Flourinert (3M)), which then serves as the carrier fluid. Activation of sample fluid reservoirs to produce regent droplets is based on the concept of dynamic reagent delivery (e.g., combinatorial barcoding) via an on demand capability. The on demand feature may be provided by one of a variety of technical capabilities for releasing delivery droplets to a primary droplet, as described herein. From this disclosure and herein cited documents and knowledge in the art, it is within the ambit of the skilled person to develop flow rates, channel lengths, and channel geometries; and establish droplets containing random or specified reagent combinations can be generated on demand and merged with the “reaction chamber” droplets containing the samples/cells/substrates of interest. By incorporating a plurality of unique tags into the additional droplets and joining the tags to a solid support designed to be specific to the primary droplet, the conditions that the primary droplet is exposed to may be encoded and recorded. For example, nucleic acid tags can be sequentially ligated to create a sequence reflecting conditions and order of same. Alternatively, the tags can be added independently appended to solid support. Non-limiting examples of a dynamic labeling system that may be used to bioninformatically record information can be found at US Provisional Patent Application entitled “Compositions and Methods for Unique Labeling of Agents” filed Sep. 21, 2012 and Nov. 29, 2012. In this way, two or more droplets may be exposed to a variety of different conditions, where each time a droplet is exposed to a condition, a nucleic acid encoding the condition is added to the droplet each ligated together or to a unique solid support associated with the droplet such that, even if the droplets with different histories are later combined, the conditions of each of the droplets are remain available through the different nucleic acids. Non-limiting examples of methods to evaluate response to exposure to a plurality of conditions can be found at US Provisional Patent Application entitled “Systems and Methods for Droplet Tagging” filed Sep. 21, 2012. Accordingly, in or as to the invention it is envisioned that there can be the dynamic generation of molecular barcodes (e.g., DNA oligonucleotides, flurophores, etc.) either independent from or in concert with the controlled delivery of various compounds of interest (drugs, small molecules, siRNA, CRISPR guide RNAs, reagents, etc.). For example, unique molecular barcodes can be created in one array of nozzles while individual compounds or combinations of compounds can be generated by another nozzle array. Barcodes/compounds of interest can then be merged with cell-containing droplets. An electronic record in the form of a computer log file is kept to associate the barcode delivered with the downstream reagent(s) delivered. This methodology makes it possible to efficiently screen a large population of cells for applications such as single-cell drug screening, controlled perturbation of regulatory pathways, etc. The device and techniques of the disclosed invention facilitate efforts to perform studies that require data resolution at the single cell (or single molecule) level and in a cost effective manner. The invention envisions a high throughput and high resolution delivery of reagents to individual emulsion droplets that may contain cells, nucleic acids, proteins, etc. through the use of monodisperse aqueous droplets that are generated one by one in a microfluidic chip as a water-in-oil emulsion. Being able to dynamically track individual cells and droplet treatments/combinations during life cycle experiments, and having an ability to create a library of emulsion droplets on demand with the further capability of manipulating the droplets through the disclosed process(es) are advantagous. In the practice of the invention there can be dynamic tracking of the droplets and create a history of droplet deployment and application in a single cell based environment. Droplet generation and deployment is produced via a dynamic indexing strategy and in a controlled fashion in accordance with disclosed embodiments of the present invention. Microdroplets can be processed, analyzed and sorted at a highly efficient rate of several thousand droplets per second, providing a powerful platform which allows rapid screening of millions of distinct compounds, biological probes, proteins or cells either in cellular models of biological mechanisms of disease, or in biochemical, or pharmacological assays. A plurality of biological assays as well as biological synthesis are contemplated. Polymerase chain reactions (PCR) are contemplated (see, e.g., US Patent Publication No. 20120219947). Methods of the invention may be used for merging sample fluids for conducting any type of chemical reaction or any type of biological assay. There may be merging sample fluids for conducting an amplification reaction in a droplet. Amplification refers to production of additional copies of a nucleic acid sequence and is generally carried out using polymerase chain reaction or other technologies well known in the art (e.g., Dieffenbach and Dveksler, PCR Primer, a Laboratory Manual, Cold Spring Harbor Press, Plainview, N.Y. [1995]). The amplification reaction may be any amplification reaction known in the art that amplifies nucleic acid molecules, such as polymerase chain reaction, nested polymerase chain reaction, polymerase chain reaction-single strand conformation polymorphism, ligase chain reaction (Barany F. (1991) PNAS 88:189-193; Barany F. (1991) PCR Methods and Applications 1:5-16), ligase detection reaction (Barany F. (1991) PNAS 88:189-193), strand displacement amplification and restriction fragments length polymorphism, transcription based amplification system, nucleic acid sequence-based amplification, rolling circle amplification, and hyper-branched rolling circle amplification. In certain embodiments, the amplification reaction is the polymerase chain reaction. Polymerase chain reaction (PCR) refers to methods by K. B. Mullis (U.S. Pat. Nos. 4,683,195 and 4,683,202, hereby incorporated by reference) for increasing concentration of a segment of a target sequence in a mixture of genomic DNA without cloning or purification. The process for amplifying the target sequence includes introducing an excess of oligonucleotide primers to a DNA mixture containing a desired target sequence, followed by a precise sequence of thermal cycling in the presence of a DNA polymerase. The primers are complementary to their respective strands of the double stranded target sequence. To effect amplification, primers are annealed to their complementary sequence within the target molecule. Following annealing, the primers are extended with a polymerase so as to form a new pair of complementary strands. The steps of denaturation, primer annealing and polymerase extension may be repeated many times (i.e., denaturation, annealing and extension constitute one cycle; there may be numerous cycles) to obtain a high concentration of an amplified segment of a desired target sequence. The length of the amplified segment of the desired target sequence is determined by relative positions of the primers with respect to each other, and therefore, this length is a controllable parameter. Methods for performing PCR in droplets are shown for example in Link et al. (U.S. Patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163), Anderson et al. (U.S. Pat. No. 7,041,481 and which reissued as RE41,780) and European publication number EP2047910 to Raindance Technologies Inc. The content of each of which is incorporated by reference herein in its entirety. The first sample fluid contains nucleic acid templates. Droplets of the first sample fluid are formed as described above. Those droplets will include the nucleic acid templates. In certain embodiments, the droplets will include only a single nucleic acid template, and thus digital PCR may be conducted. The second sample fluid contains reagents for the PCR reaction. Such reagents generally include Taq polymerase, deoxynucleotides of type A, C, G and T, magnesium chloride, and forward and reverse primers, all suspended within an aqueous buffer. The second fluid also includes detectably labeled probes for detection of the amplified target nucleic acid, the details of which are discussed below. This type of partitioning of the reagents between the two sample fluids is not the only possibility. In some instances, the first sample fluid will include some or all of the reagents necessary for the PCR whereas the second sample fluid will contain the balance of the reagents necessary for the PCR together with the detection probes. Primers may be prepared by a variety of methods including but not limited to cloning of appropriate sequences and direct chemical synthesis using methods well known in the art (Narang et al., Methods Enzymol., 68:90 (1979); Brown et al., Methods Enzymol., 68:109 (1979)). Primers may also be obtained from commercial sources such as Operon Technologies, Amersham Pharmacia Biotech, Sigma, and Life Technologies. The primers may have an identical melting temperature. The lengths of the primers may be extended or shortened at the 5′ end or the 3′ end to produce primers with desired melting temperatures. Also, the annealing position of each primer pair may be designed such that the sequence and, length of the primer pairs yield the desired melting temperature. The simplest equation for determining the melting temperature of primers smaller than 25 base pairs is the Wallace Rule (Td=2(A+T)+4(G+C)). Computer programs may also be used to design primers, including but not limited to Array Designer Software (Arrayit Inc.), Oligonucleotide Probe Sequence Design Software for Genetic Analysis (Olympus Optical Co.), NetPrimer, and DNAsis from Hitachi Software Engineering. The TM (melting or annealing temperature) of each primer is calculated using software programs such as Oligo Design, available from Invitrogen Corp.
A droplet containing the nucleic acid is then caused to merge with the PCR reagents in the second fluid according to methods of the invention described above, producing a droplet that includes Taq polymerase, deoxynucleotides of type A, C, G and T, magnesium chloride, forward and reverse primers, detectably labeled probes, and the target nucleic acid. Once mixed droplets have been produced, the droplets are thermal cycled, resulting in amplification of the target nucleic acid in each droplet. Droplets may be flowed through a channel in a serpentine path between heating and cooling lines to amplify the nucleic acid in the droplet. The width and depth of the channel may be adjusted to set the residence time at each temperature, which may be controlled to anywhere between less than a second and minutes. The three temperature zones may be used for the amplification reaction. The three temperature zones are controlled to result in denaturation of double stranded nucleic acid (high temperature zone), annealing of primers (low temperature zones), and amplification of single stranded nucleic acid to produce double stranded nucleic acids (intermediate temperature zones). The temperatures within these zones fall within ranges well known in the art for conducting PCR reactions. See for example, Sambrook et al. (Molecular Cloning, A Laboratory Manual, 3rd edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 2001). The three temperature zones can be controlled to have temperatures as follows: 95° C. (TH), 55° C. (TL), 72° C. (TM). The prepared sample droplets flow through the channel at a controlled rate. The sample droplets first pass the initial denaturation zone (TH) before thermal cycling. The initial preheat is an extended zone to ensure that nucleic acids within the sample droplet have denatured successfully before thermal cycling. The requirement for a preheat zone and the length of denaturation time required is dependent on the chemistry being used in the reaction. The samples pass into the high temperature zone, of approximately 95° C., where the sample is first separated into single stranded DNA in a process called denaturation. The sample then flows to the low temperature, of approximately 55° C., where the hybridization process takes place, during which the primers anneal to the complementary sequences of the sample. Finally, as the sample flows through the third medium temperature, of approximately 72° C., the polymerase process occurs when the primers are extended along the single strand of DNA with a thermostable enzyme. The nucleic acids undergo the same thermal cycling and chemical reaction as the droplets pass through each thermal cycle as they flow through the channel. The total number of cycles in the device is easily altered by an extension of thermal zones. The sample undergoes the same thermal cycling and chemical reaction as it passes through N amplification cycles of the complete thermal device. In other aspects, the temperature zones are controlled to achieve two individual temperature zones for a PCR reaction. In certain embodiments, the two temperature zones are controlled to have temperatures as follows: 95° C. (TH) and 60° C. (TL). The sample droplet optionally flows through an initial preheat zone before entering thermal cycling. The preheat zone may be important for some chemistry for activation and also to ensure that double stranded nucleic acid in the droplets is fully denatured before the thermal cycling reaction begins. In an exemplary embodiment, the preheat dwell length results in approximately 10 minutes preheat of the droplets at the higher temperature. The sample droplet continues into the high temperature zone, of approximately 95° C., where the sample is first separated into single stranded DNA in a process called denaturation. The sample then flows through the device to the low temperature zone, of approximately 60° C., where the hybridization process takes place, during which the primers anneal to the complementary sequences of the sample. Finally the polymerase process occurs when the primers are extended along the single strand of DNA with a thermostable enzyme. The sample undergoes the same thermal cycling and chemical reaction as it passes through each thermal cycle of the complete device. The total number of cycles in the device is easily altered by an extension of block length and tubing. After amplification, droplets may be flowed to a detection module for detection of amplification products. The droplets may be individually analyzed and detected using any methods known in the art, such as detecting for the presence or amount of a reporter. Generally, a detection module is in communication with one or more detection apparatuses. Detection apparatuses may be optical or electrical detectors or combinations thereof. Examples of suitable detection apparatuses include optical waveguides, microscopes, diodes, light stimulating devices, (e.g., lasers), photo multiplier tubes, and processors (e.g., computers and software), and combinations thereof, which cooperate to detect a signal representative of a characteristic, marker, or reporter, and to determine and direct the measurement or the sorting action at a sorting module. Further description of detection modules and methods of detecting amplification products in droplets are shown in Link et al. (U.S. patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163) and European publication number EP2047910 to Raindance Technologies Inc.
Examples of assays are also ELISA assays (see, e.g., US Patent Publication No. 20100022414). The present invention provides another emulsion library which may comprise a plurality of aqueous droplets within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise at least a first antibody, and a single element linked to at least a second antibody, wherein said first and second antibodies are different. In one example, each library element may comprise a different bead, wherein each bead is attached to a number of antibodies and the bead is encapsulated within a droplet that contains a different antibody in solution. These antibodies may then be allowed to form “ELISA sandwiches,” which may be washed and prepared for a ELISA assay. Further, these contents of the droplets may be altered to be specific for the antibody contained therein to maximize the results of the assay. Single-cell assays are also contemplated as part of the present invention (see, e.g., Ryan et al., Biomicrofluidics 5, 021501 (2011) for an overview of applications of microfluidics to assay individual cells). A single-cell assay may be contemplated as an experiment that quantifies a function or property of an individual cell when the interactions of that cell with its environment may be controlled precisely or may be isolated from the function or property under examination. The research and development of single-cell assays is largely predicated on the notion that genetic variation causes disease and that small subpopulations of cells represent the origin of the disease. Methods of assaying compounds secreted from cells, subcellular components, cell-cell or cell-drug interactions as well as methods of patterning individual cells are also contemplated within the present invention.
These and other technologies may be employed in or as to the practice of the instant invention.

Example 1: Materials and Methods

Briefly, gene expression profiles were measured at 18 time points (0.5 hr to 72 days) under Th17 conditions (IL-6, TGF-β1) or control (Th0) using Affymetrix microarrays HT_MG-430A. Differentially expressed genes were detected using a consensus over four inference methods, and cluster the genes using k-means, with an automatically derived k. Temporal regulatory interactions were inferred by looking for significant (p<5*10⁻⁵and fold enrichment>1.5) overlaps between the regulator's putative targets (e.g., based on ChIPseq) and the target gene's cluster (using four clustering schemes). Candidates for perturbation were ordered lexicographically using network-based and expression-based features. Perturbations were done using SiNW for siRNA delivery. These methods are described in more detail below.
Mice:
C57BL/6 wild-type (wt), Mt^−/−, Irf1^−/−, Fas^−/−, Irf4^fl/fl, and Cd4^Cremice were obtained from Jackson Laboratory (Bar Harbor, Me.). Stat1^−/− and 129/Sv control mice were purchased from Taconic (Hudson, N.Y.). IL-12rβ1^−/− mice were provided by Dr. Pahan Kalipada from Rush University Medical Center. IL-17Ra^−/− mice were provided by Dr. Jay Kolls from Louisiana State University/University of Pittsburgh. Irf8^fl/flmice were provided by Dr. Keiko Ozato from the National Institute of Health. Both Irf4^fl/fland Irf8^fl/flmice were crossed to Cd4^Cremice to generate Cd4^CrexIrf4^fl/fland Cd4^CrexIrf8^fl/flmice. All animals were housed and maintained in a conventional pathogen-free facility at the Harvard Institute of Medicine in Boston, Mass. (IUCAC protocols: 0311-031-14 (VKK) and 0609-058015 (AR)). All experiments were performed in accordance to the guidelines outlined by the Harvard Medical Area Standing Committee on Animals at the Harvard Medical School (Boston, Mass.). In addition, spleens fromMina^−/− mice were provided by Dr. Mark Bix from St. Jude Children's Research Hospital (IACUC Protocol: 453). Pou2af1^−/− mice were obtained from the laboratory of Dr. Robert Roeder (Kim, U. et al. The B-cell-specific transcription coactivator OCA-B/OBF-1/Bob-1 is essential for normal production of immunoglobulin isotypes. Nature 383, 542-547, doi:10.1038/383542a0 (1996)). Wild-type and Oct1^−/− fetal livers were obtained at day E12.5 and transplanted into sub-lethally irradiated Rag1^−/− mice as previously described (Wang, V. E., Tantin, D., Chen, J. & Sharp, P. A. B cell development and immunoglobulin transcription in Oct-1-deficient mice. Proc. Natl. Acad. Sci. U.S.A. 101, 2005-2010, doi:10.1073/pnas.0307304101 (2004)) (IACUC Protocol: 11-09003).
Cell Sorting and In Vitro T-Cell Differentiation in Petri Dishes:
Cd4+ T cells were purified from spleen and lymph nodes using anti-CD4 microbeads (Miltenyi Biotech) then stained in PBS with 1% FCS for 20 min at room temperature with anti-Cd4-PerCP, anti-Cd62l-APC, and anti-Cd44-PE antibodies (all Biolegend, CA).
Naïve Cd4⁺ Cd62l^highCd44^lowT cells were sorted using the BD FACSAria cell sorter. Sorted cells were activated with plate bound anti-Cd3 (2 μg/ml) and anti-Cd28 (2 μg/ml) in the presence of cytokines. For Th17 differentiation: 2 ng/mL rhTGF-β1 (Miltenyi Biotec), 25 ng/mL rmIl-6 (Miltenyi Biotec), 20 ng/ml rmIl-23 (Miltenyi Biotec), and 20 ng/ml rmIL-β1 (Miltenyi Biotec). Cells were cultured for 0.5-72 hours and harvested for RNA, intracellular cytokine staining, and flow cytometry.
Flow Cytometry and Intracellular Cytokine Staining (ICC):
Sorted naïve T cells were stimulated with phorbol 12-myristate 13-aceate (PMA) (50 ng/ml, Sigma-aldrich, MO), ionomycin (1 μg/ml, Sigma-aldrich, MO) and a protein transport inhibitor containing monensin (Golgistop) (BD Biosciences) for four hours prior to detection by staining with antibodies. Surface markers were stained in PBS with 1% FCS for 20 min at room temperature, then subsequently the cells were fixed in Cytoperm/Cytofix (BD Biosciences), permeabilized with Perm/Wash Buffer (BD Biosciences) and stained with Biolegend conjugated antibodies, i.e. Brilliant Violet 650™ anti-mouse IFN-γ (XMG1.2) and allophycocyanin-anti-IL-17A (TC11-18H10.1), diluted in Perm/Wash buffer as described (Bettelli, E. et al. Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells. Nature 441, 235-238 (2006)) (FIG. 5, FIG. 16). To measure the time-course of RORγt protein expression, a phycoerythrin-conjugated anti-Retinoid-Related Orphan Receptor gamma was used (B2D), also from eBioscience (FIG. 16). FOXP3 staining for cells from knockout mice was performed with the FOXP3 staining kit by eBioscience (00-5523-00) in accordance with their “Onestep protocol for intracellular (nuclear) proteins”. Data was collected using either a FACS Calibur or LSR II (Both BD Biosciences), then analyzed using Flow Jo software (Treestar) (Awasthi, A. et al. A dominant function for interleukin 27 in generating interleukin 10-producing anti-inflammatory T cells. Nature immunology 8, 1380-1389, doi:10.1038/ni1541 (2007); Awasthi, A. et al. Cutting edge: IL-23 receptor gfp reporter mice reveal distinct populations of IL-17-producing cells. J Immunol 182, 5904-5908, doi:10.4049/jimmunol.0900732 (2009)).
Quantification of Cytokine Secretion Using Enzyme-Linked Immunosorbent Assay (ELISA):
Naïve T cells from knockout mice and their wild type controls were cultured as described above, their supernatants were collected after 72 h, and cytokine concentrations were determined by ELISA (antibodies for IL-17 and IL-10 from BD Bioscience) or by cytometric bead array for the indicated cytokines (BD Bioscience), according to the manufacturers' instructions (FIG. 5, FIG. 16).
Microarray Data:
Naïve T cells were isolated from WT mice, and treated with IL-6 and TGF-β1. Affymetrix microarrays HT_MG-430A were used to measure the resulting mRNA levels at 18 different time points (0.5-72 h; FIG. 1b ). In addition, cells treated initially with IL-6, TGF-β1 and with addition of IL-23 after 48 hr were profiled at five time points (50-72 h). As control, time- and culture-matched WT naïve T cells stimulated under Th0 conditions were used. Biological replicates were measured in eight of the eighteen time points (1 hr, 2 hr, 10 hr, 20 hr, 30 hr, 42 hr, 52 hr, 60 hr) with high reproducibility (r²>0.98). For further validation, the differentiation time course was compared to published microarray data of Th17 cells and naïve T cells (Wei, G. et al. in Immunity Vol. 30 155-167 (2009)) (FIG. 6c ). In an additional dataset naïve T cells were isolated from WT and Il23r^−/− mice, and treated with IL-6, TGF-β1 and IL-23 and profiled at four different time points (49 hr, 54 hr, 65 hr, 72 hr). Expression data was preprocessed using the RMA algorithm followed by quantile normalization (Reich, M. et al. GenePattern 2.0. Nature genetics 38, 500-501, doi:10.1038/ng0506-500 (2006)).
Detecting Differentially Expressed Genes:
Differentially expressed genes (comparing to the Th0 control) were found using four methods: (1) Fold change. Requiring a 2-fold change (up or down) during at least two time points. (2) Polynomial fit. The EDGE software (Storey, J., Xiao, W., Leek, J., Tompkins, R. & Davis, R. in Proc. Natl. Acad. Sci. U.S.A. vol. 102 12837 (2005); Leek, J. T., Monsen, E., Dabney, A. R. & Storey, J. D. EDGE: extraction and analysis of differential gene expression. Bioinformatics 22, 507-508, doi:10.1093/bioinformatics/btk005 (2006)), designed to identify differential expression in time course data, was used with a threshold of q-value≤0.01. (3) Sigmoidal fit. An algorithm similar to EDGE while replacing the polynomials with a sigmoid function, which is often more adequate for modeling time course gene expression data (Chechik, G. & Koller, D. Timing of gene expression responses to environmental changes. J Comput Biol 16, 279-290, doi:10.1089/cmb.2008.13TT10.1089/cmb.2008.13TT [pii] (2009)), was used. A threshold of q-value≤0.01. (4) ANOVA was used. Gene expression is modeled by: time (using only time points for which there was more than one replicate) and treatment (“TGF-β1+IL-6” or “Th0”). The model takes into account each variable independently, as well as their interaction. Cases in which the p-value assigned with the treatment parameter or the interaction parameter passed an FDR threshold of 0.01 were reported.
Overall, substantial overlap between the methods (average of 82% between any pair of methods) observed. The differential expression score of a gene was defined as the number of tests that detected it. As differentially expressed genes, cases with differential expression score>3 were reported.
For the Il23r^−/− time course (compared to the WT T cells) methods 1.3 (above) were used. Here, a fold change cutoff of 1.5 was used, and genes detected by at least two tests were reported.
Clustering:
several ways for grouping the differentially expressed genes were considered, based on their time course expression data: (1) For each time point, two groups were defined: (a) all the genes that are over-expressed and (b) all the genes that are under-expressed relative to Th0 cells (see below); (2) For each time point, two groups were defined: (a) all the genes that are induced and (b) all the genes that are repressed, comparing to the previous time point; (3) K-means clustering using only the Th17 polarizing conditions. The minimal k was used, such that the within-cluster similarity (average Pearson correlation with the cluster's centroid) was higher than 0.75 for all clusters; and, (4) K-means clustering using a concatenation of the Th0 and Th17 profiles.
For methods (1, 2), to decide whether to include a gene, its original mRNA expression profiles (Th0, Th17), and their approximations as sigmoidal functions (Chechik, G. & Koller, D. Timing of gene expression responses to environmental changes. J Comput Biol 16, 279-290, doi:10.1089/cmb.2008.13TT10.1089/cmb.2008.13TT [pii] (2009)) (thus filtering transient fluctuations) were considered. The fold change levels (compared to Th0 (method 1) or to the previous time point (method 2)) were required to pass a cutoff defined as the minimum of the following three values: (1) 1.7; (2) mean+std of the histogram of fold changes across all time points; or (3) the maximum fold change across all time points. The clusters presented in FIG. 1b were obtained with method 4.
Regulatory Network Inference:
potential regulators of Th17 differentiation were identified by computing overlaps between their putative targets and sets of differentially expressed genes grouped according to methods 1-4 above. regulator-target associations from several sources were assembled: (1) in vivo DNA binding profiles (typically measured in other cells) of 298 transcriptional regulators (Linhart, C., Halperin, Y. & Shamir, R. Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome research 18, 1180-1189, doi:10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integrated platform of mammalian transcription factors. Bioinformatics 24, 2416-2417, doi:10.1093/bioinformatics/btn439 (2008); Wilson, N. K. et al. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532-544, doi:S1934-5909(10)00440-6 [pii]10.1016/j.stem.2010.07.016 (2010); Lachmann, A. et al. in Bioinformatics Vol. 26 2438-2444 (2010); Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740, doi:10.1093/bioinformatics/btr260 (2011); Jiang, C., Xuan, Z., Zhao, F. & Zhang, M. TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35, D137-140 (2007)); (2) transcriptional responses to the knockout of 11 regulatory proteins (Awasthi et al., J. Immunol 2009; Schraml, B. U. et al. The AP-1 transcription factor Batf controls T(H)17 differentiation. Nature 460, 405-409, doi:nature08114 [pii]10.1038/nature08114 (2009); Shi, L. Z. et al. HIF1alpha-dependent glycolytic pathway orchestrates a metabolic checkpoint for the differentiation of TH17 and Treg cells. The Journal of experimental medicine 208, 1367-1376, doi:10.1084/jem.20110278 (2011); Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nature immunology 12, 247-254, doi:10.1038/ni.1995 (2011); Durant, L. et al. Diverse Targets of the Transcription Factor STAT3 Contribute to T Cell Pathogenicity and Homeostasis. Immunity 32, 605-615, doi:10.1016/j.immuni.2010.05.003 (2010); Jux, B., Kadow, S. & Esser, C. Langerhans cell maturation and contact hypersensitivity are impaired in aryl hydrocarbon receptor-null mice. Journal of immunology (Baltimore, Md.: 1950) 182, 6709-6717, doi:10.4049/jimmunol.0713344 (2009); Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257-263, doi:10.1126/science.1179050 (2009); Xiao, S. et al. Retinoic acid increases Foxp3+ regulatory T cells and inhibits development of Th17 cells by enhancing TGF-beta-driven Smad3 signaling and inhibiting IL-6 and IL-23 receptor expression. J Immunol 181, 2277-2284, doi:181/4/2277 [pii] (2008)); (3) additional potential interactions obtained by applying the Ontogenet algorithm (Jojic et al., under review; regulatory model available at: to data from the mouse ImmGen consortium (January 2010 release (Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nature immunology 9, 1091-1094, doi:10.1038/ni1008-1091 (2008)), which includes 484 microarray samples from 159 cell subsets from the innate and adaptive immune system of mice; (4) a statistical analysis of cis-regulatory element enrichment in promoter regions (Elkon, R., Linhart, C., Sharan, R., Shamir, R. & Shiloh, Y. in Genome Research Vol. 13 773-780 (2003); Odabasioglu, A., Celik, M. & Pileggi, L. T. in Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design 58-65 (IEEE Computer Society, San Jose, Calif., United States, 1997)); and, (5) the TF enrichment module of the IPA software. For every TF in the database, the statistical significance of the overlap between its putative targets and each of the groups defined above using a Fisher's exact test was computed. Cases where p<5*10⁻⁵and the fold enrichment>1.5 were included.
Each edge in the regulatory network was assigned a time stamp based on the expression profiles of its respective regulator and target nodes. For the target node, the time points at which a gene was either differentially expressed or significantly induced or repressed with respect to the previous time point (similarly to grouping methods 1 and 2 above) were considered. A regulator node was defined as ‘absent’ at a given time point if: (i) it was under expressed compared to Th0; or (ii) the expression is low (<20% of the maximum value in time) and the gene was not over-expressed compared to Th0; or, (iii) up to this point in time the gene was not expressed above a minimal expression value of 100. As an additional constraint, protein expression levels were estimated using the model from Schwanhäusser, B. et al. (Global quantification of mammalian gene expression control. Nature 473, 337-342, doi:10.1038/nature10098 (2011)) and using a sigmoidal fit (Chechik & Koller, J Comput Biol 2009) for a continuous representation of the temporal expression profiles, and the ProtParam software (Wilkins, M. R. et al. Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol. 112, 531-552 (1999)) for estimating protein half-lives. It was required that, in a given time point, the predicted protein level be no less than 1.7 fold below the maximum value attained during the time course, and not be less than 1.7 fold below the Th0 levels. The timing assigned to edges inferred based on a time-point specific grouping ( grouping methods 1 and 2 above) was limited to that specific time point. For instance, if an edge was inferred based on enrichment in the set of genes induced at 1 hr (grouping method #2), it will be assigned a “1 hr” time stamp. This same edge could then only have additional time stamps if it was revealed by additional tests.
Selection of Nanostring Signature Genes:
The selection of the 275-gene signature (Table 1) combined several criteria to reflect as many aspect of the differentiation program as was possible. The following requirements were defined: (1) the signature must include all of the TFs that belong to a Th17 microarray signature (comparing to other CD4+ T cells (Wei et al., in Immunity vol. 30 155-167 (2009)), see Methods described herein); that are included as regulators in the network and have a differential expression score>1; or that are strongly differentially expressed (differential expression score=4); (2) it must include at least 10 representatives from each cluster of genes that have similar expression profiles (using clustering method (4) above); (3) it must contain at least 5 representatives from the predicted targets of each TF in the different networks; (4) it must include a minimal number of representatives from each enriched Gene Ontology (GO) category (computed across all differentially expressed genes); and, (5) it must include a manually assembled list of ˜100 genes that are related to the differentiation process, including the differentially expressed cytokines, receptor molecules and other cell surface molecules. Since these different criteria might generate substantial overlaps, a set-cover algorithm was used to find the smallest subset of genes that satisfies all of five conditions. To this list 18 genes whose expression showed no change (in time or between treatments) in the microarray data were added.
The 85-gene signature (used for the Fluidigm BioMark qPCR assay) is a subset of the 275-gene signature, selected to include all the key regulators and cytokines discussed. To this list 10 control genes (2900064A13RIK, API5, CAND1, CSNK1A1, EIF3E, EIF3H, FIP1L1, GOLGA3, HSBP1, KHDRBS1, MED24, MKLN1, PCBP2, SLC6A6, SUFU, TMED7, UBE3A, ZFP410) were added.
Selection of Perturbation Targets:
an unbiased approach was used to rank candidate regulators—transcription factor or chromatin modifier genes—of Th17 differentiation. The ranking was based on the following features: (a) whether the gene encoding the regulator belonged to the Th17 microarray signature (comparing to other CD4+ T cells (Wei et al., in Immunity vol. 30 155-167 (2009)), see Methods described herein); (b) whether the regulator was predicted to target key Th17 molecules (IL-17, IL-21, IL23r, and ROR-γt); (c) whether the regulator was detected based on both perturbation and physical binding data from the IPA software; (d) whether the regulator was included in the network using a cutoff of at least 10 target genes; (e) whether the gene encoding for the regulator was significantly induced in the Th17 time course. Only cases where the induction happened after 4 hours were considered to exclude non-specific hits; (f) whether the gene encoding the regulator was differentially expressed in response to Th17-related perturbations in previous studies. For this criterion, a database of transcriptional effects in perturbed Th17 cells was assembled, including: knockouts of Batf (Schraml et al., Nature 2009), ROR-γt (Xiao et al., unpublished), Hif1a (Shi et al., J. Exp. Med. (2011)), Stat3 and Stat5 (Yang et al., Nature Immunol (2011); Durant, L. et al. in Immunity Vol. 32 605-615 (2010), Tbx21 (Awasthi et al., unpublished), IL23r (this study), and Ahr (Jux et al., J. Immunol 2009)). Data from the Th17 response to Digoxin (Huh, J. R. et al. Digoxin and its derivatives suppress TH17 cell differentiation by antagonizing RORgammat activity. Nature 472, 486-490, doi:10.1038/nature09978 (2011)) and Halofuginone (Sundrud, M. S. et al. Halofuginone inhibits TH17 cell differentiation by activating the amino acid starvation response. Science (New York, N.Y.) 324, 1334-1338, doi:10.1126/science.1172638 (2009)), as well as information on direct binding by ROR-γt as inferred from ChIP-seq data (Xiao et al., unpublished) was also included. The analysis of the published expression data sets is described in the Methods described herein. For each regulator, the number of conditions in which it came up as a significant hit (up/down-regulated or bound) was counted; for regulators with 2 to 3 hits (quantiles 3 to 7 out of 10 bins), a score of 1 was then assign; for regulators with more than 3 hits (quantiles 8-10), a score of 2 (a score of 0 is assigned otherwise) was assigned; and, (g) the differential expression score of the gene in the Th17 time course.
The regulators were ordered lexicographically by the above features according to the order: a, b, c, d, (sum of e and f), g—that is, first sort according to a then break ties according to b, and so on. Genes that are not over-expressed during at least one time point were excluded. As an exception, predicted regulators (feature d) that had additional external validation (feature f) were retained. To validate this ranking, a supervised test was used: 74 regulators that were previously associated with Th17 differentiation were manually annotated.
All of the features are highly specific for these regulators (p<10⁻³). Moreover, using a supervised learning method (Naïve Bayes), the features provided good predictive ability for the annotated regulators (accuracy of 71%, using 5-fold cross validation), and the resulting ranking was highly correlated with the unsupervised lexicographic ordering (Spearman correlation>0.86).
This strategy was adapted for ranking protein receptors. To this end, feature c was excluded and the remaining “protein-level” features (b and d) were replaced with the following definitions: (b) whether the respective ligand is induced during the Th17 time course; and, (d) whether the receptor was included as a target in the network using a cutoff of at least 5 targeting transcriptional regulators.
Gene Knockdown Using Silicon Nanowires:
4×4 mm silicon nanowire (NW) substrates were prepared and coated with 3 μL of a 50 μM pool of four siGENOME siRNAs (Dharmcon) in 96 well tissue culture plates, as previously described (Shalek, A. K. et al. Vertical silicon nanowires as a universal platform for delivering biomolecules into living cells. Proceedings of the National Academy of Sciences of the United States of America 107, 1870-1875, doi:10.1073/pnas.0909350107 (2010)). Briefly, 150,000 naïve T cells were seeded on siRNA-laced NWs in 10 μL of complete media and placed in a cell culture incubator (37° C., 5% CO₂) to settle for 45 minutes before full media addition. These samples were left undisturbed for 24 hours to allow target transcript knockdown. Afterward, siRNA-transfected T cells were activated with αCd3/Cd28 dynabeads (Invitrogen), according to the manufacturer's recommendations, under Th17 polarization conditions (TGF-β1 & IL-6, as above). 10 or 48 hr post-activation, culture media was removed from each well and samples were gently washed with 100 μL of PBS before being lysed in 20 μL of buffer TCL (Qiagen) supplemented with 2-mercaptoethanol (1:100 by volume). After mRNA was harvested in Turbocapture plates (Qiagen) and converted to cDNA using Sensiscript RT enzyme (Qiagen), qRT-PCR was used to validate both knockdown levels and phenotypic changes relative to 8-12 non-targeting siRNA control samples, as previously described (Chevrier, N. et al. Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell 147, 853-867, doi:10.1016/j.cell.2011.10.022 (2011)). A 60% reduction in target mRNA was used as the knockdown threshold. In each knockdown experiment, each individual siRNA pool was run in quadruplicate; each siRNA was tested in at least three separate experiments (FIG. 11).
mRNA Measurements in Perturbation Assays:
the nCounter system, presented in full in Geiss et al. (Geiss, G. K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. SI. Nature Biotechnology 26, 317-325, doi:10.1038/nbt1385 (2008)), was used to measure a custom CodeSet constructed to detect a total of 293 genes, selected as described above. The Fluidigm BioMark HD system was also used to measure a smaller set of 96 genes. Finally, RNA-Seq was used to follow up and validate 12 of the perturbations.
A custom CodeSet constructed to detect a total of 293 genes, selected as described above, including 18 control genes whose expression remain unaffected during the time course was used. Given the scarcity of input mRNA derived from each NW knockdown, a Nanostring-CodeSet specific, 14 cycle Specific Target Amplification (STA) protocol was performed according to the manufacturer's recommendations by adding 5 μL of TaqMan PreAmp Master Mix (Invitrogen) and 1 μL of pooled mixed primers (500 nM each, see Table S6.1 for primer sequences) to 5 μL of cDNA from a validated knockdown. After amplification, 5 μL of the amplified cDNA product was melted at 95° C. for 2 minutes, snap cooled on ice, and then hybridized with the CodeSet at 65° C. for 16 hours. Finally, the hybridized samples were loaded into the nCounter prep station and product counts were quantified using the nCounter Digital Analyzer following the manufacturer's instructions. Samples that were too concentrated after amplification were diluted and rerun. Serial dilutions (1:1, 1:4, 1:16, & 1:64, pre-STA) of whole spleen and Th17 polarized cDNAs were used to both control for the effects of different amounts of starting input material and check for biases in sample amplification.
Nanostring nCounter Data Analysis:
For each sample, the count values were divided by the sum of counts that were assigned to a set of control genes that showed no change (in time or between treatments) in the microarray data (18 genes altogether). For each condition, a change fold ratio was computed, comparing to at least three different control samples treated with non-targeting (NT) siRNAs. The results of all pairwise comparisons (i.e. A×B pairs for A repeats of the condition and B control (NT) samples) were then pooled together: a substantial fold change (above a threshold value t) in the same direction (up/down regulation) in more than half of the pairwise comparisons was required. The threshold t was determined as max {d1, d2}, where d1 is the mean+std in the absolute log fold change between all pairs of matching NT samples (i.e., form the same batch and the same time point; d1=1.66), and where d2 is the mean+1.645 times the standard deviation in the absolute log fold change shown by the 18 control genes (determined separately for every comparison by taking all the 18×A×B values; corresponding to p=0.05, under assumption of normality). All pairwise comparisons in which both NT and knockdown samples had low counts before normalization (<100) were ignored.
A permutation test was used to evaluate the overlap between the predicted network model (FIG. 2) and the knockdown effects measured in the Nanostring nCounter (FIG. 4, FIG. 10). Two indices were computed for every TF for which predicted target were available: (i) specificity—the percentage of predicted targets that are affected by the respective knockdown (considering only genes measured by nCounter), and (ii) sensitivity—the percentage of genes affected by a given TF knockdown that are also its predicted targets in the model. To avoid circularity, target genes predicted in the original network based on knockout alone were excluded from this analysis. The resulting values (on average, 13.5% and 24.8%, respectively) were combined into an F-score (the harmonic mean of specificity and sensitivity). The calculation of F-score was then repeated in 500 randomized datasets, where the target gene labels in the knockdown result matrix were shuffled. The reported empirical p-value is:
P=(1+# randomized datasets with equal of better F-score)/(1+# randomized datasets)
mRNA Measurements on the Fluidigm BioMark HD:
cDNA from validated knockdowns was prepared for quantification on the Fluidigm BioMark HD. Briefly, 5 μL of TaqMan PreAmp Master Mix (Invitrogen), 1 μL of pooled mixed primers (500 nM each, see Table S6.1 for primers), and 1.5 μL of water were added to 2.5 μL of knockdown validated cDNA and 14 cycles of STA were performed according to the manufacturer's recommendations. After the STA, an Exonuclease I digestion (New England Biosystems) was performed to remove unincorporated primers by adding 0.8 μL Exonuclease I, 0.4 μL Exonuclease I Reaction Buffer and 2.8 μL water to each sample, followed by vortexing, centrifuging and heating the sample to 37° C. for 30 minutes. After a 15 minute 80° C. heat inactivation, the amplified sample was diluted 1:5 in Buffer TE. Amplified validated knockdowns and whole spleen and Th17 serial dilution controls (1:1, 1:4, 1:16, & 1:64, pre-STA) were then analyzed using EvaGreen and 96×96 gene expression chips (Fluidigm BioMark HD) (Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat Biotechnol 29, 1120-1127, doi:10.1038/nbt.2038 (2011)).
Fluidigm Data Analysis:
For each sample, the Ct values were subtracted from the geometric mean of the Ct values assigned to a set of four housekeeping genes. For each condition, a fold change ratio was computed, comparing to at least three different control samples treated with non-targeting (NT) siRNAs. The results of all pairwise comparisons (i.e. A×B pairs for A repeats of the condition and B control (NT) samples) were then pooled together: a substantial difference between the normalized Ct values (above a threshold value) in the same direction (up/down regulation) in more than half of the pairwise comparisons was required. The threshold t was determined as max {log 2(1.5), d1(b), d2}, where d1(b) is the mean+std in the delta between all pairs of matching NT samples (i.e., from the same batch and the same time point), over all genes in expression quantile b (1<=b<=10). d2 is the mean+1.645 times the standard deviation in the deltas shown by 10 control genes (the 4 housekeeping genes plus 6 control genes from the Nanostring signature); d2 is determined separately for each comparison by taking all the 10×A×B values; corresponding to p=0.05, under assumption of normality). All pairwise comparisons in which both NT and knockdown samples had low counts before normalization (Ct<21 (taking into account the amplification, this cutoff corresponds to a conventional Ct cutoff of 35)) were ignored.
mRNA Measurements Using RNA-Seq:
Validated single stranded cDNAs from the NW-mediated knockdowns were converted to double stranded DNA using the NEBNext mRNA Second Strand Synthesis Module (New England BioLabs) according to the manufacturer's recommendations. The samples were then cleaned using 0.9×SPRI beads (Beckman Coulter). Libraries were prepared using the Nextera XT DNA Sample Prep Kit (Illumina), quantified, pooled, and then sequenced on the HiSeq 2500 (Illumnia) to an average depth 20M reads.
RNA-Seq Data Analysis:
a Bowtie index based on the UCSC known Gene transcriptome (Fujita, P. A. et al. The UCSC Genome Browser database: update 2011. Nucleic Acids Res. 39, D876-882, doi:10.1093/nar/gkq963 (2011)) was created, and paired-end reads were aligned directly to this index using Bowtie (Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10, R25, doi:10.1186/gb-2009-10-3-r25 (2009)). Next, RSEM v1.11 (Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323, doi:10.1186/1471-2105-12-323 (2011)) was ran with default parameters on these alignments to estimate expression levels. RSEM's gene level expression estimates (tau) were multiplied by 1,000,000 to obtain transcript per million (TPM) estimates for each gene. Quantile normalization was used to further normalize the TPM values within each batch of samples. For each condition, a fold change ratio was computed, comparing to at least two different control samples treated with nontargeting (NT) siRNAs. The results of all pairwise comparisons (i.e. A×B pairs for A repeats of the condition and B control (NT) samples) were then pooled together: a significant difference between the TPM values in the same direction (up/down regulation) in more than half of the pairwise comparisons was required. The significance cutoff t was determined as max {log 2(1.5), d1(b)}, where d1(b) is the mean+1.645*std in the log fold ratio between all pairs of matching NT samples (i.e., from the same batch and the same time point), over all genes in expression quantile b (1<=b<=20). All pairwise comparisons in which both NT and knockdown samples had low counts (TPM<10) were ignored. To avoid spurious fold levels due to low expression values a small constant, set to the value of the 1st quantile (out of 10) of all TPM values in the respective batch, was add to the expression values.
A hypergeometric test was used to evaluate the overlap between the predicted network model (FIG. 2) and the knockdown effects measured by RNA-seq (FIG. 4d ). As background, all of the genes that appeared in the microarray data (and hence 20 have the potential to be included in the network) were used. As an additional test, the Wilcoxon-Mann-Whitney rank-sum test was used, comparing the absolute log fold-changes of genes in the annotated set to the entire set of genes (using the same background as before). The rank-sum test does not require setting a significance threshold; instead, it considers the fold change values of all the genes. The p-values produced by the rank-sum test were lower (i.e., more significant) than in the hypergeometric test, and therefore, in FIG. 4c , only the more stringent (hypergeometric) p-values were reported.
Profiling Tsc22d3 DNA Binding Using ChIP-Seq:
ChIP-seq for Tsc22d3 was performed as previously described (Ram, O. et al. Combinatorial Patterning of Chromatin Regulators Uncovered by Genome-wide Location Analysis in Human Cells. Cell 147, 1628-1639 (2011)) using an antibody from Abcam. The analysis of this data was performed as previously described (Ram, O. et al. Combinatorial Patterning of Chromatin Regulators Uncovered by Genome-wide Location Analysis in Human Cells. Cell 147, 1628-1639 (2011)) and is detailed in the Methods described herein.
Analysis of Tsc22d3 ChIP-Seq Data:
ChIP-seq reads were aligned to the NCBI Build 37 (UCSC mm9) of the mouse genome using Bowtie (Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. in Genome Biol Vol. 10 R25 (2009)). Enriched binding regions (peaks) were detected using MACS (Zhang, Y. et al. in Genome Biol Vol. 9 R137 (2008)) with a pvalue cutoff of 10⁻⁸. A peak was associated with a gene if it falls in proximity to its 5′ end (10 kb upstream and 1 kb downstream from transcription start site) or within the gene's body. The RefSeq transcript annotations for gene's coordinates were used.
The overlap of ChIP-seq peaks with annotated genomic regions was assessed. It was determined that a region A overlap with a peak B if A is within a distance of 50 bp from B's summit (as determined by MACS). The regions used included: (i) regulatory features annotations from the Ensemble database (Flicek, P. et al. Ensembl 2011. Nucleic Acids Res. 39, D800-806, doi:10.1093/nar/gkq1064 (2011)); (ii) regulatory 21 features found by the Oregano algorithm (Smith, R. L. et al. Polymorphisms in the IL-12beta and IL-23R genes are associated with psoriasis of early onset in a UK cohort. J Invest Dermatol 128, 1325-1327, doi:5701140 [pii] 10.1038/sj.jid.5701140 (2008)); (iii) conserved regions annotated by the multiz30way algorithm (here regions with multiz30way score>0.7 were considered); (iv) repeat regions annotated by RepeatMasker; (v) putative promoter regions—taking 10 kb upstream and 1 kb downstream of transcripts annotated in RefSeq (Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61-65, doi:10.1093/nar/gkl842 (2007)); (vi) gene body annotations in RefSeq; (vii) 3′ proximal regions (taking 1 kb upstream and 5 kb downstream to 3′ end); (viii) regions enriched in histone marks H3K4me3 and H3K27me3 in Th17 cells (Wei, G. et al. in Immunity Vol. 30 155-167 (2009)); (ix) regions enriched in binding of Stat3 and Stat5 (Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nat. Immunol. 12, 247-254, doi:10.1038/ni.1995 (2011)), Irf4 and Batf (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)), and RORγt (Xiao et al unpublished) in Th17 cells, and Foxp3 in iTreg (Xiao et al., unpublished).
For each set of peaks “x” and each set of genomic regions “y”, a binomial pvalue was used to assess their overlap in the genome as described in Mclean, C. Y. et al. in Nature biotechnology Vol. 28 nbt.1630-1639 (2010). The number of hits is defined as the number of x peaks that overlap with y. The background probability in sets (i)-(vii) is set to the overall length of the region (in bp) divided by the overall length of the genome. The background probability in sets (viii)-(ix) is set to the overall length of the region divided by the overall length of annotated genomic regions: this includes annotated regulatory regions (as defined in sets i, and ii), regions annotated as proximal to genes (using the definitions from set v-vii), carry a histone mark in Th17 cells (using the definition from set viii), or bound by transcription regulators in Th17 cells (using the definitions from set ix).
For the transcription regulators (set ix), an additional “gene-level” test was also included: here the overlap between the set of bound genes using a hypergeometric p-value was evaluated. A similar test was used to evaluate the overlap between the bound genes and genes that are differentially expressed in Tsc22d3 knockdown.
The analysis was repeated with a second peak-calling software (Scripture) (Guttman, M. et al. in Nature biotechnology Vol. 28 503-510 (2010); Garber, M. et al. A High-Throughput Chromatin Immunoprecipitation Approach Reveals Principles of Dynamic Gene Regulation in Mammals. Molecular cell, doi:10.1016/j.molcel.2012.07.030 (2012)), and obtained consistent results in all the above tests. Specifically, similar levels of overlap with the Th17 factors tested, both in terms of co-occupied binding sites and in terms of common target genes, was seen.
Estimating Statistical Significance of Monochromatic Interactions Between Modules:
The functional network in FIG. 4b consists of two modules: positive and negative. Two indices were computed: (1) within-module index: the percentage of positive edges between members of the same module (i.e., down-regulation in knockdown/knockout); and, (2) between-module index: the percentage of negative edges between members of the same module that are negative. The network was shuffled 1,000 times, while maintaining the nodes' out degrees (i.e., number of outgoing edges) and edges' signs (positive/negative), and re-computed the two indices. The reported p-values were computed using a t-test.
Using Literature Microarray Data for Deriving a Th17 Signature and for Identifying Genes Responsive to Th17-Related Perturbations:
To define the Th17 signatures genes, the gene expression data from Wei et al., in Immunity, vol. 30 155-167 (2009) was downloaded and analyzed, and the data was preprocessed using the RMA algorithm, followed by quantile normalization using the default parameters in the ExpressionFileCreator module of the 23 GenePattern suite (Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500-501, doi:10.1038/ng0506-500 (2006)). This data includes replicate microarray measurements from Th17, Th1, Th2, iTreg, nTreg, and Naïve CD4+ T cells. For each gene, it was evaluated whether it is over-expressed in Th17 cells compared to all other cell subsets using a one-sided t-test. All cases that had a p-value<0.05 were retained. As an additional filtering step, it was required that the expression level of a gene in Th17 cells be at least 1.25 fold higher than its expression in all other cell subsets. To avoid spurious fold levels due to low expression values, a small constant (c=50) was added to the expression values.
To define genes responsive to published Th17-related perturbations, gene expression data from several sources that provided transcriptional profiles of Th17 cells under various conditions (listed above) were downloaded and analyzed. These datasets were preprocessed as above. To find genes that were differentially expressed in a given condition (compared to their respective control), the fold change between the expression levels of each probeset in the case and control conditions was computed. To avoid spurious fold levels due to low expression values, a small constant as above was added to the expression values. Only cases where more than 50% of all of the possible case-control comparisons were above a cutoff of 1.5 fold change were reported. As an additional filter, when duplicates are available, a Z-score was computed as above and only cases with a corresponding p-value<0.05 were reported.
Genes:
The abbreviations set forth below in Table 11 are used herein to identify the genes used throughout the disclosure, including but not limited to those shown in Tables 1-9 of the specification.

TABLE 11

Gene Abbreviations, Entrez ID Numbers and Brief Description

Symbol	Entrez ID	Description

AAK1	22848	AP2 associated kinase 1
ABCG2	9429	ATP-binding cassette, sub-family G (WHITE), member 2
ACP5	54	acid phosphatase 5, tartrate resistant
ACVR1B	91	activin A receptor, type 1B
ACVR2A	92	activin receptor IIA
ADAM10	102	a disintegrin and metallopeptidase domain 10
ADAM17	6868	a disintegrin and metallopeptidase domain 17
ADRBK1	156	adrenergic receptor kinase, beta 1
AES	166	amino-terminal enhancer of split
AHR	196	aryl-hydrocarbon receptor
AIM1
	202	absent in melanoma 1
AKT1	207	thymoma viral proto-oncogene 1
ALPK2	115701	alpha-kinase 2
ANKHD1	54882	ankyrin repeat and KH domain containing 1
ANP32A	8125	acidic (leucine-rich) nuclear phosphoprotein 32 family,
		member A
ANXA4	307	annexin A4
AQP3	360	aquaporin 3
ARHGEF3	50650	Rho guanine nucleotide exchange factor (GEF) 3
ARID3A	1820	AT rich interactive domain 3A (BRIGHT-like)
ARID5A	10865	AT rich interactive domain 5A (MRF1-like)
ARL5A	26225	ADP-ribosylation factor-like 5A
ARMCX2	9823	armadillo repeat containing, X-linked 2
ARNTL	406	aryl hydrocarbon receptor nuclear translocator-like
ASXL1	171023	additional sex combs like 1 (Drosophila)
ATF2	1386	activating transcription factor 2
ATF3	467	activating transcription factor 3
ATF4	468	activating transcription factor 4
AURKB	9212	aurora kinase B
AXL	558	AXL receptor tyrosine kinase
B4GALT1	2683	UDP-Gal: betaGlcNAc beta 1,4-galactosyltransferase,
		polypeptide 1
BATF	10538	basic leucine zipper transcription factor, ATF-like
BATF3	55509	basic leucine zipper transcription factor, ATF-like 3
BAZ2B	29994	bromodomain adjacent to zinc finger domain, 2B
BCL11B	64919	B-cell leukemia/lymphoma 11B
BCL2L11	10018	BCL2-like 11 (apoptosis facilitator)
BCL3	602	B-cell leukemia/lymphoma 3
BCL6	604	B-cell leukemia/lymphoma 6
BHLH40	8553	Basic Helix-Loop-Helix Family, Member E40
BLOC1S1	2647	biogenesis of lysosome-related organelles complex-1,
		subunit 1
BMP2K	55589	BMP2 inducible kinase
BMPR1A	657	bone morphogenetic protein receptor, type 1A
BPGM	669	2,3-bisphosphoglycerate mutase
BSG	682	basigin
BTG1	694	B-cell translocation gene 1, anti-proliferative
BTG2	7832	B-cell translocation gene 2, anti-proliferative
BUB1	699	budding uninhibited by benzimidazoles 1 homolog
		(S. cerevisiae)
C14ORF83	161145	RIKEN cDNA 6330442E10 gene
C16ORF80	29105	gene trap locus 3
C21ORF66	94104	RIKEN cDNA 1810007M14 gene
CAMK4	814	calcium/calmodulin-dependent protein kinase IV
CARM1	10498	coactivator-associated arginine methyltransferase 1
CASP1	834	caspase 1
CASP3	836	caspase 3
CASP4	837	caspase 4, apoptosis-related cysteine peptidase
CASP6	839	caspase 6
CASP8AP2	9994	caspase 8 associated protein 2
CBFB	865	core binding factor beta
CBX4	8535	chromobox homolog 4 (Drosophila Pc class)
CCL1	6346	chemokine (C-C motif) ligand 1
CCL20	6364	chemokine (C-C motif) ligand 20
CCL4	6351	chemokine (C-C motif) ligand 4
CCND2	894	cyclin D2
CCR4	1233	chemokine (C-C motif) receptor 4
CCR5	1234	chemokine (C-C motif) receptor 5
CCR6	1235	chemokine (C-C motif) receptor 6
CCR8	1237	chemokine (C-C motif) receptor 8
CCRN4L	25819	CCR4 carbon catabolite repression 4-like
		(S. cerevisiae)
CD14	929	CD14 antigen
CD2	914	CD2 antigen
CD200	4345	CD200 antigen
CD226	10666	CD226 antigen
CD24	934	CD24a antigen
CD247	919	CD247 antigen
CD27	939	CD27 antigen
CD274	29126	CD274 antigen
CD28	940	CD28 antigen
CD3D	915	CD3 antigen, delta polypeptide
CD3G	917	CD3 antigen, gamma polypeptide
CD4	920	CD4 antigen
CD40LG	959	CD40 ligand
CD44	960	CD44 antigen
CD53	963	CD53 antigen
CD5L	922	CD5 antigen-like
CD63	967	CD63 antigen
CD68	968	CD68 antigen
CD70	970	CD70 antigen
CD74	972	CD74 antigen (invariant polypeptide of major
		histocompatibility complex, cl
CD80	941	CD80 antigen
CD83	9308	CD83 antigen
CD84	8832	CD84 antigen
CD86	942	CD86 antigen
CD9	928	CD9 antigen
CD96	10225	CD96 antigen
CDC25B	994	cell division cycle 25 homolog B (S. pombe)
CDC42BPA	8476	CDC42 binding protein kinase alpha
CDC5L	988	cell division cycle 5-like (S. pombe)
CDK5	1020	cyclin-dependent kinase 5
CDK6	1021	cyclin-dependent kinase 6
CDKN3	1033	cyclin-dependent kinase inhibitor 3
CDYL	9425	chromodomain protein, Y chromosome-like
CEBPB	1051	CCAAT/enhancer binding protein (C/EBP), beta
CENPT	80152	centromere protein T
CHD7	55636	chromodomain helicase DNA binding protein 7
CHMP1B	57132	chromatin modifying protein 1B
CHMP2A	27243	charged multivesicular body protein 2A
CHRAC1	54108	chromatin accessibility complex 1
CIC	23152	capicua homolog (Drosophila)
CITED2	10370	Cbp/p300-interacting transactivator, with Glu/Asp-rich
		carboxy-terminal dom
CLCF1	23529	cardiotrophin-like cytokine factor 1
CLK1	1195	CDC-like kinase 1
CLK3	1198	CDC-like kinase 3
CMTM6	54918	CKLF-like MARVEL transmembrane domain containing 6
CNOT2	4848	CCR4-NOT transcription complex, subunit 2
CREB1	1385	cAMP responsive element binding protein 1
CREB3L2	64764	cAMP responsive element binding protein 3-like 2
CREG1	8804	cellular repressor of E1A-stimulated genes 1
CREM	1390	cAMP responsive element modulator
CSDA	8531	cold shock domain protein A
CSF1R	1436	colony stimulating factor 1 receptor
CSF2	1437	colony stimulating factor 2 (granulocyte-macrophage)
CTLA4	1493	cytotoxic T-lymphocyte-associated protein 4
CTSD	1509	cathepsin D
CTSW	1521	cathepsin W
CXCL10	3627	chemokine (C-X-C motif) ligand 10
CXCR3	2833	chemokine (C-X-C motif) receptor 3
CXCR4	7852	chemokine (C-X-C motif) receptor 4
CXCR5	643	chemochine (C-X-C motif) receptor 5
DAPP1	27071	dual adaptor for phosphotyrosine and 3-
		phosphoinositides 1
DAXX	1616	Fas death domain-associated protein
DCK	1633	deoxycytidine kinase
DCLK1	9201	doublecortin-like kinase 1
DDIT3	1649	DNA-damage inducible transcript 3
DDR1	780	discoidin domain receptor family, member 1
DGKA	1606	diacylglycerol kinase, alpha
DGUOK	1716	deoxyguanosine kinase
DNAJC2	27000	DnaJ (Hsp40) homolog, subfamily C, member 2
DNTT	1791	deoxynucleotidyltransferase, terminal
DPP4	1803	dipeptidylpeptidase 4
DUSP1	1843	dual specificity phosphatase 1
DUSP10	11221	dual specificity phosphatase 10
DUSP14	11072	dual specificity phosphatase 14
DUSP16	80824	dual specificity phosphatase 16
DUSP2	1844	dual specificity phosphatase 2
DUSP22	56940	dual specificity phosphatase 22
DUSP6	1848	dual specificity phosphatase 6
E2F1	1869	E2F transcription factor 1
E2F4	1874	E2F transcription factor 4
E2F8	79733	E2F transcription factor 8
ECE2	9718	endothelin converting enzyme 2
EGR1	1958	early growth response 1
EGR2	1959	early growth response 2
EIF2AK2	5610	eukaryotic translation initiation factor 2-alpha kinase 2
ELK3	2004	ELK3, member of ETS oncogene family
ELL2	22936	elongation factor RNA polymerase II 2
EMP1	2012	epithelial membrane protein 1
ENTPD1	953	ectonucleoside triphosphate diphosphohydrolase 1
ERCC5	2073	excision repair cross-complementing rodent repair
		deficiency, complementati
ERRFI1	54206	ERBB receptor feedback inhibitor 1
ETS1	2113	E26 avian leukemia oncogene 1, 5′ domain
ETS2	2114	E26 avian leukemia oncogene 2, 3′ domain
ETV6	2120	ets variant gene 6 (TEL oncogene)
EZH1	2145	enhancer of zeste homolog 1 (Drosophila)
FAS	355	Fas (TNF receptor superfamily member 6)
FASLG	356	Fas ligand (TNF superfamily, member 6)
FCER1G	2207	Fc receptor, IgE, high affinity I, gamma polypeptide
FCGR2B	2213	Fc receptor, IgG, low affinity IIb
FES	2242	feline sarcoma oncogene
FLI1	2313	Friend leukemia integration 1
FLNA	2316	filamin, alpha
FOSL2	2355	fos-like antigen 2
FOXJ2	55810	forkhead box J2
FOXM1	2305	forkhead box M1
FOXN3	1112	forkhead box N3
FOXO1	2308	forkhead box O1
FOXP1	27086	forkhead box P1
FOXP3	50943	forkhead box P3
FRMD4B	23150	FERM domain containing 4B
FUS	2521	fusion, derived from t(12; 16) malignant liposarcoma
		(human)
FZD7	8324	frizzled homolog 7 (Drosophila)
GAP43	2596	growth associated protein 43
GATA3	2625	GATA binding protein 3
GATAD1	57798	GATA zinc finger domain containing 1
GATAD2B	57459	GATA zinc finger domain containing 2B
GEM	2669	GTP binding protein (gene overexpressed in skeletal
		muscle)
GFI1	2672	growth factor independent 1
GJA1	2697	gap junction protein, alpha 1
GK	2710	glycerol kinase
GLIPR1	11010	GLI pathogenesis-related 1 (glioma)
GMFB	2764	glia maturation factor, beta
GMFG	9535	glia maturation factor, gamma
GRN	2896	granulin
GUSB	2990	glucuronidase, beta
HCLS1	3059	hematopoietic cell specific Lyn substrate 1
HDAC8	55869	histone deacetylase 8
HIF1A	3091	hypoxia inducible factor 1, alpha subunit
HINT3	135114	histidine triad nucleotide binding protein 3
HIP1R	9026	huntingtin interacting protein 1 related
HIPK1	204851	homeodomain interacting protein kinase 1
HIPK2	28996	homeodomain interacting protein kinase 2
HK1	3098	hexokinase 1
HK2	3099	hexokinase 2
HLA-A	3105	major histocompatibility complex, class I, A
HLA-DQA1	3117	histocompatibility 2, class II antigen A, alpha
HMGA1	3159	high mobility group AT-hook 1
HMGB2	3148	high mobility group box 2
HMGN1	3150	high mobility group nucleosomal binding domain 1
ICOS	29851	inducible T-cell co-stimulator
ID1	3397	inhibitor of DNA binding 1
ID2	3398	inhibitor of DNA binding 2
ID3	3399	inhibitor of DNA binding 3
IER3	8870	immediate early response 3
IFI35	3430	interferon-induced protein 35
IFIH1	64135	interferon induced with helicase C domain 1
IFIT1	3434	interferon-induced protein with tetratricopeptide
		repeats 1
IFITM2	10581	interferon induced transmembrane protein 2
IFNG	3458	interferon gamma
IFNGR1	3459	interferon gamma receptor 1
IFNGR2	3460	interferon gamma receptor 2
IKZF1	10320	IKAROS family zinc finger 1
IKZF3	22806	IKAROS family zinc finger 3
IKZF4	64375	IKAROS family zinc finger 4
IL10	3586	interleukin 10
IL10RA	3587	interleukin 10 receptor, alpha
IL12RB1	3594	interleukin 12 receptor, beta 1
IL12RB2	3595	interleukin 12 receptor, beta 2
IL15RA	3601	interleukin 15 receptor, alpha chain
IL17A	3605	interleukin 17A
IL17F	112744	interleukin 17F
IL17RA	23765	interleukin 17 receptor A
IL18R1	8809	interleukin 18 receptor 1
IL1R1	3554	interleukin 1 receptor, type I
IL1RN	3557	interleukin 1 receptor antagonist
IL2	3558	interleukin 2
IL21	59067	interleukin 21
IL21R	50615	interleukin 21 receptor
IL22	50616	interleukin 22
IL23R	149233	interleukin 23 receptor
IL24	11009	interleukin 24
IL27RA	9466	interleukin 27 receptor, alpha
IL2RA	3559	interleukin 2 receptor, alpha chain
IL2RB	3560	interleukin 2 receptor, beta chain
IL2RG	3561	interleukin 2 receptor, gamma chain
IL3	3562	interleukin 3
IL4	3565	interleukin 4
IL4R	3566	interleukin 4 receptor, alpha
IL6ST	3572	interleukin 6 signal transducer
IL7R	3575	interleukin 7 receptor
IL9	3578	interleukin 9
INHBA	3624	inhibin beta-A
INPP1	3628	inositol polyphosphate-1-phosphatase
IRAK1BP1	134728	interleukin-1 receptor-associated kinase 1 binding
		protein 1
IRF1	3659	interferon regulatory factor 1
IRF2	3660	interferon regulatory factor 2
IRF3	3661	interferon regulatory factor 3
IRF4	3662	interferon regulatory factor 4
IRF7	3665	interferon regulatory factor 7
IRF8	3394	interferon regulatory factor 8
IRF9	10379	interferon regulatory factor 9
ISG20	3669	interferon-stimulated protein
ITGA3	3675	integrin alpha 3
ITGAL	3683	integrin alpha L
ITGAV	3685	integrin alpha V
ITGB1	3688	integrin beta 1 (fibronectin receptor beta)
ITK	3702	IL2-inducible T-cell kinase
JAK2	3717	Janus kinase 2
JAK3	3718	Janus kinase 3
JARID2	3720	jumonji, AT rich interactive domain 2
JMJD1C	221037	jumonji domain containing 1C
JUN	3725	Jun oncogene
JUNB	3726	Jun-B oncogene
KAT2B	8850	K(lysine) acetyltransferase 2B
KATNA1	11104	katanin p60 (ATPase-containing) subunit A1
KDM6B	23135	lysine (K)-specific demethylase 6B
KLF10	7071	Kruppel-like factor 10
KLF13	51621	Kruppel-like factor 13
KLF6	1316	Kruppel-like factor 6
KLF7	8609	Kruppel-like factor 7 (ubiquitous)
KLF9	687	Kruppel-like factor 9
KLRD1	3824	killer cell lectin-like receptor, subfamily D, member 1
LAD1	3898	ladinin
LAMP2	3920	lysosomal-associated membrane protein 2
LASS4	79603	LAG1 homolog, ceramide synthase 4
LASS6	253782	LAG1 homolog, ceramide synthase 6
LEF1	51176	lymphoid enhancer binding factor 1
LGALS3BP	3959	lectin, galactoside-binding, soluble, 3 binding protein
LGTN	1939	ligatin
LIF	3976	leukemia inhibitory factor
LILRB1, LILRB2,	10859, 10288, 11025,	leukocyte immunoglobulin-like receptor, subfamily B
LILRB3, LILRB4,	11006, 10990	(with TM and ITIM domains), members 1--5
LILRB5
LIMK2	3985	LIM motif-containing protein kinase 2
LITAF	9516	LPS-induced TN factor
LMNB1	4001	lamin B1
LRRFIP1	9208	leucine rich repeat (in FLII) interacting protein 1
LSP1	4046	lymphocyte specific 1
LTA	4049	lymphotoxin A
MAF	4094	avian musculoaponeurotic fibrosarcoma (v-maf) AS42
		oncogene homolog
MAFF	23764	v-maf musculoaponeurotic fibrosarcoma oncogene
		family, protein F (avian)
MAFG	4097	v-maf musculoaponeurotic fibrosarcoma oncogene
		family, protein G (avian)
MAML2	84441	mastermind like 2 (Drosophila)
MAP3K5	4217	mitogen-activated protein kinase kinase kinase 5
MAP3K8	1326	mitogen-activated protein kinase kinase kinase 8
MAP4K2	5871	mitogen-activated protein kinase kinase kinase kinase 2
MAP4K3	8491	mitogen-activated protein kinase kinase kinase kinase 3
MAPKAPK2	9261	MAP kinase-activated protein kinase 2
MATR3	9782	matrin 3
MAX	4149	Max protein
MAZ	4150	MYC-associated zinc finger protein (purine-binding
		transcription factor)
MBNL1	4154	muscleblind-like 1 (Drosophila)
MBNL3	55796	muscleblind-like 3 (Drosophila)
MDM4	4194	transformed mouse 3T3 cell double minute 4
MEN1	4221	multiple endocrine neoplasia 1
MFHAS1	9258	malignant fibrous histiocytoma amplified sequence 1
MGLL	11343	monoglyceride lipase
MIER1	57708	mesoderm induction early response 1 homolog
		(Xenopus laevis
MINA	84864	myc induced nuclear antigen
MKNK2	2872	MAP kinase-interacting serine/threonine kinase 2
MORF4L1	10933	mortality factor 4 like 1
MORF4L2	9643	mortality factor 4 like 2
MS4A6A	64231	membrane-spanning 4-domains, subfamily A, member 6B
MST4	51765	serine/threonine protein kinase MST4
MT1A	4489	metallothionein 1
MT2A	4502	metallothionein 2
MTA3	57504	metastasis associated 3
MXD3	83463	Max dimerization protein 3
MXI1	4601	Max interacting protein 1
MYC	4609	myelocytomatosis oncogene
MYD88	4615	myeloid differentiation primary response gene 88
MYST4	23522	MYST histone acetyltransferase monocytic leukemia 4
NAGK	55577	N-acetylglucosamine kinase
NAMPT	10135	nicotinamide phosphoribosyltransferase
NASP	4678	nuclear autoantigenic sperm protein (histone-binding)
NCF1C	654817	neutrophil cytosolic factor 1
NCOA1	8648	nuclear receptor coactivator 1
NCOA3	8202	nuclear receptor coactivator 3
NEK4	6787	NIMA (never in mitosis gene a)-related expressed kinase 4
NEK6	10783	NIMA (never in mitosis gene a)-related expressed kinase 6
NFATC1	4772	nuclear factor of activated T-cells, cytoplasmic,
		calcineurin-dependent 1
NFATC2	4773	nuclear factor of activated T-cells, cytoplasmic,
		calcineurin-dependent 2
NFE2L2	4780	nuclear factor, erythroid derived 2, like 2
NFIL3	4783	nuclear factor, interleukin 3, regulated
NFKB1	4790	nuclear factor of kappa light polypeptide gene enhancer
		in B-cells 1, p105
NFKBIA	4792	nuclear factor of kappa light polypeptide gene enhancer
		in B-cells inhibito
NFKBIB	4793	nuclear factor of kappa light polypeptide gene enhancer
		in B-cells inhibito
NFKBIE	4794	nuclear factor of kappa light polypeptide gene enhancer
		in B-cells inhibito
NFKBIZ	64332	nuclear factor of kappa light polypeptide gene enhancer
		in B-cells inhibito
NFYC	4802	nuclear transcription factor-Y gamma
NKG7	4818	natural killer cell group 7 sequence
NMI	9111	N-myc (and STAT) interactor
NOC4L	79050	nucleolar complex associated 4 homolog (S. cerevisiae)
NOTCH1	4851	Notch gene homolog 1 (Drosophila)
NOTCH2	4853	Notch gene homolog 2 (Drosophila)
NR3C1	2908	nuclear receptor subfamily 3, group C, member 1
NR4A2	4929	nuclear receptor subfamily 4, group A, member 2
NR4A3	8013	nuclear receptor subfamily 4, group A, member 3
NUDT4	11163	nudix (nucleoside diphosphate linked moiety X)-type motif 4
OAS2	4939	2′-5′ oligoadenylate synthetase 2
PACSIN1	29993	protein kinase C and casein kinase substrate in neurons 1
PAXBP1	94104	PAX3 and PAX7 binding protein 1
PCTK1	5127	PCTAIRE-motif protein kinase 1
PDCD1	5133	programmed cell death 1
PDCD1LG2	80380	programmed cell death 1 ligand 2
PDK3	5165	pyruvate dehydrogenase kinase, isoenzyme 3
PDPK1	5170	3-phosphoinositide dependent protein kinase-1
PDXK	8566	pyridoxal (pyridoxine, vitamin B6) kinase
PECI	10455	peroxisomal delta3, delta2-enoyl-Coenzyme A
		isomerase
PELI2	57161	pellino 2
PGK1	5230	phosphoglycerate kinase 1
PHACTR2	9749	phosphatase and actin regulator 2
PHF13	148479	PHD finger protein 13
PHF21A	51317	PHD finger protein 21A
PHF6	84295	PHD finger protein 6
PHLDA1	22822	pleckstrin homology-like domain, family A, member 1
PHLPP1	23239	PH domain and leucine rich repeat protein phosphatase 1
PI4KA	5297	phosphatidylinositol 4-kinase, catalytic, alpha
		polypeptide
PIM1	5292	proviral integration site 1
PIM2	11040	proviral integration site 2
PIP4K2A	5305	phosphatidylinositol-5-phosphate 4-kinase, type II,
		alpha
PKM2	5315	pyruvate kinase, muscle
PLAC8	51316	placenta-specific 8
PLAGL1	5325	pleiomorphic adenoma gene-like 1
PLAUR	5329	plasminogen activator, urokinase receptor
PLEK	5341	pleckstrin
PLEKHF2	79666	pleckstrin homology domain containing, family F (with
		FYVE domain) member 2
PLK2	10769	polo-like kinase 2 (Drosophila)
PMEPA1	56937	prostate transmembrane protein, androgen induced 1
PML	5371	promyelocytic leukemia
PNKP	11284	polynucleotide kinase 3′-phosphatase
POU2AF1	5450	POU domain, class 2, associating factor 1
POU2F2	5452	POU domain, class 2, transcription factor 2
PPME1	51400	protein phosphatase methylesterase 1
PPP2R5A	5525	protein phosphatase 2, regulatory subunit B (B56),
		alpha isoform
PPP3CA	5530	protein phosphatase 3, catalytic subunit, alpha isoform
PRC1	9055	protein regulator of cytokinesis 1
PRDM1	639	PR domain containing 1, with ZNF domain
PRF1	5551	perforin 1 (pore forming protein)
PRICKLE1	144165	prickle like 1 (Drosophila)
PRKCA	5578	protein kinase C, alpha
PRKCD	5580	protein kinase C, delta
PRKCH	5583	protein kinase C, eta
PRKCQ	5588	protein kinase C, theta
PRKD3	23683	protein kinase D3
PRNP	5621	prion protein
PROCR	10544	protein C receptor, endothelial
PRPF4B	8899	PRP4 pre-mRNA processing factor 4 homolog B (yeast)
PRPS1	5631	phosphoribosyl pyrophosphate synthetase 1
PSMB9	5698	proteasome (prosome, macropain) subunit, beta type 9
		(large multifunctional
PSTPIP1	9051	proline-serine-threonine phosphatase-interactingprotein 1
PTEN	5728	phosphatase and tensin homolog
PTK2B	2185	PTK2 protein tyrosine kinase 2 beta
PTP4A1	7803	protein tyrosine phosphatase 4a1
PTPLA	9200	protein tyrosine phosphatase-like (proline instead of
		catalytic arginine),
PTPN1	5770	protein tyrosine phosphatase, non-receptor type 1
PTPN18	26469	protein tyrosine phosphatase, non-receptor type 18
PTPN6	5777	protein tyrosine phosphatase, non-receptor type 6
PTPRC	5788	protein tyrosine phosphatase, receptor type, C
PTPRCAP	5790	protein tyrosine phosphatase, receptor type, C
		polypeptide-associated prote
PTPRE	5791	protein tyrosine phosphatase, receptor type, E
PTPRF	5792	protein tyrosine phosphatase, receptor type, F
PTPRJ	5795	protein tyrosine phosphatase, receptor type, J
PTPRS	5802	protein tyrosine phosphatase, receptor type, S
PVR	5817	poliovirus receptor
PYCR1	5831	pyrroline-5-carboxylate reductase 1
RAB33A	9363	RAB33A, member of RAS oncogene family
RAD51AP1	10635	RAD51 associated protein 1
RARA	5914	retinoic acid receptor, alpha
RASGRP1	10125	RAS guanyl releasing protein 1
RBPJ	3516	recombination signal binding protein for
		immunoglobulin kappa J region
REL	5966	reticuloendotheliosis oncogene
RELA	5970	v-rel reticuloendotheliosis viral oncogene homolog A
		(avian)
RFK	55312	riboflavin kinase
RIPK1	8737	receptor (TNFRSF)-interacting serine-threonine kinase 1
RIPK2	8767	receptor (TNFRSF)-interacting serine-threonine kinase 2
RIPK3	11035	receptor-interacting serine-threonine kinase 3
RNASEL	6041	ribonuclease L (2′,5′-oligoisoadenylate synthetase-
		dependent)
RNF11	26994	ring finger protein 11
RNF5	6048	ring finger protein 5
RORA	6095	RAR-related orphan receptor alpha
RORC	6097	RAR-related orphan receptor gamma
RPP14	11102	ribonuclease P 14 subunit (human)
RPS6KB1	6198	ribosomal protein S6 kinase, polypeptide 1
RUNX1	861	runt related transcription factor 1
RUNX2	860	runt related transcription factor 2
RUNX3	864	runt related transcription factor 3
RXRA	6256	retinoid X receptor alpha
SAP18	10284	Sin3-associated polypeptide 18
SAP30	8819	sin3 associated polypeptide
SATB1	6304	special AT-rich sequence binding protein 1
SEMA4D	10507	sema domain, immunoglobulin domain (Ig),
		transmembrane domain (TM) and shor
SEMA7A	8482	sema domain, immunoglobulin domain (Ig), and GPI
		membrane anchor, (semaphor
SERPINB1	1992	serine (or cysteine) peptidase inhibitor, clade B,
		member 1a
SERPINE2	5270	serine (or cysteine) peptidase inhibitor, clade E,
		member 2
SERTAD1	29950	SERTA domain containing 1
SGK1	6446	serum/glucocorticoid regulated kinase 1
SH2D1A	4068	SH2 domain protein 1A
SIK1	150094	salt-inducible kinase 1
SIRT2	22933	sirtuin 2 (silent mating type information regulation 2,
		homolog) 2 (S. cere
SKAP2	8935	src family associated phosphoprotein 2
SKI	6497	ski sarcoma viral oncogene homolog (avian)
SKIL	6498	SKI-like
SLAMF7	57823	SLAM family member 7
SLC2A1	6513	solute carrier family 2 (facilitated glucose transporter),
		member 1
SLC3A2	6520	solute carrier family 3 (activators of dibasic and neutral
		amino acid trans
SLK	9748	STE20-like kinase (yeast)
SMAD2	4087	MAD homolog 2 (Drosophila)
SMAD3	4088	MAD homolog 3 (Drosophila)
SMAD4	4089	MAD homolog 4 (Drosophila)
SMAD7	4092	MAD homolog 7 (Drosophila)
SMARCA4	6597	SWI/SNF related, matrix associated, actin dependent
		regulator of chromatin,
SMOX	54498	spermine oxidase
SOCS3	9021	suppressor of cytokine signaling 3
SP1	6667	trans-acting transcription factor 1
SP100	6672	nuclear antigen Sp100
SP4	6671	trans-acting transcription factor 4
SPHK1	8877	sphingosine kinase 1
SPOP	8405	speckle-type POZ protein
SPP1	6696	secreted phosphoprotein 1
SPRY1	10252	sprouty homolog 1 (Drosophila)
SRPK2	6733	serine/arginine-rich protein specific kinase 2
SS18	6760	synovial sarcoma translocation, Chromosome 18
STARD10	10809	START domain containing 10
STAT1	6772	signal transducer and activator of transcription 1
STAT2	6773	signal transducer and activator of transcription 2
STAT3	6774	signal transducer and activator of transcription 3
STAT4	6775	signal transducer and activator of transcription 4
STAT5A	6776	signal transducer and activator of transcription 5A
STAT5B	6777	signal transducer and activator of transcription 5B
STAT6	6778	signal transducer and activator of transcription 6
STK17B	9262	serine/threonine kinase 17b (apoptosis-inducing)
STK19	8859	serine/threonine kinase 19
STK38	11329	serine/threonine kinase 38
STK38L	23012	serine/threonine kinase 38 like
STK39	27347	serine/threonine kinase 39, STE20/SPS1 homolog
		(yeast)
STK4	6789	serine/threonine kinase 4
SULT2B1	6820	sulfotransferase family, cytosolic, 2B, member 1
SUZ12	23512	suppressor of zeste 12 homolog (Drosophila)
TAF1B	9014	TATA box binding protein (Tbp)-associated factor, RNA
		polymerase I, B
TAL2	6887	T-cell acute lymphocytic leukemia 2
TAP1	6890	transporter 1, ATP-binding cassette, sub-family B
		(MDR/TAP)
TBPL1	9519	TATA box binding protein-like 1
TBX21	30009	T-box 21
TCERG1	10915	transcription elongation regulator 1 (CA150)
TEC	7006	cytoplasmic tyrosine kinase, Dscr28C related
		(Drosophila)
TFDP1	7027	transcription factor Dp 1
TFEB	7942	transcription factor EB
TGFB1	7040	transforming growth factor, beta 1
TGFB3	7043	transforming growth factor, beta 3
TGFBR1	7046	transforming growth factor, beta receptor I
TGFBR3	7049	transforming growth factor, beta receptor III
TGIF1	7050	TGFB-induced factor homeobox 1
TGM2	7052	transglutaminase 2, C polypeptide
THRAP3	9967	thyroid hormone receptor associated protein 3
TIMP2	7077	tissue inhibitor of metalloproteinase 2
TK1	7083	thymidine kinase 1
TK2	7084	thymidine kinase 2, mitochondrial
TLE1	7088	transducin-like enhancer of split 1, homolog of
		Drosophila E(spl)
TLR1	7096	toll-like receptor 1
TMEM126A	84233	transmembrane protein 126A
TNFRSF12A	51330	tumor necrosis factor receptor superfamily, member 12a
TNFRSF13B	23495	tumor necrosis factor receptor superfamily, member 13b
TNFRSF1B	7133	tumor necrosis factor receptor superfamily, member 1b
TNFRSF25	8718	tumor necrosis factor receptor superfamily, member 25
TNFRSF4	7293	tumor necrosis factor receptor superfamily, member 4
TNFRSF9	3604	tumor necrosis factor receptor superfamily, member 9
TNFSF11	8600	tumor necrosis factor (ligand) superfamily, member 11
TNFSF8	944	tumor necrosis factor (ligand) superfamily, member 8
TNFSF9	8744	tumor necrosis factor (ligand) superfamily, member 9
TNK2	10188	tyrosine kinase, non-receptor, 2
TOX4	9878	TOX high mobility group box family member 4
TP53	7157	transformation related protein 53
TRAF3	7187	Tnf receptor-associated factor 3
TRAT1	50852	T cell receptor associated transmembrane adaptor 1
TRIM24	8805	tripartite motif-containing 24
TRIM25	7706	tripartite motif-containing 25
TRIM28	10155	tripartite motif-containing 28
TRIM5	85363	tripartite motif containing 5
TRIP12	9320	thyroid hormone receptor interactor 12
TRPS1	7227	trichorhinophalangeal syndrome I (human)
TRRAP	8295	transformation/transcription domain-associated protein
TSC22D3	1831	TSC22 domain family, member 3
TSC22D4	81628	TSC22 domain family, member 4
TWF1	5756	twinfilin, actin-binding protein, homolog 1 (Drosophila)
TXK	7294	TXK tyrosine kinase
UBE2B	7320	ubiquitin-conjugating enzyme E2B, RAD6 homology
		(S. cerevisiae)
UBIAD1	29914	UbiA prenyltransferase domain containing 1
ULK2	9706	Unc-51 like kinase 2 (C. elegans)
VAV1	7409	vav 1 oncogene
VAV3	10451	vav 3 oncogene
VAX2	25806	ventral anterior homeobox containing gene 2
VRK1	7443	vaccinia related kinase 1
VRK2	7444	vaccinia related kinase 2
WDHD1	11169	WD repeat and HMG-box DNA binding protein 1
WHSC1L1	54904	Wolf-Hirschhorn syndrome candidate 1-like 1 (human)
WNK1	65125	WNK lysine deficient protein kinase 1
XAB2	56949	XPA binding protein 2
XBP1	7494	X-box binding protein 1
XRCC5	7520	X-ray repair complementing defective repair in Chinese
		hamster cells
5
YBX1	4904	Y box protein 1
ZAK	51776	RIKEN cDNA B230120H23 gene
ZAP70	7535	zeta-chain (TCR) associated protein kinase
ZBTB32	27033	zinc finger and BTB domain containing 32
ZEB1	6935	zinc finger E-box binding homeobox 1
ZEB2	9839	zinc finger E-box binding homeobox 2
ZFP161	7541	zinc finger protein 161
ZFP36L1	677	zinc finger protein 36, C3H type-like 1
ZFP36L2	678	zinc finger protein 36, C3H type-like 2
ZFP62	92379	zinc finger protein 62
ZNF238	10472	zinc finger protein 238
ZNF281	23528	zinc finger protein 281
ZNF326	284695	zinc finger protein 326
ZNF703	80139	zinc finger protein 703
ZNRF1	84937	zinc and ring finger 1
ZNRF2	223082	zinc and ring finger 2

Primers for Nanostring STA and qRT-PCR/Fluidigm and siRNA Sequences:
Table S6.1 presents the sequences for each forward and reverse primer used in the Fluidigm/qRT-PCR experiments and Nanostring nCounter gene expression profiling. Table S6.2 presents the sequences for RNAi used for knockdown analysis.

TABLE S6.1

Primer Sequences

		SEQ		SEQ
	Gene	ID		ID
Assay	Name	NO:	Forward Sequence	NO:	Reverse Sequence

Nanostring	1700097N02Rik	1	GGC CAG AGC TTG	2	AGC AAG CCA GCC
STA			ACC ATC		AAA CAG

Nanostring	Aim1	3	AGC CAA TTT TGA	4	GGA AGC CCT GCA
STA			AGG GCA		TTT CCT

Nanostring	Arntl	5	TAT AAC CCC TGG	6	GTT GCA GCC CTC
STA			GCC CTC		GTT GTC

Nanostring	Bcl6	7	GTC GGG ACA TCT	8	GGA GGA TGC AAA
STA			TGA CGG		ACC CCT

Nanostring	Ccl20	9	GCA TGG GTA CTG	10	TGA GGA GGT TCA
STA			CTG GCT		CAG CCC

Nanostring	Cd24a	11	GGA CGC GTG AAA	12	TGC ACT ATG GCC
STA			GGT TTG		TTA TCG G

Nanostring	Cd80	13	TGC CTA AGC TCC	14	ACG GCA AGG CAG
STA			ATT GGC		CAA TAC

Nanostring	Csnk1a1	15	GGG TAT TGG GCG	16	CCA CGG CAG ACT
STA			TCA CTG		GGT TCT

Nanostring	Ddr1	17	ATG CAC ACT CTG	18	CCA AGG ACC TGC
STA			GGA GCC		AAA GAG G

Nanostring	Emp1	19	AGC TGC CAT ACC	20	AGG CAC ATG GGA
STA			ACT GGC		TCT GGA

Nanostring	Flna	21	CTT CAC TGC ATT	22	CAC AGG ACA ACG
STA			CGC CCT		GAA GCA

Nanostring	Gata3	23	CAC CGC CAT GGG	24	TGG GAT CCG GAT
STA			TTA GAG		TCA GTG

Nanostring	2900064A13Rik	25	AAG GAA AAA TGC	26	TCT CCC GTC TCA
STA			GAG CAA GA		TGT CAG G

Nanostring	Anxa4	27	ATG GGG GAC AGA	28	TGC CTA AGC CCT
STA			CGA GGT		TCA TGG

Nanostring	Atf4	29	GAT GAT GGC TTG	30	TGG CCA ATT GGG
STA			GCC AGT		TTC ACT

Nanostring	Bmpr1a	31	CAT TTG GGA AAT	32	ATG GGC CCA ACA
STA			GGC TCG		TTC TGA

Nanostring	Ccl4	33	AAG CTC TGC GTG	34	ACC ACA GCT GGC
STA			TCT GCC		TTG GAG

Nanostring	Cd274	35	CGT GGA TCC AGC	36	ATC ATT CGC TGT
STA			CAC TTC		GGC GTT

Nanostring	Cd86	37	ATC TGC CGT GCC	38	ACG AGC CCA TGT
STA			CAT TTA		CCT TGA

Nanostring	Ctla2b	39	GGC TCA ACA GCA	40	TTA ATT TGA AGA
STA			GGA AGC		CAT CAT GGC A

Nanostring	Dntt	41	CCC AGA AGC CAC	42	TTC CAG CCC TTT
STA			AGA GGA		CCT TCC

Nanostring	Ercc5	43	GTG CCA TTT GAC	44	CTG GCC TAC CCT
STA			ACA GCG		CCA CCT

Nanostring	Foxm1	45	CAA GCC AGG CTG	46	TGG GTC GTT TCT
STA			GAA GAA		GCT GTG

Nanostring	Gem	47	GAC ACG CTT CGG	48	CAA CTG TGA TGA
STA			GTT CAC		GGC CAG C

Nanostring	6330442E10Rik	49	CCC AGC ATT AAG	50	AGG AGC AAC AGG
STA			GCT CCA		GGA CCT

Nanostring	Api5	51	CAG CTT TGA ACA	52	AGC TGA CTG AAA
STA			CAG GGT CTT		TTC CTC CCT

Nanostring	B4galt1	53	TCA CAG TGG ACA	54	CAC TCA CCC TGG
STA			TCG GGA		GCA TCT

Nanostring	Cand1	55	CTA CTG CAG GGA	56	GGG TCC CTC TTT
STA			GGA GCG		AGG GCA

Nanostring	Ccr4	57	GTC CGT GCA GTT	58	GGT TTG GGG ACA
STA			TGG CTT		GGC TTT

Nanostring	Cd28	59	CCT TTG CAG TGA	60	CGT TTT GAA AAT
STA			GTT GGG A		CTG CAG AGA A

Nanostring	Cd9	61	GCG GGA AAC ACT	62	TGC TGA AGA TCA
STA			CAA AGC		TGC CGA

Nanostring	Ctsw	63	GCC ACT GGA GCT	64	TGA CCT CTC CTG
STA			GAA GGA		CCC GTA

Nanostring	Dpp4	65	CCC TGC TCC TGC	66	AAA TCT TCC GAC
STA			ATC TGT		CCA GCC

Nanostring	Errfi1	67	TCC TGC TTT TCC	68	CCA GCA ACA CAA
STA			CAT CCA		GAC CAG C

Nanostring	Foxo1	69	TCC AGT CTG GGC	70	GGC AGC AGA GGG
STA			AAG AGG		TGG ATA

Nanostring	Gfi1	71	ATG TCT TCC CTG	72	AAG CCC AAA GCA
STA			CCT CCC		CAG ACG

Nanostring	Abcg2	73	GGA ACA TCG GCC	74	CAT TCC AGC GGC
STA			TTC AAA		ATC ATA

Nanostring	Aqp3	75	CGG CAC AGC TGG	76	GGT TGA CGG CAT
STA			AAT CTT		AGC CAG

Nanostring	Batf	77	CTA CCC AGA GGC	78	AAC TAT CCA CCC
STA			CCA GTG		CCT GCC

Nanostring	Casp1	79	TCC TGA GGG CAA	80	GAT TTG GCT TGC
STA			AGA GGA		CTG GG

Nanostring	Ccr5	81	AAC TGA ATG GGG	82	TTA CAG CCG CCT
STA			AGG TTG G		TTC AGG

Nanostring	Cd4	83	CCA GCC CTG GAT	84	GCC ACT TTC ATC
STA			CTC CTT		ACC ACC A

Nanostring	Cebpb	85	TGC ACC GAG GGG	86	AAC CCC GCA GGA
STA			ACA C		ACA TCT

Nanostring	Cxcl10	87	TGC CGT CAT TTT	88	CGT GGC AAT GAT
STA			CTG CCT		CTC AAC A

Nanostring	Egr2	89	AGG ACC TTG ATG	90	CTG GCA TCC AGG
STA			GAG CCC		GTC AAC

Nanostring	Etv6	91	CAT GAG GGA GGA	92	AAA TCC CTG CTA
STA			TGC TGG		TCA AAA ATC C

Nanostring	Foxp1	93	GCT CTC TGT CTC	94	ACT CAC AAC CCA
STA			CAA GGG C		GAC CGC

Nanostring	Gja1	95	GGC CTG ATG ACC	96	TCC CTA CTT TTG
STA			TGG AGA		CCG CCT

Nanostring	Acly	97	GAG GGC TGG GAC	98	GCA GCT GCC CAG
STA			CAT TG		AAT CTT

Nanostring	Arhgef3	99	GCA GCA GGC TGT	100	TTC CTC CCC ACT
STA			TTC TTA CC		CAT CCA

Nanostring	BC021614	101	AAG GAG GGC AAG	102	GAG CTT GGG TCG
STA			GAC CAG		GGA TTT

Nanostring	Casp3	103	GGA GAT GGC TTG	104	ACT CGA ATT CCG
STA			CCA GAA		TTG CCA

Nanostring	Ccr6	105	GCC AGA TCC ATG	106	TTT GGT TGC CTG
STA			ACT GAC G		GAC GAT

Nanostring	Cd44	107	CAG GGA ACA TCC	108	TAG CAT CAC CCT
STA			ACC AGC		TTG GGG

Nanostring	Chd7	109	CAT TGT CAG TGG	110	GAA TCA CAG GCT
STA			GCG TCA		CGC CC

Nanostring	Cxcr3	111	CCA GAT CTA CCG	112	CAT GAC CAG AAG
STA			CAG GGA		GGG CAG

Nanostring	Eif3e	113	GTC AAC CAG GGA	114	CAG TTT TCC CCA
STA			TGG CAG		GAG CGA

Nanostring	Fas	115	GCT GTG GAT CTG	116	CCC CCA TTC ATT
STA			GGC TGT		TTG CAG

Nanostring	Foxp3	117	TGG AAA CAC CCA	118	GGC AAG ACT CCT
STA			GCC ACT		GGG GAT

Nanostring	Glipr1	119	TGG ATG GCT TCG	120	TGC AGC TGT GGG
STA			TCT GTG		TTG TGT

Nanostring	Acvr1b	121	GTG CCG ACA TCT	122	GCA CTC CCG CAT
STA			ATG CCC		CAT CTT

Nanostring	Arid5a	123	GGC CTC GGG TCT	124	CTA GGC AGC TGG
STA			TTC AGT		GCT CAC

Nanostring	Bcl11b	125	GGA GGG GTG GCT	126	AAG ATT CTC GGG
STA			TTC AA		GTC CCA

Nanostring	Casp4	127	GGA ACA GCT GGG	128	GCC TGG GTC CAC
STA			CAA AGA		ACT GAA

Nanostring	Ccr8	129	GTG GGT GTT TGG	130	ATC AAG GGG ATG
STA			GAC TGC		GTG GCT

Nanostring	Cd5l	131	TGG GGG TAC CAC	132	GGG CGT GTA GCC
STA			GAC TGT		TTG AGA

Nanostring	Clcf1	133	AAT CCT CCT CGA	134	TGA CAC CTG CAA
STA			CTG GGG		TGC TGC

Nanostring	Cxcr4	135	CCG ATA GCC TGT	136	GTC GAT GCT GAT
STA			GGA TGG		CCC CAC

Nanostring	Eif3h	137	AGC CTT CGC CAT	138	CGC CTT CAG CGA
STA			GTC AAC		GAG AGA

Nanostring	Fasl	139	GCA AAT AGC CAA	140	GTT GCA AGA CTG
STA			CCC CAG		ACC CCG

Nanostring	Frmd4b	141	GGA GTC CCA GTC	142	TGG ACC TTC TTC
STA			CCA CCT		TCC CCC

Nanostring	Golga3	143	TCC AAC CAG GTG	144	TCA TCT CAG AGT
STA			GAG CAC		CCA GCC G

Nanostring	Acvr2a	145	ATG GCA AAC TTG	146	CAA GAT CTG TGC
STA			GAC CCC		AGG GCA

Nanostring	Arl5a	147	CGG ATT TGA GCG	148	AGT CAC TGG TGG
STA			CTT CTG		GTG GGA

Nanostring	Bcl2l11	149	TGG CAA GCC CTC	150	AAA CAC ACA CAA
STA			TCA CTT		CCA CGC A

Nanostring	Casp6	151	TGC TCA AAA TTC	152	CAC GGG TAC GTC
STA			ACG AGG TG		ATG CTG

Nanostring	Cd2	153	CAC CCT GGT CGC	154	GGT TGT GTT GGG
STA			AGA GTT		GCA TTC

Nanostring	Cd70	155	CTG GCT GTG GGC	156	GGA GTT GTG GTC
STA			ATC TG		AAG GGC

Nanostring	Cmtm6	157	TGC TGG TGT AGG	158	TCT CAG CAA TCA
STA			CGT CTT T		CAG TGC AA

Nanostring	Cxcr5	159	TGG CCT TAA TGT	160	TGC TGG CTT GCC
STA			GCC TGT C		CTT TAC

Nanostring	Eif3m	161	TGG CTT GTT ACA	162	CCG ATG TGT GCT
STA			TGA GCA AAA		GTG ACT G

Nanostring	Fip111	163	GGA TAC GAA TGG	164	CCA ACG CTT GAA
STA			GAC TGG AA		CTG GCT

Nanostring	Fzd7	165	TTC CCT GCA ATA	166	TGA AGT AAT CTG
STA			GAA GTC TGG		TCC TCC CGA

Nanostring	Grn	167	CCG GCC TAC TCA	168	AAC TTT ATT GGA
STA			TCC TGA		GCA ACA CAC G

Nanostring	Ahr	169	GTT GTG ATG CCA	170	CAA GCG TGC ATT
STA			AAG GGC		GGA CTG

Nanostring	Armcx2	171	TCC AAT CTT GCC	172	TTC CAG CAC TTT
STA			ACC ACC		GGG AGC

Nanostring	Bcl3	173	CCA GGT TTT GCA	174	CCT CCC AGA CCC
STA			CCA AGG		CTC TGT

Nanostring	Ccl1	175	CAC TGA TGT GCC	176	TGA GGC GCA GCT
STA			TGC TGC		TTC TCT

Nanostring	Cd247	177	TAC CAT CCC AGG	178	GCA GGT TGG CAG
STA			GAA GCA		CAG TCT

Nanostring	Cd74	179	GCT TCC GAA ATC	180	CGC CAT CCA TGG
STA			TGC CAA		AGT TCT

Nanostring	Csf2	181	GGC CAT CAA AGA	182	GCT GTC ATG TTC
STA			AGC CCT		AAG GCG

Nanostring	Daxx	183	GTT GAC CCC GCA	184	ATT CCG AGG AGG
STA			CTG TCT		CTT TGG

Nanostring	Elk3	185	CCT GTG GAC CCA	186	GAC GGA GTT CAG
STA			GAT GCT		CTC CCA

Nanostring	Fli1	187	GAT TCT GAG AAA	188	GCC AGT GTT CCA
STA			GGA GTA CGC A		GTT GCC

Nanostring	Gap43	189	GCG AGA GAG CGA	190	CCA CGG AAG CTA
STA			GTG AGC		GCC TGA

Nanostring	Gusb	191	ATG GAG CAG ACG	192	AAA GGC CGA AGT
STA			CAA TCC		TTT GGG

Nanostring	H2-Q10	193	GTG GGC ATC TGT	194	TGG AGC GGG AGC
STA			GGT GGT		ATA GTC

Nanostring	Ifi35	195	CAG AGT CCC ACT	196	AGG CAC AAC TGT
STA			GGA CCG		CAG GGC

Nanostring	Il12rb2	197	GCA GCC AAC TCA	198	GTG ATG CTC CCT
STA			AAA GGC		GGT TGG

Nanostring	Il22	199	TCA GAC AGG TTC	200	TCT TCT CGC TCA
STA			CAG CCC		GAC GCA

Nanostring	Il4ra	201	CCT TCA GCC CCA	202	AGC TCA GCC TGG
STA			GTG GTA		GTT CCT

Nanostring	Irf3	203	AAG GGA CAC TTC	204	TTT CCT GCA GTT
STA			CCG GAG		CCC CAG

Nanostring	Katna1	205	CGG TGC GGG AAC	206	CAT TTG GTC AAG
STA			TAT CC		AAC TCC CTG

Nanostring	Lad1	207	GAA GGA GCT GTC	208	GCA TCC AGG GAT
STA			AGG CCA		GTG GAC

Nanostring	Ly6c2	209	GTC CTT CCA ATG	210	CCT CCA GGG CCA
STA			ACC CCC		AGA ATA G

Nanostring	Mina	211	GTC TGC CGG AGC	212	TAA TGT GGA GGG
STA			ATC AGT		AGG CCC

Nanostring	Nampt	213	CAA GGA GAT GGC	214	TGG GAT CAG CAA
STA			GTG GAT		CTG GGT

Nanostring	Nkg7	215	TGG CCC TCT GGT	216	TTT CAT ACT CAG
STA			CTC AAC		CCC GAC G

Nanostring	Hif1a	217	AAG AAC TTT TGG	218	GCA CTG TGG CTG
STA			GCC GCT		GGA GTT

Nanostring	Ifih1	219	GCT GAA AAC CCA	220	ACT TCA CTG CTG
STA			AAA TAC GA		TGC CCC

Nanostring	Il17a	221	ATC AGG ACG CGC	222	GAC GTG GAA CGG
STA			AAA CAT		TTG AGG

Nanostring	Il23r	223	CAC TGC AAG GCA	224	CGT TTG GTT TGT
STA			GCA GG		TGT TGT TTT G

Nanostring	Il6st	225	TCG GAC GGC AAT	226	GTT GCT GGA GAT
STA			TTC ACT		GCT GGG

Nanostring	Irf9	227	ACT GAT CGT CGC	228	TTG GTC TGT CTT
STA			GTC TCC		CCA AGT GCT

Nanostring	Kcmf1	229	CTG ACC ACC CGA	230	TCC AGG TAA CGC
STA			TGC AGT		TGC ACA

Nanostring	Lamp2	231	GGC TGC AGC TGA	232	AAG CTG AGC CAT
STA			ACA TCA		TAG CCA AA

Nanostring	Maf	233	AGG CAG GAG GAT	234	TCA TGG GGG TGG
STA			GGC TTC		AGG AC

Nanostring	Mkln1	235	GGT TTG CCC ATC	236	GGA TCC ATT TGG
STA			AAC TCG		GCC TTT

Nanostring	Ncf1	237	GCA AAG GAC AGG	238	TTT GAC ACC CTC
STA			ACT GGG		CCC AAA

Nanostring	Notch1	239	GCA GGC AAA TGC	240	GTG GCC ATT GTG
STA			CTC AAC		CAG ACA

Nanostring	Hip1r	241	CTC GAG CAG CTG	242	CCA GCA GGG ACC
STA			GGA CC		CTC TTT

Nanostring	Ifit1	243	TCA TTC GCT ATG	244	GGC CTG TTG TGC
STA			CAG CCA		CAA TTC

Nanostring	Il17f	245	AAG AAC CCC AAA	246	CAG CGA TCT CTG
STA			GCA GGG		AGG GGA

Nanostring	Il24	247	TCT CCA CTC TGG	248	CTG CAT CCA GGT
STA			CCA ACA		CAG GAG A

Nanostring	Il7r	249	TGG CCT AGT CTC	250	CGA GCG GTT TGC
STA			CCC GAT		ACT GT

Nanostring	Isg20	251	CTG TGG AAG ATG	252	GTG GTT GGT GGC
STA			CCA GGG		AGT GGT

Nanostring	Khdrbs1	253	GTT CGT GGA ACC	254	TCC CCT TGA CTC
STA			CCA GTG		TGG CTG

Nanostring	Lgals3bp	255	GGC CAC AGA GCT	256	CCA GCT CAC TCT
STA			TCA GGA		TGG GGA

Nanostring	Maff	257	TCT GAC TCT TGC	258	TGG CAC AAT CCA
STA			AGG CCC		AAG CCT

Nanostring	Mt1	259	ACT ATG CGT GGG	260	GCA GGA GCT GGT
STA			CTG GAG		GCA AGT

Nanostring	Ncoa1	261	GCC TCC AGC CCA	262	TGA GGG ATT TAT
STA			TCC TAT		TCG GGG A

Nanostring	Notch2	263	TAC GAG TGC ACC	264	GCA GCG TCC TGG
STA			TGC CAA		AAT GTC

Nanostring	Hsbp1	265	ATC ACG TGA CCA	266	CTC TGA TAC CCT
STA			CAG CCC		GCC GGA

Nanostring	Ifng	267	TCT GGG CTT CTC	268	TCC TTT TGC CAG
STA			CTC CTG		TTC CTC C

Nanostring	Il17ra	269	GGG GCT GAG CTG	270	TGG TGT TCA GCT
STA			CAG AGT		GCA GGA

Nanostring	Il27ra	271	AAG GCT GGC CTC	272	GGG CAG GGA ACC
STA			GAA CTT		AAA CTT

Nanostring	Il9	273	TGG TGA CAT ACA	274	TGT GTG GCA TTG
STA			TCC TTG CC		GTC AGC

Nanostring	Itga3	275	GCT TCA CCC AGA	276	CCC ATA TGT TGG
STA			ACA CCG		TGC CGT

Nanostring	Kif2a	277	TGC CGA ATA CAC	278	TCC GCC GGT TCT
STA			CAA GCA		TTA CAA

Nanostring	Lif	279	GGG GCA GGT AGT	280	TCG GGA TCA AGG
STA			TGC TCA		ACA CAG A

Nanostring	Map3k5	281	CCA TCT TGG AGT	282	GCT CAG TCA GGC
STA			GCG AGA A		CCT TCA

Nanostring	Mt2	283	TGT GCT GGC CAT	284	AGG CAC AGG AGC
STA			ATC CCT		AGT TGG

Nanostring	Nfatc2	285	AGC TCC ACG GCT	286	CGT TTC GGA GCT
STA			ACA TGG		TCA GGA

Nanostring	Nr3c1	287	CAA GTG ATT GCC	288	CAT TGG TCA TAC
STA			GCA GTG		ATG CAG GG

Nanostring	Icos	289	CGG CCG ATC ATA	290	TTC CCT GGG AGC
STA			GGA TGT		TGT CTG

Nanostring	Ifngr2	291	CGA AAC AAC AGC	292	CGG TGA ACC GTC
STA			AAA TGC C		CTT GTC

Nanostring	Il1r1	293	ACC CGA GGT CCA	294	TCT CAT TCC GAG
STA			GTG GTA		GGC TCA

Nanostring	Il2ra	295	TGC AAG AGA GGT	296	GTT CCC AAG GAG
STA			TTC CGA		GTG GCT

Nanostring	Inhba	297	AGC AGA AGC ACC	298	TCC TGG CAC TGC
STA			CAC AGG		TCA CAA

Nanostring	Itgb1	299	TGG AAA ATT CTG	300	TTG GCC CTT GAA
STA			CGA GTG TG		ACT TGG

Nanostring	Klf10	301	CCC TCC AAA AGG	302	GGC AAA AAC AAA
STA			GCC TAA		GTC CCC A

Nanostring	Litaf	303	AGT GCA CAG AAG	304	CCA GCA AAT GGA
STA			GGC TGC		GAA ATG G

Nanostring	Max	305	AGG ACG CCT GCT	306	GCT GCA AAT CTG
STA			CTA CCA		TCC CCA

Nanostring	Mta3	307	CGG AGA AGC AGA	308	ACT TTG GGC CCA
STA			AGC ACC		CTC TGA

Nanostring	Nfe2l2	309	GCC GCT TAG AGG	310	TGC TCC AGC TCG
STA			CTC ATC		ACA ATG

Nanostring	Nudt4	311	TGG GGT GCC ATC	312	ATT CCA CAT GGC
STA			CAG TAT		TTT GGC

Nanostring	Id2	313	TCA GCC ATT TCA	314	TAA CGT TTT CGC
STA			CCA GGA G		TCC CCA

Nanostring	Ikzf4	315	GGG GTC TAG CCC	316	GCC GGG GAG AGA
STA			AAT TCC		GGT TAG

Nanostring	Il1rn	317	TGG TAA GCT TTC	318	TCA TCA CAT CAG
STA			CTT CTT TCC		GAA GGG C

Nanostring	Il2rb	319	GCA CCC CAT CCT	320	CAA GTC CAG CTC
STA			CAG CTA		GGT GGT

Nanostring	Irf1	321	TAA GCA CGG CTG	322	CAG CAG AGC TGC
STA			GGA CAT		CCT TGT

Nanostring	Jak3	323	CTC CCC AGC GAT	324	CAG CCC AAA CCA
STA			TGT CAT		GTC AGG

Nanostring	Klf6	325	GAG CGG GAA CTC	326	GGG AAA ATG ACC
STA			AGG ACC		ACT GCG

Nanostring	Lmnb1	327	TGC CCT AGG GGA	328	CAA GCG GGT CTC
STA			CAA AAA		ATG CTT

Nanostring	Mbn13	329	TGG AGC ATG AAT	330	TGA GGG TCC CAT
STA			CCA CAC C		GAG TGG

Nanostring	Mxi1	331	CTC AGG AGA TGG	332	CCT CGT CAC TCC
STA			AGC GGA		CGA CAC

Nanostring	Nfil3	333	CAC GGT GGT GAA	334	GAA AGG AGG GAG
STA			GGT TCC		GGA GGA

Nanostring	Oas2	335	TGC CTG TGC TTG	336	GAA GAA GGG CCA
STA			CTC TGA		GAA GGG

Nanostring	Id3	337	CCG AGG AGC CTC	338	GTC TGG ATC GGG
STA			TTA GCC		AGA TGC

Nanostring	Il10	339	ACT GCC TTC AGC	340	CAG CTT CTC ACC
STA			CAG GTG		CAG GGA

Nanostring	Il21	341	CCT GGA GTG GTA	342	TGC GTT GGT TCT
STA			TCA TCG C		GAT TGT G

Nanostring	Il3	343	CAC ACC ATG CTG	344	CTC CTT GGC TTT
STA			CTC CTG		CCA CGA

Nanostring	Irf4	345	CAG AGA AAC GCA	346	AGT CCA CCA GCT
STA			TTC CTG G		GGC TTT T

Nanostring	Jun	347	TAT TGG CCG GCA	348	GCC TGG CAC TTA
STA			GAC TTT		CAA GCC

Nanostring	Klf9	349	AGG GAA GGA AGA	350	TGG CCA TGT AAA
STA			CGC CAC		AGC CAA A

Nanostring	Lrrfip1	351	GTC TCC AAC GCC	352	ATC TCT TCC CTT
STA			CAG CTA		TGC CGC

Nanostring	Med24	353	ACT GCT AGG GGT	354	TGA GCC ATA GGT
STA			CCT GGG		CTG GGC

Nanostring	Myd88	355	GAA GCT GTT TGG	356	TCA TTC CTC CCC
STA			CTT CGC		CAG ACA

Nanostring	Nflcbie	357	TCG AGG CGC TCA	358	CGG ACA ACA TCT
STA			CAT ACA		GGC TGA

Nanostring	Pcbp2	359	CTC AAC TGA GCG	360	AGG GTT GAG GCA
STA			GGC AAT		CAT GGA

Nanostring	Ier3	361	CCT TCT CCA GCT	362	CCT CTT GGC AAT
STA			CCC TCC		GTT GGG

Nanostring	Il10ra	363	GTA AAG GCC GGC	364	TTT CCA GTG GAG
STA			TCC AGT		GAT GTG C

Nanostring	Il21r	365	AGG TCT GGC CAC	366	GGC CAC AGT CAC
STA			AAC ACC		GTT CAA

Nanostring	Il4	367	AGG GCT TCC AAG	368	TGC TCT TTA GGC
STA			GTG CTT		TTT CCA GG

Nanostring	Irf7	369	GAG GCT GAG GCT	370	ATC CTG GGG ACA
STA			GCT GAG		CAC CCT

Nanostring	Kat2b	371	GGT GCT TTG AGC	372	GCC CTG CAC AAG
STA			AGT TCT GA		CAA AGT

Nanostring	Klrd1	373	GCC TGG CTA TGG	374	CCG TGG ACC TTC
STA			GAG GAT		CTT GTC

Nanostring	Lsp1	375	CCT GAG CCC TAC	376	GGG CAG CTC TAT
STA			CAC CAA		GGA GGG

Nanostring	Mgll	377	CGC GCA GTA GTC	378	AAG ATG AGG GCC
STA			TGG CTC		TTG GGT

Nanostring	Myst4	379	CAA CAA AGG GCA	380	TTC AAC ACA AGG
STA			GCA AGC		GCA GAG G

Nanostring	Nfkbiz	381	TTA GCT GGA TGA	382	ATG TTG CTG CTG
STA			GCC CCA		TGG TGG

Nanostring	Peli2	383	GCC AGA CGG TAG	384	CGT GCT GTG TAT
STA			TGG TGG		GGC TCG

Nanostring	Phlda1	385	GAT GAC GGA GGG	386	GGG GTT GAG GCT
STA			CAA AGA		GGA TCT

Nanostring	Prdm1	387	ACC CTG GCT ATG	388	GGG AAG CTG GAT
STA			CAC CTG		TGA GCA

Nanostring	Pstpip1	389	GAG AGC GAG GAC	390	CCT TCC ACA TCA
STA			CGA GTG		CAG CCC

Nanostring	Rela	391	TGC GAC AAG GTG	392	GAG CTC GCG ATC
STA			CAG AAA		AGA AGG

Nanostring	Runx3	393	GCC CCT TCC CAC	394	CTC CCC CTG CTG
STA			CAT TTA		CTA CAA

Nanostring	Sgk1	395	GGC TAG GCA CAA	396	AGC GCT CCC TCT
STA			GGC AGA		GGA GAT

Nanostring	Smox	397	ACA GCC TCG TGT	398	GGC CAT TGG CTT
STA			GGT GGT		CTG CTA

Nanostring	Stat4	399	GCC TCT ATG GCC	400	ACT TCC AGG AGT
STA			TCA CCA		TGG CCC

Nanostring	Tbx21	401	TGG GAA GCT GAG	402	GCC TTC TGC CTT
STA			AGT CGC		TCC ACA

Nanostring	Tmed7	403	TGG TTA GCG TAG	404	CCC ATG GGG ATA
STA			GGC AGG		TGC ACT

Nanostring	Traf3	405	ATC TGT GGG CGC	406	GGA CTG TCA AGA
STA			TCT GAC		TGG GGC

Nanostring	Vav3	407	TTC TGG CAG GGA	408	TTT GGT CCT GTG
STA			CGA AAC		CCT TAC AA

Nanostring	Plac8	409	TGC TCC CCA AAA	410	AGG AAT GCC GTA
STA			TTC CAA		TCG GGT

Nanostring	Prf1	411	ACC AAC CAG GAC	412	CCC TGT GGA CAG
STA			TGC TGC		GAG CAC

Nanostring	Ptprj	413	TCA CCT GGA GCA	414	TGG TAC CAT TGG
STA			ATG CAA		CAT CCG

Nanostring	Rfk	415	TTT CCC TCT TGG	416	TCC CTC CCC ACA
STA			TGG CCT		CCA CTA

Nanostring	Rxra	417	TTG TTG GGC GAC	418	TGG AGA GTT GAG
STA			TTT TGC		GGA CGA A

Nanostring	Skap2	419	TGG GTG AAC ATT	420	AAA CAG CAA CCC
STA			CCT GCC		TCA CCG

Nanostring	Socs3	421	TGC AGG AGA GCG	422	GAA CTG GCT GCG
STA			GAT TCT		TGC TTC

Nanostring	Stat5a	423	CCT CCG CTA GAA	424	GCT CTT ACA CGA
STA			GCT CCC		GAG GCC C

Nanostring	Tgfb1	425	CGC CTG AGT GGC	426	ATG TCA TGG ATG
STA			TGT CTT		GTG CCC

Nanostring	Tmem126a	427	CTG CTT GAA TAT	428	CCA ACT AGT GCA
STA			GGA TCA GCA		CCC CGT

Nanostring	Trat1	429	CAA TGG ATG CCA	430	CCT TGC CAG TCC
STA			ACG TTT C		CTG TGT

Nanostring	Vax2	431	GGC CCC CGT GGA	432	CAC ACA CAC ACG
STA			CTA TAC		CAC ACG

Nanostring	Plagl1	433	TTG AGA CTG TAT	434	GCA GGG TCT TCA
STA			CCC CCA GC		AAG GTC AG

Nanostring	Pricklel	435	TGG GTT TCC AGT	436	GCC TTT ATT AAA
STA			TGC AGT T		CAC CTC CCT G

Nanostring	Pycr1	437	CCC TGG GTG TGT	438	AAG GGG TTG AAA
STA			GCA GTC		GGG GTG

Nanostring	Rngtt	439	CCC AAA AGA CTG	440	TCC ACA GGG TAA
STA			CAT CGG		GGC TGA A

Nanostring	Sav1	441	CGA CCC CCA ATG	442	TAG CCC ACC CTG
STA			TAA GGA		ATG GAA

Nanostring	Ski	443	GGT CCC CTG CAG	444	CTT CCG TTT TCG
STA			TGT CTG		TGG CTG

Nanostring	Spp1	445	CCA TGA CCA CAT	446	CCA AGC TAT CAC
STA			GGA CGA		CTC GGC

Nanostring	Stat5b	447	ACT CAG CGC CCA	448	GCT CTG CAA AGG
STA			CTT CAG		CGT TGT

Nanostring	Tgfb3	449	GCC AAA GTC CCC	450	AAG GAA GGC AGG
STA			TGG AAT		AGG AGG

Nanostring	Tnfrsf12a	451	GGG AGC CTT CCA	452	GGC ATT ATA GCC
STA			AGG TGT		CCT CCG

Nanostring	Trim24	453	CGG TGG TCC TTC	454	TGC AGA GCC ATT
STA			GCC		CAA CAC A

Nanostring	Xbp1	455	GGA CCT CAT CAG	456	GCA GGT TTG AGA
STA			CCA AGC		TGC CCA

Nanostring	Plekhf2	457	CGG CAA TAT TGT	458	GGG CGT CTT CCC
STA			TAT CCA GAA		ACT TTT

Nanostring	Prkca	459	TGC TGT CCC AGG	460	CAA ATA GCC CAG
STA			GAT GAT		GAT ACC CA

Nanostring	Rab33a	461	GCT GGC TTG GCA	462	TTG ATC TTC TCG
STA			TCC TT		CCC TCG

Nanostring	Rora	463	GAT GTG GCA GCT	464	TTG AAG ACA TCG
STA			GTG TGC		GGG CTC

Nanostring	Sema4d	465	TTC TTG GGC AGT	466	TCG CGG GAT CAT
STA			GAA CCC		CAA CTT

Nanostring	Slamf7	467	CTC CAT GAA GCT	468	TTG ATT ACG CAG
STA			CAG CCA A		GTG CCA

Nanostring	Spry1	469	AGG ACT TCC CTT	470	AGC CAG GAT TCA
STA			CAC GCC		ACT TTG TGA

Nanostring	Stat6	471	TGC TTT TGC CAG	472	ACG CCC AGG GAG
STA			TGT GAC C		TTT ACA

Nanostring	Tgfbr1	473	TGA TGT CAG CTC	474	TCT GCA GCG AGA
STA			TGG GCA		ACC AAA

Nanostring	Tnfrsf13b	475	GGA AGG CAC CAG	476	CTC GTC GCA AGC
STA			GGA TCT		CTC TGT

Nanostring	Trim25	477	TCT GCC TTG TGC	478	ACG GGT GCA TCA
STA			CTG ACA		GCC TAA

Nanostring	Xrcc5	479	AGG GGA CCT GGA	480	GAC AAG TTG GGG
STA			CTC TGG		CCA ATG

Nanostring	Pmepa1	481	GTG ACC GCT TGA	482	GCT GTG TCG GCT
STA			TGG GG		GAT GAA

Nanostring	Prkd3	483	CCT GGC CTC TCA	484	AGA GGC CTT TCA
STA			GTT CCA		GCA GGC

Nanostring	Rad51ap1	485	AGC AGC CAA GTG	486	TGC CAC AAG GAG
STA			CGG TAG		AGG TCC

Nanostring	Rorc	487	CCT CTG ACC CGT	488	GCT TCC AGA AGC
STA			CTC CCT		CAG GGT

Nanostring	Sema7a	489	ATG AAA GGC TAT	490	GTG CAC AAT GGT
STA			GCC CCC		GGC CTT

Nanostring	Slc2a1	491	GAC CCT GCA CCT	492	GAA GCC AGC CAC
STA			CAT TGG		AGC AAT

Nanostring	Stard10	493	AGG ACC CAG GAG	494	ATC TCC ACA GCC
STA			AGT CGG		TGC ACC

Nanostring	Sufu	495	ATG GGG AGT CCT	496	TAG GCC CTG CAT
STA			TCT GCC		CAG CTC

Nanostring	Tgfbr3	497	TCT GGG ATT TGC	498	GTG CAG GAA GAG
STA			CAT CCA		CAG GGA

Nanostring	Tnfrsf25	499	CGA GCC ATG TGG	500	GAG GCT GAG AGA
STA			GAA AAG		TGG GCA

Nanostring	Trps1	501	TTG TAA CGC ACT	502	CGT GCC TTT TTG
STA			TTG AGA TCC		GTA GCC

Nanostring	Zeb1	503	AAG CGC TGT GTC	504	GTG AGA TGC CCC
STA			CCT TTG		AGT GCT

Nanostring	Pm1	505	AAT TTG GGT CCT	506	GCT CGA GAT GCC
STA			CTC GGC		AGT GCT

Nanostring	Prnp	507	CCT CCC ACC TGG	508	CCG TCA CAG GAG
STA			GAT AGC		GAC CAA

Nanostring	Rasgrp1	509	CAA GCA TGC AAA	510	CGT TAT GAG CGG
STA			GTC TGA GC		GGT TTG

Nanostring	Rpp14	511	GCA GCA GTG GTC	512	TGT CAC CAA CAG
STA			TGG TCA		GGG CTT

Nanostring	Serpinb1a	513	CAA GGT GCT GGA	514	GCG GCC CAG GTT
STA			GAT GCC		AGA GTT

Nanostring	Slc6a6	515	GGT GCG TTC CTC	516	AGG CCA GGA TGA
STA			ATA CCG		CGA TGT

Nanostring	Stat1	517	GAG GTA GAG GCC	518	TTT AAG CTC TGC
STA			TGG GGA		CGC CTC

Nanostring	Sult2b1	519	CGA TGT CGT GGT	520	GTC CTG CTG CAG
STA			CTC CCT		CTC CTC

Nanostring	Tgif1	521	GGA CCC AGT CCA	522	CGG CAA TCA GGA
STA			AAC CCT		CCG TAT

Nanostring	Tnfsf11	523	AAC AAG CCT TTC	524	AGA GAT CTT GGC
STA			AGG GGG		CCA GCC

Nanostring	Tsc22d3	525	TGC CAG TGT GCT	526	CTG TGC ACA AAG
STA			CCA GAA		CCA TGC

Nanostring	Zfp161	527	CGC CAA GAT TTC	528	TCC CCG ATT TCT
STA			CGT GA		TCC ACA

Nanostring	Pou2af1	529	GCC CAC TGG CCT	530	TGG GAT ATC AAA
STA			TCA TTT		GAA ACT GTC A

Nanostring	Procr	531	GCC AAA ACG TCA	532	ACG GCC ACA TCG
STA			CCA TCC		AAG AAG

Nanostring	Rbpj	533	TCC CTT AAA ACA	534	CTT CCC CTT GAC
STA			GGA GCC A		AAG CCA

Nanostring	Runx1	535	GCC TGA GAA AAC	536	CAT GTG CCT GAT
STA			GGT AGG G		GGA TTT TT

Nanostring	Serpine2	537	TGA GCC ATC AAA	538	GCT TGT TCA CCT
STA			GGC AAA		GGC CC

Nanostring	Smad3	539	ACG TGC CCC TGT	540	GAG TGG TGG GAC
STA			CTG AAG		AGG GC

Nanostring	Stat2	541	GCA ACC AGG AAC	542	TCT TCG GCA AGA
STA			GCA GAC		ACC TGG

Nanostring	Tal2	543	GGT GGA GGC AGC	544	CAT CCT CAT CTG
STA			AGA GTG		GCA GGC

Nanostring	Tgm2	545	CAG TCT CAG TGC	546	ATG TCC TCC CGG
STA			GAG CCA		TCA TCA

Nanostring	Tnfsf8	547	ACG CCC CCA GAG	548	CTG GGT CAG GGG
STA			AAG AGT		AAG GAG

Nanostring	Ube3a	549	TCG CAT GTA CAG	550	CTT TGG AAA CGC
STA			TGA AAG AAG A		CTC CCT

Nanostring	Zfp238	551	GCC TTG ATT GAC	552	AAG AAA AAG GGA
STA			ATG GGG		AAA ACA ACC A

Nanostring	Prc1	553	TCC CAA CCC TGT	554	CAG TGT GGG CAG
STA			GCT CAT		AAC TGG

Nanostring	Psmb9	555	TGG TTA TGT GGA	556	GGA AGG GAC TTC
STA			CGC AGC		TGG GGA

Nanostring	Rel	557	GCC CCT CTG GGA	558	GGG GTG AGT CAC
STA			TCA ACT		TGG TGG

Nanostring	Runx2	559	AAA TCC TCC CCA	560	TGC AGA GTT CAG
STA			AGT GGC		GGA GGG

Nanostring	Sertad1	561	CTG GGT GCC TTG	562	CGC CTC ATC CAA
STA			GAC TTG		CTC TGG

Nanostring	Smarca4	563	TAC CGT GCC TCA	564	CCC CGG TCT TCT
STA			GGG AAA		GCT TTT

Nanostring	Stat3	565	TTC AGC GAG AGC	566	AAA TGC CTC CTC
STA			AGC AAA		CTT GGG

Nanostring	Tap1	567	TCT CTC TTG CCT	568	GGC CCG AAA CAC
STA			TGG GGA		CTC TCT

Nanostring	Timp2	569	GCT GGA CGT TGG	570	CTC ATC CGG GGA
STA			AGG AAA		GGA GAT

Nanostring	Tnfsf9	571	GTT TCC CAC ATT	572	AGC CCG GGA CTG
STA			GGC TGC		TCT ACC

Nanostring	Ubiad1	573	TAC AGA GCG CTT	574	GCC ACC ATG CCA
STA			GTC CCC		TGT TTT

Nanostring	Zfp281	575	CCA GAC GTA GTT	576	TGC TGC TGG CAG
STA			GGG CAG A		TTG GTA

Nanostring	Zfp410	577	CTG AAA GAG CCT	578	CCA TCA TGC ACT
STA			CAC GGC		CTG GGA

Fluidigm	B2M	579	TTC TGG TGC TTG	580	CAG TAT GTT CGG
& QPCR			TCT CAC TGA		CTT CCC ATT C

Fluidigm	Aim1	581	GAC GAC TCC TTT	582	AAA TTT TCT CCA
& QPCR			CAG ACC AAG T		TCA TAA GCA ACC

Fluidigm	Cd44	583	GCA TCG CGG TCA	584	CAC CGT TGA TCA
& QPCR			ATA GTA GG		CCA GCT T

Fluidigm	Ifngr2	585	TCC TGT CAC GAA	586	ACG GAA TCA GGA
& QPCR			ACA ACA GC		TGA CTT GC

Fluidigm	Il6st	587	TCC CAT GGG CAG	588	CCA TTG GCT TCA
& QPCR			GAA TAT AG		GAA AGA GG

Fluidigm	Klf7	589	AAG TGT AAC CAC	590	TCT TCA TAT GGA
& QPCR			TGC GAC AGG		GCG CAA GA

Fluidigm	Mt2	591	CAT GGA CCC CAA	592	AGC AGG AGC AGC
& QPCR			CTG CTC		AGC TTT

Fluidigm	Nudt4	593	CTG CTG TGA GGG	594	CGA GCA GTC TGC
& QPCR			AAG TGT ATG A		CTA GCT TT

Fluidigm	Pstpip1	595	AGC CCT CCT GTG	596	TGG TCT TGG GAC
& QPCR			GTG TGA TA		TTC CAT GT

Fluidigm	Rxra	597	GCT TCG GGA CTG	598	GCG GCT TGA TAT
& QPCR			GTA GCC		CCT CAG TG

Fluidigm	Sod1	599	CCA GTG CAG GAC	600	GGT CTC CAA CAT
& QPCR			CTC ATT TT		GCC TCT CT

Fluidigm	Tgfb1	601	TGG AGC AAC ATG	602	CAG CAG CCG GTT
& QPCR			TGG AAC TC		ACC AAG

Fluidigm	GAPDH	603	GGC AAA TTC AAC	604	AGA TGG TGA TGG
& QPCR			GGC ACA GT		GCT TCC C

Fluidigm	Atf4	605	ATG ATG GCT TGG	606	CCA TTT TCT CCA
& QPCR			CCA GTG		ACA TCC AAT C

Fluidigm	Cmtm6	607	GAT ACT GGA AAA	608	AAT GGG TGG AGA
& QPCR			GTC AAG TCA TCG		CAA AAA TGA

Fluidigm	Il10	609	CAG AGC CAC ATG	610	GTC CAG CTG GTC
& QPCR			CTC CTA GA		CTT TGT TT

Fluidigm	Il7r	611	CGA AAC TCC AGA	612	AAT GGT GAC ACT
& QPCR			ACC CAA GA		TGG CAA GAC

Fluidigm	Lamp2	613	TGC AGA ATG GGA	614	GGC ACT ATT CCG
& QPCR			GAT GAA TTT		GTC ATC C

Fluidigm	Myc	615	CCT AGT GCT GCA	616	TCT TCC TCA TCT
& QPCR			TGA GGA GA		TCT TGC TCT TC

Fluidigm	Pcbp2	617	CAG CAT TAG CCT	618	ATG GAT GGG TCT
& QPCR			GGC TCA GTA		GCT CTG TT

Fluidigm	Rasgrp1	619	GTT CAT CCA TGT	620	TCA CAG CCA TCA
& QPCR			GGC TCA GA		GCG TGT

Fluidigm	Satb1	621	ATG GCG TTG CTG	622	CTT CCC AAC CTG
& QPCR			TCT CTA GG		GAT GAG C

Fluidigm	Stat1	623	GCA GCA CAA CAT	624	TCT GTA CGG GAT
& QPCR			ACG GAA AA		CTT CTT GGA

Fluidigm	Tgif1	625	CTC AGA GCA AGA	626	CGT TGA TGA ACC
& QPCR			GAA AGC ACT G		AGT TAC AGA CC

Fluidigm	HMBS	627	TCC CTG AAG GAT	628	AAG GGT TTT CCC
& QPCR			GTG CCT AC		GTT TGC

Fluidigm	B4galt1	629	GCC ATC AAT GGA	630	CAT TTG GAC GTG
& QPCR			TTC CCT AA		ATA TAG ACA TGC

Fluidigm	Foxo1	631	CTT CAA GGA TAA	632	GAC AGA TTG TGG
& QPCR			GGG CGA CA		CGA ATT GA

Fluidigm	Il16	633	CCA CAG AAG GAG	634	GTG TTT TCC TGG
& QPCR			AGT CAA GGA		GGA TGC T

Fluidigm	Irf1	635	GAG CTG GGC CAT	636	TCC ATG TCT TGG
& QPCR			TCA CAC		GAT CTG G

Fluidigm	Lmnb1	637	GGG AAG TTT ATT	638	ATC TCC CAG CCT
& QPCR			CGC TTG AAG A		CCC ATT

Fluidigm	Myd88	639	TGG CCT TGT TAG	640	AAG TAT TTC TGG
& QPCR			ACC GTG A		CAG TCC TCC TC

Fluidigm	Pmepa1	641	GCT CTT TGT TCC	642	CTA CCA CGA TGA
& QPCR			CCA GCA T		CCA CGA TTT

Fluidigm	Rbpj	643	AGT CTT ACG GAA	644	CCA ACC ACT GCC
& QPCR			ATG AAA AAC GA		CAT AAG AT

Fluidigm	Sema4d	645	GAC CCT GGT AAC	646	TCA CGA CGT CAT
& QPCR			ACC ACA GG		GCC AAG

Fluidigm	Stat3	647	GGA AAT AAC GGT	648	CAT GTC AAA CGT
& QPCR			GAA GGT GCT		GAG CGA CT

Fluidigm	Timp2	649	CGT TTT GCA ATG	650	GGA ATC CAC CTC
& QPCR			CAG ACG TA		CTT CTC G

Fluidigm	HPRT	651	TCC TCC TCA GAC	652	CCT GGT TCA TCA
& QPCR			CGC TTT T		TCG CTA ATC

Fluidigm	Cand1	653	GAA CTT CCG CCA	654	CTG GTA AGG CGT
& QPCR			GCT TCC		CCA GTA ATC T

Fluidigm	Foxp1	655	CTG CAC ACC TCT	656	GGA AGC GGT AGT
& QPCR			CAA TGC AG		GTA CAG AGG T

Fluidigm	Il17ra	657	TGG GAT CTG TCA	658	ATC ACC ATG TTT
& QPCR			TCG TGC T		CTC TTG ATC G

Fluidigm	Irf4	659	ACA GCA CCT TAT	660	ATG GGG TGG CAT
& QPCR			GGC TCT CTG		CAT GTA GT

Fluidigm	LOC100048299	661	CCA GCA AGA CAT	662	GAT CTT GCC TTC
& QPCR	/// Max		TGA TGA CC		TCC AGT GC

Fluidigm	Nampt	663	CCT GTT CCA GGC	664	TCA TGG TCT TTC
& QPCR			TAT TCT GTT C		CCC CAA G

Fluidigm	Pml	665	AGG AAC CCT CCG	666	TTC CTC CTG TAT
& QPCR			AAG ACT ATG		GGC TTG CT

Fluidigm	Rel	667	TTG CAG AGA TGG	668	CAC CGA ATA CCC
& QPCR			ATA CTA TGA AGC		AAA TTT TGA A

Fluidigm	Sema7a	669	GGA GAG ACC TTC	670	AAG ACA AAG CTA
& QPCR			CAT GTG CT		TGG TCC TGG T

Fluidigm	Stat5a	671	AAG ATC AAG CTG	672	CAT GGG ACA GCG
& QPCR			GGG CAC TA		GTC ATA C

Fluidigm	Trim25	673	CCC TAC GAC CCT	674	TGT GGC TGT GCA
& QPCR			AAG TCA AGC		TGA TAG TG

Fluidigm	pgk1	675	TAC CTG CTG GCT	676	CAC AGC CTC GGC
& QPCR			GGA TGG		ATA TTT CT

Fluidigm	Casp6	677	TGA AAT GCT TTA	678	GTG GCT TGA AGT
& QPCR			ACG ACC TCA G		CGA CAC CT

Fluidigm	Hif1a	679	GCA CTA GAC AAA	680	CGC TAT CCA CAT
& QPCR			GTT CAC CTG AGA		CAA AGC AA

Fluidigm	Il21r	681	GGA GTG ACC CCG	682	AGG AGC AGC AGC
& QPCR			TCA TCT T		ATG TGA G

Fluidigm	Irf8	683	GAG CCA GAT CCT	684	GGC ATA TCC GGT
& QPCR			CCC TGA CT		CAC CAG T

Fluidigm	Lsp1	685	CAA AGC GAG AGA	686	AAG TGG ACT TTG
& QPCR			CCA GAG GA		GCT TGG TG

Fluidigm	Nfatc2	687	GAT CGT AGG CAA	688	CTT CAG GAT GCC
& QPCR			CAC CAA GG		TGC ACA

Fluidigm	Pou2af1	689	CAT GCT CTG GCA	690	ACT CGA ACA CCC
& QPCR			AAA ATC C		TGG TAT GG

Fluidigm	Rela	691	CCC AGA CCG CAG	692	GCT CCA GGT CTC
& QPCR			TAT CCA T		GCT TCT T

Fluidigm	Skap2	693	GTG CTC CCG ACA	694	CCC ATT CCT CAG
& QPCR			AAC GTA TC		CAT CTT TG

Fluidigm	Stat5b	695	CGA GCT GGT CTT	696	CTG GCT GCC GTG
& QPCR			TCA AGT CA		AAC AAT

Fluidigm	Xbp1	697	TGA CGA GGT TCC	698	TGC AGA GGT GCA
& QPCR			AGA GGT G		CAT AGT CTG

Fluidigm	PPIA	699	ACG CCA CTG TCG	700	GCA AAC AGC TCG
& QPCR			CTT TTC		AAG GAG AC

Fluidigm	Cd2	701	TGG GAT GAC TAG	702	AGT GGA TCA TGG
& QPCR			GCT GGA GA		GCT TTG AG

Fluidigm	Icos	703	CGG CAG TCA ACA	704	TCA GGG GAA CTA
& QPCR			CAA ACA A		GTC CAT GC

Fluidigm	Il24	705	AGA ACC AGC CAC	706	GTG TTG AAG AAA
& QPCR			CTT CAC AC		GGG CCA GT

Fluidigm	Khdrbs1	707	CTC GAC CCG TCC	708	TTG ACT CTC CCT
& QPCR			TTC ACT C		TCT GAA TCT TCT

Fluidigm	Lta	709	TCC CTC AGA AGC	710	GAG TTC TGC TTG
& QPCR			ACT TGA CC		CTG GGG TA

Fluidigm	Nfatc3	711	GGG GCA GTG AAA	712	GCT TTT CAC TAT
& QPCR			GCC TCT		AGC CCA GGA G

Fluidigm	Prf1	713	AAT ATC AAT AAC	714	CAT GTT TGC CTC
& QPCR			GAC TGG CGT GT		TGG CCT A

Fluidigm	Rora	715	TTA CGT GTG AAG	716	GGA GTA GGT GGC
& QPCR			GCT GCA AG		ATT GCT CT

Fluidigm	Ski	717	GAG AAA GAG ACG	718	TCA AAG CTC TTG
& QPCR			TCC CCA CA		TAG GAG TAG AAG C

Fluidigm	Stat6	719	TCT CCA CGA GCT	720	GAC CAC CAA GGG
& QPCR			TCA CAT TG		CAG AGA C

Fluidigm	Xrcc5	721	GAA GAT CAC ATC	722	CAG GAT TCA CAC
& QPCR			AGC ATC TCC A		TTC CAA CCT

Fluidigm	RPL13A	723	ATC CCT CCA CCC	724	GCC CCA GGT AAG
& QPCR			TAT GAC AA		CAA ACT T

Fluidigm	Cd24a	725	CTG GGG TTG CTG	726	AGA TGT TTG GTT
& QPCR			CTT CTG		GCA GTA AAT CTG

Fluidigm	Id2	727	GAC AGA ACC AGG	728	AGC TCA GAA GGG
& QPCR			CGT CCA		AAT TCA GAT G

Fluidigm	Il2ra	729	TGT GCT CAC AAT	730	CTC AGG AGG AGG
& QPCR			GGA GTA TAA GG		ATG CTG AT

Fluidigm	Klf10	731	AGC CAA CCA TGC	732	GGC TTT TCA GAA
& QPCR			TCA ACT TC		ATT AGT TCC ATT

Fluidigm	Maf	733	TTC CTC TCC CGA	734	CCA CGG AGC ATT
& QPCR			ATT TTT CA		TAA CAA GG

Fluidigm	Nfe2l2	735	CAT GAT GGA CTT	736	CCT CCA AAG GAT
& QPCR			GGA GTT GC		GTC AAT CAA

Fluidigm	Prkca	737	ACA GAC TTC AAC	738	CTG TCA GCA AGC
& QPCR			TTC CTC ATG GT		ATC ACC TT

Fluidigm	Runx1	739	CTC CGT GCT ACC	740	ATG ACG GTG ACC
& QPCR			CAC TCA CT		AGA GTG C

Fluidigm	Slc2a1	741	ATG GAT CCC AGC	742	CCA GTG TTA TAG
& QPCR			AGC AAG		CCG AAC TGC

Fluidigm	Sufu	743	TGT TGG AGG ACT	744	AGG CCA GCT GTA
& QPCR			TAG AAG ATC TAA		CTC TTT GG
			CC

Fluidigm	Zeb1	745	GCC AGC AGT CAT	746	TAT CAC AAT ACG
& QPCR			GAT GAA AA		GGC AGG TG

Fluidigm	Ywhaz	747	AAC AGC TTT CGA	748	TGG GTA TCC GAT
& QPCR			TGA AGC CAT		GTC CAC AAT

Fluidigm	Cd4	749	ACA CAC CTG TGC	750	GCT CTT GTT GGT
& QPCR			AAG AAG CA		TGG GAA TC

Fluidigm	Ifi35	751	TGA GAG CCA TGT	752	CTC CTG CAG CCT
& QPCR			CTG TGA CC		CAT CTT G

Fluidigm	Il4ra	753	GAG TGG AGT CCT	754	CAG TGG AAG GCG
& QPCR			AGC ATC ACG		CTG TAT C

Fluidigm	Klf6	755	TCC CAC TTG AAA	756	ACT TCT TGC AAA
& QPCR			GCA CAT CA		ACG CCA CT

Fluidigm	Mina	757	GAA TCT GAG GAC	758	TGG GAA AGT ACA
& QPCR			CGG ATC G		ACA AAT CTC CA

Fluidigm	Notch1	759	CTG GAC CCC ATG	760	AGG ATG ACT GCA
& QPCR			GAC ATC		CAC ATT GC

Fluidigm	Prkd3	761	TGG CTA CCA GTA	762	TGG TAA ACG CTG
& QPCR			TCT CCG TGT		CTG ATG TC

Fluidigm	Runx3	763	TTC AAC GAC CTT	764	TTG GTG AAC ACG
& QPCR			CGA TTC GT		GTG ATT GT

Fluidigm	Smarca4	765	AGA GAA GCA GTG	766	ATT TCT TCT GCC
& QPCR			GCT CAA GG		GGA CCT C

Fluidigm	Tap1	767	TTC CCT CAG GGC	768	CTG TCG CTG ACC
& QPCR			TAT GAC AC		TCC TGA C

Fluidigm	Zfp36l1	769	TTC ACG ACA CAC	770	TGA GCA TCT TGT
& QPCR			CAG ATC CT		TAC CCT TGC

Fluidigm	B2M	771	TTC TGG TGC TTG	772	CAG TAT GTT CGG
& QPCR			TCT CAC TGA		CTT CCC ATT C

Fluidigm	1700097N02Rik	773	CCA GAG CTT GAC	774	TCC TTT ACA AAT
& QPCR			CAT CAT CAG		CAT ACA GGA CTG G

Fluidigm	Armcx2	775	CCC TTC ACC CTG	776	CTT CCT CGA ATT
& QPCR			GTC CTT		AGG CCA GA

Fluidigm	Ccr4	777	CTC AGG ATC ACT	778	GGC ATT CAT CTT
& QPCR			TTC AGA AGA GC		TGG AAT CG

Fluidigm	Cebpb	779	TGA TGC AAT CCG	780	CAC GTG TGT TGC
& QPCR			GAT CAA		GTC AGT C

Fluidigm	Emp1	781	AAG AGA GGA CCA	782	CTT TTT GGT GAC
& QPCR			GAC CAG CA		TTC TGA GTA GAG
					AAT

Fluidigm	Ier3	783	CAG CCG AAG GGT	784	AAA TCT GGC AGA
& QPCR			GCT CTA C		AGA TGA TGG

Fluidigm	Itga3	785	AGG GGG AGA CCA	786	GCC ATT GGA GCA
& QPCR			GAG TTC C		GGT CAA

Fluidigm	Lrrfip1	787	AGT CTC AGC GGC	788	GCA AAC TGG AAC
& QPCR			AAT ACG AG		TGC AGG AT

Fluidigm	Nfkbiz	789	CAG CTG GGG AAG	790	GGC AAC AGC AAT
& QPCR			TCA TTT TT		ATG GAG AAA

Fluidigm	Ptprj	791	CCA ATG AGA CCT	792	GTA GGA GGC AGT
& QPCR			TGA ACA AAA CT		GCC ATT TG

Fluidigm	Stat4	793	CGG CAT CTG CTA	794	TGC CAT AGT TTC
& QPCR			GCT CAG T		ATT GTT AGA AGC

Fluidigm	GAPDH	795	GGC AAA TTC AAC	796	AGA TGG TGA TGG
& QPCR			GGC ACA GT		GCT TCC C

Fluidigm	Acvr1b	797	AGA GGG TGG GGA	798	TGC TTC ATG TTG
& QPCR			CCA AAC		ATT GTC TCG

Fluidigm	Arntl	799	GCC CCA CCG ACC	800	TGT CTG TGT CCA
& QPCR			TAC TCT		TAC TTT CTT GG

Fluidigm	Ccr8	801	AGA AGA AAG GCT	802	GGC TCC ATC GTG
& QPCR			CGC TCA GA		TAA TCC AT

Fluidigm	Chd7	803	GAG GAC GAA GAC	804	CAG TGT ATC GCT
& QPCR			CCA GGT G		TCC TCT TCA C

Fluidigm	Fas	805	TGC AGA CAT GCT	806	CTT AAC TGT GAG
& QPCR			GTG GAT CT		CCA GCA AGC

Fluidigm	Il17f	807	CCC AGG AAG ACA	808	CAA CAG TAG CAA
& QPCR			TAC TTA GAA GAA A		AGA CTT GAC CA

Fluidigm	Itgb1	809	TGG CAA CAA TGA	810	ATG TCG GGA CCA
& QPCR			AGC TAT CG		GTA GGA CA

Fluidigm	Map3k5	811	CAA GAA ATT AGG	812	ACA CAG GAA ACC
& QPCR			CAC CTG AAG C		CAG GGA TA

Fluidigm	Notch2	813	TGC CTG TTT GAC	814	GTG GTC TGC ACA
& QPCR			AAC TTT GAG T		GTA TTT GTC AT

Fluidigm	Rorc	815	ACC TCT TTT CAC	816	TCC CAC ATC TCC
& QPCR			GGG AGG A		CAC ATT G

Fluidigm	Tgfbr1	817	CAG CTC CTC ATC	818	CAG AGG TGG CAG
& QPCR			GTG TTG G		AAA CAC TG

Fluidigm	HMBS	819	TCC CTG AAG GAT	820	AAG GGT TTT CCC
& QPCR			GTG CCT AC		GTT TGC

Fluidigm	Aes	821	TGC AAG CGC AGT	822	TGA CGT AAT GCC
& QPCR			ATC ACA G		TCT GCA TC

Fluidigm	Batf	823	AGA AAG CCG ACA	824	CGG AGA GCT GCG
& QPCR			CCC TTC A		TTC TGT

Fluidigm	Cd247	825	CCA GAG ATG GGA	826	AGT GCA TTG TAT
& QPCR			GGC AAA C		ACG CCT TCC

Fluidigm	Clcf1	827	TAT GAC CTC ACC	828	GGG CCC CAG GTA
& QPCR			CGC TAC CT		GTT CAG

Fluidigm	Fip111	829	CGT TTC CCT ATG	830	CCC ACT GCT TGG
& QPCR			GCA ATG TC		TGG TGT

Fluidigm	Il1r1	831	TTG ACA TAG TGC	832	TCG TAT GTC TTT
& QPCR			TTT GGT ACA GG		CCA TCT GAA GC

Fluidigm	Jun	833	CCA GAA GAT GGT	834	CTG ACC CTC TCC
& QPCR			GTG GTG TTT		CCT TGC

Fluidigm	Mbnl3	835	GCC AAG AGT TTG	836	CTT GCA GTT CTC
& QPCR			CCA TGT G		ACG AGT GC

Fluidigm	Nr3c1	837	TGA CGT GTG GAA	838	CAT TTC TTC CAG
& QPCR			GCT GTA AAG T		CAC AAA GGT

Fluidigm	Rpp14	839	GGA ACG CGG TTA	840	CAT CTT CCA ACA
& QPCR			TTC CAG T		TGG ACA CCT

Fluidigm	Tmem126a	841	TAG CGA AGG TTG	842	GGT TTA TGA CTT
& QPCR			CGG TAG AC		TCC ATC TTG GAC

Fluidigm	HPRT	843	TCC TCC TCA GAC	844	CCT GGT TCA TCA
& QPCR			CGC TTT T		TCG CTA ATC

Fluidigm	Ahr	845	TGC ACA AGG AGT	846	AGG AAG CTG GTC
& QPCR			GGA CGA		TGG GGT AT

Fluidigm	BC021614	847	CAC ATT CAA GGC	848	GTA TTG GAT TGG
& QPCR			TTC CTG TTT		TAC AGG GTG AG

Fluidigm	Cd274	849	CCA TCC TGT TGT	850	TCC ACA TCT AGC
& QPCR			TCC TCA TTG		ATT CTC ACT TG

Fluidigm	Cmtm7	851	TCG CCT CCA TAG	852	CTC GCT AGG CAG
& QPCR			TGA TAG CC		AGG AAG C

Fluidigm	Flna	853	GCA AGT GCA CAG	854	TTG CCT GCT GCT
& QPCR			TCA CAG GT		TTT GTG T

Fluidigm	Il2	855	GCT GTT GAT GGA	856	TTC AAT TCT GTG
& QPCR			CCT ACA GGA		GCC TGC TT

Fluidigm	Lad1	857	CTA CAG CAG TTC	858	TGT CTT TCC TGG
& QPCR			CCT CAA ACG		GGC TCA T

Fluidigm	Mta3	859	CTT TGT CGT GTA	860	TTG GTA GCT GGA
& QPCR			TCA TTG GGT ATT		GTT TGC AG

Fluidigm	Peci	861	AAC GGT GCT GTG	862	CAG CTG GGC CAT
& QPCR			TTA CTG AGG		TTA CTA CC

Fluidigm	Sap30	863	CGG TGC AGT GTC	864	CTC CCG CAA ACA
& QPCR			AGC TTC		ACA GAG TT

Fluidigm	Tnfrsf12a	865	CCG CCG GAG AGA	866	CTG GAT CAG TGC
& QPCR			AAA GTT		CAC ACC T

Fluidigm	pgk1	867	TAC CTG CTG GCT	868	CAC AGC CTC GGC
& QPCR			GGA TGG		ATA TTT CT

Fluidigm	AI451617	869	CAA CTG CAG AGT	870	TGT GTC TGC CTG
& QPCR	///		TTG GAG GA		TCC TGA CT
	Trim30

Fluidigm	Bcl11b	871	TCC CAG AGG GAA	872	CCA GAC CCT CGT
& QPCR			CTC ATC AC		CTT CCT C

Fluidigm	Cd28	873	CTG GCC CTC ATC	874	GGC GAC TGC TTT
& QPCR			AGA ACA AT		ACC AAA ATC

Fluidigm	Ctla2b	875	GCC TCC TCT GTC	876	AAG CAG AGG ATG
& QPCR			AGT TGC TC		AGC AGG AA

Fluidigm	Foxp3	877	TCA GGA GCC CAC	878	TCT GAA GGC AGA
& QPCR			CAG TAC A		GTC AGG AGA

Fluidigm	Il21	879	GAC ATT CAT CAT	880	TCA CAG GAA GGG
& QPCR			TGA CCT CGT G		CAT TTA GC

Fluidigm	Lif	881	AAA CGG CCT GCA	882	AGC AGC AGT AAG
& QPCR			TCT AAG G		GGC ACA AT

Fluidigm	Myst4	883	GCA ACA AAG GGC	884	AGA CAT CTT TAG
& QPCR			AGC AAG		GAA ACC AAG ACC

Fluidigm	Peli2	885	TAC ACC TTG CGA	886	GGA CGT TGG TCT
& QPCR			GAG ACC AG		CAC TTT CC

Fluidigm	Sgk1	887	GAT TGC CAG CAA	888	TTG ATT TGT TGA
& QPCR			CAC CTA TG		GAG GGA CTT G

Fluidigm	Tnfrsf25	889	CCC TGG CTT ATC	890	AGA TGC CAG AGG
& QPCR			CCA GAC T		AGT TCC AA

Fluidigm	PPIA	891	ACG CCA CTG TCG	892	GCA AAC AGC TCG
& QPCR			CTT TTC		AAG GAG AC

Fluidigm	Aqp3	893	CTG GGG ACC CTC	894	TGG TGA GGA AGC
& QPCR			ATC CTT		CAC CAT

Fluidigm	Bcl3	895	GAA CAA CAG CCT	896	TCT GAG CGT TCA
& QPCR			GAA CAT GG		CGT TGG

Fluidigm	Cd74	897	GCC CTA GAG AGC	898	TGG TAC AGG AAG
& QPCR			CAG AAA GG		TAA GCA GTG G

Fluidigm	Ctsw	899	GGT TCA ACC GGA	900	TGG GCA AAG ATG
& QPCR			GTT ACT GG		CTC AGA C

Fluidigm	Gem	901	GAC AGC ATG GAC	902	ACG ACC AGG GTA
& QPCR			AGC GAC T		CGC TCA TA

Fluidigm	Il27ra	903	AGT TCC GGT ACA	904	ACA GGA GTC AGC
& QPCR			AGG AAT GC		CCA TCT GT

Fluidigm	Litaf	905	TCC TGT GGC AGT	906	CTA CGC AGA ACG
& QPCR			CTG TGT CT		GGA TGA AG

Fluidigm	Ncfl	907	GGA CAC CTT CAT	908	CTG CCA CTT AAC
& QPCR			TCG CCA TA		CAG GAA CAT

Fluidigm	Plekhf2	909	GTC GGC GAC TAG	910	TCC ACC ATC TTT
& QPCR			GAG GAC T		TGC TAA TAA CC

Fluidigm	Smad3	911	TCA AGA AGA CGG	912	CCG ACC ATC CAG
& QPCR			GGC AGT T		TGA CCT

Fluidigm	Tnfsf8	913	GAG GAT CTC TTC	914	TTG TTG AGA TGC
& QPCR			TGT ACC CTG AAA		TTT GAC ACT TG

Fluidigm	RPL13A	915	ATC CCT CCA CCC	916	GCC CCA GGT AAG
& QPCR			TAT GAC AA		CAA ACT T

Fluidigm	Arhgef3	917	GTT GGT CCC ATC	918	GAT TGC TGC AGT
& QPCR			CTC GTG		AGC TGT CG

Fluidigm	Bcl6	919	CTG CAG ATG GAG	920	GCC ATT TCT GCT
& QPCR			CAT GTT GT		TCA CTG G

Fluidigm	Cd86	921	GAA GCC GAA TCA	922	CAG CGT TAC TAT
& QPCR			GCC TAG C		CCC GCT CT

Fluidigm	Cxcr4	923	TGG AAC CGA TCA	924	GGG CAG GAA GAT
& QPCR			GTG TGA GT		CCT ATT GA

Fluidigm	Glipr1	925	TGC CCT AAT GGA	926	TTA TAT GGC CAC
& QPCR			GCA AAT TTT A		GTT GGG TAA

Fluidigm	Il2rb	927	AGC ATG GGG GAG	928	GGG GCT GAA GAA
& QPCR			ACC TTC		GGA CAA G

Fluidigm	LOC100045833	929	TCT TGT GGC CCT	930	GCA ATG CAG AAT
& QPCR	/// Ly6c1		ACT GTG TG		CCA TCA GA
	/// Ly6c2

Fluidigm	Ncoa1	931	TGG CAT GAA CAT	932	GCC AAC ATC TGA
& QPCR			GAG GTC AG		GCA TTC AA

Fluidigm	Prc1	933	TGG AAA CTT TTC	934	TTT CCC CCT CGG
& QPCR			CTA GAG TTT GAG A		TTT GTA A

Fluidigm	Smox	935	GAT GCT TCG ACA	936	GGA ACC CCG GAA
& QPCR			GTT CAC AGG		GTA TGG

Fluidigm	Ubiad1	937	GTC TGG CTC CTT	938	AGT GAT GAG GAT
& QPCR			TCT CTA CAC AG		GAC GAG GTC

Fluidigm	Ywhaz	939	AAC AGC TTT CGA	940	TGG GTA TCC GAT
& QPCR			TGA AGC CAT		GTC CAC AAT

Fluidigm	Arid5a	941	CAG AGC AGG AGC	942	GCC AAG TTC ATC
& QPCR			CAG AGC		ATA CAC GTT C

Fluidigm	Casp3	943	GAG GCT GAC TTC	944	AAC CAC GAC CCG
& QPCR			CTG TAT GCT T		TCC TTT

Fluidigm	Cd9	945	GAT ATT CGC CAT	946	TGG TAG GTG TCC
& QPCR			TGA GAT AGC C		TTG TAA AAC TCC

Fluidigm	Elk3	947	GAG GGG CTT TGA	948	TGT CCT GTG TGC
& QPCR			GAG TGC T		CTG TCT TG

Fluidigm	Golga3	949	ACA GAA AGT GGC	950	TCT CGC TGG AAC
& QPCR			AGA TGC AG		AAT GTC AG

Fluidigm	Irf9	951	TGA GGC CAC CAT	952	AGC AGC AGC GAG
& QPCR			TAG AGA GG		TAG TCT GA

Fluidigm	LOC100046232	953	GGA CCA GGG AGC	954	GTC CGG CAC AGG
& QPCR	/// Nfil3		AGA ACC		GTA AAT C

Fluidigm	Nfkbie	955	CCT GGA CCT CCA	956	TCC TCT GCA ATG
& QPCR			ACT GAA GA		TGG CAA T

Fluidigm	Prnp	957	TCC AAT TTA GGA	958	GCC GAC ATC AGT
& QPCR			GAG CCA AGC		CCA CAT AG

Fluidigm	Stat2	959	GGA ACA GCT GGA	960	GTA GCT GCC GAA
& QPCR			ACA GTG GT		GGT GGA

Fluidigm	Zfp161	961	GGA GTG AGG AAG	962	TGG ATT CGG GAG
& QPCR			TTC GGA AA		TCT CCA T

Fluidigm	B2M	963	TTC TGG TGC TTG	964	CAG TAT GTT CGG
& QPCR			TCT CAC TGA		CTT CCC ATT C

Fluidigm	Abcg2	965	GCC TTG GAG TAC	966	AAA TCC GCA GGG
& QPCR			TTT GCA TCA		TTG TTG TA

Fluidigm	Ccr5	967	GAG ACA TCC GTT	968	GTC GGA ACT GAC
& QPCR			CCC CCT AC		CCT TGA AA

Fluidigm	Cxcr3	969	AGG CAG CAC GAG	970	GGC ATC TAG CAC
& QPCR			ACC TGA		TTG ACG TTC

Fluidigm	Fli1	971	AGA CCA TGG GCA	972	GCC CCA GGA TCT
& QPCR			AGA ACA CT		GAT AAG G

Fluidigm	Gzmb	973	GCT GCT CAC TGT	974	TGG GGA ATG CAT
& QPCR			GAA GGA AGT		TTT ACC AT

Fluidigm	Il10ra	975	GCT CCC ATT CCT	976	AAG GGC TTG GCA
& QPCR			CGT CAC		GTT CTG T

Fluidigm	Il3	977	TAC ATC TGC GAA	978	GGC TGA GGT GGT
& QPCR			TGA CTC TGC		CTA GAG GTT

Fluidigm	Klrd1	979	GGA TTG GAA TGC	980	TGC TCT GGC CTG
& QPCR			ATT ATA GTG AAA A		ATA ACT GAG

Fluidigm	Plac8	981	CAG ACC AGC CTG	982	CCA AGA CAA GTG
& QPCR			TGT GAT TG		AAA CAA AAG GT

Fluidigm	Sertad1	983	TCC CTC TTC GTT	984	GCT TGC GCT TCA
& QPCR			CTG ATT GG		GAC CTT T

Fluidigm	Tnfsf9	985	CGC CAA GCT ACT	986	CGT ACC TCA GAC
& QPCR			GGC TAA AA		CTT GAG ATA GGT

Fluidigm	GAPDH	987	GGC AAA TTC AAC	988	AGA TGG TGA TGG
& QPCR			GGC ACA GT		GCT TCC C

Fluidigm	Acvr2a	989	CCC TCC TGT ACT	990	GCA ATG GCT TCA
& QPCR			TGT TCC TAC TCA		ACC CTA GT

Fluidigm	Ccr6	991	TTC GCC ACT CTA	992	TCT GGT GTA GAA
& QPCR			ATC AGT AGG AC		AGG GAA GTG G

Fluidigm	Cxcr5	993	GAA TGA CGA CAG	994	GCC CAG GTT GGC
& QPCR			AGG TTC CTG		TTC TTA T

Fluidigm	Foxm1	995	ACT TTA AGC ACA	996	GGA GAG AAA GGT
& QPCR			TTG CCA AGC		TGT GAC GAA

Fluidigm	Hip1r	997	AGT GAG CAA GCT	998	GAA GCC AGG TAC
& QPCR			GGA CGA C		TGG GTG TG

Fluidigm	Il12rb1	999	CGC AGC CGA GTA	1000	AAC GGG AAA TCT
& QPCR			ATG TAC AAG		GCA CCT C

Fluidigm	Il9	1001	GCC TCT GTT TTG	1002	GCA TTT TGA CGG
& QPCR			CTC TTC AGT T		TGG ATC A

Fluidigm	LOC100046643	1003	TAG GTC AGA TCG	1004	GTG GGG TCC TCT
& QPCR	/// Spry1		GGT CAT CC		TTC AAG G

Fluidigm	Prdm1	1005	TGC GGA GAG GCT	1006	TGG GTT GCT TTC
& QPCR			CCA CTA		CGT TTG

Fluidigm	Socs3	1007	ATT TCG CTT CGG	1008	AAC TTG CTG TGG
& QPCR			GAC TAG C		GTG ACC AT

Fluidigm	Trim24	1009	ATC CAG CAG CCT	1010	GGC TTA GGG CTG
& QPCR			TCC ATC T		TGA TTC TG

Fluidigm	HMBS	1011	TCC CTG AAG GAT	1012	AAG GGT TTT CCC
& QPCR			GTG CCT AC		GTT TGC

Fluidigm	Anxa4	1013	TGA TGC TCT TAT	1014	CGT CTG TCC CCC
& QPCR			GAA GCA GGA C		ATC TCT T

Fluidigm	Cd5l	1015	GAG GAC ACA TGG	1016	ACC CTT GTG TAG
& QPCR			ATG GAA TGT		CAC CTC CA

Fluidigm	Daxx	1017	CAG GCC ACT GGT	1018	TCC GTC TTA CAC
& QPCR			CTC TCC		AGT TCA AGG A

Fluidigm	Gap43	1019	CGG AGA CTG CAG	1020	GGT TTG GCT TCG
& QPCR			AAA GCA G		TCT ACA GC

Fluidigm	Id3	1021	GAG GAG CTT TTG	1022	GCT CAT CCA TGC
& QPCR			CCA CTG AC		CCT CAG

Fluidigm	Il12rb2	1023	TGT GGG GTG GAG	1024	TCT CCT TCC TGG
& QPCR			ATC TCA GT		ACA CAT GA

Fluidigm	Inhba	1025	ATC ATC ACC TTT	1026	TCA CTG CCT TCC
& QPCR			GCC GAG TC		TTG GAA AT

Fluidigm	Maff	1027	GAC AAG CAC GCA	1028	CAT TTT CGC AGA
& QPCR			CTG AGC		AGA TGA CCT

Fluidigm	Prickle1	1029	ATG GAT TCT TTG	1030	TGA CGG TCT TGG
& QPCR			GCG TTG TC		CTT GCT

Fluidigm	Spp1	1031	GGA GGA AAC CAG	1032	TGC CAG AAT CAG
& QPCR			CCA AGG		TCA CTT TCA C

Fluidigm	Trps1	1033	ACT CTG CAA ACA	1034	TCT TTT TCC GGA
& QPCR			ACA GAA GAC G		CCA TAT CTG T

Fluidigm	HPRT	1035	TCC TCC TCA GAC	1036	CCT GGT TCA TCA
& QPCR			CGC TTT T		TCG CTA ATC

Fluidigm	Bcl2l11	1037	GGA GAC GAG TTC	1038	AAC AGT TGT AAG
& QPCR			AAC GAA ACT T		ATA ACC ATT TGA
					GG

Fluidigm	Cd80	1039	TCG TCT TTC ACA	1040	TTG CCA GTA GAT
& QPCR			AGT GTC TTC AG		TCG GTC TTC

Fluidigm	Dntt	1041	GAG CAG CAG CTC	1042	GAT GTC GCA GTA
& QPCR			TTG CAT AA		CAA AAG CAA C

Fluidigm	Gata3	1043	TTA TCA AGC CCA	1044	TGG TGG TGG TCT
& QPCR			AGC GAA G		GAC AGT TC

Fluidigm	Ifih1	1045	CTA TTA ACC GTG	1046	CAC CTG CAA TTC
& QPCR			TTC AAA ACA TGA A		CAA AAT CTT A

Fluidigm	Il15ra	1047	CCA GTG CCA ACA	1048	TTG GGA GAG AAA
& QPCR			GTA GTG ACA		GCT TCT GG

Fluidigm	Irf7	1049	CTT CAG CAC TTT	1050	TGT AGT GTG GTG
& QPCR			CTT CCG AGA		ACC CTT GC

Fluidigm	Mgll	1051	TCG GAA CAA GTC	1052	TCA GCA GCT GTA
& QPCR			GGA GGT		TGC CAA AG

Fluidigm	Procr	1053	AGC GCA AGG AGA	1054	GGG TTC AGA GCC
& QPCR			ACG TGT		CTC CTC

Fluidigm	Stard10	1055	GAG CTG CGT CAT	1056	TGC AGG CCT TGT
& QPCR			CAC CTA CC		ACA TCT TCT

Fluidigm	Tsc22d3	1057	GGT GGC CCT AGA	1058	TCA AGC AGC TCA
& QPCR			CAA CAA GA		CGA ATC TG

Fluidigm	pgk1	1059	TAC CTG CTG GCT	1060	CAC AGC CTC GGC
& QPCR			GGA TGG		ATA TTT CT

Fluidigm	Casp1	1061	CCC ACT GCT GAT	1062	GCA TAG GTA CAT
& QPCR			AGG GTG AC		AAG AAT GAA CTG
					GA

Fluidigm	Cd83	1063	TGG TTC TGA AGG	1064	CAA CCA GAG AGA
& QPCR			TGA CAG GA		AGA GCA ACA C

Fluidigm	Dpp4	1065	CGG TAT CAT TTA	1066	GTA GAG TGT AGA
& QPCR			GTA AAG AGG CAA A		GGG GCA GAC C

Fluidigm	Gfi1	1067	TCC GAG TTC GAG	1068	GAG CGG CAC AGT
& QPCR			GAC TTT TG		GAC TTC T

Fluidigm	Ifit1	1069	TCT AAA CAG GGC	1070	GCA GAG CCC TTT
& QPCR			CTT GCA G		TTG ATA ATG T

Fluidigm	Il17a	1071	CAG GGA GAG CTT	1072	GCT GAG CTT TGA
& QPCR			CAT CTG TGT		GGG ATG AT

Fluidigm	Isg20	1073	TTG GTG AAG CCA	1074	CTT CAG GGC ATT
& QPCR			GGC TAG AG		GAA GTC GT

Fluidigm	Mt1	1075	CAC CAG ATC TCG	1076	AGG AGC AGC AGC
& QPCR			GAA TGG AC		TCT TCT TG

Fluidigm	Psmb9	1077	CGC TCT GCT GAG	1078	CTC CAC TGC CAT
& QPCR			ATG CTG		GAT GGT T

Fluidigm	Sult2b1	1079	ACT TCC TGT TTA	1080	AAC TCA CAG ATG
& QPCR			TCA CCT ATG AGG A		CGT TGC AC

Fluidigm	Vav3	1081	TTA CAC GAA GAT	1082	CAA CAC TGG ATA
& QPCR			GAG TGC AAA TG		GGA CTT TAT TCA
					TC

Fluidigm	PPIA	1083	ACG CCA CTG TCG	1084	GCA AAC AGC TCG
& QPCR			CTT TTC		AAG GAG AC

Fluidigm	Casp4	1085	TCC AGA CAT TCT	1086	TCT GGT TCC TCC
& QPCR			TCA GTG TGG A		ATT TCC AG

Fluidigm	Creb3l2	1087	CCA GCC AGC ATC	1088	AGC AGG TTC CTG
& QPCR			CTC TGT		GAT CTC AC

Fluidigm	Egr2	1089	CTA CCC GGT GGA	1090	AAT GTT GAT CAT
& QPCR			AGA CCT C		GCC ATC TCC

Fluidigm	Gja1	1091	TCC TTT GAC TTC	1092	CCA TGT CTG GGC
& QPCR			AGC CTC CA		ACC TCT

Fluidigm	Ifitm2	1093	TGG TCT GGT CCC	1094	CTG GGC TCC AAC
& QPCR			TGT TCA AT		CAC ATC

Fluidigm	Il1rn	1095	TGT GCC AAG TCT	1096	TTC TTT GTT CTT
& QPCR			GGA GAT GA		GCT CAG ATC AGT

Fluidigm	Jak3	1097	TGG AAG ACC CGG	1098	GTC TAG CGC TGG
& QPCR			ATA GCA		GTC CAC

Fluidigm	Mxi1	1099	CAA AGC CAA AGC	1100	AGT CGC CGC TTT
& QPCR			ACA CAT CA		AAA AAC CT

Fluidigm	Rad51ap1	1101	AAA GCA AGA GGC	1102	TGC ATT GCT GCT
& QPCR			CCA AGT G		AGA GTT CC

Fluidigm	Tbx21	1103	TCA ACC AGC ACC	1104	AAA CAT CCT GTA
& QPCR			AGA CAG AG		ATG GCT TGT G

Fluidigm	Xcl1	1105	GAG ACT TCT CCT	1106	GGA CTT CAG TCC
& QPCR			CCT GAC TTT CC		CCA CAC C

Fluidigm	RPL13A	1107	ATC CCT CCA CCC	1108	GCC CCA GGT AAG
& QPCR			TAT GAC AA		CAA ACT T

Fluidigm	Ccl20	1109	AAC TGG GTG AAA	1110	GTC CAA TTC CAT
& QPCR			AGG GCT GT		CCC AAA AA

Fluidigm	Csf2	1111	GCA TGT AGA GGC	1112	CGG GTC TGC ACA
& QPCR			CAT CAA AGA		CAT GTT A

Fluidigm	Errfi1	1113	TGC TCA GGA GCA	1114	TGG AGA TGG ACC
& QPCR			CCT AAC AAC		ACA CTC TG

Fluidigm	Gp49a ///	1115	TGG AGT CCT GGT	1116	TGT GTG TTC TTC
& QPCR	Lilrb4		GTC ATT CC		ACA GAA GCA TT

Fluidigm	Ifng	1117	ATC TGG AGG AAC	1118	TTC AAG ACT TCA
& QPCR			TGG CAA AA		AAG AGT CTG AGG
					TA

Fluidigm	Il22 ///	1119	TTT CCT GAC CAA	1120	TCT GGA TGT TCT
& QPCR	Iltifb		ACT CAG CA		GGT CGT CA

Fluidigm	Kat2b	1121	GGA GAA ACT CGG	1122	CAG CCA TTG CAT
& QPCR			CGT GTA CT		TTA CAG GA

Fluidigm	Nkg7	1123	TCT ACC TAG GCT	1124	CCG ACG GGT TCT
& QPCR			GGG TCT CCT		ACA GTG AG

Fluidigm	Serpinb1a	1125	GGA TTT TCT GCA	1126	GAC AAC AGT TCT
& QPCR			TGC CTC TG		GGG ATT TTC C

Fluidigm	Tgm2	1127	CTC ACG TTC GGT	1128	TCC CTC CTC CAC
& QPCR			GCT GTG		ATT GTC A

Fluidigm	Zfp238	1129	TGC ATC TGT CTC	1130	TCT GGA AAC TCC
& QPCR			TCT TAG TCT GCT		ATA CTG TCT TCA

Fluidigm	Ywhaz	1131	AAC AGC TTT CGA	1132	TGG GTA TCC GAT
& QPCR			TGA AGC CAT		GTC CAC AAT

Fluidigm	Ccl4	1133	GCC CTC TCT CTC	1134	GAG GGT CAG AGC
& QPCR			CTC TTG CT		CCA TTG

Fluidigm	Cxcl10	1135	GCT GCC GTC ATT	1136	TCT CAC TGG CCC
& QPCR			TTC TGC		GTC ATC

Fluidigm	Etv6	1137	TCC CTT TCG CTG	1138	GGG CGT GTA TGA
& QPCR			TGA GAC AT		AAT TCG TT

Fluidigm	Grn	1139	TGG CTA ATG GAA	1140	CAT CAG GAC CCA
& QPCR			ATT GAG GTG		CAT GGT CT

Fluidigm	Ikzf4	1141	GCA GAC ATG CAC	1142	TGA GAG CTC CCT
& QPCR			ACA CCA C		CTC CAG AT

Fluidigm	Il23r	1143	CCA AGT ATA TTG	1144	AGC TTG AGG CAA
& QPCR			TGC ATG TGA AGA		GAT ATT GTT GT

Fluidigm	Klf9	1145	CTC CGA AAA GAG	1146	GCG AGA ACT TTT
& QPCR			GCA CAA GT		TAA GGC AGT C

Fluidigm	Phlda1	1147	CGC ACC AGC CTC	1148	TTC CGA AGT CCT
& QPCR			TTC ACT		CAA AAC CTT

Fluidigm	Serpine2	1149	TTG GGT CAA AAA	1150	CCT TGA AAT ACA
& QPCR			TGA GAC CAG		CTG CAT TAA CGA

Fluidigm	Tnfrsf13b	1151	GAG CTC GGG AGA	1152	TGG TCG CTA CTT
& QPCR			CCA CAG		AGC CTC AAT

Fluidigm	Zfp281	1153	GGA GAG GAC GGC	1154	TTT TCA TAC CCC
& QPCR			GTT ATT TT		GGA GGA G

TABLE S6.2

RNAi sequences

Duplex				SEQ
Catalog	Gene	GENE	Gene	ID
Number	Symbol	ID	Accession	NO:	Sequence

D-040676-01	Acvr2a	11480	NM_007396	1155	CAAAGAAUCUAGUCUAUGA

D-040676-02	Acvr2a	11480	NM_007396	1156	UGACAGGACUGAUUGUAUA

D-040676-03	Acvr2a	11480	NM_007396	1157	GCAGAAACAUGCAGGAAUG

D-040676-04	Acvr2a	11480	NM_007396	1158	GGCAAUAUGUGUAAUGAAA

D-044066-01	Ahr	11622	NM_013464	1159	CCAAUGCACGCUUGAUUUA

D-044066-02	Ahr	11622	NM_013464	1160	GAAGGAGAGUUCUUGUUAC

D-044066-03	Ahr	11622	NM_013464	1161	CCGCAAGAUGUUAUUAAUA

D-044066-04	Ahr	11622	NM_013464	1162	CCAGUUCUCUUAUGAGUGC

D-054696-01	Arid5a	214855	NM_145996	1163	GGAAGAACGUGUAUGAUGA

D-054696-02	Arid5a	214855	NM_145996	1164	GAAGAGGGAUUCGCUCAUG

D-054696-03	Arid5a	214855	NM_145996	1165	CCUCUAAACUUCACCGGUA

D-054696-04	Arid5a	214855	NM_145996	1166	GGUCAUCCCUGCUUUCCCA

D-040483-02	ARNTL	11865	NM_007489	1167	GCAUCGAUAUGAUAGAUAA

D-040483-03	ARNTL	11865	NM_007489	1168	CAGUAAAGGUGGAAGAUAA

D-040483-04	ARNTL	11865	NM_007489	1169	GAAAUACGGGUGAAAUCUA

D-040483-17	ARNTL	11865	NM_007489	1170	UGUCGUAGGAUGUGACCGA

D-049093-01	Batf	53314	NM_016767	1171	GAACGCAGCUCUCCGCAAA

D-049093-02	Batf	53314	NM_016767	1172	UCAAACAGCUCACCGAGGA

D-049093-03	Batf	53314	NM_016767	1173	GAGGAAAGUUCAGAGGAGA

D-049093-04	Batf	53314	NM_016767	1174	UCAAGUACUUCACAUCAGU

D-058452-01	CCR5	12774	NM_009917	1175	GGAGUUAUCUCUCAGUGUU

D-058452-02	CCR5	12774	NM_009917	1176	UGAAGUUUCUACUGGUUUA

D-058452-03	CCR5	12774	NM_009917	1177	GCUAUGACAUCGAUUAUGG

D-058452-04	CCR5	12774	NM_009917	1178	UGAAACAAAUUGCGGCUCA

D-062489-01	CCR6	12458	NM_009835	1179	GCACAUAUGCGGUCAACUU

D-062489-02	CCR6	12458	NM_009835	1180	CCAAUUGCCUACUCCUUAA

D-062489-03	CCR6	12458	NM_009835	1181	GAACGGAUGAUUAUGACAA

D-062489-04	CCR6	12458	NM_009835	1182	UGUAUGAGAAGGAAGAAUA

D-040286-04	EGR1	13653	NM_007913	1183	CGACAGCAGUCCCAUCUAC

D-040286-01	EGR1	13653	NM_007913	1184	UGACAUCGCUCUGAAUAAU

D-040286-02	EGR1	13653	NM_007913	1185	ACUCCACUAUCCACUAUUA

D-040286-03	EGR1	13653	NM_007913	1186	AUGCGUAACUUCAGUCGUA

D-040303-01	Egr2	13654	NM_010118	1187	GAAGGUAUCAUCAAUAUUG

D-040303-02	Egr2	13654	NM_010118	1188	GAUCUCCCGUAUCCGAGUA

D-040303-03	Egr2	13654	NM_010118	1189	UCUCUACCAUCCGUAAUUU

D-040303-04	Egr2	13654	NM_010118	1190	UGACAUGACUGGAGAGAAG

D-058294-01	ELK3	13713	NM_013508	1191	GUAGAGAUCAGCCGGGAGA

D-058294-02	ELK3	13713	NM_013508	1192	GAUCAGGUUUGUGACCAAU

D-058294-03	ELK3	13713	NM_013508	1193	UCUUUAAUGUUGCCAAAUG

D-058294-04	ELK3	13713	NM_013508	1194	UGAGAUACUAUUACGACAA

D-050997-21	Ets1	23871	NM_001038642	1195	GCUUAGAGAUGUAGCGAUG

D-050997-22	Ets1	23871	NM_001038642	1196	CCUGUUACACCUCGGAUUA

D-050997-23	Ets1	23871	NM_001038642	1197	CAGCUACGGUAUCGAGCAU

D-050997-24	Ets1	23871	NM_001038642	1198	UCAAGUAUGAGAACGACUA

D-040983-01	ETS2	23872	NM_011809	1199	GAUCAACAGCAAUACAUUA

D-040983-02	ETS2	23872	NM_011809	1200	UGAAUUUGCUCAACAACAA

D-040983-03	ETS2	23872	NM_011809	1201	UAGAGCAGAUGAUCAAAGA

D-040983-04	ETS2	23872	NM_011809	1202	GAAUGACUUUGGAAUCAAG

D-058395-01	Etv6	14011	NM_007961	1203	GAACAAACAUGACCUAUGA

D-058395-02	Etv6	14011	NM_007961	1204	CAAAGAGGAUUUCCGCUAC

D-058395-03	Etv6	14011	NM_007961	1205	GCAUUAAGCAGGAACGAAU

D-058395-04	Etv6	14011	NM_007961	1206	CGCCACUACUACAAACUAA

D-045283-04	Fas	14102	NM_007987	1207	GAGUAAAUACAUCCCGAGA

D-045283-03	Fas	14102	NM_007987	1208	GGAGGCGGGUUCAUGAAAC

D-045283-02	Fas	14102	NM_007987	1209	CGCAGAACCUUAGAUAAAU

D-045283-01	Fas	14102	NM_007987	1210	GUACCAAUCUCAUGGGAAG

D-041127-01	Foxo1	56458	NM_019739	1211	GAAGACACCUUUACAAGUG

D-041127-02	Foxo1	56458	NM_019739	1212	GGACAACAACAGUAAAUUU

D-041127-03	Foxo1	56458	NM_019739	1213	GGAGAUACCUUGGAUUUUA

D-041127-04	Foxo1	56458	NM_019739	1214	GAAAUCAGCAAUCCAGAAA

D-040670-01	GATA3	14462	NM_008091	1215	GAAGAUGUCUAGCAAAUCG

D-040670-02	GATA3	14462	NM_008091	1216	CGGAAGAUGUCUAGCAAAU

D-040670-03	GATA3	14462	NM_008091	1217	GUACAUGGAAGCUCAGUAU

D-040670-04	GATA3	14462	NM_008091	1218	AGAAAGAGUGCCUCAAGUA

D-060495-01	Id2	15902	NM_010496	1219	CAUCUGAAUUCCCUUCUGA

D-060495-02	Id2	15902	NM_010496	1220	GAACACGGACAUCAGCAUC

D-060495-03	Id2	15902	NM_010496	1221	GUCGAAUGAUAGCAAAGUA

D-060495-04	Id2	15902	NM_010496	1222	CGGUGAGGUCCGUUAGGAA

D-051517-01	Ikzf4	22781	NM_011772	1223	GAUGGUGCCUGACUCAAUG

D-051517-02	Ikzf4	22781	NM_011772	1224	CGACUGAACGGCCAACUUU

D-051517-03	Ikzf4	22781	NM_011772	1225	GUGAAGGCCUUUAAGUGUG

D-051517-04	Ikzf4	22781	NM_011772	1226	GAACUCACACCUGUCAUCA

D-040810-01	IL17RA	16172	NM_008359	1227	GGACAGAUUUGAGGAGGUU

D-040810-02	IL17RA	16172	NM_008359	1228	GAAUAGUACUUGUCUGGAU

D-040810-03	IL17RA	16172	NM_008359	1229	UCUGGGAGCUCGAGAAGAA

D-040810-04	IL17RA	16172	NM_008359	1230	GAGAGCAACUCCAAAAUCA

D-040007-04	IL6ST	16195	NM_010560	1231	GUCCAGAGAUUUCACAUUU

D-040007-03	IL6ST	16195	NM_010560	1232	AGACUUACCUUGAAACAAA

D-040007-02	IL6ST	16195	NM_010560	1233	GAACUUCACUGCCAUUUGU

D-040007-01	IL6ST	16195	NM_010560	1234	GCACAGAGCUGACCGUGAA

D-057981-04	IL7R	16197	NM_008372	1235	GGAUUAAACCUGUCGUAUG

D-057981-03	IL7R	16197	NM_008372	1236	UAAGAUGCCUGGCUAGAAA

D-057981-02	IL7R	16197	NM_008372	1237	GCAAACCGCUCGCCUGAGA

D-057981-01	IL7R	16197	NM_008372	1238	GAAAGUCGUUUAUCGCAAA

D-043796-04	IRF4	16364	NM_013674	1239	CCAUAUCAAUGUCCUGUGA

D-043796-03	IRF4	16364	NM_013674	1240	CGAGUUACCUGAACACGUU

D-043796-02	IRF4	16364	NM_013674	1241	UAUCAGAGCUGCAAGUGUU

D-043796-01	IRF4	16364	NM_013674	1242	GGACACACCUAUGAUGUUA

D-040737-01	Irf8	15900	NM_008320	1243	GGACAUUUCUGAGCCAUAU

D-040737-02	Irf8	15900	NM_008320	1244	GAGCGAAGUUCCUGAGAUG

D-040737-03	Irf8	15900	NM_008320	1245	GCAAGGGCGUGUUCGUGAA

D-040737-04	Irf8	15900	NM_008320	1246	GCAACGCGGUGGUGUGCAA

D-042246-04	ITGA3	16400	NM_013565	1247	GCGAUGACUGGCAGACAUA

D-042246-03	ITGA3	16400	NM_013565	1248	GAGUGGCCCUAUGAAGUUA

D-042246-02	ITGA3	16400	NM_013565	1249	GGACAAUGUUCGCGAUAAA

D-042246-01	ITGA3	16400	NM_013565	1250	CCAGACACCUCCAACAUUA

D-043776-01	Jun	16476	NM_010591	1251	GAACAGGUGGCACAGCUUA

D-043776-02	Jun	16476	NM_010591	1252	GAAACGACCUUCUACGACG

D-043776-03	Jun	16476	NM_010591	1253	CCAAGAACGUGACCGACGA

D-043776-04	Jun	16476	NM_010591	1254	GCCAAGAACUCGGACCUUC

D-041158-04	JUNB	16477	NM_008416	1255	CAACCUGGCGGAUCCCUAU

D-041158-03	JUNB	16477	NM_008416	1256	CAACAGCAACGGCGUGAUC

D-041158-02	JUNB	16477	NM_008416	1257	UGGAACAGCCUUUCUAUCA

D-041158-01	JUNB	16477	NM_008416	1258	ACACCAACCUCAGCAGUUA

D-049885-01	Kat2b	18519	NM_020005	1259	GCAGUAACCUCAAAUGAAC

D-049885-02	Kat2b	18519	NM_020005	1260	UCACAUAUGCAGAUGAGUA

D-049885-03	Kat2b	18519	NM_020005	1261	GAAGAACCAUCCAAAUGCU

D-049885-04	Kat2b	18519	NM_020005	1262	AAACAAGCCCAGAUUCGAA

D-047145-02	LRRFIP1	16978	NM_001111312	1263	GAAGGGCUCCCGUAACAUG

D-047145-17	LRRFIP1	16978	NM_001111312	1264	AAAGAGGCCCUGCGGCAAA

D-047145-18	LRRFIP1	16978	NM_001111312	1265	GCUCGAGAGAUCCGGAUGA

D-047145-19	LRRFIP1	16978	NM_001111312	1266	AGACACAGUAAAUGACGUU

D-063455-01	Mina	67014	NM_025910	1267	GUAAACAGUUGCCAAGGUU

D-063455-02	Mina	67014	NM_025910	1268	GCACCUACCAGAACAAUUC

D-063455-03	Mina	67014	NM_025910	1269	GAAAUGGAACGGAGACGAU

D-063455-04	Mina	67014	NM_025910	1270	GGUCACCAAUUCGUGUUAA

D-040813-01	MYC	17869	NM_010849	1271	GACGAGACCUUCAUCAAGA

D-040813-02	MYC	17869	NM_010849	1272	GACAGCAGCUCGCCCAAAU

D-040813-03	MYC	17869	NM_010849	1273	GAAUUUCUAUCACCAGCAA

D-040813-04	MYC	17869	NM_010849	1274	GUACAGCCCUAUUUCAUCU

D-063057-04	MYD88	17874	NM_010851	1275	GAUGAUCCGGCAACUAGAA

D-063057-03	MYD88	17874	NM_010851	1276	GUUAGACCGUGAGGAUAUA

D-063057-02	MYD88	17874	NM_010851	1277	CGACUGAUUCCUAUUAAAU

D-063057-01	MYD88	17874	NM_010851	1278	GCCUAUCGCUGUUCUUGAA

D-041128-01	NCOA1	17977	NM_010881	1279	GAACAUGAAUCCAAUGAUG

D-041128-02	NCOA1	17977	NM_010881	1280	GAACAUGGGAGGACAGUUU

D-041128-03	NCOA1	17977	NM_010881	1281	UCAAGAAUCUGCUACCAAA

D-041128-04	NCOA1	17977	NM_010881	1282	CCAAGAAGAUGGUGAAGAU

D-047764-01	Nfkb1	18033	NM_008689	1283	GACAUGGGAUUUCAGGAUA

D-047764-02	Nfkb1	18033	NM_008689	1284	GGAUUUCGAUUCCGCUAUG

D-047764-03	Nfkb1	18033	NM_008689	1285	CUACGGAACUGGGCAAAUG

D-047764-04	Nfkb1	18033	NM_008689	1286	GGAAACGCCAGAAGCUUAU

D-041110-01	NOTCH1	18128	NM_008714	1287	GAACAACUCCUUCCACUUU

D-041110-02	NOTCH1	18128	NM_008714	1288	GGAAACAACUGCAAGAAUG

D-041110-03	NOTCH1	18128	NM_008714	1289	GAACCAGGCUACACAGGAA

D-041110-04	NOTCH1	18128	NM_008714	1290	GAAGGUGUAUACUGUGAAA

D-045970-01	Nr3c1	14815	NM_008173	1291	GAUCGAGCCUGAGGUGUUA

D-045970-02	Nr3c1	14815	NM_008173	1292	UUACAAAGAUUGCAGGUAU

D-045970-03	Nr3c1	14815	NM_008173	1293	GCCAAGAGUUAUUUGAUGA

D-045970-04	Nr3c1	14815	NM_008173	1294	GCAUGUAUGACCAAUGUAA

D-048514-04	PML	18854	NM_008884	1295	GCGCAAGUCCAAUAUCUUC

D-048514-03	PML	18854	NM_008884	1296	AGUGGUACCUCAAGCAUGA

D-048514-02	PML	18854	NM_008884	1297	GCGCAGACAUUGAGAAGCA

D-048514-01	PML	18854	NM_008884	1298	CAGCAUAUCUACUCCUUUA

D-048879-01	POU2AF1	18985	NM_011136	1299	GAAGAAAGCGUGGCCAUAC

D-048879-02	POU2AF1	18985	NM_011136	1300	CGGAGUAUGUGUCCCAUGA

D-048879-03	POU2AF1	18985	NM_011136	1301	UCACUAAUGUCACGCCAAG

D-048879-04	P0U2AF1	18985	NM_011136	1302	GCAACACGUACGAGCUCAA

D-043069-09	Prdm1	12142	NM_007548	1303	GGAGAGACCCACCUACAUA

D-043069-10	Prdm1	12142	NM_007548	1304	GCAAUACAGUAGUGAGAAA

D-043069-11	Prdm1	12142	NM_007548	1305	GGAAGGACAUCUACCGUUC

D-043069-21	Prdm1	12142	NM_007548	1306	GUACAUACAUAGUGAACGA

D-042664-04	PROCR	19124	NM_011171	1307	UAUCUGACCCAGUUCGAAA

D-042664-03	PROCR	19124	NM_011171	1308	UAACUCCGAUGGCUCCCAA

D-042664-02	PROCR	19124	NM_011171	1309	GUAAGUUUCCGGCCAAAGA

D-042664-01	PROCR	19124	NM_011171	1310	CCAAACAGGUCGCUCUUAC

D-042742-01	Rbpj	19664	NM_001080928	1311	CCAAACGACUCACUAGGGA

D-042742-02	Rbpj	19664	NM_001080928	1312	UCUCAACCCUGUGCGUUUA

D-042742-03	Rbpj	19664	NM_001080928	1313	GCAGACGGCAUUACUGGAU

D-042742-04	Rbpj	19664	NM_001080928	1314	GUAGAAGCCGAAACAAUGU

D-040776-01	Rela	19697	NM_009045	1315	GGAGUACCCUGAAGCUAUA

D-040776-02	Rela	19697	NM_009045	1316	GAAGAAGAGUCCUUUCAAU

D-040776-03	Rela	19697	NM_009045	1317	UAUGAGACCUUCAAGAGUA

D-040776-04	Rela	19697	NM_009045	1318	GAAUCCAGACCAACAAUAA

D-042209-01	Rorc	19885	NM_011281	1319	UGAGUAUAGUCCAGAACGA

D-042209-02	Rorc	19885	NM_011281	1320	CAAUGGAAGUCGUCCUAGU

D-042209-03	Rorc	19885	NM_011281	1321	GAGUGGAACAUCUGCAAUA

D-042209-04	Rorc	19885	NM_011281	1322	GCUCAUCAGCUCCAUAUUU

D-048982-01	RUNX1	12394	NM_001111022	1323	UGACCACCCUGGCGAGCUA

D-048982-02	RUNX1	12394	NM_001111022	1324	GCAACUCGCCCACCAACAU

D-048982-03	RUNX1	12394	NM_001111022	1325	GAGCUUCACUCUGACCAUC

D-048982-04	RUNX1	12394	NM_001111022	1326	ACAAAUCCGCCACAAGUUG

D-045547-01	Satb1	20230	NM_009122	1327	CAAAGGAUAUGAUGGUUGA

D-045547-02	Satb1	20230	NM_009122	1328	GAAACGAGCCGGAAUCUCA

D-045547-03	Satb1	20230	NM_009122	1329	GAAGGGAGCACAGACGUUA

D-045547-04	Satb1	20230	NM_009122	1330	GCACGCGGAAUUUGUAUUG

D-042265-01	SKI	20481	NM_011385	1331	GACCAUCUCUUGUUUCGUG

D-042265-02	SKI	20481	NM_011385	1332	GGAAAGAGAUUGAGCGGCU

D-042265-03	SKI	20481	NM_011385	1333	GCUGGUUCCUCCAAUAAGA

D-042265-04	SKI	20481	NM_011385	1334	UGAAGGAGAAGUUCGACUA

D-040687-04	SMAD4	17128	NM_008540	1335	GAAGGACUGUUGCAGAUAG

D-040687-03	SMAD4	17128	NM_008540	1336	GCAAAGGAGUGCAGUUGGA

D-040687-02	SMAD4	17128	NM_008540	1337	GAAGUAGGACUGCACCAUA

D-040687-01	SMAD4	17128	NM_008540	1338	AAAGAGCAAUUGAGAGUUU

D-041135-01	Smarca4	20586	NM_011417	1339	GGUCAACGGUGUCCUCAAA

D-041135-02	Smarca4	20586	NM_011417	1340	GAUAAUGGCCUACAAGAUG

D-041135-03	Smarca4	20586	NM_011417	1341	GAGCGAAUGCGGAGGCUUA

D-041135-04	Smarca4	20586	NM_011417	1342	CAACGGGCCUUUCCUCAUC

D-051590-01	SMOX	228608	NM_145533	1343	GCACAGAGAUGCUUCGACA

D-051590-02	SMOX	228608	NM_145533	1344	CCACGGGAAUCCUAUCUAU

D-051590-03	SMOX	228608	NM_145533	1345	AGAAUGGCGUGGCCUGCUA

D-051590-04	SMOX	228608	NM_145533	1346	UGAGGAAUUCAGCGAUUUA

D-043282-01	Sp4	20688	NM_009239	1347	GGACAACAGCAGAUUAUUA

D-043282-02	Sp4	20688	NM_009239	1348	GACAAUAGGUGCUGUUAGU

D-043282-03	Sp4	20688	NM_009239	1349	AAUUAGACCUGGCGUUUCA

D-043282-04	Sp4	20688	NM_009239	1350	GGAGUUCCAGUAACAAUCA

D-061490-01	Tgif1	21815	NM_009372	1351	GCAAAUAGCACCCAGCAAC

D-061490-02	Tgif1	21815	NM_009372	1352	CAAACGAGCGGCAGAGAUG

D-061490-03	Tgif1	21815	NM_009372	1353	UCAGUGAUCUGCCAUACCA

D-061490-04	Tgif1	21815	NM_009372	1354	GCCAAGAUUUCAGAAGCUA

D-047483-04	TRIM24	21848	NM_145076	1355	AAACUGACCUGUCGAGACU

D-047483-03	TRIM24	21848	NM_145076	1356	CCAAUACGUUCACCUAGUG

D-047483-02	TRIM24	21848	NM_145076	1357	GAUCAGCCUAGCUCAGUUA

D-047483-01	TRIM24	21848	NM_145076	1358	GCAAGCGGCUGAUUACAUA

D-065500-01	TRPS1	83925	NM_032000	1359	GCAAAUGGCGGAUAUGUAU

D-065500-02	TRPS1	83925	NM_032000	1360	GCGAGCAGAUUAUUAGAAG

D-065500-03	TRPS1	83925	NM_032000	1361	CUACGGUUCUGGAGUAAAU

D-065500-04	TRPS1	83925	NM_032000	1362	GAAGUUCGAGAGUCAAACA

D-055209-02	Tsc22d3	14605	NM_010286	1363	GUGAGCUGCUUGAGAAGAA

D-055209-17	Tsc22d3	14605	NM_010286	1364	CUGUACGACUCCAGGAUUU

D-055209-18	Tsc22d3	14605	NM_010286	1365	CUAUAUAGCCAUAAUGCGU

D-055209-19	Tsc22d3	14605	NM_010286	1366	CAGUGAGCCUGUCGUGUCA

D-060426-04	UBE2B	22210	NM_009458	1367	CAGAAUCGAUGGAGUCCCA

D-060426-03	UBE2B	22210	NM_009458	1368	GAUGGUAGCAUAUGUUUAG

D-060426-02	UBE2B	22210	NM_009458	1369	GGAAUGCAGUUAUAUUUGG

D-060426-01	UBE2B	22210	NM_009458	1370	GAAGAGAGUUUCGGCCAUU

D-047149-02	VAX2	24113	NM_011912	1371	GGACUUGCCUGCUGGCUAC

D-047149-03	VAX2	24113	NM_011912	1372	UGACACAGGUAGCGCGAGU

D-047149-04	VAX2	24113	NM_011912	1373	CUACAGCAGACUAGAACAA

D-047149-17	VAX2	24113	NM_011912	1374	GCACUGAGUUGGCCCGACA

D-040825-04	XBP1	22433	NM_013842	1375	UCUCAAACCUGCUUUCAUC

D-040825-03	XBP1	22433	NM_013842	1376	GAGUCAAACUAACGUGGUA

D-040825-02	XBP1	22433	NM_013842	1377	GGAUCACCCUGAAUUCAUU

D-040825-01	XBP1	22433	NM_013842	1378	UGACAUGUCUUCUCCACUU

D-051513-01	Zeb1	21417	NM_011546	1379	GAACCCAGCUUGAACGUCA

D-051513-02	Zeb1	21417	NM_011546	1380	GAAAGAGCACUUACGGAUU

D-051513-03	Zeb1	21417	NM_011546	1381	GGUUUGGUAUCUCCCAUAA

D-051513-04	Zeb1	21417	NM_011546	1382	GAAGUGUAUUAGCUUGAUG

D-058937-01	ZFP161	22666	NM_009547	1383	CCUCCGCUCUGACAUAUUU

D-058937-02	ZFP161	22666	NM_009547	1384	GAUUCUCGGUAUCCGGUUU

D-058937-03	ZFP161	22666	NM_009547	1385	CCGCCAAGAUUUCCGUGAA

D-058937-04	ZFP161	22666	NM_009547	1386	AAAGACCAUUUGCGUGUCA

D-057818-01	ZFP281	226442	NM_177643	1387	GCACCACCGCGAUGUAUUA

D-057818-02	ZFP281	226442	NM_177643	1388	GAACAACGUACCAGAUUGA

D-057818-03	ZFP281	226442	NM_177643	1389	AAGCAAGGCCCGAUAAGUA

D-057818-04	ZFP281	226442	NM_177643	1390	GAUCAGUACUCUGGCAAAU

D-041703-01	ZFP36L1	12192	NM_007564	1391	UCAAGACGCCUGCCCAUUU

D-041703-02	ZFP36L1	12192	NM_007564	1392	UCAGCAGCCUUAAGGGUGA

D-041703-03	ZFP36L1	12192	NM_007564	1393	GGAGCUGGCGAGCCUCUUU

D-041703-04	ZFP36L1	12192	NM_007564	1394	CGAAUCCCCUCACAUGUUU

Example 2: A Transcriptional Time Course of Th17 Differentiation

The differentiation of naïve CD4+ T-cells into Th17 cells was induced using TGF-β1 and IL-6, and measured transcriptional profiles using microarrays at eighteen time points along a 72 hr time course during the differentiation of naïve CD4+ T-cells into Th17 cells, induced by a combination of the anti-inflammatory cytokine TGF-β1 and the proinflammatory cytokine IL-6 (FIG. 1, FIG. 6A, FIG. 6B and FIG. 6C, see Methods in Example 1). As controls, mRNA profiles were measured for cells that were activated without the addition of differentiating cytokines (Th0). 1,291 genes that were differentially expressed specifically during Th17 differentiation were identified by comparing the Th17 differentiating cells to the control cells (see Methods in Example 1) and partitioned into 20 co-expression clusters (k-means clustering, see Methods in Example 1, FIG. 1b and FIG. 7) that displayed distinct temporal profiles. These clusters were used to characterize the response and reconstruct a regulatory network model, as described below (FIG. 2a ).
Three Main Waves of Transcription and Differentiation:
There are three transcriptional phases as the cells transition from a naïve-like state (t=0.5 hr) to Th17 (t=72 hr; FIG. 1c and FIG. 6c ): early (up to 4 hr), intermediate (4-20 hr), and late (20-72 hr). Each corresponds, respectively, to a differentiation phase (Korn et al., Annu Rev Immunol 2009): (1) induction, (2) onset of phenotype and amplification, and (3) stabilization and IL-23 signaling.
The early phase is characterized by transient induction (e.g., Cluster C5, FIG. 1b ) of immune response pathways (e.g., IL-6 and TGF-β signaling; FIG. 1d ). The first transition point (t=4 hr) is marked by a significant increase in the expression level of ROR-γt, which is not detectable at earlier time points. The second transition (t=20 hr) is accompanied by significant changes in cytokine expression, with induction of Th17 signature cytokines (e.g., IL-17) that strengthen the Th17 phenotype and a concomitant decrease in other cytokines (e.g., IFN-γ) that belong to other T cell lineages.
Some early induced genes display sustained expression (e.g., Cluster C10, FIG. 1b ); these are enriched for transcription regulators (TRs) also referred to herein as transcription factors (TFs), including the key Th17 factors Stat3, Irf4 and Batf, and the cytokine and receptor molecules IL-21, Lif, and Il2ra.
The transition to the intermediate phase (t=4 hr) is marked by induction of ROR-γt (master TF; FIG. 6d ) and another 12 TFs (Cluster C20, FIG. 1b ), both known (e.g., Ahr) and novel (e.g., Trps1) to Th17 differentiation. At the 4 hr time point, the expression of ROR-γt, the master TF of Th17 differentiation, significantly increases (FIG. 6d )—marking the beginning of the accumulation of differentiation phenotypes (‘intermediate phase’)—and remains elevated throughout the rest of the time course. Another 12 factors show a similar pattern (Cluster 8 C20, FIG. 1b ). These include Ahr and Rbpj, as well as a number of factors (e.g., Etv6 and Trps1) not described previously as having roles in Th17 differentiation. Overall, the 585 genes that are induced between 4 and 20 hrs are differentially expressed and substantially distinct from the early response genes (FIG. 1b ; e.g., clusters C20, C14, and C1).
During the transition to the late phase (t=20 hr), mRNAs of Th17 signature cytokines are induced (e.g., IL-17a, IL-9; cluster C19) whereas mRNAs of cytokines that signal other T cell lineages are repressed (e.g., IFN-γ and IL-4). Regulatory cytokines from the IL-10 family are also induced (IL-10, IL-24), possibly as a self-limiting mechanism related to the emergence of ‘pathogenic’ or ‘non-pathogenic’ Th17 cells (Lee et al., Induction and Molecular Signature of Pathogenic Th17 Cells, Nature Immunol 13, 991-999; doi:10.1038/ni.2416). Around 48 hr, the cells induce IL23r (data not shown), which plays an important role in the late phase (FIG. 8A, 8B).
Between 20 and 42 hrs post activation (i.e., starting 16 hrs after the induction of ROR-γt expression), there is a substantial change compared to Th0 in the expression of 821 genes, including many major cytokines (e.g., cluster C19, FIG. 1b ). The expression of Th17-associated inflammatory cytokines, including IL-17a, IL-24, IL-9 and lymphotoxin alpha LTA (Elyaman, W. et al. Notch receptors and Smad3 signaling cooperate in the induction of interleukin-9-producing T cells. Immunity 36, 623-634, doi:10.1016/j.immuni.2012.01.020 (2012)), is strongly induced (FIG. 1d ), whereas other cytokines and chemokines are repressed or remain at their low basal level (Clusters C8 and C15, FIG. 1b and FIG. 7). These include cytokines that characterize other T-helper cell types, such as IL-2 (Th17 differentiation inhibitor), IL-4 (Th2), and IFN-γ (Th1), and others (Csf1, Tnfsf9/4 and Ccl3). Finally, regulatory cytokines from the IL-10 family are also induced (IL-10, IL-24), possibly as a self-limiting mechanism. Thus, the 20 hr time point might be crucial for the emergence of the proposed ‘pathogenic’ versus ‘nonpathogenic’/regulatory Th17 cells (Lee et al., Nature Immunol 2012).
Most expression changes in the 1,055 genes differentially expressed in the remainder of the time course (>48 hr) are mild, occur in genes that responded during the 20-42 hr period (FIG. 1, e.g., clusters C18, C19, and C20), and typically continue on the same trajectory (up or down). Among the most strongly late-induced genes is the TF Hif1a, previously shown to enhance Th17 development via interaction with ROR-γt (Dang, E. V. et al. Control of T(H)17/T(reg) balance by hypoxia-inducible factor 1. Cell 146, 772-784, doi:10.1016/j.cell.2011.07.033 (2011)). The genes over-expressed at the latest time point (72 hr) are enriched for apoptotic functions (p<10⁻⁶), consistent with the limited survival of Th17 cells in primary cultures, and include the Th2 cytokine IL-4 (FIG. 8a ), suggesting that under TGF-β1+IL-6 treatment, the cells might have a less stable phenotype.
The peak of induction of IL-23r mRNA expression occurs at 48 hr and, at this time point one begins to see IL-23r protein on the cell surface (data not shown). The late phase response depends in part on IL-23, as observed when comparing temporal transcriptional profiles between cells stimulated with TGF-β1+IL-6 versus TGF-β1+IL-6+IL-23, or between WT and IL-23r−/− cells treated with TGF-β1+IL-6+IL-23 (FIG. 8). For instance, in IL-23r-deficient Th17 cells, the expression of IL-17ra, IL-1r1, IL-21r, ROR-γt, and Hif1a is decreased, and IL-4 expression is increased. The up-regulated genes in the IL-23r−/− cells are enriched for other CD4+ T cell subsets, suggesting that, in the absence of IL-23 signaling, the cells start to dedifferentiate, thus further supporting the hypothesis that IL-23 may have a role in stabilizing the phenotype of differentiating Th17 cells.

Example 3: Inference of Dynamic Regulatory Interactions

Without wishing to be bound by any one theory, it was hypothesized that each of the clusters (FIG. 1b ) encompasses genes that share regulators active in the relevant time points. To predict these regulators, a general network of regulator-target associations from published genomics profiles was assembled (Linhart, C., Halperin, Y. & Shamir, R. Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome research 18, 1180-1189, doi:10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integrated platform of mammalian transcription factors. Bioinformatics 24, 2416-2417, doi:10.1093/bioinformatics/btn439 (2008); Wilson, N. K. et al. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532-544, doi:10.1016/j.stem.2010.07.016 (2010); Lachmann, A. et al. in Bioinformatics Vol. 26 2438-2444 (2010); Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740, doi:10.1093/bioinformatics/btr260 (2011); Jiang, C., Xuan, Z., Zhao, F. & Zhang, M. TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35, D137-140 (2007); Elkon, R., Linhart, C., Sharan, R., Shamir, R. & Shiloh, Y. in Genome Research Vol. 13 773-780 (2003); Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091-1094, doi:10.1038/ni1008-1091 (2008)) (FIG. 2a , see Methods in Example 1)
The general network of regulator-target associations from published genomics profiles was assembled as follows: in vivo protein-DNA binding profiles for 298 regulators (Linhart, C., Halperin, Y. & Shamir, R. Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome research 18, 1180-1189, doi:10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integrated platform of mammalian transcription factors. Bioinformatics 24, 2416-2417, doi:10.1093/bioinformatics/btn439 (2008); Wilson, N. K. et al. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532-544, doi:10.1016/j.stem.2010.07.016 (2010); Lachmann, A. et al. in Bioinformatics Vol. 26 2438-2444 (2010); Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740, doi:10.1093/bioinformatics/btr260 (2011); Jiang, C., Xuan, Z., Zhao, F. & Zhang, M. TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35, D137-140 (2007), 825 DNA cis-regulatory elements scored in each gene's promoter (Elkon, R., Linhart, C., Sharan, R., Shamir, R. & Shiloh, Y. Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome research 13, 773-780, doi:10.1101/gr.947203 (2003)), transcriptional responses to the knockout of 11 regulatory proteins, and regulatory relations inferred from co-expression patterns across 159 immune cell types (Heng, T. S. & Painter, M. W. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091-1094, doi:10.1038/ni1008-1091 (2008)) (see Methods in Example 1). While most protein-DNA binding profiles were not measured in Th17 cells, DNA-binding profiles in Th17 cells of a number of key TFs, including Irf4 and Batf (Glasmacher, E. et al. A Genomic Regulatory Element That Directs Assembly and Function of Immune-Specific AP-1-IRF Complexes. Science, doi:10.1126/science.1228309 (2012)), Stat3 and Stat5 (Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nat. Immunol. 12, 247-254, doi:10.1038/ni.1995 (2011)), and Rorc (Xiao et al., unpublished) has been included.
A regulator was then connected to a gene from its set of putative targets only if there was also a significant overlap between the regulator's putative targets and that gene's cluster (see Methods in Example 1). Since different regulators act at different times, the connection between a regulator and its target may be active only within a certain time window. To determine this window, each edge was labeled with a time stamp denoting when both the target gene is regulated (based on its expression profile) and the regulator node is expressed at sufficient levels (based on its mRNA levels and inferred protein levels (Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342, doi:10.1038/nature10098 (2011)); see Methods in Example 1). For the target gene, the time points in which it is either differentially expressed compared to the Th0 condition or is being induced or repressed compared with preceding time points in the Th17 time course were considered. For the regulator node, only time points where the regulator is sufficiently expressed and not repressed relative to the Th0 condition were included. To this end, the regulator's predicted protein expression level was inferred from its mRNA level using a recently proposed model (Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342, doi:10.1038/nature10098 (2011)) (see Methods in Example 1). In this way, a network ‘snapshot’ was derived for each of the 18 time points (FIG. 2b-d ). Overall, 9,159 interactions between 71 regulators and 1,266 genes were inferred in at least one network.
Substantial Regulatory Re-Wiring During Differentiation:
The active factors and interactions change from one network to the next. The vast majority of interactions are active only at some time window (FIG. 2c ), even for regulators (e.g., Batf) that participate in all networks. Based on similarity in active interactions, three network classes were identified (FIG. 2c ), corresponding to the three differentiation phases (FIG. 2d ). All networks in each phase were collapsed into one model, resulting in three consecutive network models (FIG. 9A, 9B). Among the regulators, 33 are active in all of the networks (e.g. many known master regulators such as Batf1, Irf4, and Stat3), whereas 18 are active in only one (e.g. Stat1 and Irf1 in the early network; ROR-γt in the late network). Indeed, while ROR-γt mRNA levels are induced at −4h, ROR-γt protein levels increase at approximately 20h and further rise over time, consistent with the model (FIG. 9).
Densely Interconnected Transcriptional Circuits in Each Network:
At the heart of each network is its ‘transcriptional circuit’, connecting active TFs to target genes that themselves encode TFs. For example, the transcriptional circuit in the early response network connects 48 factors that are predicted to act as regulators to 72 factors whose own transcript is up- or down-regulated during the first four hours (a subset of this model is shown in FIG. 2e ). The circuit automatically highlights many TFs that were previously implicated in immune signaling and Th17 differentiation, either as positive or negative regulators, including Stat family members, both negative (Stat1, Stat5) and positive (Stat3), the pioneering factor Batf, TFs targeted by TGF-β signaling (Smad2, Runx1, and Irf7), several TFs targeted by TCR signaling (Rel, Nfkb1, and Jun), and several interferon regulatory factors (Irf4 and Irf1), positioned both as regulators and as target genes that are strongly induced. In addition, 34 regulators that were not previously described to have a role in Th17 differentiation were identified (e.g., Sp4, Egr2, and Smarca4). Overall, the circuit is densely intraconnected (Novershtern et al., Cell 2011), with 16 of the 48 regulators themselves transcriptionally controlled (e.g., Stat1, Irf1, Irf4, Batf). This suggests feedback circuitry, some of which may be auto-regulatory (e.g., for Irf4, Stat3 and Stat1).
As in the early network, there is substantial cross-regulation between the 64 TFs in the intermediate and late transcriptional circuits, which include major Th17 regulators such as ROR-γt, Irf4, Batf, Stat3, and Hif1a (FIG. 2e ).
Ranking Novel Regulators for Systematic Perturbation:
In addition to known Th17 regulators, the network includes dozens of novel factors as predicted regulators (FIG. 2d ), induced target genes, or both (FIG. 2E). It also contains receptor genes as induced targets, both previously known in Th17 cells (e.g., IL-1R1, IL-17RA) and novel (e.g., Fas, Itga3). This suggests substantial additional complexity compared to current knowledge, but must be systematically tested to validate the role and characterize the function of each candidate.
Candidate regulators were ranked for perturbation (FIG. 2a, 3a , see Methods in Example 1), guided by features that reflect a regulatory role (FIG. 3a , “Network Information”) and a role as target (FIG. 3a , “Gene Expression Information”).
To this end, a scoring scheme was devised to rank candidate regulators for perturbation (FIG. 2a , FIG. 3a , FIG. 10, Methods), guided by protein activity (participation as a regulator node, FIG. 3a , “Network Information”) and mRNA level (changes in expression as a target, FIG. 3a , “Gene Expression Information”; Methods). Under each criterion, several features were considered for selecting genes to perturb (see Methods in Example 1). In “Network Information”, it was considered whether the gene acts as regulator in the network, the type of experimental support for this predicted role, and whether it is predicted to target key Th17 genes. In “Gene Expression Information”, it was considered changes in mRNA levels of the encoding gene in the time course data (preferring induced genes), under IL23R knockout, or in published data of perturbation in Th17 cells (e.g., Batf knockout (Schraml, B. U. et al. in Nature Vol. 460 405-409 (2009)); See Methods for the complete list); and whether a gene is more highly expressed in Th17 cells as compared to other CD4+ subsets, based on genome wide expression profiles (Wei, G. et al. in Immunity Vol. 30 155-167 (2009)).
The genes were computationally ordered to emphasize certain features (e.g., a predicted regulator of key Th17 genes) over others (e.g., differential expression in the time course data). A similar scheme was used to rank receptor proteins (see Methods in Example 1). Supporting their quality, the top-ranked factors are enriched (p<10⁻³) for manually curated Th17 regulators (FIG. 10), and correlate well (Spearman r>0.86) with a ranking learned by a supervised method (see Methods in Example 1). 65 genes were chose for perturbation: 52 regulators and 13 receptors. These included most of the top 44 regulators and top 9 receptors (excluding a few well-known Th17 genes and/or those for which knockout data already existed), as well as additional representative lower ranking factors.

Example 4: Nanowire-Based Perturbation of Primary T Cells

While testing the response of naïve CD4+ T cells from knock-out mice deleted for key factors is a powerful strategy, it is limited by the availability of mouse strains or the ability to generate new ones. In unstimulated primary mouse T cells, viral- or transfection-based siRNA delivery has been nearly impossible because it either alters differentiation or cell viability (Dardalhon, V. et al. Lentivirus-mediated gene transfer in primary T cells is enhanced by a central DNA flap. Gene therapy 8, 190-198 (2001); McManus, M. et al. Small interfering RNA-mediated gene silencing in T lymphocytes. The Journal of Immunology 169, 5754 (2002)). a new delivery technology based on silicon nanowires (NWs) (Shalek et al., Proc Natl Acad Sci U.S.A. 2010; Shalek, A. K. et al. Nanowire-Mediated Delivery Enables Functional Interrogation of Primary Immune Cells: Application to the Analysis of Chronic Lymphocytic Leukemia. Nano Lett. 12, 6498-6504, doi:10.1021/nl3042917 (2012)) was, therefore, used, which was optimized to effectively (>95%) deliver siRNA into naïve T cells without activating them (FIGS. 3b and c ) (Shalek et al., Nano Lett 2012).
Recently, it was demonstrated that NWs are able to effectively penetrate the membranes of mammalian cells and deliver a broad range of exogenous molecules in a minimally invasive, non-activating fashion (Shalek et al., Proc. Natl. Acad. Sci. U.S.A. 2010; Shalek, et al., Nano Lett. 2012). In particular, the NW-T cell interface (FIG. 3b ) was optimized to effectively (>95%) deliver siRNAs into naïve murine T cells. This delivery neither activates nor induces differentiation of naïve T cells and does not affect their response to conventional TCR stimulation with anti-CD3/CD28 (FIG. 3c ) (Shalek, et al., Nano Lett. 2012)). Importantly, NW-delivered siRNAs yielded substantial target transcript knockdowns, prior to and even up to 48h after anti-CD3/CD28 activation, despite rapid cellular proliferation (FIG. 3d ).
It was then attempted to perturb 60 genes with NW-mediated siRNA delivery and efficient knockdown (<60% transcript remaining at 48 hr post activation) was achieved for 34 genes (FIG. 3d and FIG. 11, Table S6.2). Knockout mice were obtained for seven other genes, two of which (Irf8 and Il17ra) were also in the knockdown set. Altogether, 39 of the 65 selected genes were successfully perturbed—29 regulators and 10 receptors—including 21 genes not previously associated with Th17 differentiation.
Nanowire-Based Screen Validates 39 Regulators in the Th17 Network:
the effects of the perturbation on gene expression were profiled at two time points. 28 of the perturbations were profiled at 10 hr after the beginning of differentiation, soon after the induction of ROR-γt (FIG. 6), and all of the perturbations were profiled at 48 hr, when the Th17 phenotype becomes more established (FIG. 1b ). Two of the perturbations (Il17ra and Il21r knockouts) were also profiled at 60 hr.
In particular, the effects of perturbations at 48 hr post-activation on the expression of 275 signature genes were measured using the Nanostring nCounter system (Il17ra and Il21r knockouts were also measured at 60 hr).
The signature genes were computationally chosen to cover as many aspects of the differentiation process as possible (see Methods in Example 1): they include most differentially expressed cytokines, TFs, and cell surface molecules, as well as representatives from each cluster (FIG. 1b ), enriched function, and predicted targets in each network. For validation, a signature of 85 genes was profiled using the Fluidigm BioMark system, obtaining highly reproducible results (FIG. 12).
The signature genes for expression analysis were computationally chosen to cover as many aspects of the differentiation process as possible (see Methods in Example 1). They include the majority of the differentially expressed cytokines, TFs, and cell surface genes, as well as representative genes from each expression cluster (FIG. 1b ), enriched biological function, and predicted targets of the regulators in each network. Importantly, since the signature includes most of the genes encoding the perturbed regulators, the connections between them (FIG. 4a , ‘perturbed’), including feedback and feed-forward loops, could be determined.
The statistical significance of a perturbation's effect on a signature gene was scored by comparing to non-targeting siRNAs and to 18 control genes that were not differentially expressed (see Methods in Example 1, FIG. 4a , all non-grey entries are significant). Perturbation of 26 of the tested regulators had a significant effect on the expression of at least 25 signature genes at the 48 hr time point (10% of signature genes that had any response). On average, a perturbation affected 40 genes, and 80% of the signature genes were affected by at least one regulator. Supporting the original network model (FIG. 2), there is a significant overlap between the genes affected by a regulator's knockdown and its predicted targets (p≤0.01, permutation test; see Methods in Example 1).
To study the network's dynamics, the effect of 28 of the perturbations at 10 hr (shortly after the induction of ROR-γt) was measured using the Fluidigm Biomark system. It was found that 30% of the functional interactions are present with the same activation/repression logic at both 10 hr and 48 hr, whereas the rest are present only in one time point (FIG. 13). This is consistent with the extent of rewiring in the original model (FIG. 2b ).
Whenever possible, the function of each regulator was classified as either positive or negative for Th17 differentiation. Specifically, at the 48 hr time point, perturbation of 22 of the regulators significantly attenuated IL-17A or IL-17F expression (‘Th17 positive regulators’, FIG. 4b , blue) and perturbation of another five, significantly increased IL-17 levels (‘Th17 negative regulators’, FIG. 4b , red). 12 of these strongly positive or negative regulators were not previously associated with Th17 cells (FIG. 4b , light grey halos around blue and red nodes). A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. Next, the role of these strong positive and negative regulators in the development of the Th17 phenotype was focused on.
Two Coupled Antagonistic Circuits in the Th17 Network:
Characterizing each regulator by its effect on Th17 signature genes (e.g. IL17A, IL17F, FIG. 4b , grey nodes, bottom), it was found that at 48 hr the network is organized into two antagonistic modules: a module of 22 ‘Th17 positive factors’ (FIG. 4b , blue nodes: 9 novel) whose perturbation decreased the expression of Th17 signature genes (FIG. 4b , grey nodes, bottom), and a module of 5 ‘Th17 negative factors’ (FIG. 4b , red nodes: 3 novel) whose perturbation did the opposite. A color version of these figures can be found in Yosef et al., “Dynamic regulatory network controlling Th17 cell differentiation, Nature, vol. 496: 461-468 (2013)/doi: 10.1038/nature11981. Each of the modules is tightly intra-connected through positive, self-reinforcing interactions between its members (70% of the intra-module edges), whereas most (88%) inter-module interactions are negative. This organization, which is statistically significant (empirical p-value<10⁻³; see Methods in Example 1, FIG. 14), is reminiscent to that observed previously in genetic circuits in yeast (Segre, D., Deluna, A., Church, G. M. & Kishony, R. Modular epistasis in yeast metabolism. Nat. Genet. 37, 77-83, doi:10.1038/ng1489 (2005); Peleg, T., Yosef, N., Ruppin, E. & Sharan, R. Network-free inference of knockout effects in yeast. PLoS Comput Biol 6, e1000635, doi:10.1371/journal.pcbi.1000635 (2010)). At 10 hrs, the same regulators do not yield this clear pattern (p>0.5), suggesting that at that point, the network is still malleable.
The two antagonistic modules may play a key role in maintaining the balance between Th17 and other T cell subsets and in self-limiting the pro-inflammatory status of Th17 cells. Indeed, perturbing Th17 positive factors also induces signature genes of other T cell subsets (e.g., Gata3, FIG. 4b , grey nodes, top), whereas perturbing Th17 negative factors suppresses them (e.g., Foxp3, Gata3, Stat4, and Tbx21).

Example 5: Validation and Characterization of Novel Factors

The studies presented herein focused on the role of 12 of the positive or negative factors (including 11 of the 12 novel factors that have not been associated with Th17 cells; FIG. 4b , light grey halos). RNA-Seq was used after perturbing each factor to test whether its predicted targets (FIG. 2) were affected by perturbation (FIG. 4c , Venn diagram, top). Highly significant overlaps (p<10⁻⁵) for three of the factors (Egr2, Irf8, and Sp4) that exist in both datasets were found, and a border-line significant overlap for the fourth (Smarca4) was found, validating the quality of the edges in the network.
Next, the designation of each of the 12 factors as ‘Th17 positive’ or ‘Th17 negative’ was assessed by comparing the set of genes that respond to that factor's knockdown (in RNA-Seq) to each of the 20 clusters (FIG. 1b ). Consistent with the original definitions, knockdown of a ‘Th17 positive’ regulator down-regulated genes in otherwise induced clusters, and up-regulated genes in otherwise repressed or un-induced clusters (and vice versa for ‘Th17 negative’ regulators; FIG. 4d and FIG. 15a,b ). The genes affected by either positive or negative regulators also significantly overlap with those bound by key CD4+ transcription regulators (e.g., Foxp3 (Marson, A. et al. Foxp3 occupancy and regulation of key target genes during T cell stimulation. Nature 445, 931-935, doi:10.1038/nature05478 (2007); Zheng, Y. et al. Genome-wide analysis of Foxp3 target genes in developing and mature regulatory T cells. Nature 445, 936-940, doi:10.1038/nature05563 (2007)), Batf, Irf4, and ROR-γt (Glasmacher, E. et al. A Genomic Regulatory Element That Directs Assembly and Function of Immune-Specific AP-1-IRF Complexes. Science (New York, N.Y.), doi:10.1126/science.1228309 (2012); Ciofani, M. et al. A Validated Regulatory Network for Th17 Cell Specification. Cell, doi:10.1016/j.cell.2012.09.016 (2012)), Xiao et al., unpublished data). For instance, genes that are down-regulated following knockdown of the ‘Th17-positive’ regulator Mina are highly enriched (p<10⁻⁶) in the late induced clusters (e.g., C19, C20). Conversely, genes in the same late induced clusters become even more up-regulated following knockdown of the ‘Th17 negative’ regulator Sp4.
Mina Promotes the Th17 Program and Inhibits the Foxp3 Program:
Knockdown of Mina, a chromatin regulator from the Jumonji C (JmjC) family, represses the expression of signature Th17 cytokines and TFs (e.g. ROR-γt, Batf, Irf4) and of late-induced genes (clusters C9, C19; p<10⁻⁵), while increasing the expression of Foxp3, the master TF of Treg cells. Mina is strongly induced during Th17 differentiation (cluster C7), is down-regulated in IL23r−/− Th17 cells, and is a predicted target of Batf (Glasmacher, E. et al. A Genomic Regulatory Element That Directs Assembly and Function of Immune-Specific AP-1-IRF Complexes. Science, doi:10.1126/science.1228309 (2012)), ROR-γt (Glasmacher et al., Science 2012), and Myc in the model (FIG. 5a ). Mina was shown to suppress Th2 bias by interacting with the TF NFAT and repressing the IL-4 promoter (Okamoto, M. et al. Mina, an Il4 repressor, controls T helper type 2 bias. Nat. Immunol. 10, 872-879, doi:10.1038/ni.1747 (2009)). However, in the cells, Mina knockdown did not induce Th2 genes, suggesting an alternative mode of action via positive feedback loops between Mina, Batf and ROR-γt (FIG. 5a , left). Consistent with this model, Mina expression is reduced in Th17 cells from ROR-γt-knockout mice, and the Mina promoter was found to be bound by ROR-γt by ChIP-Seq (data not shown). Finally, the genes induced by Mina knockdown significantly overlap with those bound by Foxp3 in Treg cells (Marson et al., Nature 2007; Zheng et al., Nature 2007) (P<10⁻²⁵) and with a cluster previously linked to Foxp3 activity in Treg cells (Hill, J. A. et al. Foxp3 transcription-factor-dependent and -independent regulation of the regulatory T cell transcriptional signature. Immunity 27, 786-800, doi:S1074-7613(07)00492-X [pii]10.1016/j.immuni.2007.09.010 (2007)) (FIG. 15c ). When comparing to previously defined transcriptional signatures of Treg cells (compared to conventional T cells, (Hill, J. A. et al. Foxp3 transcription-factor-dependent and -independent regulation of the regulatory T cell transcriptional signature. Immunity 27, 786-800, doi:10.1016/j.immuni.2007.09.010 (2007))), genes that are induced in the Mina knockdown are enriched in a cluster tightly linked to functional activity of FoxP3. Conversely, genes down-regulated in the Mina knockdown are more directly responsive to TCR and IL-2 and less responsive to Foxp3 in Treg cells (FIG. 15c ).
To further analyze the role of Mina, IL-17a and Foxp3 expression was measured following differentiation of naïve T cells from Mina−/− mice. Mina−/− cells had decreased IL-17a and increased Foxp3 compared to wild-type (WT) cells, as detected by intracellular staining (FIG. 5a ). Cytokine analysis of the corresponding supernatants confirmed a decrease in IL-17a production and an increase in IFN-γ (FIG. 5a ) and TNF-α (FIG. 16a ). Under Th17 differentiation conditions, loss of Mina resulted in a decrease in IL-17 expression and increase in FoxP3, as detected by intracellular staining (FIG. 5a ). Cytokine analysis of the supernatants from these differentiating cultures confirmed a decrease in IL-17 production with a commensurate increase in IFNγ (FIG. 5a ) and TNFα (FIG. 16a ).
The reciprocal relationship between Tregs/Th17 cells has been well described (Korn, T. et al. IL-21 initiates an alternative pathway to induce proinflammatory T(H)17 cells. Nature 448, 484-487, doi:10.1038/nature05970 (2007)), and it was assumed that this is achieved by direct binding of the ROR-γt/Foxp3 TFs. However, the analysis suggests a critical role for the regulator Mina in mediating this process. This suggests a model where Mina, induced by ROR-γt and Batf, promotes transcription of ROR-γt, while suppressing induction of Foxp3, thus affecting the reciprocal Tregs/Th17 balance (Korn, et al., Nature 2007)) by favoring rapid Th17 differentiation.
Fas Promotes the Th17 Program and Suppresses IFN-γ Expression:
Fas, the TNF receptor superfamily member 6, is another Th17 positive regulator (FIG. 5b ). Fas is induced early, and is a target of Stat3 and Batf in the model. Fas knockdown represses the expression of key Th17 genes (e.g., IL-17a, IL-17f, Hif1a, Irf4, and Rbpj) and of the induced cluster C14, and promotes the expression of Th1-related genes, including IFN-γ receptor 1 and Klrd1 (Cd94; by RNA-Seq, FIG. 4, FIG. 5b , and FIG. 15). Fas and Fas-ligand deficient mice are resistant to the induction of autoimmune encephalomyelitis (EAE) (Waldner, H., Sobel, R. A., Howard, E. & Kuchroo, V. K. Fas- and FasL-deficient mice are resistant to induction of autoimmune encephalomyelitis. J Immunol 159, 3100-3103 (1997)), but have no defect in IFN-γ or Th1 responses. The mechanism underlying this phenomenon was never studied.
To explore this, T cells from Fas−/− mice (FIG. 5b , FIG. 16c ) were differentiated. Consistent with the knockdown analysis, expression of IL-17a was strongly repressed and IFN-γ production was strongly increased under both Th17 and Th0 polarizing conditions (FIG. 5b ). These results suggest that besides being a death receptor, Fas may play an important role in controlling the Th1/Th17 balance, and Fas−/− mice may be resistant to EAE due to lack of Th17 cells.
Pou2af1 Promotes the Th17 Program and Suppresses IL-2 Expression:
Knockdown of Pou2af1 (OBF1) strongly decreases the expression of Th17 signature genes (FIG. 5c ) and of intermediate- and late-induced genes (clusters C19 and C20, p<10⁻⁷), while increasing the expression of regulators of other CD4+ subsets (e.g., Foxp3, Stat4, Gata3) and of genes in non-induced clusters (clusters C2 and C16 p<10⁻⁹). Pou2af1's role in T cell differentiation has not been explored (Teitell, M. A. OCA-B regulation of B-cell development and function. Trends Immunol 24, 546-553 (2003)). To investigate its effects, T cells from Pou2af1−/− mice were differentiated (FIG. 5c , FIG. 16b ). Compared to WT cells, IL-17a production was strongly repressed. Interestingly, IL-2 production was strongly increased in Pou2af1−/− T cells under non-polarizing (Th0) conditions. Thus, Pou2af1 may promote Th17 differentiation by blocking production of IL-2, a known endogenous repressor of Th17 cells (Laurence, A. et al. Interleukin-2 signaling via STAT5 constrains T helper 17 cell generation. Immunity 26, 371-381, doi:S1074-7613(07)00176-8 [pii]10.1016/j.immuni.2007.02.009 (2007)). Pou2af1 acts as a transcriptional co-activator of the TFs OCT1 or OCT2 (Teitell, Trends Immunol 2003). IL-17a production was also strongly repressed in Oct1-deficient cells (FIG. 16d ), suggesting that Pou2af1 may exert some of its effects through this co-factor.
TSC22d3 May Limit Th17 Differentiation and Pro-Inflammatory Function:
Knockdown of the TSC22 domain family protein 3 (Tsc22d3) increases the expression of Th17 cytokines (IL-17a, IL-21) and TFs (ROR-γt, Rbpj, Batf), and reduces Foxp3 expression. Previous studies in macrophages have shown that Tsc22d3 expression is stimulated by glucocorticoids and IL-10, and it plays a key role in their anti-inflammatory and immunosuppressive effects (Choi, S.-J. et al. Tsc-22 enhances TGF-beta signaling by associating with Smad4 and induces erythroid cell differentiation. Mol. Cell. Biochem. 271, 23-28 (2005)). Tsc22d3 knockdown in Th17 cells increased the expression of IL-10 and other key genes that enhance its production (FIG. 5d ). Although IL-10 production has been shown (Korn et al., Nature 2007; Peters, A., Lee, Y. & Kuchroo, V. K. The many faces of Th17 cells. Curr. Opin. Immunol. 23, 702-706, doi:10.1016/j.coi.2011.08.007 (2011); Chaudhry, A. et al. Interleukin-10 signaling in regulatory T cells is required for suppression of Th17 cell-mediated inflammation. Immunity 34, 566-578, doi:10.1016/j.immuni.2011.03.018 (2011)) to render Th17 cells less pathogenic in autoimmunity, co-production of IL-10 and IL-17a may be the indicated response for clearing certain infections like Staphylococcus aureus at mucosal sites (Zielinski, C. E. et al. Pathogen-induced human TH17 cells produce IFN-γ or IL-10 and are regulated by IL-1β. Nature 484, 514-518, doi:10.1038/nature10957 (2012)). This suggests a model where Tsc22d3 is part of a negative feedback loop for the induction of a Th17 cell subtype that coproduce IL-17 and IL-10 and limits their pro-inflammatory capacity. Tsc22d3 is induced in other cells in response to the steroid Dexamethasone (Jing, Y. et al. A mechanistic study on the effect of dexamethasone in moderating cell death in Chinese Hamster Ovary cell cultures. Biotechnol Prog 28, 490-496, doi:10.1002/btpr.747 (2012)), which represses Th17 differentiation and ROR-γt expression (Hu, M., Luo, Y. L., Lai, W. Y. & Chen, P. F. [Effects of dexamethasone on intracellular expression of Th17 cytokine interleukin 17 in asthmatic mice]. Nan Fang Yi Ke Da Xue Xue Bao 29, 1185-1188 (2009)). Thus, Tsc22d3 may mediate this effect of steroids.
To further characterize Tsc22d3's role, ChIP-Seq was used to measure its DNA-binding profile in Th17 cells and RNA-Seq following its knockdown to measure its functional effects. There is a significant overlap between Tsc22d3's functional and physical targets (P<0.01, e.g., IL-21, Irf4; see Methods in Example 1). For example, Tsc22d3 binds in proximity to IL-21 and Irf4, which also become up regulated in the Tsc22d3 knockdown. Furthermore, the Tsc22d3 binding sites significantly overlap those of major Th17 factors, including Batf, Stat3, Irf4, and ROR-γt (>5 fold enrichment; FIG. 5d , and see Methods in Example 1). This suggests a model where Tsc22d3 exerts its Th17-negative function as a transcriptional repressor that competes with Th17 positive regulators over binding sites, analogous to previous findings in CD4+ regulation (Ciofani et al., Cell 2012; Yang, X. P. et al. Opposing regulation of the locus encoding IL-17 through direct, reciprocal actions of STAT3 and STAT5. Nat. Immunol. 12, 247-254, doi:10.1038/ni.1995 (2011)).

Example 6: Protein C Receptor (PROCR) Regulates Pathogenic Phenotype of Th17 Cells

Th17 cells, a recently identified T cell subset, have been implicated in driving inflammatory autoimmune responses as well as mediating protective responses against certain extracellular pathogens. Based on factors such as molecular signature, Th17 cells are classified as pathogenic or non-pathogenic. (See e.g., Lee et al., “Induction and molecular signature of pathogenic Th17 cells,” Nature Immunology, vol. 13(10): 991-999 and online methods).
It should be noted that 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 will be 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 or towards another T cell 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 or towards another T cell phenotype is desirable. For example, pathogenic Th17 cells are believed to be involved in immune responses such as autoimmunity and/or inflammation. Thus, inhibition of pathogenic Th17 cell differentiation or otherwise decreasing the balance of Th17 T cells towards non-pathogenic Th17 cells or towards another T cell phenotype is desirable in therapeutic strategies for treating or otherwise ameliorating a symptom of an immune-related disorder such as an autoimmune disease or an inflammatory disorder. In another example, depending on the infection, non-pathogenic or pathogenic Th17 cells are believed to be desirable in building a protective immune response in infectious diseases and other pathogen-based disorders. Thus, inhibition of non-pathogenic Th17 cell differentiation or otherwise decreasing the balance of Th17 T cells towards pathogenic Th17 cells or towards another T cell phenotype or vice versa is desirable in therapeutic strategies for treating or otherwise ameliorating a symptom of an immune-related disorder such as infectious disease.
Th17 cells are considered to be pathogenic when they exhibit a distinct pathogenic signature where one or more of the following genes or products of these genes is upregulated in TGF-β3-induced Th17 cells as compared to TGF-β1-induced Th17 cells: Cxcl3, Il22, Il3, Ccl4, Gzmb, Lrmp, Ccl5, Casp1, Csf2, Ccl3, Tbx21, Icos, Il7r, Stat4, Lgals3 or Lag3. Th17 cells are considered to be non-pathogenic when they exhibit a distinct non-pathogenic signature where one or more of the following genes or products of these genes is down-regulated in TGF-β3-induced Th17 cells as compared to TGF-β1-induced Th17 cells: Il6st, Il1rn, lkzf3, Maf, Ahr, Il9 or Il10.
A temporal microarray analysis of developing Th17 cells was performed to identify cell surface molecules, which are differentially expressed in Th17 cells and regulate the development of Th17 cells. PROCR was identified as a receptor that is differentially expressed in Th17 cells and found its expression to be regulated by Th17-specific transcription regulators.
Protein C receptor (PROCR; also called EPCR or CD201) is primarily expressed on endothelial cells, CD8⁺ dendritic cells and was also reported to be expressed to lower levels on other hematopoietic and stromal cells. It binds to activated protein C as well as factor VII/VIIa and factor Xa and was shown to have diverse biological functions, including anticoagulant, cytoprotective, anti-apoptotic and anti-inflammatory activity. However, prior to these studies, the function of PROCR in T cells had not been explored.
The biological function of PROCR and its ligand activated protein C in Th17 cells was analyzed, and it was found that it decreased the expression of some of the genes identified as a part of the pathogenic signature of Th17 cells. Furthermore, PROCR expression in Th17 cells reduced the pathogenicity of Th17 cells and ameliorated disease in a mouse model for human multiple sclerosis.
These results imply that PROCR functions as a regulatory gene for the pathogenicity of Th17 cells through the binding of its ligand(s). It is therefore conceivable that the regulation of this pathway might be exploited for therapeutic approaches to inflammatory and autoimmune diseases.
These studies are the first to describe the Th17-specific expression of PROCR and its role in reducing autoimmune Th17 pathogenicity. Thus, activation of PROCR through antibodies or other agonists are useful as a therapeutic strategy in an immune response such as inflammatory autoimmune disorders. In addition, blocking of PROCR through antibodies or other inhibitors could be exploited to augment protective Th17 responses against certain infectious agents and pathogens.
PROCR is Expressed in Th17 Cells:
The membrane receptor PROCR (Protein C receptor; also called EPCR or CD201) is present on epithelial cells, monocytes, macrophages, neutrophils, eosinophils, and natural killer cells but its expression had not previously been reported on T cells (Griffin J H, Zlokovic B V, Mosnier L O. 2012. Protein C anticoagulant and cytoprotective pathways. Int J Hematol 95: 333-45). However, the detailed transcriptomic analysis of Th17 cells described herein has identified PROCR as an important node for Th17 cell differentiation (Yosef N, Shalek A K, Gaublomme J T, Jin H, Lee Y, Awasthi A, Wu C, Karwacz K, Xiao S, Jorgolli M, Gennert D, Satija R, Shakya A, Lu D Y, Trombetta J J, Pillai M R, Ratcliffe P J, Coleman M L, Bix M, Tantin D, Park H, Kuchroo V K, Regev A. 2013. Dynamic regulatory network controlling TH17 cell differentiation. Nature 496: 461-8). PROCR shares structural homologies with the CD1/MHC molecules and binds activated protein C (aPC) as well as blood coagulation factor VII and the Vγ4Vδ5 TCR of γδ T cells. Due to its short cytoplasmic tail PROCR does not signal directly, but rather signals by associating with the G-protein-coupled receptor PAR1 (FIG. 30a ; (Griffin et al, Int J Hematol 95: 333-45 (2012))). To analyze PROCR expression on Th subsets, CD4+ T cells were differentiated in vitro under polarizing conditions and determined PROCR expression. As indicated by the network analysis of Th17 cells, high levels of PROCR could be detected in cells differentiated under Th17 conditions (FIG. 31b ). To study expression of PROCR on Th17 cells during an immune response, mice were immunized with MOG/CFA to induce EAE. PROCR was not expressed on T cells in spleen and lymph nodes. In contrast, it could be detected on Th17 cells infiltrating the CNS (FIG. 31c ). These data indicate that PROCR is expressed on Th17 cells in vitro and in vivo, where it is largely restricted to T cells infiltrating the target organ. To investigate the functions of PROCR in Th17 cells, studies were designed to test how loss of PROCR would affect IL-17 production using T cells from a PROCR hypomorphic mutant (PROCRd/d). PROCR deficiency causes early embryonic lethality (embryonic day 10.5) (Gu J M, Crawley J T, Ferrell G, Zhang F, Li W, Esmon N L, Esmon C T. 2002. Disruption of the endothelial cell protein C receptor gene in mice causes placental thrombosis and early embryonic lethality. J Biol Chem 277: 43335-43), whereas hypomorphic expression of PROCR, which retain only small amounts (<10% of wild-type) of PROCR, is sufficient to completely abolish lethality and mice develop normally under steady state conditions (Castellino F J, Liang Z, Volkir S P, Haalboom E, Martin J A, Sandoval-Cooper M J, Rosen E D. 2002. Mice with a severe deficiency of the endothelial protein C receptor gene develop, survive, and reproduce normally, and do not present with enhanced arterial thrombosis after challenge. Thromb Haemost 88: 462-72). When challenged in a model for septic shock, PROCRd/d mice show compromised survival compared to WT mice (Iwaki T, Cruz D T, Martin J A, Castellino F J. 2005. A cardioprotective role for the endothelial protein C receptor in lipopolysaccharide-induced endotoxemia in the mouse. Blood 105: 2364-71). Naïve CD4+ PROCRd/d T cells differentiated under Th17 conditions produced less IL-17 compared to WT naïve CD4+ T cells (FIG. 31d ). Effector memory PROCRd/d T cells cultured with IL-23 produced more IL-17 than WT memory T cells. Therefore PROCR, similar to PD-1, promotes generation of Th17 cells from naïve CD4 T cells, but inhibits the function of Th17 effector T cells.
Knockdown Analysis of PROCR in Tumor Model:
FIG. 44 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 5×10⁵B16F10 melanoma cells. As shown in FIG. 44, inhibition of PROCR slowed tumor growth. Thus, inhibition of PROCR is useful for impeding tumor growth and in other therapeutic applications for treatment of cancer.
PD-1 and PROCR Affect Th17 Pathogenicity:
Th17 cells are very heterogeneous and the pathogenicity of Th17 subsets differs depending on the cytokine environment during their differentiation (Zielinski C E, Mele F, Aschenbrenner D, Jarrossay D, Ronchi F, Gattorno M, Monticelli S, Lanzavecchia A, Sallusto F. 2012. Pathogen-induced human TH17 cells produce IFN-gamma or IL-10 and are regulated by IL-1beta. Nature 484: 514-8; Lee Y, Awasthi A, Yosef N, Quintana F J, Peters A, Xiao S, Kleinewietfeld M, Kunder S, Sobel R A, Regev A, Kuchroo V. 2012. Induction and molecular signature of pathogenic Th17 cells. Nat Immunol In press; and Ghoreschi K, Laurence A, Yang X P, Tato C M, McGeachy M J, Konkel J E, Ramos H L, Wei L, Davidson T S, Bouladoux N, Grainger J R, Chen Q, Kanno Y, Watford W T, Sun H W, Eberl G, Shevach E M, Belkaid Y, Cua D J, Chen W, O'Shea J J. 2010. Generation of pathogenic T(H)17 cells in the absence of TGF-beta signalling. Nature 467: 967-71). In addition to the cytokine milieu, several costimulatory pathways have been implicated in regulating differentiation and function of T helper subsets, including Th17 cells. CTLA-4-B7 interactions inhibit Th17 differentiation (Ying H, Yang L, Qiao G, Li Z, Zhang L, Yin F, Xie D, Zhang J. 2010. Cutting edge: CTLA-4-B7 interaction suppresses Th17 cell differentiation. J Immunol 185: 1375-8). Furthermore, the work described herein revealed that ICOS plays a critical role in the maintenance of Th17 cells (Bauquet A T, Jin H, Paterson A M, Mitsdoerffer M, Ho I C, Sharpe A H, Kuchroo V K. 2009. The costimulatory molecule ICOS regulates the expression of c-Maf and IL-21 in the development of follicular T helper cells and TH-17 cells. Nat Immunol 10: 167-75).
Based on the detailed genomic analysis of pathogenic vs. non-pathogenic Th17 cells herein, it has been determined that the molecular signatures that define pathogenic vs. non-pathogenic effector Th17 cells in autoimmune disease (Lee Y, Awasthi A, Yosef N, Quintana F J, Peters A, Xiao S, Kleinewietfeld M, Kunder S, Sobel R A, Regev A, Kuchroo V. 2012. Induction and molecular signature of pathogenic Th17 cells. Nat Immunol In press). Interestingly, PROCR is part of the signature for non-pathogenic Th17 cells and its expression is highly increased in non-pathogenic subsets (FIG. 32a ). Furthermore, PROCR seems to play a functional role in regulating Th17 pathogenicity as engagement of PROCR by its ligand aPC induces some non-pathogenic signature genes, while Th17 cells from PROCRd/d mice show decreased expression of these genes (FIG. 32b ). To study whether PROCR could also affect pathogenicity of Th17 cells in an in vivo model of autoimmunity, an adoptive transfer model for EAE was used. To induce disease, MOG-specific 2D2 TCR transgenic T cells were differentiated under Th17 conditions and then transferred into naïve recipients. As shown in FIG. 32c , forced overexpression of PROCR on Th17 cells ameliorated disease, confirming that PROCR drives conversion of pathogenic towards non-pathogenic Th17 cells. In addition, it was found that PD-1:PD-L1 interactions limit the pathogenicity of effector Th17 cells in vivo. When MOG35-55-specific (2D2) Th17 effector cells were transferred into WT vs. PD-L1−/− mice, PD-L1−/− recipients rapidly developed signs of EAE (as early as day 5 post transfer), and EAE severity was markedly increased with most experiments needed to be terminated due to rapid onset of morbidity in PD-L1−/− recipients (FIG. 32d ). The number of CNS-infiltrating cells was significantly increased in PD-L1−/− recipients with a greater percentage of 2D2+ IL-17+ in PD-L1−/− recipients compared to WT mice. Therefore both PD-1 and PROCR seem to control pathogenicity of effector Th17 cells.
Several co-inhibitory molecules have been implicated in T cell dysfunction during antigen persistence. PD-1 and Tim-3, in particular, have wide implications in cancer and chronic viral infections such as HIV, HCV in human and LCMV in mice. Autoreactive T cell responses in mice and human are characterized with reduced expression of inhibitory molecules. The ability to induce T cell dysfunction in autoimmune settings could be clinically beneficial. MS patients that respond to Copaxone treatment show significantly elevated levels of expression of PROCR and PD-L1. It has been previously demonstrated that increasing Tim-3 expression and promoting T cell exhaustion provides the ability to limit encephalitogenecity of T cells and reduce EAE severity (Rangachari M, Zhu C, Sakuishi K, Xiao S, Karman J, Chen A, Angin M, Wakeham A, Greenfield E A, Sobel R A, Okada H, McKinnon P J, Mak T W, Addo M M, Anderson A C, Kuchroo V K. 2012. Bat3 promotes T cell responses and autoimmunity by repressing Tim-3-mediated cell death and exhaustion. Nat Med 18: 1394-400). Studies were, therefore, designed to determine whether the novel inhibitory molecule PROCR, which is selectively enriched in Th17 cells, could also play a role in T cell exhaustion. It was found that PROCR is expressed in exhausted tumor infiltrating lymphocytes that express both PD-1 and Tim-3 (FIG. 33a ). Consistent with this observation, it was found that PROCR was most enriched in antigen-specific exhausted CD8 T cells (FIG. 33b ) during chronic LCMV infection. While T cell exhaustion is detrimental in chronic viral infection and tumor immunity, induction of exhaustion may play a beneficial role in controlling potentially pathogenic effector cells that cause autoimmune diseases. Regulating the expression and/or function of PD-1 and PROCR might provide the avenues to accomplish this task in controlling autoimmunity.

Example 7: Fas in Th Cell Differentiation

Fas, also known as FasR, CD95, APO-1, TNFRSF6, is a member of the TNF receptor superfamily. Binding of FasL leads to FAS trimers that bind FADD (death domains), which activates caspase-8 and leads to apoptosis. Fas also exhibits apoptosis independent effects such as interaction with Akt, STAT3, and NF-κB in liver cells and interaction with NF-κB and MAPK pathways in cancer cells.
Lpr mice are dominant negative for Fas (transposon intron 1), creating a functional knockout (KO). These mice exhibit lymphoproliferative disease (lpr); age dependent >25-fold size increase of LN, Spleen; expansion of Thy1+B220+CD4−CD8−TCRa/b+ T cells. These mice produce spontaneous anti-dsDNA Ab, systemic autoimmunity, which makes them a model of systemic lupus erythematosus (SLE), but these mice are resistant to experimental autoimmune encephalomyelitis (EAE). Gld mice are dominant negative for FasL. Fas flox mice that are CD4Cre-/CD19Cre-/CD4Cre-CD19Cre-/LckCre-Fasflox exhibit no lymphoproliferation and no expansion of Thy1+B220+CD4−CD8−TCRa/b+ T cells. These mice do exhibit progressive lymphopenia, inflammatory lung fibrosis, and wasting syndrome. Fas flox mice that are MxCre+poly(IC)-Fasflox exhibit an 1pr phenotype. Fas flox mice that are MOGCre-Fasflox are resistant to EAE. Fas flox mice that are LysMCre-Fasflox exhibit lymphoproliferation and glomerulonephritis.
Although Fas (CD95) has been identified as a receptor mediating apoptosis, the data herein clearly show that Fas is important for Th17 differentiation and development of EAE. The data herein demonstrates that Fas-deficient mice have a defect in Th17 cell differentiation and preferentially differentiate into Th1 and Treg cells. The expansion of Treg cells and inhibition of Th17 cells in Fas-deficient mice might be responsible for disease resistance in EAE.
Fas-deficient cells are impaired in their ability to differentiate into Th17 cells, and they produce significantly lower levels of IL-17 when cultured in vitro under Th17 conditions (IL-1β+IL-6+IL-23). Furthermore, they display reduced levels of IL-23R, which is crucial for Th17 cells as IL-23 is required for Th17 stability and pathogenicity. In contrast, Fas inhibits IFN-γproduction and Th1 differentiation, as cells derived from Fas-deficient mice secrete significantly higher levels of IFN-γ. Similarly, Fas-deficient cells more readily differentiate into Foxp3+ Tregs and secrete higher levels of the Treg effector cytokine IL-10. It therefore seems as if Fas suppresses the differentiation into Tregs and IFN-γ-producing Th1 cells while promoting Th17 differentiation. In inflammatory autoimmune disorders, such as EAE, Fas therefore seems to promote disease progression by shifting the balance in T helper cells away from the protective Tregs and from IFN-γ-producing Th1 cells towards pathogenic Th17 cells.

Example 8: Targeting CD5L in Modulation of Th17 Pathogenic State

CD5L (CD5 antigen-like; AIM (apoptosis inhibitor of macrophage)) has been identified as a novel regulator of the Th17 pathogenic state. CD5L is a 54-kD protein belongs to the macrophage scavenger receptor cysteine-rich domain superfamily; other family member include CD5, CD6, CD36, MARCO etc. CD5L is expressed by macrophage and adipocytes and is incorporated into cells through CD36 (Kurokawa J. et al. 2010). CD5L can be induced by activation of RXR/LXR (Valledor A F et al. 2004) and inhibits lipid induced apoptosis of thymocytes and macrophage. CD5L is involved in obesity-associated autoantibody production (Arai S et. al. 2013) and plays a role in lipid metabolism. CD5L promotes lipolysis in adipocytes, potentially preventing obesity onset (Miyazaki T et al. 2011) and inhibits de novo lipid synthesis by inhibiting fatty acid synthase (Kurokawa J. et al. 2010).
FIGS. 34A-34C are a series of graphs demonstrating the expression of CD5L on Th17 cells. FIGS. 35A-35C are a series of illustrations and graphs depicting how CD5L deficiency does not alter Th17 differentiation. FIGS. 36A-36C are a series of illustrations and graphs depicting how CD5L deficiency alters Th17 memory by affecting survival or stability.
FIGS. 37A-37B are a series of graphs depicting how CD5L deficiency results in more severe and prolonged EAE with higher Th17 responses. FIGS. 38A-38C are a series of illustrations and graphs depicting how loss of CD5L converts non-pathogenic Th17 cells into pathogenic effector Th17 cells. FIGS. 39A-39B are a series of graphs depicting how CD5L-deficient Th17 cells (TGF-β+IL-6) develop a pathogenic phenotype.

Example 9: Single Cell Analysis of Functional Th17 Data

Single cell analysis of target genes that can be exploited for therapeutic and/or diagnostic uses allows for the identification of genes that either cannot be identified at a population level or are not otherwise ready apparent as a suitable target gene at the population level.
Single-cell RNA sequencing provides a unique opportunity to characterize different sub-types of Th17 cells and to gain better understanding of the regulatory mechanisms that underlie their heterogeneity and plasticity. In particular, the studies described herein were designed to identify subpopulations of Th17 cells both in-vitro and in-vivo, and to map the potential divergent mechanisms at play. These results provide important mechanistic insights with the potential for therapeutic relevance in treatment of autoimmune-disease.
Using a microfluidic technology (Fluidigm C1) for preparation of single-cell mRNA SMART-Seq libraries, differentiated Th17 cells (96 cells at a time) were profiled in-vitro under pathogenic and non-pathogenic polarizing conditions at two time points (48h and 96h into the differentiation process). In addition, Th17 cells isolated from the central nervous system and lymph nodes were profiled at the peak of disease of mice immunized with experimental autoimmune encephalomyelitis (EAE; a mouse model of multiple sclerosis). A computational pipeline was then developed for processing and analyzing the resulting data set (˜1000 cells altogether). The results offer a vantage point into the sources and functional implications of expression patterns observed at the single cell level, expression modality, i.e., map how a gene is expressed across the population, and variability, i.e., how tightly the expression level of a gene is regulated.
For instance, it was found that the signature cytokine IL-17A exhibits one of the highest levels of variability in the cell's transcriptome in-vitro. This variation strongly correlates with an unsupervised partition of the cells into sub-populations, which spans the spectrum between potentially pathogenic cells (high levels of IL-17A and low levels of immunosuppressive cytokines like IL-10) to non-pathogenic cells (opposite expression profiles).
The specific genes that characterize the two extreme states provide appealing target genes and include candidates that were not detected by previous, population-level approaches (Yosef, N. et al. Dynamic regulatory network controlling TH17 cell differentiation. Nature 496, 461-468, doi:10.1038/nature11981 (2013)). To identify the most promising target genes, a gene prioritization scheme, which combines the single cell RNA-seq results with multiple other sources of information (e.g., transcription factor binding), was developed. High-ranking targets were then further analyzed using the respective knockout mice.
The following provides single cell analysis methods and conditions to induce various T cell phenotypes:

- Condition Th0: cells are activated with CD3/CD28, but no cytokines are added to the media as a control
- Condition T16: CD3/CD28 activation+TGFβ1+IL6 are added to media to produce non-pathogenic Th17 conditions
- Condition T36: CD3/CD28 activation+TGFβ3+IL6 are added to media to produce pathogenic Th17 conditions
- Condition B623: CD3/CD28 activation+IL1β+IL6+IL23 are added to media to produce pathogenic Th17 conditions
- Condition T: CD3/CD28 activation+TGFβ1 are added to media to produce Treg conditions

Under condition Th0, proliferation of cell is activated but the cells are not influenced toward a specific outcome. Under conditions T16, T36 and B623, the activated, proliferating cells are influenced toward a specific Th17 cell outcome, as indicated above. Again, 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. They are being used to connote different Th17 cell phenotypes with different identifying characteristics.
The following methods were used in the studies described herein: Mice: C57BL/6 wild-type, CD4−/−(2663). Mice were obtained from Jackson Laboratory. IL-17A-GFP mice were from Biocytogen. In addition, spleens and lymph nodes from GPR65−/− mice were provided by Yang Li. ZBTB32−/− mice were obtained from the laboratory of Pier Paolo Pandolfi. Cell sorting and in vitro T-cell differentiation: CD4+ T cells were purified from spleen and lymph nodes using anti-CD4 microbeads (Miltenyi Biotech) then stained in PBS with 1% FCS for 20 min at room temperature with anti-CD4-PerCP, anti-CD62l-APC and anti-CD44-PE antibodies (all Biolegend). Naïve CD4+CD62l highCD44low T cells were sorted using the BD FACSAria cell sorter. Sorted cells were activated with plate-bound anti-CD3 (2 μg ml-1) and anti-CD28 (2 μg ml-1) in the presence of cytokines. For TH17 differentiation, the following reagents were used: 2 ng/ml recombinant human TGF-β1 and recombinant human TGF-β3 (Miltenyi Biotec), 25 ng/ml recombinant mouse IL-6 (Miltenyi Biotec), 20 ng/ml recombinant mouse IL-23 (R&D Biosystems) and 20 ng/ml recombinant mouse IL-1β (Miltenyi Biotec). Cells were cultured for 48-96 h and collected for RNA, intracellular cytokine staining, flow-fish.
CyTOF and flow cytometry: Active induction of EAE and disease analysis: For active induction of EAE, mice were immunized by subcutaneous injection of 100 μg MOG (35-55) (MEVGWYRSPFSRVVHLYRNGK) (SEQ ID NO: 1395) in CFA, then received 200 ng pertussis toxin intraperitoneally (List Biological Laboratory) on days 0 and 2. Mice were monitored and were assigned scores daily for development of classical and atypical signs of EAE according to the following criteria: 0, no disease; 1, decreased tail tone or mild balance defects; 2, hind limb weakness, partial paralysis or severe balance defects that cause spontaneous falling over; 3, complete hind limb paralysis or very severe balance defects that prevent walking; 4, front and hind limb paralysis or inability to move body weight into a different position; 5, moribund state (ger, A., Dardalhon, V., Sobel, R. A., Bettelli, E. & Kuchroo, V. K. Th1, Th17, and Th9 effector cells induce experimental autoimmune encephalomyelitis with different pathological phenotypes. Journal of immunology 183, 7169-7177, doi:10.4049/jimmunol.0901906 (2009)).
Isolation of T-cells from EAE mice at the peak of disease: At the peak of disease, mice T-cells were collected from the draining lymph nodes and the CNS. For isolation from the CNS, mice were perfused through the left ventricle of the heart with cold PBS. The brain and the spinal cord were flushed out with PBS by hydrostatic pressure. CNS tissue was minced with a sharp razor blade and digested for 20 min at 37° C. with collagenase D (2.5 mg/ml; Roche Diagnostics) and DNaseI (1 mg/ml; Sigma). Mononuclear cells were isolated by passage of the tissue through a cell strainer (70 μm), followed by centrifugation through a Percoll gradient (37% and 70%). After removal of mononuclear cells, the lymphocytes were washed, stained and sorted for CD3 (Biolegend), CD4 (Biolegend), 7AAD and IL17a-GFP or FOXP3-GFP.
Whole transcriptome amplification: Cell lysis and SMART-Seq (amskold, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnology 30, 777-782 (2012)) whole transcriptome amplification (WTA) was performed on the C1 chip using the C1 Single-Cell Auto Prep System (C1 System) using the SMARTer Ultra Low RNA Kit for Illumina Sequencing (Clontech) with the following modifications:
Cell Lysis Mix:


Composition	Stock Conc.	Volume

C1 Loading Reagent	20X	0.60 ul

SMARTer Kit RNase Inhibitor	40	x	0.30 ul
SMARTer Kit
3′ SMART CDS Primer II A	12	μM	4.20 ul

SMARTer Kit Dilution Buffer	1X	6.90 ul

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


Composition	Stock Conc.	Volume

C1 Loading Reagent	20.0	x	0.45 ul
SMARTer Kit 5X First-Strand Buffer	5.0	x	4.20 ul
(RNase-Free)
SMARTer Kit Dithiothreitol	100	mM	0.53 ul
SMARTer Kit dNTP Mix (dATP, dCTP, dGTP,	10	mM	2.10 ul
and dTTP, each at 10 mM)
SMARTer Kit SMARTer II A Oligonucleotide	12	uM	2.10 ul
SMARTer Kit RNase Inhibitor	40	x	0.53 ul
SMARTer Kit SMARTScribe ™ Reverse	100.0	x	2.10 ul
Transcriptase

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


Composition	Stock Conc.	Volume

PCR Water

—

35.2 ul

10X Advantage

2 PCR Buffer	10.0	x	5.6 ul
	50X dNTP Mix	10	mM	2.2 ul
	IS PCR primer	12	uM	2.2 ul
	50X Advantage
2 Polymerase Mix	50.0	x	2.2 ul
	C1 Loading Reagent	20.0	x	2.5 ul

Cycling Conditions III:
a) 95° C., 1 min
b) 5 cycles of:
i) 95° C., 20s
ii) 58° C., 4 min
ii) 68° C., 6 min
c) 9 cycles of:
i) 95° C., 20s
ii) 64° C., 30s
ii) 68° C., 6 min
d) 7 cycles of:
i) 95° C., 30s
ii) 64° C., 30s
ii) 68° C., 7 min
e) 72° C., 10 min
Library preparation and RNA-Seq: WTA products were harvested from the C1 chip and cDNA libraries were prepared using Nextera XT DNA Sample preparation reagents (Illumina) as per the manufacturer's recommendations, with minor modifications. Specifically, reactions were run at 1/4 the recommended volume, the tagmentation step was extended to 10 minutes, and the extension time during the PCR step was increased from 30s to 60s. After the PCR step, all 96 samples were pooled without library normalization, cleaned twice with 0.9× AMPure XP SPRI beads (Beckman Coulter), and eluted in buffer TE. The pooled libraries were quantified using Quant-IT DNA High-Sensitivity Assay Kit (Invitrogen) and examined using a high sensitivity DNA chip (Agilent). Finally, samples were sequenced deeply using either a HiSeq 2000 or a HiSeq 2500 sequencer.
RNA-Seq of population controls: Population controls were generated by extracting total RNA using RNeasy plus Micro RNA kit (Qiagen) according to the manufacturer's recommendations. Subsequently, 1 μL of RNA in water was added to 2 μL of lysis reaction mix, thermocycled using cycling conditions I (as above). Next, 4 μL of the RT Reaction Mix were added and the mixture was thermocycled using cycling conditions II (as above). Finally, 1 μL of the total RT reaction was added to 9 μL of PCR mix and that mixture was thermocycled using cycling conditions III (as above). Products were quantified, diluted to 0.125 ng/μL and libraries were prepared, cleaned, and tested as above.
Flow cytometry and intracellular cytokine staining: Sorted naïve T cells were stimulated with phorbol 12-myristate 13-aceate (PMA) (50 ng/ml, Sigma-aldrich), ionomycin (1 μg/ml, Sigma-aldrich) and a protein transport inhibitor containing monensin (Golgistop) (BD Biosciences) for 4 h before detection by staining with antibodies. Surface markers were stained in PBS with 1% FCS for 20 min at room temperature, then subsequently the cells were fixed in Cytoperm/Cytofix (BD Biosciences), permeabilized with Perm/Wash Buffer (BD Biosciences) and stained with Biolegend conjugated antibodies, that is, Brilliant violet 650 anti-mouse IFN-γ (XMG1.2) and allophycocyanin-anti-IL-17A (TC11-18H10.1), diluted in Perm/Wash buffer as described14. Foxp3 staining was performed with the Foxp3 staining kit by eBioscience (00-5523-00) in accordance with their ‘One-step protocol for intracellular (nuclear) proteins’. Data were collected using either a FACS Calibur or LSR II (Both BD Biosciences), then analyzed using Flow Jo software (Treestar).
Quantification of cytokine secretion using ELISA: Naïve T cells from knockout mice and their wild-type controls were cultured as described above, their supernatants were collected after 48h and 96h, and cytokine concentrations were determined by ELISA (antibodies for IL-17 and IL-10 from BD Bioscience) or by cytometric bead array for the indicated cytokines (BD Bioscience), according to the manufacturers' instructions.
RNA-FlowFish analysis of RNA-expression: Cells prepared under the same conditions as the RNA-seq samples were prepared with the QuantiGene® ViewRNA ISH Cell Assay kit from Affymetrix following the manufacturers protocol. High throughput image acquisition at 60× magnification with an ImageStream X MkII allows for analysis of high-resolution images, including brightfield, of single cells. Genes of interest were targeted by type 1 probes, housekeeping genes by type 4 probes, and nuclei were stained with dapi. Single cells were selected based on cell properties like area, aspect ratio (brightfield images) and nuclear staining. As a negative control, the Bacterial DapB gene (Type 1 probe) was used. Spot counting was performed with the amnis IDEAS software to obtain the expression distributions.
CyTOF analysis of protein-expression: In-vitro differentiated cells were cultured and harvested at 72h, followed by a 3h stimulation similar to the flow cytometry protocol described above. Subsequently samples were prepared as described previously15. In-vivo cells isolated from lymph nodes and CNS from reporter mice were, due to their limited numbers, imbedded in a pool of CD3+ T-cells isolated from a CD4−/− mouse, to allow for proper sample preparation. The cells from the CD4−/− mouse were stained and sorted for CD3+CD4-7AAD-cells to insure that low amounts of CD4+ staining during CyTOF staining would be obtained, and CD4+ cells from LN and CNS could be identified in silico.
RNA-Seq Profiling of Single Cells During Th17 Differentiation:
The mRNA levels of CD4+naïve T cells differentiated in vitro were profiled under two types of polarizing conditions: Tgfβ1+IL6 and Tgfβ3+IL6. While both treatments lead to IL17-production (Ghoreschi, K., Laurence, A., Yang, X. P., Hirahara, K. & O'Shea, J. J. T helper 17 cell heterogeneity and pathogenicity in autoimmune disease. Trends Immunol 32, 395-401 (2011)), only the latter results in autoimmunity upon adoptive transfer (ostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119-124 (2012)). Microfluidic chips (Fluidigm C1) were used for the preparation of single-cell mRNA SMART-Seq libraries. Each polarizing condition was sampled at 48 hr and 96 hr into the differentiation process. In addition to these single cell RNA-seq libraries, their corresponding bulk populations of at least 10,000 cells, with at least two replicates for each condition and at an average depth of 15 million reads, were also sequenced.
RNA-seq reads were aligned to the NCBI Build 37 (UCSC mm9) of the mouse genome using Top-Hat. The resulting alignments were processed by Cufflinks to evaluate the expression of transcripts. An alternative pipeline based on the RSEM (RNA-Seq by expectation maximization) software for mRNA quantification was also employed. Unless stated otherwise, the results obtained with this alternative pipeline were similar to the ones presented herein.
Library quality metrics, such as genomic alignment rates, ribosomal RNA contamination, and 3′ or 5′ coverage bias, were computed for each library. Cells that had low values of these parameters were filtered; the remaining cells (˜80% of the total profiled cells) had similar quality metrics. As an additional preprocessing step, principal components that significantly (p<1e-3) correlated with library quality metrics were subtracted. Finally, unless stated otherwise, genes from each sample that were not appreciably expressed (fragments per kilobase of exon per million (FPKM)>10) in at least 20% of the sample's cells were discarded, retaining on average ˜6k genes for the in vitro samples and ˜3k genes for the in vivo samples.
Although the gene expression levels of population replicates were tightly correlated with one another (Pearson r>0.97, log-scale), there were substantial differences in expression between individual cells (0.72<r<0.82, mean: 0.78; FIG. 1b ). Despite this extensive cell-to-cell variation, the average expression across all single cells correlated well with the population data (r>0.92).
Single Cell Profiles Reveal IL17-Related Heterogeneity In Vitro:
Considering the distribution of the expression from individual genes across cells differentiated with Tgfβ1+IL6, a wide spectrum of behaviors was observed. About 40% of the analyzed genes were constitutively expressed in all cells. Reassuringly, this set of genes is highly enriched for housekeeping genes (p<x). However, constitutive expression of TH17 signature cytokines (for example, IL17f, IL9 and IL21) and early-acting transcription factors (e.g. Rorc, Irf4, Batf, Stat3, Hif1a, and Mina) was also seen. The remaining genes exhibit a bimodal expression patterns with high mRNA levels in at least 20% of the cells and a much lower (often undetectable) levels in the remaining cells. Interestingly, the bimodal genes include key TH17 signature cytokines, chemokines and their receptors (for example, IL23r, IL17a, Ccl20). Bimodatlity was also seen for regulatory cytokines from the IL-10 family (IL10, IL24, IL27ra), as previously observed in population-level data. Finally, a small representation (usually <30% of cells) was seen for transcription factors and cytokines that characterize other T-cell lineages (for example, IL12rb2, Stat4 [Th1], Ccr4, and Gata3 [Th2], and low levels of Foxp3 [iTreg]). Expression of genes from the IL10 module possibly represent a self-limiting mechanism, which is active in a subset of the cells and might play a role in the ‘non-pathogenic’ effects of TH17 cells differentiated with Tgfβ1. Expression from other T cell subsets may represent a contamination of the sample with non-Th17 cells or, rather reflect a more complex picture of “hybrid” double positive cells.
High-throughput, high resolution, flow RNA-fluorescence in situ hybridization (RNA-FlowFISH), an amplification-free imaging technique, was performed to verify that heterogeneity in the single-cell expression data reflected true biological differences, rather than library preparation biases and technical noise associated with the amplification of small amounts of cellular RNA. For 9 genes, selected to cover a wide range of expression and variation levels, the heterogeneity detected by RNA-FLowFISH closely mirrored the sequencing data. For example, expression of housekeeping genes (such as β-actin (Actb) and β2-microglobulin (B2m)) and key Th17 transcription factors (e.g., Rorc, Irf4, Batf) matched a log-normal distribution in both single-cell RNA-Seq and RNA-FISH measurements. By contrast, other signature genes (e.g., IL17a, IL2) showed significantly greater levels of heterogeneity, recapitulating the RNA-SEQ results.
Identification of Cell Sub-Populations:
To quantify this behavior, a model by Shalek et al. (Shalek, et al. “Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells.” Nature 2013 May 19. doi: 10.1038/nature1217), describing the distribution of a given gene across cells using three parameters: (alpha)—the % of expressing cells; (sigma): the standard deviation of expression for the expressing cells; and (Mu): the average level of expression for expressing cells, was adapted. In this adapted model, these parameters are inferred by fitting the expression distribution with a mixture-model of two distributions: a log normal distribution for expressing cells and an exponential for non-expressing cells. Interestingly, it was found that the signature cytokine IL17a exhibited one of the highest levels of variability in the cell's transcriptome in-vitro. Additional cytokines, chemokines and their receptors, including Ccl20, IL2, IL10, IL9 and IL24, were among the highly variable genes. While these key genes exhibit strong variability, it was not clear to what extent these patterns are informative for the cell's state. To investigate this, the correlation between signature genes of various CD4+ lineages and all other expressed genes was computed. Clustering this map reveals a clear distinction between regulatory cytokines (IL10 module) and pro-inflammatory molecules (IL17, Rorc). Expression from the IL10 module possibly represents a self-limiting mechanism, which is active in a subset of the cells and plays a role in the ‘non-pathogenic’ effects of TH17 cells differentiated with Tgfβ1.
To investigate this, principle component analysis was conducted on the space of cells. It was found that the PCA can adequately separate IL17a expressing cells from cells that did not express IL17a. In addition, it was found that the first PC positively correlated with IL17a and negatively correlated with IL10. The depiction of the cells in the space of the first two PC therefore spans the spectrum between potentially pathogenic cells (high levels of IL-17a and low levels of immunosuppressive cytokines like IL-10) to non-pathogenic cells (opposite expression profiles). The PCs were characterized by computing correlations with other cell properties.
GPR65 Promotes Th17 Differentiation and Suppresses IL2:
A first set of experiments identified the target gene GPR65, a glycosphingolipid receptor that is genetically associated with autoimmune disorders such as multiple sclerosis, ankylosing spondylitis, inflammatory bowel disease, and Crohn's disease. GPR65 has shown a positive correlation with the module of genes associated with an inflammatory response, referred to herein as the IL17 module, and negatively correlated with the module of genes associated with a regulatory cytokine profile, referred to herein as the IL10. The IL17 module includes genes such as BATF, STAT4, MINA, IL17F, CTLA4, ZBTB32 (PLZP), IL2, IL17A, and RORC. The IL10 module includes genes such as IL10, IRF4, IL9, IL24, and SMAD3. Genes that are known to have a positive correlation with the IL17 module include BATF, HIF1A, RORC, and MINA. Genes that are known to have a negative correlation with the IL17 module include FOXP3, AHR, TRP53, IKZF3, IRF4, IRF1, IL10, IL23, and IL9. As described throughout the disclosure, novel regulators of the IL17 module include DEC1, CD5L, and ZBTB32 (PLZP).
To explore the role of GPR65, GPR65−/− mice were obtained and differentiated naïve T-cells under various T cell conditions (Th0, T16, T36, B623, T). FIGS. 40A and 40B demonstrate that IL17A expression is reduced in GPR65 knock out cells, for example, in FIG. 40A by 42% for T16 condition, by 48% for T36 condition, and by 73% for B623 condition, and in FIG. 40B by 20% in T16 condition and 13% for T36 condition. In addition, the B623 condition showed increased interferon gamma (IFNγ) production, a cytokine that is normally attributed to Th1 cells, and associated with eliciting a severe immune response. These results demonstrate that GPR65 is a regulator of Th17 differentiation. Thus, modulation of GPR65 can be used to influence a population of T cells toward or away from a Th17 phenotype.
A second set of experiments identified the target gene DEC1 also known as Bhlhe40. DEC1 is a basic helix-loop-helix transcription factor that is known to be highly induced in a CD28-dependent manner upon T cell activation (Martinez-Llordella et al. “CD28-inducible transcription factor DEC1 is required for efficient autoreactive CD4+ T cell response.” J Exp Med. 2013 Jul. 29; 210(8):1603-19. doi: 10.1084/jem.20122387. Epub 2013 Jul. 22). DEC1 is required for the development of experimental autoimmune encephalomyelitis and plays a critical role in the production of the proinflammatory cytokines GM-CSF, IFNγ, and IL-2 (Bluestone, 2013). Prior to the studies presented herein, DEC1 was not previously known to be associated with T cells generally, or with Th17 cells in particular.
To explore the role of DEC1, DEC1−/− mice were obtained and differentiated naïve T-cells under various T cell conditions (Th0, T16, T36, B623, T). As shown in FIG. 41A, IL-17A expression was unchanged in the non-pathogenic condition, i.e., T16, but expression was reduced in the pathogenic conditions T36 and B623, e.g., about 55% decrease for T36 condition and about 43% decrease for B623 condition. As shown in FIG. 41B, the DEC1 knockout cells also demonstrated an increase in FOXP3 positive cells. FIG. 41C demonstrates that the cytokine secretion assay (CBA) largely supports the ICC data seen in FIG. 41A by demonstrating a decrease for IL17A for all Th17 conditions and an increase in IL-10 production for all Th17 conditions. These results demonstrate that DEC1 is a promoter of pathogenic Th17 differentiation. Thus, modulation of DEC1 can be used to influence a population of T cells toward or away from the Th17 pathogenic phenotype.
A third set of experiments identified the target gene PLZP also known as Zbtb32. PLZP is a transcription factor that is known to be a repressor of GATA-3. PLZP has been shown to negatively regulate T-cell activation (I-Cheng Ho, 2004) and to regulate cytokine expression activation (SC Miaw, 2000).
To explore the role of PLZP, PLZP−/− mice were obtained and differentiated naïve T-cells under various T cell conditions (Th0, T16, T36, B623, T). As shown in FIG. 42A, IL-17A production was decreased in the pathogenic Th17 cell conditions T36 and B623. These results demonstrate that PLZP is a promoter of pathogenic Th17 differentiation. Thus, modulation of PLZP can be used to influence a population of T cells toward or away from the Th17 pathogenic phenotype.
A fourth set of experiments identified the target gene TCF4 (transcription factor 4), a basis helix-loop-helix transcription factor. TCF4 is known to be related to super-pathways including the MAPK signaling pathway and the myogenesis pathway.
To explore the role of TCF4, TCF4−/− mice were obtained and differentiated naïve T-cells under various T cell conditions (Th0, T16, T36, B623, T). As shown in FIG. 43, IL-17A production was decreased in the pathogenic Th17 cell condition B623. These results demonstrate that TCF4 can be used as a promoter of pathogenic Th17 differentiation. Thus, modulation of TCF4 can be used to influence a population of T cells toward or away from the Th17 pathogenic phenotype.

Example 10: CD5L, a Regulator of Intracellular Lipid Metabolism, Restrains Pathogenicity of Th17 Cells

IL-17-producing Th17 cells are present at the sites of tissue inflammation and have been implicated in the pathogenesis of a number of autoimmune diseases in humans and relevant murine models (Kleinewietfeld and Hafler 2013, Lee, Collins et al. 2014). However, not all IL-17 producing Th17 cells induce autoimmune tissue inflammation and disease (‘pathogenic’). Th17 cells that line the normal gut mucosa are thought to play an important role in tissue homeostasis by preventing tissue invasion of gut microflora and promoting epithelial barrier functions (Guglani and Khader 2010). In addition, Th17 cells play a crucial role in host defence against pathogens such as fungi (Candida albicans) and extracellular bacteria (Staphalococcus aureus) (Gaffen, Hernandez-Santos et al. 2011, Romani 2011). Therefore, Th17 cells show a great degree of diversity in their function: on one hand, they are potent inducers of tissue inflammation and autoimmunity, and on the other hand, they promote tissue homeostasis and barrier function. The extracellular signals and intracellular mechanisms that control these opposing functions of Th17 cells in vivo are only partially known and intensively studied.
Different types of Th17 cells with distinct effector functions can be generated in vitro by different combination of cytokines. It has been shown (Bettelli, Carrier et al. 2006; Veldhoen, Hocking et al. 2006; Harrington et al., 2006) that two cytokines, IL-6 and TGFβ1, can induce differentiation of naïve T cells into Th17 cells in vitro, although these cells are poor inducers of Experimental Autoimmune Encephalomyelitis (EAE), an autoimmune disease model of the central nervous system. Exposure of these cells to the proinflammatory cytokine IL-23 can make them into disease-inducing, pathogenic, cells (McGeachy, Bak-Jensen et al. 2007, Awasthi, Riol-Blanco et al. 2009, Jager, Dardalhon et al. 2009, McGeachy, Chen et al. 2009). Indeed, other combinations of cytokines, such as IL-1β+IL-6+IL-23 (Ghoreschi, Laurence et al. 2010) or TGFβ3+IL-6+IL-23, can induce differentiation of Th17 cells that elicit potent EAE with severe tissue inflammation upon adoptive transfer in vivo. Comparison of gene expression profiles of Th17 cells generated with these distinct in vitro differentiation protocols led to the identification of a gene signature that distinguishes pathogenic from non-pathogenic Th17 cells, consisting of a proinflammatory module of 16 genes expressed in pathogenic Th17 cells (e.g., T-bet, GMCSF and IL-23R) and a regulatory module of 7 genes expressed in non-pathogenic cells (e.g., IL-10). Exposure of non-pathogenic Th17 cells to IL-23 converts them into a pathogenic phenotype, with the diminished expression of the regulatory module and the induced expression of the proinflammatory module, suggesting that IL-23 is a master cytokine that dictates the functional phenotype of Th17 cells.
In humans, two different subtypes of Th17 cells have also been described with specificity for different types of pathogens. Th17 cells that co-produce IL-17 with IFNγ were generated in response to Candida albicans, whereas Th17 cells that co-produce IL-17 with IL-10 have specificity for Staphylococcus aureus infection (Zielinski, Mele et al.). Both IL-1 and IL-23 contributed to the induction of each of these functionally-distinct subtypes of Th17 cells in response to antigen. Comparison of these human Th17 cell subsets with pathogenic and non-pathogenic Th17 cells in mice suggest that the C. albicans-specific Th17 cells may mirror the pathogenic Th17 cells, with expression of the proinflammatory module, whereas S. aureus-specific Th17 cells are more similar to the non-pathogenic Th17 cells that has been described in the mouse models of autoimmunity.
Identifying the key molecular switches that drive pathogenic and non-pathogenic Th17 cells will allow selective inhibition of pathogenic Th17 cells, while sparing non-pathogenic, potentially tissue-protective, Th17 cells. To date, the intracellular mechanisms by which IL-23 evokes the pathogenic phenotype in differentiating Th17 cells is not well understood. Genomic approaches provide a compelling unbiased approach to find such candidate mechanisms (Yosef et al. 2014), but it is likely that pathogenic and non-pathogenic cells co-exist in vivo, and co-differentiate in vitro, limiting the power to detect subtler signals. Indeed, previous signature comparing populations of pathogenic and non-pathogenic-derived cells did not find strong candidate regulators, but rather effector molecules. The advent of single cell RNA-Seq opens the way to identify such subtler, yet physiologically important, regulators.
Here, single-cell RNA-Seq profiles of Th17 cells from in vivo autoimmune lesions and from in vitro differentiation were used to identify a novel regulator of Th17 pathogenicity, CD5L (CD5-Like). CD5L is predominantly expressed in non-pathogenic Th17 cells and is down-regulated upon exposure to IL-23. CD5L deficiency converts non-pathogenic Th17 cells into disease-inducing pathogenic Th17 cells, by regulating the Th17 cell lipidome, altering the balance between polyunsaturated fatty acyls (PUFA) and saturated lipids, and in turn affecting the activity and binding of Rorγt, the master transcription factor of Th17 cell differentiation. Thus, CD5L is now identified as a critical regulator that distinguishes Th17 cell functional states, and T-cell lipid metabolism as an integral component of the pathways regulating the pathogenicity of Th17 cells.
Results:
Th17 cells play a critical role in host defense against extracellular pathogens and maintenance of gut tissue homeostasis, but have also been implicated in the pathogenic induction of multiple autoimmune diseases. The mechanisms implicated in balancing such ‘pathogenic’ and ‘non-pathogenic’ Th17 cell states remain largely unknown. Here, single-cell RNA-Seq was used to identify CD5L (CD5-Like) as one of the novel regulators that is selectively expressed in non-pathogenic but not in pathogenic Th17 cells. While CD5L does not affect Th17 differentiation, it serves as a major functional switch, as loss of CD5L converts ‘non-pathogenic’ Th17 cells into ‘pathogenic’ Th17 cells that promote autoimmune disease in mice in vivo. It is shown that CD5L mediates this effect by modulating the intracellular lipidome, such that Th17 cells deficient in CD5L show increased expression of saturated lipids, including cholesterol metabolites, and decreased expression of poly unsaturated fatty acyls (PUFA). This in turn alters the ligand availability to and function of Rorγt, the master transcription factor of Th17 cells, and T cell function. This study identified CD5L as a critical regulator of the functional state of Th17 cells and highlighted the importance of lipid saturation and lipid metabolism in balancing immune protection and disease in T cells.
Single-Cell RNA-Seq Identifies CD5L as a High-Ranking Candidate Regulator of Pathogenicity:
To identify candidate regulators of Th17 cell function, single-cell RNA-Seq profiles were analyzed from Th17 cells isolated from the CNS during EAE in vivo or differentiated in vitro under non-pathogenic (TGFβ1+IL-6) and pathogenic (IL-1β+IL-6+IL-23) conditions. Briefly three lines of evidence were used to rank genes for their potential association with pathogenicity: (1) co-variation analysis of a transcript's expression across single Th17 cells differentiated in vitro (in the non-pathogenic conditions), which showed the presence of two anti-correlated modules: a “pro-inflammatory module” (positively correlated with the expression of Il17a) and a “regulatory module” (positively correlated with the expression of Il10); (2) Principle Components Analysis (PCA) of single Th17 cells differentiated under either condition, which showed that cells span a pathogenicity spectrum, such that a cell's location on PC1 is related to the expression of pathogenic genes; and (3) PCA of single Th17 isolated from the CNS and lymph node during EAE in vivo, which showed that cells span a wide functional spectrum along the first PC (from effector to memory to exhausted state) and the second PC (from a naïve-like to terminally differentiated state).
Cd5l (Cd5-like) was one of the high-ranking genes by single-cell analysis of potential regulators, showing a surprising combination of two key features: (1) it is only expressed in vitro in Th17 cells derived under non-pathogenic conditions (FIG. 45D); but (2) in those non-pathogenic cells, its was expressed as a member in co-variance with the other genes in the proinflammatory module in Th17 cells. First, the vast majority (˜80%) of Th17 cells derived under the pathogenic condition (IL-1β+IL-6+IL-23) lacked Cd5l expression, whereas Th17 cells differentiated under the non-pathogenic (TGF-b1+IL-6) condition predominantly expressed Cd5l (FIG. 45C). Furthermore, most of sorted IL-17A+(GFP+, where GFP is under the control of IL-17 promoter) cells differentiated under the non-pathogenic condition (TGFβ1+IL-6) expressed Cd5l (FIG. 45D, top left panel), consistent with its original association with the IL17 module (in non-sorted cells; below). In contrast, Th17 cells differentiated under two different pathogenic conditions (IL-1+IL-6+IL-23 or TGFβ3+IL-6) lacked Cd5l expression in a majority of the T cells. Similarly, none of the encephalitogenic Th17 cells (CD4⁺ IL-17A.GFP⁺) sorted from the central nervous system (CNS) of mice undergoing active EAE expressed any Cd5l at the single-cell level (FIG. 45D, lower right panel). Second, CD5L is highly positively correlated with the defining signature of the pro-inflammatory model, and negatively correlated with the regulatory module. In particular, it is among the top 5 genes in the proinflammatory module whose expression is also most strongly correlated with the expression of previously-defined pathogenic gene signature (FIG. 45A, empirical p-value<0.05). Furthermore, non-pathogenic Th17 cells expressing higher levels of Cd5l also have lower scores for the aforementioned PC1, as does the pathogenicity signature (FIG. 45B, Pearson correlation of 0.44; p<10⁻⁷).
CD5L is a member of the scavenger receptor cysteine rich superfamily (Sarrias M R et al. 2004). Its expression was previously reported in macrophages (Miyazaki, Hirokami et al. 1999), and it has been shown to bind to cytosolic fatty acid synthase in adipocytes following endocytosis. It has also been reported to be a receptor for Pathogen Associated Molecular Patterns (PAMPs), and may have a function in regulating innate immune responses (Martinez V G et al. 2014). However, it has not been reported to be expressed in T cell and therefore it's role in T cell function has not been identified.
CD5L expression is specifically associated with non-pathogenic Th17 cells in vitro and in vivo: It was hypothesized that CD5L's exclusive expression in Th17 cells differentiated under non-pathogenic conditions but in association with the IL17 inflammatory module, may indicate a unique role in regulating the transition between a non-pathogenic and pathogenic state. While co-expression with the inflammatory module and correlation with a pathogenicity signature (FIG. 45A,B) per se could have suggested a function as a positive regulator of pathogenicity, the apparent absent of CD5L from Th17 cell differentiated in vitro under pathogenic conditions or isolated from lesions in the CNS (FIG. 45C,D) suggest a more nuanced role. In particular, it was hypothesized that CD5L may be a negative regulator of pathogenicity, explaining its absence from truly pathogenic cells. Notably, mRNAs of negative regulators of state-changes in cells are often co-regulated with the modules that they negatively regulate in eukaryotes from yeast (Segal et al., Nature Genetics 2003; Pe'er et al. Bioinformatics 2002) to human (Amit et al Nature Genetics 2007).
To test this hypothesis, the initial finding that CD5L is uniquely expressed in non-pathogenic Th17 cells both in vitro and in vivo with qPCR (FIG. 45E, F) and protein expression analyses (FIG. 45G) of naïve CD4 T cells cultured under various differentiation conditions was first validated and extended. At the mRNA level, little to no Cd5l expression was found in Th0, Th1 or Th2 helper T cells (FIG. 45E), high expression in Th17 cells differentiated with TGFβ1+IL-6, but little to no expression in Th17 cells differentiated IL-1β+IL-6+IL-23 or in iTregs (FIG. 45E). Importantly, similar patterns are observed for CD5L protein expression by flow cytometry (FIG. 45G).
Next, it was explored whether CD5L expression is associated with less pathogenic Th17 cells in vivo. First, Cd5l expression was analyzed in Th17 cells isolated from mice following immunization with myelin oligodendrocyte glycoprotein (MOG_35-55) in complete Freund's adjuvant (CFA). Th17 cells (CD3⁺CD4⁺IL-17.GFP⁺) were sorted from the periphery (spleen) and it was found that Cd5l was only expressed in IL-17⁺ but not IL-17⁻ T cells (FIG. 45H, left panel). In striking contrast, Cd5l was not expressed in Th17 cells from the CNS despite significant expression of Il17 (FIG. 45H, right panel), consistent with the single-cell RNA-seq data (FIG. 45D). Next, Cd5l expression was analyzed on Th17 cells isolated from naïve mice that line the gut mucosa and are not associated with inflammation. IL-17A.GFP⁺ and IL-17A.GFP⁻ CD4 T cells were isolated from the mesenteric lymph node (mLN) and the lamina propria (LP) of naïve mice, where Th17 cells are thought to contribute to tissue homeostasis and mucosal barrier function. IL-17⁺ but not IL-17⁻ T cells harvested from mLN and LP of normal gut mucosa expressed high levels of Cd5l (FIG. 45I and data not shown). Thus, CD5L is a gene expressed in non-pathogenic (but not in pathogenic) Th17 cells in vivo.
Finally, it was tested whether IL-23 exposure, known to make Th17 cells more pathogenic, can directly regulate Cd5l expression. It was hypothesized that if CD5L is a positive regulator of IL23-dependent pathogenicity its expression will be increased by IL23, whereas if it is a negative regulator, its expression will be suppressed. As IL-23R is induced after 48 hours of T-cell activation, naïve T cells were differentiated with TGFβ1+IL-6 for 48h and then expanded with or without IL-23 in fresh media. The addition of IL-23 significantly suppressed Cd5l expression as compared to PBS control (FIG. 45F), consistent with these cells acquiring a pro-inflammatory module and becoming pathogenic Th17 cells, and with the hypothetical assignment of CD5L as a negative regulator of pathogenicity.
CD5L Represses/Dampens Th17 Cell Effector Function without Affecting Th17 Differentiation of Naïve T Cells:
To analyze whether CD5L plays any functional role in vivo, wildtype (WT) and Cd5l deficient mice were immunized with MOG_35-55/CFA to induce EAE. CD5L^−/− mice exhibited significantly more severe clinical EAE that persisted for at least 28 days, whereas WT mice began recovering 12 days post immunization (FIG. 46A). Next, the phenotype of CD4 T cells was analyzed during the course of EAE. Similar frequencies of FoxP3+ Treg cells were found in WT and CD5L^−/− mice, suggesting that the increased severity of the disease was not due to a decreased number of Tregs in Cd5l deficient mice (FIG. 50A). On the other hand, a significantly higher percentage of IL-17-producing CD4 T cells and a lower percentage of IFNγ⁺ CD4 T cells in the CNS of CD5L^−/− mice (FIGS. 46A and 51B) was observed. Moreover, in response to MOG reactivation in vitro, cells from the draining lymph node (dLN) of CD5L^−/− mice showed higher proliferative responses and produced more IL-17 (FIG. 51C, D). This is consistent with either a direct or indirect role for CD5L in defining the function of Th17 cells.
To determine whether CD5L's effect is due to a direct role in the differentiation of Th17 cells, naïve WT and CD5L^−/− CD4 T cells were analyzed under the non-pathogenic Th17 cell condition and analyzed whether CD5L directly regulated the expression of signature Th17 genes. The loss of CD5L did not affect Th17 differentiation of naïve T cells, as measured by IL-17 expression by intracellular cytokine staining or by ELISA (FIG. 46B, C), nor that of other signature Th17 genes including Il17f, Il21, Il23r or Rorc (FIG. 46D). However, under the non-pathogenic Th17 differentiation condition, WT Th17 cells produce IL-10, whereas CD5L^−/− Th17 cells showed a decrease in the expression of IL-10 as determined by ELISA (FIG. 46C) or qPCR analysis (FIG. 46D). These observations suggest that CD5L does not regulate Th17 cell differentiation directly, that Th17 cell differentiation alone cannot explain the increased susceptibility to EAE in CD5L^−/− mice, but that CD5L may indeed affect the internal state of differentiated Th17 cells.
Next, it was determined whether CD5L has any role in expanding or maintaining effector/memory Th17 cells. To this end, naïve Th17 cells differentiated under the non-pathogenic conditions were washed and re-plated without IL-23. Upon restimulation, the CD5L^−/− Th17 cells had a significantly higher percentage of IL-17A⁺ cells and IL-23R⁺ cells (FIG. 46E), suggesting that CD5L deficiency leads to more stably expanding Th17 cells. Consistent with this result, CD5L^−/− Th17 cells expressed more Il17a and Il123r and less Il10 as determined by qPCR (FIG. 46F). Thus, CD5L does not regulate initial Th17 cell differentiation of the naïve T cells but does control their expansion and/or effector functions over time. Consistent with this result, effector memory cells (CD4⁺CD62L⁻CD44⁺) isolated directly ex vivo from CD5L^−/− mice expressed significantly higher IL-17 and lower IL-10 levels (FIG. 46G). This higher percentage of effector memory T cells producing IL-17 might reflect the greater stability and higher frequency of Th17 cells that persist in the repertoire of CD5L^−/− mice. To address whether Th17 cells isolated in vivo also produced more IL-17 on a per cell basis, RORγt⁺ (GFP⁺) effector/memory T cells were sorted from WT and CD5L^−/− mice, their cytokine production upon activation ex vivo was analyzed. The RORγt.GFP⁺ T cells from the CD5L^−/− mice showed much higher production of IL-17 and lower production of IL-10 suggesting that RORγt⁺ cells are better IL-17 producers in the absence of CD5L (FIG. 46H).
CD5L is a Major Switch that Regulates Pathogenicity of Th17 Cells:
To study whether loss of CD5L can convert non-pathogenic Th17 cells into pathogenic, disease-inducing Th17 cells, CD5L^−/− mice were crossed to 2D2 transgenic mice that express TCRs specific for MOG 35-55/IA^b.Naïve 2D2 transgenic T cells carrying CD5L deficiency were differentiated under the non-pathogenic (TGFβ1+IL-6) Th17 condition and then transferred into WT recipients. Prior to transfer, a similar frequency of IL-17⁺ T cells was generated from WT and CD5L ^−/− 2 D2 naïve cells (FIG. 47A), consistent with the observation that CD5L does not affect Th17 differentiation of naïve T cells.
Next, clinical and histological disease progression in the recipients of WT and CD5L ^−/− 2 D2 cells was compared. As expected, many recipients (6/13) of WT 2D2 Th17 cells showed very little to no signs of clinical or histological EAE. Strikingly, all (12/12) CD5L ^−/− 2 D2 recipients developed severe EAE with optic neuritis. Moreover, CD5L ^−/− 2 D2 recipients had significant weight loss and developed more ectopic lymphoid follicle-like structures in the CNS, a hallmark of disease induced by highly pathogenic IL-23-treated Th17 cells (FIG. 47B, C) (Peters, Pitcher et al. 2011). Thus, T cell intrinsic expression of CD5L plays a pivotal role in restraining the pathogenicity of Th17 cells. After adoptive transfer, the T cells were isolated from the CNS of mice undergoing EAE. The 2D2 CD5L^−/− T cells retained a much higher frequency of IL-17 producing T cells and a reduced level of IL-10 as compared to the WT 2D2 T cells (FIG. 47D). Upon adoptive transfer, WT 2D2 T cells acquired production of IFNγ in vivo, whereas only a very small proportion of CD5L ^−/− 2 D2 T cells produced IFNγ, suggesting that CD5L may also regulate the stability of Th17 cells. Consistent with this observation, when the naïve WT and CD5L ^−/− 2 D2 T cells were transferred into WT hosts and immunized the mice with MOG_35-55/CFA without inducing EAE (no pertussis toxin was given), CD5L ^−/− 2 D2 T cells accumulated a higher frequency of IL-17A⁺ T cells compared to WT. Strikingly, while the WT T cells expressed IL-10, none of the CD5L ^−/− 2 D2 T cells expressed IL-10 (FIG. 47E).
As IL-23 can suppress the expression of CD5L, and since CD5L functions to restrain Th17 cell pathogenicity, it was reasoned that sustained CD5L expression should antagonize the IL-23 driven pathogenicity of Th17 cells. To test this hypothesis, a retroviral vector for ectopic expression of CD5L in Th17 cells was generated. Naïve 2D2 T cells were differentiated under pathogenic differentiation conditions (IL-1β+IL-6+IL-23), transduced with CD5L, transferred into WT recipients and followed for weight loss and the development of clinical EAE. Prior to transfer, 2D2 T cells transduced with CD5L had similar IL-17 expression and increased IL-10 expression (FIG. 51A). After transfer, ectopic expression of CD5L in Th17 cells differentiated under pathogenic conditions reduced their pathogenicity when compared to the WT control in that they led to reduced weight loss in mice and a significant decrease in the induction of EAE (FIG. 51B, C). Furthermore, CD5L over-expressing 2D2 T cells transferred in vivo, lost IL-17 production and most of the transferred cells began producing IFNγ (FIG. 51D). Therefore, CD5L does not regulate Th17 differentiation of naïve T cells, but affects the functional state of Th17 cells in that the loss of CD5L converts non-pathogenic Th17 cells into pathogenic Th17 cells that stably produce IL-17 in vivo and its sustained over-expression in pathogenic Th17 cells converts them to a less pathogenic and less stable phenotype in that these cells lose the expression of IL-17 and acquire an IFNγ producing Th1 phenotype in vivo. These two data sets unequivocally support the role of CD5L as a negative regulator of the functional pathogenic state of Th17 cells.
Consistent with these functional findings, CD5L also regulates the expression of the pathogenic/non-pathogenic gene signature previously defined in Th17 cells. To show this, naïve WT and CD5L^−/− T cells were differentiated under the non-pathogenic TGFβ1+IL-6 condition and rested them in fresh media without adding any exogenous IL-23 for 48 hours followed by mRNA expression analysis by qPCR. CD5L deficient Th17 cells differentiated under the non-pathogenic condition significantly upregulated several effector molecules of the pathogenic signature, including Il123r, Il3, Ccl4, Gzmb, Lrmp, Lag3 and Sgk1, and downregulated several genes of the non-pathogenic signature, including Il10, Il9 and Maf (FIG. 47F). Several other signature genes, however, were not affected by CD5L, suggesting a more nuanced mechanism.
CD5L Shifts the Th17 Cell Lipidome Balance from Saturated to Unsaturated Lipids, Modulating Rorγt Ligand Availability and Function:
Since CD5L is known to regulate lipid metabolism, by binding to fatty acid synthase in the cytoplasm of adipocytes (Kurokawa, Arai et al. 2010), it was speculated that CD5L may also regulate Th17-cell function by specifically regulating lipid metabolites in T cells. To test this hypothesis, it was analyzed whether lipid metabolism is regulated by CD5L and is associated with the increased pathogenicity observed in Th17 cells from CD5L^−/− mice. The lipidome of WT and CD5L^−/− Th17 cells differentiated under the non-pathogenic (TGFβ1+IL-6) and pathogenic (TGFβ1+IL-6+IL-23) conditions was profiled. It was possible to resolve and identify around 200 lipid metabolites intracellularly or in the supernatant of differentiating Th17 cells using mass spectrometry and liquid chromatography. Of those metabolites that were differentially expressed between WT and CD5L^−/−, a striking similarity between the lipidome of CD5L^−/− Th17 cells differentiated under the non-pathogenic condition and WT Th17 cells differentiated under the pathogenic condition (FIG. 48A) was observed. Among other metabolic changes, CD5L deficiency significantly increased the levels of saturated lipids (SFA), including metabolites that carry saturated fatty acyl and cholesterol ester (CE) as measured by liquid chromatography and mass spectrometry (FIGS. 48B and 52A), and free cholesterol as shown by microscopy (FIG. 52B). Moreover, the absence of CD5L resulted in a significant reduction in metabolites carrying poly-unsaturated fatty acyls (PUFA) (FIG. 48B). Similar increase in CE and reduction in PUFA is observed in the lipidome of Th17 cells differentiated under either of two pathogenic conditions (IL-1β+IL-6+IL-23 and TGFβ3+IL-6+IL-23) compared to non-pathogenic WT cells (FIG. 48C and FIG. 51A). Thus, Th17 cell pathogenicity is associated with a shift in the balance of lipidome saturation as reflected in the increase in saturated lipids and decrease in PUFA metabolites.
Cholesterol metabolites, such as oxysterols, have been previously reported to function as agonistic ligands of Rorγt (Jin, Martynowski et al. 2010, Soroosh, Wu et al. 2014). Previous ChIP-Seq analysis (Xiao, Yosef et al. 2014) suggests that Rorγt binds at several sites in the promoter and intronic regions of Il23r and Il17 (FIG. 48D) and near CNS-9 of Il10, where other transcription factors, such as cMaf, which regulates Il10 expression, also binds. As showed above, CD5L restrains the expression of IL-23R and IL-17 and promotes IL-10 production in Rorγt⁺ Th17 cells, and because CD5L-deficient Th17 cells contain higher cholesterol metabolite and lower PUFA (FIG. 48A,B). Putting these data together, it was hypothesized that CD5L regulates the expression of IL-23R, IL-17 and IL-10 by affecting the binding of Rorγt to these targets, through affecting the SFA-PUFA balance.
To test this hypothesis, it was first assessed if CD5L modulates Rorγt activity by using ChIP-PCR and luciferase reporter assays. Consistent with the hypothesis, ChIP of Rorγt showed significantly higher binding of Rorγt in the Il17 and Il23r region and significantly reduced binding to the Il10 region in CD5L-deficient Th17 cells compared to WT (FIGS. 48D,E and 52C). Consistently, ectopic overexpression of CD5L is sufficient to suppress Rorγt-dependent transcription of Il17 and Il23r promoter luciferase reporters (FIGS. 48F and 52D) and to enhance the transcription of the Il10 reporter in the presence of Rorγt (FIG. 48G).
Next, it was tested whether changing the lipidome balance of WT Th17 cells with the addition of SFA or PUFA can regulate Rorγt binding to genomic regions (FIGS. 48DE and 52C), finding that in the presence of SFA, there is a significant increase in the enrichment of Rorγt-binding to Il17 and Il23r genomic elements, whereas there was a decrease in the binding of Rorγt in the presence of PUFA (FIGS. 48D and 52C). Addition of PUFA also significantly increased the enrichment of Rorγt binding to the Il10 CNS-9 region (FIG. 48E), suggesting that manipulation of the lipid content of Th17 cells can indeed modulate Rorγt DNA binding ability.
Finally, it was reasoned that if CD5L regulates Rorγt transcriptional activity by limiting Rorγt ligand(s), the addition of exogenous agonists of Rorγt would rescue the CD5L induced suppression. Indeed, addition of 7, 27 dihydroxycholesterol, previously shown as an endogenous ligand of Rorγt (Soroosh, Wu et al. 2014), rescued the CD5L-driven suppression of Il17 reporter transcription, suggesting ligand availability partly contributes to the regulation of Rorγt function by CD5L (FIG. 48H). On the other hand, the addition of PUFA decreased Rorγt driven Il17a transcription in control cells, but not in those expressing CD5L (FIG. 48I), suggesting the function of PUFA may depend on the Rorγt ligand. Indeed, while Rorγt can strongly transactivate Il23r enhancer in the presence of an agonistic ligand, the addition of PUFA to the agonist ligand almost completely inhibited Rorγt-mediated Il23r transactivation and enhanced Il10 transactivation (FIG. 48J,K). This observation suggests that PUFA may modulate Rorγt ligand binding and thus affect the ability of Rorγt to transactivate Il23r and Il10. On the other hand, while the addition of SFA by itself has little impact on Rorγt-dependent transcription, it nevertheless modified the function of the oxysterol (FIG. 48J,K). Thus, CD5L regulates the expression of Il23r and Il10, members of the pathogenic/non-pathogenic signature, by shifting lipidome balance and limiting Rorγt ligand availability as well as function.
PUFA and SFA can Regulate Th17 Cell Function and Contribute to CD5L-Dependent Regulation of Th17 Cells:
As CD5L-deficient Th17 cells differentiated under the non-pathogenic condition have altered balance in lipid saturation, and since PUFA and SFA can modulate Rorγt binding and functional activity, the relevance of fatty acid moeities to Th17 cell function and its contribution to CD5L-driven Th17 cell pathogenicity was analyzed. The effect of adding PUFA and SFA on the generation of Th17 cells was first tested. WT Th17 cells were differentiated with TGFβ1+IL-6 and expanded using IL-23 in fresh media with the presence of either PUFA or SFA. PUFA suppressed the percentage of IL-17⁺ and IL-23R.GFP⁺ CD4 T cells (FIG. 49A), suggesting that PUFA can limit Th17 cell function under the pathogenic condition. On the other hand, addition of SFA increased the expression of both IL-17 and IL-23R expression, but this effect was not significant, possibly because the already very high levels of SFA in the pathogenic Th17 cells could not be further altered by the addition of exogenous SFA. This result is consistent with qPCR analysis of Il17 and Il23r expression and further, the effect of PUFA is abolished in Rorγt^−/− Th17 cells (FIG. 49B), suggesting the function of PUFA requires Rorγt expression. CD5L^−/− Th17 cells differentiated under the non-pathogenic condition are also sensitive to PUFA treatment, resulting in reduced percentage of IL-17+CD4+ T cells (FIG. 49C).
Next, the contribution of lipid saturation to Th17 cell pathogenicity was studied. It was speculated that if the balance of lipid saturation distinguishes non-pathogenic WT Th17 cells and pathogenic CD5L^−/− Th17 cells, the addition of SFA to WT and PUFA to CD5L^−/− Th17 cells (TGFβ1+IL-6) can result in reciprocal changes in transcriptional signature relevant to Th17 cell pathogenicity. Therefore (using the Nanostring nCounter) the expression of a 316 gene signature of Th17 cell differentiation and function in SFA- or control-treated WT Th17 cells and in PUFA- or control-treated CD5L^−/− Th17 cells differentiated with TGFβ1+IL-6 was analyzed. It was found that PUFA-treated CD5L^−/− Th17 cells resemble WT non-pathogenic Th17 cells, and SFA-treated WT non-pathogenic Th17 cells are more similar to CD5L^−/− Th17 cells (FIG. 49D). qPCR analysis confirmed that PUFA and SFA reciprocally regulated the expression of key genes in the pathogenicity signatures, including Il10, Il23r, Ccl5, Csf2 and Lag3 (FIG. 49D). (Notably, in some cases PUFA and SFA have the same effects; for example, Il22 expression is increased following treatment by either fatty acid.) Taken together, these observations suggest that the balance of lipid saturation contributes to CD5L-dependent regulation of Th17 cells by regulating the Th17 cell transcriptome.
Discussion:
Th17 cells are a T helper cell lineage capable of diverse functions ranging from maintaining gut homeostasis, mounting host defense against pathogens, to inducing autoimmune diseases. How Th17 cells can mediate such diverse and opposing functions remains a critical question to be addressed. This is especially important since anti-IL-17 and Th17-based therapies have been highly efficacious in some autoimmune diseases, but have had no impact in other diseases (Genovese, Van den Bosch et al. 2010, Hueber, Sands et al. 2012, Leonardi, Matheson et al. 2012, Papp, Leonardi et al. 2012, Baeten and Kuchroo 2013, Patel, Lee et al. 2013), even when Th17 cells have been genetically linked to the disease process (Cho 2008, Lees, Barrett et al. 2011). Using single-cell genomics this issue has been addressed and identified novel functional regulators of Th17 cells have been identified.
Here, CD5L is highlighted and investigated as one of the novel regulators that affects the pathogenicity of Th17 cells. It is shown that: (1) CD5L is highly expressed only in non-pathogenic Th17 cells but in them co-varies with a pro-inflammatory module, a pattern consistent with being a negative modulator of pathogenicity; (2) CD5L does not affect Th17 differentiation but affects long-term expansion and the functional phenotype of Th17 cells; (3) CD5L-deficiency converts non-pathogenic Th17 cells into pathogenic Th17 cells; and (4) CD5L regulates lipid metabolism in Th17 cells and alters the balance between SFA and PUFA.
Seemingly paradoxically, CD5L is expressed only in non-pathogenic Th17 cells, but in co-variance with the pro-inflammatory module. This initial observation led us to hypothesize that CD5L is a negative regulator of a non-pathogenic to pathogenic transition, since such negative regulators are often known to co-vary in regulatory networks with the targets they repress, in organisms from yeast. Functional analysis bears out this hypothesis, suggesting that CD5L might indeed be expressed to restrain the pro-inflammatory module in the non-pathogenic Th17 cells. Thus, other genes with this specific pattern—exclusive expression in non-pathogenic cells but in co-variance with the pro-inflammatory module may also be repressors that quench pro-inflammatory effector functions. Thus, depending on the environmental context or trigger, non-pathogenic Th17 cells can be readily converted into pro-inflammatory or pathogenic Th17 cells, by inhibiting the expression of a single gene like CD5L. This is supported by the data, which clearly show that IL-23R signalling can suppress CD5L expression and that the persistent expression of CD5L inhibits the pro-inflammatory function of Th17 cells. In addition to suppressing the pro-inflammatory module, CD5L may also promote the function of the regulatory module, thereby acting as a switch to allow rapid responses to environmental triggers, such that Th17 cells can change their functional phenotype without having to depend on other intermediary pathways. It is also apparent that the expression of CD5L can stabilize the function of non-pathogenic Th17 cells, so that the regulatory module and proinflammatory module could co-exist in a cell population. This observation also highlights the molecular difference between the regulatory module and the proinflammatory module that are co-expressed in non-pathogenic Th17 cells, suggesting that the non-pathogenic Th17 cells that can produce both IL-17 and IL-10 have a unique role in physiological processes. This is consistent with the recent discovery that Th17 cells that can develop in the small intestine in response to gut microbiome (Esplugues, Huber et al. 2011), as well as that Th17 cells that can also co-produce IL-10 and are presumably important for protective immunity against S. aureus infection on the mucosal surfaces of the lung (Zielinski, Mele et al.) do not mediate autoimmunity or tissue injury.
Both pathogenic and non-pathogenic Th17 cells are present in the draining lymph nodes but pathogenic Th17 cells appear at the site of tissue inflammation (CNS) and non-pathogenic Th17 cells appear in the gut or other mucosal surfaces, where they promote mucosal barrier function and also maintain tissue homeostasis. This is mirrored in the expression of CD5L, which is highly expressed in Th17 cells in the gut at the steady state, but not in the CNS at the peak of autoimmune tissue inflammation. IL-23, which is present in the CNS during EAE, can suppress CD5L and convert non-pathogenic Th17 cells into pathogenic Th17 cells. At the steady state, it is not known what promotes CD5L expression and non-pathogenicity in the gut. TGFβ is an obvious candidate given the abundance of TGFβ in the intestine and its role in both differentiation of IL-10 producing CD4 T cells in vivo (Maynard, Harrington et al. 2007, Konkel and Chen 2011) and the differentiation of Th17 cells in vitro (Bettelli, Carrier et al. 2006, Veldhoen, Hocking et al. 2006). Specific commensal bacteria (Ivanov, Atarashi et al. 2009, Yang, Torchinsky et al. 2014) and metabolites from microbiota (Arpaia, Campbell et al. 2013) have also been implicated in regulating T cell differentiation. Notably, CD5L is reported as a secreted protein (Miyazaki, Hirokami et al. 1999) and plays a role in recognizing PAMP (Martinez V G et al. 2014). It is possible that, in vivo, CD5L expressed by non-pathogenic Th17 cells in the gut can interact with the microbiota and maintains gut tolerance and a non-pathogenic Th17 phenotype. Therefore, the two functional states of Th17 cells may be highly plastic, and depending on the milieu, either pathogenic or non-pathogenic Th17 cells can be generated by sensing changes in the tissue micro-environment. It is clear, however, the expression of CD5L in non-pathogenic Th17 cells is critical for maintaining the non-pathogenic functional state of Th17 cells and IL-23 rapidly suppresses CD5L, which renders these cells pathogenic. This hypothesis also predicts non-pathogenic Th17 cells can be easily converted into pathogenic Th17 cells by production of IL-23 locally in the gut during inflammatory bowel disease.
How does CD5L regulate the pathogenicity of Th17 cells? In this study, evidence is provided that CD5L can regulate Th17 cell function at least in part by regulating intracellular lipid metabolism in Th17 cells. CD5L was shown to inhibit the de novo synthesis of fatty acid through direct binding to fatty acid synthase (Kurokawa, Arai et al. 2010), although this has not been demonstrated in T cells. It was discovered that in Th17 cells CD5L is not a general inhibitor of fatty acid synthesis, but regulates the balance of PUFA vs. SFA. It is shown that PUFA limits ligand-dependent function for Rorγt, such that in the presence of CD5L or PUFA, Rorγt binding to the Il17a and Il23r is enhanced, along with reduced transactivation of both genes, whereas binding at and expression from the Il10 locus is enhanced. Notably, Rorγt's ability to regulate Il10 expression was not reported previously. Since CD5L does not impact overall Th17 cell differentiation, this suggests a highly nuanced effect of CD5L and lipid balance on Rorγt function, enhancing its binding to and transcactivation at some loci, reducing it in others, and likely not affecting its function at other loci, such as those needed for general Th17 cell differentiation. How this is achieved mechanistically remains to be investigated. For example, the regulation of Il10 transcription is complex and depends on diverse transcription factors and epigenetic modifications. In Th17 cells, Stat3 and c-Maf can promote the expression of Il10 (Stumhofer, Silver et al. 2007, Xu, Yang et al. 2009). As Stat3, C-Maf and Rorγt can all bind to the same Il10 enhancer element, it is therefore possible that, depending on the quality and quantity of the available ligands, Rorγt may interact with other transcription factors and regulate Il10 transcription. More generally, this supports a hypothesis where the spectrum of Rorγt ligands depends—at least in part—on the CD5L-regulated PUFA vs. SFA lipid balance in the cell, and where different ligands impact distinct specificity on Rorγt, allowing it to assume a spectrum of functional states, related for example to distinct functional states. Further studies would be required to fully elucidate such a mechanism.
Several metabolic pathways have been associated with Th17 cell differentiation. HIF1a can promote Th17 cell differentiation through direct transactivation of Rorγt (Dang, Barbi et al. 2011, Shi, Wang et al. 2011) and acetyl-coA carboxylase can regulate Th17/Treg balance through the glycolytic and lipogenic pathway (Berod, Friedrich et al. 2014). Both HIF1a and acetyl-coA carboxylase are associated with obesity and mice harbouring mutations in genes that regulate Th17 cell differentiation and function have been shown to acquire an obese phenotype (Winer, Paltser et al. 2009, Ahmed and Gaffen 2010, Jhun, Yoon et al. 2012, Mathews, Wurmbrand et al. 2014). Thus, there appears to be an association between Th17 cell development and obesity. A hallmark of obesity is the accumulation of saturated fat and cholesterol. In this study, evidence is provided that at the cellular level, lipidome saturation can promote Th17 cell function by regulating Rorγt function.
In addition to regulating the pathogenicity of Th17 cells, CD5L deficient Th17 cells appeared to retain a more stable Th17 phenotype in vivo. Th17 cells from CD5L deficient naïve 2D2 T cells differentiated under non-pathogenic conditions remain mostly IL-17⁺ and IFNγ⁻ upon transfer into a WT host in contrast to WT 2D2 cells, which attain more IFNγ⁺ expression. Moreover, transfer of undifferentiated naïve CD5L^−/− CD4⁺ 2D2 T cells resulted in higher frequency of IL-17A⁺ cells following immunization as compared with WT 2D2 T cells. As CD5L does not regulate Th17 cell differentiation of naïve T cells, this suggests that the Th17 cellular phenotype may be more stable in the absence of CD5L. It is possible that Th17 cell stability is in part dependent on ligand availability. Therefore, sensing of the microenvironment by Th17 cells may change CD5L expression and regulate Rorγt ligand availability, which in turn may affect Th17 phenotype and function.
Thus, by using single cell genomics and computational analysis, CD5L has been identified as a novel repressor of pathogenicity of Th17 cells, highlighting the power of single cell genomics to identify molecular switches that affect Th17 cell functions, otherwise obscured by population-level genomic profiles. CD5L appears to be a molecular switch that does not affect Th17 differentiation per se but one that impacts the function (pathogenic vs. non-pathogenic phenotype) of Th17 cells, potentially by regulating the quality and/or quantity of available Rorγt ligands, allowing a single master regulator to possibly assume multiple functional states. The results connect the lipidome to essential functions of immune cells, opening new avenues for sensitive and specific therapeutic intervention.

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Example 11: GPR65 Promotes Th17 Differentiation and is Essential for EAE

GPR65, a glycosphingolipid receptor, is co-expressed with the pro-inflammatory module (FIGS. 4B and S6E), suggesting that it might have a role in promoting pathogenicity. GPR65 is also highly expressed in the in vivo Th17 cells harvested from the CNS that attain a Th1-like effector/memory phenotype (FIG. 2D). Importantly, genetic variations in GPR65 are associated with multiple sclerosis (International Multiple Sclerosis Genetics et al., 2011), ankylosing spondylitis (International Genetics of Ankylosing Spondylitis et al., 2013), inflammatory bowel disease (Jostins et al., 2012), and Crohn's disease (Franke et al., 2010).
The role of GPR65 was tested in Th17 differentiation in vitro and in the development of autoimmunity in vivo. Naïve T-cells isolated from Gpr65^−/− mice in vitro were differentiated with TGF-β1+IL-6 (non-pathogenic condition) or with IL-1β+IL-6+IL-23 (pathogenic condition) for 96 hours. In both cases, there was a ˜40% reduction of IL-17a positive cells in Gpr65^−/− cells compared to their wild type controls as measured by intracellular cytokine staining (ICC) (FIG. 5A). Memory cells from Gpr65^−/− mice that were reactivated with IL-23 also showed a ˜45% reduction in IL-17a-positive cells compared to wild type (FIG. S6). Consistently, an enzyme-linked immunosorbent assay (ELISA) of the supernatant showed a reduced secretion of IL-17a (p<0.01) and IL-17f (p<2.5×10⁻⁵) (FIG. 5B) and increased IL-10 secretion (p<0.01, FIG. S6C) under pathogenic (IL-1β+IL-6+ L-23) differentiation conditions.
To further validate the effect of GPR65 on Th17 function, RNA-seq profiles were measured of a bulk population of Gpr65^−/− Th17 cells, differentiated in vitro under TGF-β1+IL-6 for 96 hours. Supporting a role for GPR65 as a driver of pathogenicity of Th17 cells, it was found that genes up-regulated (compared to wild type) in Gpr65^−/− cells are significantly enriched (P<18.5×10^−/−, hypergeometric test) for the genes characterizing the more regulatory cells under TGF-β1+IL-6 (positive PC1, FIG. 4C) and for genes down-regulated in the pathogenicity signature (Lee et al., 2012) (P<1.4λ10⁻⁴, hypergeometric test).
To determine the effect of loss of GPR65 on tissue inflammation and autoimmune disease in vivo, CD4+ lymphocytes and splenocytes derived from Gpr65^−/− mice were transferred into RAG-1^−/− mice followed by MOG_35-55immunization. It was found that in the absence of GPR65-expressing T cells, mice are protected from EAE (FIG. 5D) and far fewer IL-17A and IFN-γ positive cells are recovered from the LN and spleen compared to controls transferred with wild-type cells (FIG. S6B). Furthermore, in vitro restimulation of the spleen and LN cells from the immunized mice with MOG₃₅_55 showed that loss of GPR65 resulted in dramatic reduction of MOG-specific IL-17A or IFN-γ positive cells compared to their wild-type controls (FIG. 5C), suggesting that GPR65 regulates the generation of encephalitogenic T cells in vivo. Taken together, the data strongly validates that GPR65 is a positive regulator of the pathogenic Th17 phenotype, and its loss results in protection from EAE.

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Example 12: MOG-Stimulated Plzp^−/− Cells have a Defect in Generating Pathogenic Th17 Cells

PLZP (ROG), a transcription factor, is a known repressor of GATA3 (Miaw et al., 2000) (Th2 master regulator), and regulates cytokine expression (Miaw et al., 2000) in T-helper cells. Since Plzp is co-expressed with the pro-inflammatory module, it was hypothesized that it may regulate pathogenicity in Th17 cells. (It was, however, not possible to undertake an EAE experiment since PLZP^−/− mice are not available on the EAE-susceptible background.)
While in vitro differentiated Plzp^−/− cells produced IL-17A at comparable levels to wild-type (FIG. S8A), a MOG-driven recall assay revealed that Plzp^−/− cells have a defect in IL-17A production that becomes apparent with increasing MOG concentration during restimulation (FIG. 5H). Furthermore, Plzp^−/− cells also produced less IL-17A than wild-type cells when reactivated in the presence of IL-23, which acts to expand previously in vivo differentiated Th17 cells (FIG. S8B). Finally, Plzp^−/− T cells secreted less IL-17A, IL-17F (FIG. 5I), IFN-γ, IL-13 and GM-CSF (FIG. S8C). These observations suggest that PLZP regulates the expression of a wider range of inflammatory cytokines. At 48 hours into the differentiation of Plzp^−/− cells, Irf1 (FC=5.1), Il-9 (FC=1.8) and other transcripts of the regulatory module are up regulated compared to WT (Table S10), whereas transcripts from the pro-inflammatory module, such as Ccl-20 (FC=0.38), Tnf (FC=0.10) and Il-17a (FC=0.42), are repressed.
Thus, by single cell genomics and covariance analysis, a number of novel regulators of pathogenicity of Th17 cells that affect development of Th17 cells in vitro and autoimmunity in vivo have been identified.

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

Claims

1. A method of diagnosing, prognosing and/or staging an immune response involving T cell balance, comprising detecting a first level of expression, activity and/or function of CD5L and comparing the detected level to a control of level of CD5L expression, activity and/or function, wherein a difference in the detected level and the control level indicates that the presence of an immune response in the subject.

2. The method of claim 1, wherein an immune response is monitored in a subject comprising detecting a level of expression, activity and/or function of CD5L at a first time point, detecting a level of expression, activity and/or function of CD5L at a second time point, and comparing the first detected level of expression, activity and/or function with the second detected level of expression, activity and/or function, wherein a change in the first and second detected levels indicates a change in the immune response in the subject.

3. The method of claim 1, wherein a patient population at risk or suffering from an immune response is identified comprising detecting a level of expression, activity and/or function of CD5L in the patient population and comparing the level of expression, activity and/or function of CD5L in a patient population not at risk or suffering from an immune response, wherein a difference in the level of expression, activity and/or function of CD5L in the patient populations identifies the patient population as at risk or suffering from an immune response.

4. A method for monitoring subjects undergoing a treatment or therapy for an aberrant immune response to determine whether the patient is responsive to the treatment or therapy comprising detecting a level of expression, activity and/or function of IL17A in the absence of the treatment or therapy and comparing the level of expression, activity and/or function of IL17A in the presence of the treatment or therapy, wherein a difference in the level of expression, activity and/or function of IL17A in the presence of the treatment or therapy indicates whether the patient is responsive to the treatment or therapy, wherein the treatment or therapy is specific for CD5L.

5. The method of claim 1, wherein the immune response is an autoimmune response or an inflammatory response.

6. The method of claim 5 wherein the inflammatory response is associated with an autoimmune response, an infectious disease and/or a pathogen-based disorder.

7. The method of claim 4, wherein the treatment or therapy is an antagonist of CD5L in an amount sufficient to switch Th17 cells from a non-pathogenic to pathogenic signature; or wherein the treatment or therapy is an agonist that enhances or increases the expression of CD5L in an amount sufficient to switch Th17 cells from a pathogenic to a non-pathogenic signature.

8. The method according to claim 7, wherein the treatment or therapy targets T cells and the T cells are naïve T cells, partially differentiated T cells, differentiated T cells, a combination of naïve T cells and partially differentiated T cells, a combination of naïve T cells and differentiated T cells, a combination of partially differentiated T cells and differentiated T cells, or a combination of naïve T cells, partially differentiated T cells and differentiated T cells.

9. A method of modulating T cell balance, the method comprising contacting a Th17 cell or a population of T cells comprising Th17 cells or T cells capable of differentiating into Th17 cells with an exogenous T cell modulating agent in an amount sufficient to modify maintenance and/or function of the Th17 cell or population of T cells by altering balance between pathogenic and non-pathogenic Th17 cells as compared to maintenance and/or function of the Th17 cell or population of T cells in the absence of the T cell modulating agent, wherein the T cell modulating agent is specific for CD5L.

10. The method of claim 9, wherein the T cell modulating agent is an antagonist of CD5L in an amount sufficient to switch Th17 cells from a non-pathogenic to pathogenic signature.

11. The method of claim 9, wherein the T cell modulating agent is an agonist that enhances or increases the expression of CD5L in an amount sufficient to switch Th17 cells from a pathogenic to non-pathogenic signature.

12. The method according to claim 9, wherein the population of T cells comprise naïve T cells, partially differentiated T cells, differentiated T cells, a combination of naïve T cells and partially differentiated T cells, a combination of naïve T cells and differentiated T cells, a combination of partially differentiated T cells and differentiated T cells, or a combination of naïve T cells, partially differentiated T cells and differentiated T cells.

13. A method of drug discovery for the treatment of a disease or condition involving an immune response involving Th17 cells comprising the steps of:

(a) providing a population of cells or tissue comprising Th17 cells;

(b) providing a CD5L specific compound or plurality of compounds to be screened for their efficacy in the treatment of said disease or condition;

(c) contacting said compound or plurality of compounds with said population of cells or tissue;

(d) 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 the genes of Table 1 or Table 2;

(e) comparing the detected level to a control of level of one or more signature genes or one or more products of one or more signature genes selected from the genes of Table 1 or Table 2 or gene product expression, activity and/or function; and,

(e) evaluating the difference between the detected level and the control level to determine the immune response elicited by said CD5L specific compound or plurality of compounds.