Introduction

GWAS has linked hundreds of regions of the human genome to autoimmune disease susceptibility. The majority of GWAS variants are located in non-coding regions of the genome, and likely contribute to disease risk by modulating cis-regulatory element (cRE) activity to influence gene expression¹. Identifying causal variants and effector genes molecularly responsible for increased disease risk is critical for identifying targets for downstream molecular study and therapeutic intervention. An understanding of how non-coding variants function is often limited by incomplete knowledge of the mechanism of action, i.e., whether a variant is located in a cRE, in which cell types a cRE is active, and which genes are regulated by which cRE.

CD4+ T cells are key regulators of innate and adaptive immune responses that combat infection by orchestrating the activity of other immune cells. In their quiescent, naïve state, ‘helper’ T cells traffic between the blood and secondary lymphoid tissues as part of an immunosurveillance program maintained by transcription factors such as KLF2, TOB, and FOXO^2–4. Upon encountering specific antigen, a cascade of signals activated through the TCR and costimulatory receptors results in large-scale changes in gene expression driven by factors like NFKB, NFAT, IRF4, and STATs, leading to proliferation and differentiation into specialized Th1, Th2, and Th17 subsets capable of participating in protective immunity^5,6. CD4+ T cells are also key players in the induction and pathogenesis of autoimmunity. The cis-regulatory architecture of CD4+ T cells is enriched for autoimmune disease GWAS variants^7–14, and T cells from autoimmune patients harbor distinct epigenetic and transcriptomic signatures linking dysregulated gene expression with disease pathogenesis. Autoimmune variants may contribute to the breakdown of immune self-tolerance by shifting the balance between autoreactive conventional vs. regulatory CD4+ T cells, by altering cytokine production, or by promoting auto-antigen production^15,16.

Chromatin conformation assays allow for identification of putative target genes of autoimmune variant-containing cRE in close physical proximity to gene promoters. Previous work using promoter-capture HiC and HiC in combination with other epigenetic marks have implicated sets of autoimmune variants and effector genes that may participate in T cell activation^17–19. In addition to chromatin conformation approaches, multiple complementary approaches have been developed to link disease associated SNPs to their downstream effector genes. Expression quantitative trait mapping (eQTL) and chromatin co-accessibility data can be used to implicate effector genes through statistical association of genotypes with readouts of expression or other molecular markers²⁰. However, these approaches do not account for the relevant 3D structure of the genome in the nucleus, and are highly susceptible to trans-effects and other confounding factors.

In this study, we measured the impact of TCR-CD28 activation on the autoimmune-associated cis-regulatory architecture of CD4+ helper T cells, identifying hundreds of effector genes not implicated by prior studies and leading to the discovery of a stretch of evolutionarily conserved enhancers required for normal expression of the canonical T cell activation gene IL2 whose activity is influenced by autoimmune risk variants. The set of variant-connected effector genes defined by 3D physical proximity to autoimmune-associated cRE is enriched for genes that regulate T cell activation, as validated pharmacologically in this study and by CRISPR-based screens in orthogonal studies^21–23.

Results

Gene expression dynamics as a function of naïve CD4+ T cell activation

To explore the universe of genes and cREs that are affected by and may contribute to CD4+ activation, we characterized the dynamics of gene expression, chromatin accessibility, and 3D chromatin conformation in human CD4+CD62L^hiCD44^lo naïve T cells purified directly ex vivo and in response to in vitro activation through the T cell receptor (TCR) and CD28 for 8 or 24 hours using RNA-seq (N=3 donors), ATAC-seq (N=3 donors), and HiC (N=2 donors, Fig. 1A). As sequencing depth and restriction enzyme cutting frequency affects the resolution and reliability of HiC experiments²⁴, we constructed libraries with two four-cutter restriction enzymes and sequenced to a total of approximately 4 billion unique-valid reads per timepoint. We verified the reproducibility of replicate ATAC-seq and RNA-seq libraries with principal component analysis and HiC libraries using distance-controlled, stratum-adjusted correlation coefficient (SCC). Replicate samples were highly correlated and clustered by activation stage (Fig. S1A-C). As expected, quiescent naïve CD4+ T cells expressed high levels of SELL, TCF7, CCR7, and IL7R, and rapidly upregulated CD69, CD44, HLADR, and IL2RA upon stimulation (Fig 1B, Table S1). Genome-scale gene set variance analysis based on MsigDB hallmark pathways showed that genes involved in KRAS and Hedgehog signaling are actively enriched in quiescent naive CD4+ T cells, while stimulated cell transcriptomes are enriched for genes involved in cell cycle, metabolism, and TNF-, IL-2/STAT5-, IFNg-, Notch-, and MTORC1-mediated signaling pathways (Table S2).

Figure 1:

To define global gene expression dynamics during the course of CD4+ T cell activation, we performed pairwise comparisons and k-means clustering, identifying 4390 differentially expressed genes after 8 hours of stimulation (3289 upregulated and 1101 downregulated) and 3611 differentially expressed genes between 8 hours and 24 hours (3015 upregulated and 596 downregulated, Fig. S1D) that could be further separated into five clusters based on their distinct trajectories (Fig. 1C, Fig. S1E-G, Table S1). Genes upregulated early upon activation (cluster 1; n=1621 genes) are enriched for pathways involved in the unfolded protein response, cytokine signaling, and translation (e.g., IL2, IFNG, TNF, IL3, IL8, IL2RA, ICOS, CD40LG, FASLG, MYC, FOS, JUNB, REL, NFKB1, STAT5A, Table S3). Genes downregulated late (cluster 2; n=1600 genes) are moderately enriched for receptor tyrosine kinase signaling, cytokine signaling, and extracellular matrix organization. Genes monotonically increasing (cluster 3; n=2676 genes) are highly enriched for pathways involved in infectious disease and RNA stability, translation and metabolism, and moderately enriched for pathways involved in the unfolded protein response, cellular responses to stress, regulation of apoptosis, and DNA repair (e.g., TBX21, BHLHE40, IL12RB2, STAT1, CCND2, CDK4, PRMT1, ICAM1, EZH2, Table S3). Genes downregulated early (cluster 4; n=1628 genes) are enriched for pathways involved in inositol phosphate biosynthesis, neutrophil degranulation, and metabolism of nucleotides (e.g., KLF2, IL7R, RORA, IL10RA, Table S3), and genes upregulated late (cluster 5; n=2154 genes) are highly enriched for pathways involved in cell cycle, DNA unwinding, DNA repair, chromosomal maintenance, beta-oxidation of octanoyl-CoA, and cellular response to stress (e.g., CDK2, E2F1, CDK1, CCNE1, CCNA2, PCNA, WEE1, CDC6, ORC1, MCM2, Table S3). These patterns are consistent with known changes in the cellular processes that operate during T cell activation, confirming that in vitro stimulation of CD4+ T cells recapitulates gene expression programs known to be engaged during a T cell immune response.

Dynamic changes in chromosomal architecture and genome accessibility during naïve CD4+ T cell activation

To understand the cis-regulatory dynamics underlying the observed activation-induced changes in gene expression, we examined CD4+ T cell nuclear chromosome conformation and chromatin accessibility as a function of stimulation state using HiC and ATAC-seq. The human genome consists of ∼3 meters of DNA that is incorporated into chromatin and compacted into the ∼500 cubic micron nucleus of a cell in a hierarchically ordered manner. This degree of compaction results in only ∼1% of genomic DNA being accessible to the machinery that regulates gene transcription²⁵, therefore a map of open chromatin regions (OCR) in a cell represents its potential gene regulatory landscape. Open chromatin mapping of human CD4+ T cells at all states identified a consensus cis-regulatory landscape of 181,093 reproducible OCR (Table S4). Of these, 14% (25,291) exhibited differential accessibility following 8 hours of stimulation (FDR<0.05). Most differentially accessible regions (DAR) became more open (18,887), but some DAR (6,629) showed reduced accessibility (Fig. 2A, Table S5). The change in accessibility over the next 16 hours of stimulation showed the opposite dynamic, with 6,629 regions exhibiting reduced accessibility, and only 4,417 DAR becoming more open (total of 11,046 DAR, Fig. 2A, Table S5). These OCR represent the set of putative cRE with dynamic activity during T cell activation.

Figure 2:

The vast majority of putative cRE are located in intergenic or intronic regions of the genome far from gene promoters, meaning that the specific impact of a given cRE on gene expression cannot be properly interpreted from a one-dimensional map of genomic or epigenomic features. To predict which cRE may regulate which genes in CD4+ T cells across different states of activation, we created three-dimensional maps of cRE-gene proximity in the context of genome structure. The highest order of 3D nuclear genome structure is represented by A/B compartments, which are large chromosomal domains that self-associate into transcriptionally active (A) vs. inactive (B) regions²⁶. In agreement with prior studies, we find that OCR located in active A compartments exhibit higher average accessibility than those OCR located in less active B compartments (Fig S2A; Table S6), a trend observed across all cell states. Likewise, genes located in A compartments show higher average expression than those located in B compartments (Fig S2B). A quantitative comparison across cell states showed that 94% of the CD4+ T cell genome remained stably compartmentalized into A (42%) and B (52%), indicating that activation does not cause a major shift in the large-scale organization of the genome within the nucleus.

Within each A or B compartment, the genome is further organized into topologically associating domains (TADs)²⁷. These structures are defined by the fact that genomic regions within them have the potential to interact with each other in 3D, but have low potential to interact with regions outside the TAD. The location of TAD boundaries can influence gene expression by limiting the access of cRE to specific, topologically associated genes. While ∼80% of TAD boundaries remained stable across all states, 20% of TAD boundaries (8925) changed as a function of T cell activation (Fig. 2C, Fig. S2C and D, Table S7). TAD boundary dynamics were categorized and 2198 boundaries exhibited a change in strength, 2030 boundaries shifted position, and 4697 boundaries exhibited more complex changes such as loss of a boundary resulting in merger of two neighboring TAD, addition of a boundary splitting one TAD into two, or a combination of any of these changes. Genes nearby dynamic TAD boundaries are enriched for pathways involved in RNA metabolism, cellular response to stress, and the activity of PTEN, p53, JAK/STAT, Runx and Hedgehog (Table S6). We also detected chromatin stripes, which are TAD-like structures that consist of a genomic anchor region that is brought into contact with a larger loop domain via an active extrusion mechanism. Chromatin stripes are contained within and/or overlap TAD regions, and are enriched for active enhancers and super-enhancers^28,29. We identified 1526 chromatin stripes in quiescent naïve CD4+ T cells, 1676 stripes in 8 hour stimulated cells, and 2028 stripes at 24 hours post-stimulation (Fig S3A, Table S8). Consistent with prior studies in other cell types, chromatin stripes were preferentially located in A compartments, and genes and OCR within stripe regions showed increased expression and chromatin accessibility (Fig. S3B-D).

Within active topological structures, transcriptional enhancers can regulate the expression of distant genes by looping to physically interact with gene promoters^30,31. To identify potential regulatory cRE-gene interactions, we identified high confidence loop contacts across all cell states using Fit-HiC (merged 1kb, 2kb, and 4kb resolutions, Fig. 2D). This approach detected 933,755 loop contacts in quiescent naïve CD4+ T cells, 900,267 loop contacts in 8-hour stimulated cells, and 551,802 contacts in 24-hour stimulated cells (2,099,627 total unique loops). Approximately 23% of these loops involved a gene promoter at one end and an OCR at the other, and these promoter-interacting OCR were enriched for enhancer signatures based on flanking histone marks from CD4+ T cells in the epigenome roadmap database³² (Fig. S2E). T cell activation resulted in significant reorganization of the open chromatin-promoter interactome, as 907 promoter-OCR exhibited increased contact and 1333 showed decreased contact following 8 hours of stimulation (Fig. 2E). Continued stimulation over the next 16 hours was associated with an increase in the contact frequency of 41 promoter-OCR pairs, while only 4 pairs exhibited decreased contact (Fig. 2E). Activation-induced changes in chromatin architecture and gene expression were highly correlated, as genomic regions exhibiting increased promoter connectivity became more accessible at early stages of stimulation, which was associated with increased gene transcription from connected promoters (Fig. 2F, Table S9). The accessibility of promoter-connected OCR and the expression of their associated genes decreased globally from 8 to 24 hours of stimulation (Fig. 2F). We compared these loop calls to a prior chromatin capture analysis in CD4+ T cells by Burren et al.¹⁸ and found that roughly 40% of stable loops in both stimulated and unstimulated cells were identical in both studies, despite differing in approach (HiC vs. PCHiC), analysis (HiC vs. CHiCAGO), sample (naïve CD4+ vs. total CD4+), timepoint (8 vs. 4 hour), and donor individuals (Fig. S2F). As expected, unstimulated samples were more similar than activated samples.

We next focused on the 5 sets of genes with the dynamic expression patterns defined in Fig. 1D, and identified 57,609 OCR that contact dynamic gene promoters in at least one stage. Most dynamic genes contacted between 1 and ∼35 OCR, with a median of 10 OCR per gene, but a handful of dynamic genes were observed in contact with over 100 distinct open chromatin regions (Fig. S2G). Similarly, most OCR were connected to a single dynamic gene, but many contacted more than one gene (median 2 genes per OCR), suggesting that most dynamic genes have a complex regulatory architecture. Increased gene expression upon activation correlated with an increase in the accessibility and promoter contact frequency of distant cRE (Fig. S2H), as exemplified by GEM and IRF4 (Fig. S4A,B). Conversely, the 3D regulatory architecture of genes like KLF2 and DPEP2, which were downregulated following stimulation, exhibited decreased contact and accessibility (Fig. S4C,D).

Transcription factor footprints enriched in dynamic open chromatin identify regulators of T cell activation

To explore what factors drive dynamic changes in the regulatory architecture of the CD4+ T cell genome during activation, we conducted a quantitative footprinting analysis at 1,173,159 transcription factor (TF) motifs located in the consensus CD4+ T cell open chromatin landscape using the average accessibility of the region surrounding each motif as a measure of regulatory activity (Table S10). Activation of naïve CD4+ T cells resulted in increased chromatin accessibility around bZIP motifs at tens of thousands of genomic regions by 8 hours post-stimulation (Fig. 3A and C, Table S10), which was associated with increased expression of Fos and Jun family members constituting the AP-1 complex, as well as the bZIP factors BATF, BACH1, and BACH2 (Fig. 3A). The activity of NFE2 and SMAD was increased without increased expression (Fig. 3A and C), likely due to post-translational regulation of these factors by phosphorylation³³. Conversely, the motifs for a number of TF exhibited significantly reduced accessibility early after stimulation, including those for EGR2 and FOXP3 that are known to negatively regulate T cell activation^34,35 (Fig. 3A). By 24 hours post-activation, bZIP activity remained largely unchanged compared to 8 hours (Fig. 3B), but a number of factors showed decreased activity. These include several members of the Sp family, the Myc cofactor MAZ that also cooperates with CTCF to regulate chromatin looping³⁶, KLF2, which controls genes involved in naïve CD4+ T cell quiescence and homing^2,37, NRF1, a factor implicated in age-associated T cell hypofunction³⁸, and EGR2 and 3, which are known to oppose T cell activation and promote tolerance^34,39,40 (Fig. 3B and C).

Figure 3:

To explore how transcription factor activity may operate via the CD4+ T cell open chromatin landscape to regulate distinct programs of dynamic gene expression during TCR/CD28-induced activation, we focused on TF motifs enriched among those OCR specifically contacting promoters of dynamic genes identified by our clustering analysis (Fig. S5A). The set of OCR contacting dynamic gene promoters were enriched for the motifs of 89 expressed (TPM>5) transcription factors, as compared to motifs present in the total open chromatin landscape (Fig. 3D). The majority of this TF activity was enriched in OCR connected to genes highly upregulated at 24 hours post-activation (clusters 3 and 5, Fig. 3D), with the exception of CREB3, ELF1, ESRRA, GABPA, RELA, XPB1, ZNF384, and the transcriptional repressor IKZF1 known as a strong negative regulator of T cell activation and differentiation^41–44. Conversely, motifs for IKZF1, ZNF384, GABPA, ESRRA, and ELF1 were highly enriched in the set of OCR contacting genes down-regulated early after activation (cluster 4, Fig. 3D). Motifs for KLF2 and the metabolic gene regulator SREBF1 were likewise enriched in OCR connected to down-regulated genes. Open chromatin regions interacting with genes in cluster 2 are negatively enriched for this set of TF except for CTCF (Fig. 3D).

Finally, we integrated TF footprint, promoter connectome, and gene co-expression data to construct TF-gene regulatory networks likely operating at each timepoint. The connections between regulatory nodes are based on physical promoter-TF footprint interactions with confidence weighted by gene co-expression (Fig. 3E, Table S11). Highly co-expressed genes at the core of the unstimulated CD4+ T cell regulatory network encode transcription factors such as KLF2, ETS1, IKZF1, and TCF7^45–47 that are known to be involved in T cell gene silencing, quiescence, homeostasis, and homing. Genes connected to the top factors in this network were enriched for pathways involved in immune signaling, DNA replication and repair, protein secretion, and programmed cell death (Fig S5B). Costimulation through the TCR and CD28 induced a set of core network genes active at both time points with known roles in T cell activation and differentiation (NFKB1, JUNB, MYC, IRF4, STAT5, STAT1, LEF1, ATF4). Also part of this set is PLAGL2, an oncogene in the Wnt pathway that regulates hypoxia-induced genes⁴⁸ with no prior defined role in T cell activation. Additional nodes specifically implicated at 8 hours post-activation are HIF1A, the major sensor of cellular hypoxia⁴⁹, and XBP1, a major transcriptional mediator of the unfolded ER protein response with defined roles in T cell activation, differentiation, and exhaustion^50,51. Factors specifically implicated at 24 hours post-activation include E2F1, a transcriptional regulator of both cell cycle and apoptosis in T cells^52,53, BHLHE40, a factor known to control multiple aspects of T cell metabolism and differentiation⁵⁴, and the Myc cofactor MAZ that has not been previously studied in the context of T cell function. Genes connected to factors in the activated T cell networks were enriched for pathways involved in cytokine signaling, the interferon response, transcription, cell cycle, DNA replication and repair, and programmed cell death (Fig S5B). Together, these data indicate that concurrent but separable stimulation-induced gene programs are the result of the activity of distinct sets of DNA binding factors mobilized by antigen and costimulatory receptor signaling in naïve CD4+ T cells.

Identification of autoimmune variants associated with CD4+ T cell cRE and predicted effector genes

Following our established variant-to-gene (V2G) mapping approach to implicate functional SNPs and their effector genes using the combination of GWAS and chromatin conformation capture data^55–59, we intersected promoter-interacting OCR with autoimmune SNPs from the 95% credible set derived from 15 autoimmune diseases (Fig. 4A, Tables S12 and S13). Constraining the GWAS SNPs in this way reduced the credible set size from an average of 14 variants per sentinel to 3 variants per sentinel. To determine whether open chromatin in physical contact with dynamically regulated genes in CD4+ T cells is enriched for autoimmune disease heritability, we performed a partitioned LD score regression analysis. This landscape was enriched for variants associated with susceptibility to inflammatory bowel disease (IBD), ulcerative colitis (UC), type I diabetes (T1D), lupus (SLE), celiac disease (CEL), allergy (ALG), eczema (ECZ), and rheumatoid arthritis (RA), but not for variants associated with psoriasis (PSO) or juvenile idiopathic arthritis (JIA) (Fig. 4B, Table S14). The OCR connected to genes upregulated early and/or progressively upon activation (clusters 1 and 3) were most strongly enriched for ALG, CEL, IBD/UC, RA and T1D heritability, while SLE and ECZ heritability was most enriched in OCR connected to genes upregulated later post-activation (clusters 3 and 5, Fig. 4B). SLE was also the only disease (besides PSO and JIA) that was not enriched in open chromatin connected to down-regulated genes (cluster 4, Fig. 4B).

Figure 4:

The promoter-connected open chromatin landscape for all CD4+ T cell states in this study contains 2606 putatively causal variants linked to ∼half of the sentinel signals (423) for the 15 diseases analyzed, and are in contact with a total of 1836 genes (Table S13). A total of 1151 autoimmune variants localized to the promoters of 400 genes (−1500/+500bp from the TSS, Table S15). These variants were on average 103 kb from the TSS of their connected gene (Fig. 4C), and each variant contacted an average of 5 genes (Fig. 4D). The majority of linked SNPs interact with genes in addition to the nearest gene, and ∼half of linked SNPs ‘skip’ the nearest gene to target only distant genes (Fig. 4E). Approximately 60% of connected genes were implicated across all timepoints (Fig. 4F, Table S13), while ∼40% (753) are dynamically regulated (clusters 1-5) in response to TCR/CD28 co-stimulation. Examples of SNP-genes pairs that exhibit dynamic accessibility, chromosome contact, and expression in response to T cell activation are the SIK1, PARK7, DUSP5, CLEC2D, TRIP10, GPR108 and IL2 loci (Fig. 4G, Table S13). The TRIP10 and GPR108 promoters were each captured in contact with a high confidence variant rs1077667 (PP>0.99), which is located in an intron of TNFSF14 and is associated with multiple sclerosis (Fig. 4H). The accessibility of this SNP and it’s contact with TRIP10 and GPR108 increased following activation (Fig. 4H). Conversely, the allergy-associated SNP rs7380290 is accessible and contacts the SIK1 promoter in resting cells, but shows reduced accessibility and promoter connectivity upon activation (Fig. 4I). TRIP10 encodes a cytoskeletal binding protein involved in endocytosis that suppresses glucose uptake in response to insulin signaling⁶⁰, GPR108 encodes an orphan G-protein coupled receptor, and SIK1 encodes a salt-inducible kinase with roles in cancer, epilepsy, and myeloid signaling⁶¹. None of these genes have been previously studied in the context of T cell activation.

Comparative predictive power against orthogonal variant-to-gene mapping approaches

In an attempt to measure the predictive power of this approach relative to other V2G approaches, we compared our chromatin capture-based autoimmune GWAS effector gene predictions to the predictions of three other chromosome capture-based studies in human CD4+ T cells^17–19, two single-cell eQTL studies in human CD4+ T cells^13,62, and a set of 449 genes that when mutated in humans cause inborn errors of immunity⁶³. We integrated the chromatin-based datasets with the same 95% credible set of autoimmune variants to create comparable physical contact maps. Our HiC-ATAC-seq approach in naïve and activated human CD4+ implicated 1947 effector genes, 400 of which through proxies in open promoters and 1836 through contacts with distal variants, of which 752 were dynamically regulated throughout activation. The capture-HiC datasets from Burren et al., Yang et al., and Javierre et al. implicated 1408, 2572, and 2368 genes, respectively, and a total of 369 autoimmune GWAS effector genes were predicted in common by all 3D chromatin-based approaches (Fig. S6A, Table S16). The scRNA-seq eQTL studies by Soskic et al. and Schmiedel et al. implicated 171 and 221 genes, with 51 eGenes in common (Fig. S6A). The Soskic variant-gene pairs were further co-localized with autoimmune GWAS SNPs, and 41 of these eQTL were also predicted by our study (Fig. S6B). This concordance is 10-fold higher than obtained by a random sampling of genes within 500 kb of autoimmune variants (Fig. S6C). However, 1665 of the genes implicated in our study were not identified in these statistical approaches. A total of 12 genes were implicated by all CD4+ T cell-based approaches queried (APIP, ERAP2, FIGNL1, IL18R1, METTL21B, MRPL51, PPIF, PXK, RMI2, RNASET2, SERPINB1, VAMP1). We find that 809 genes from our CD4 T cell V2G data were not implicated by these other chromatin-based datasets, and altogether, 729 autoimmune effector genes were uniquely predicted by our study.

To measure the predictive power and sensitivity of the sets of genes implicated by each approach above, a precision-recall analysis was performed against the human inborn errors in immunity (HIEI) ‘gold standard’ gene set. The set of genes from our study with disease-associated SNPs located in their open promoters overlapped with 5% of the genes from the HIEI truth set (Fig. S6D). However, including distal autoimmune variants captured interacting with gene promoters from our HiC data increased the sensitivity of the approach 3-fold to 16% (Fig. S6D), confirming the predictive value of long-range promoter-cRE/SNP contacts. This level of sensitivity is comparable to that of other chromosome capture-based approaches for connecting SNPs to genes using in CD4+ T cells (Fig. S6D). Conversely, the eQTL-based approaches were 3- to 4-fold less sensitive than the chromatin-constrained approaches in predicting HIEI genes (Fig. S6D).

The fraction of HIEI genes among the set of genes implicated by our CD4 V2G study is ∼3.5%, a predictive power greater than that of the Yang (2.5%) and Javierre (3%) approaches, but less than that of Burren (Fig. S6D). While inclusion of variants in distal cRE improves sensitivity over proximal cRE alone, it reduces precision, as the predictive power of only genes from our study with accessible disease-associated SNPs in their promoters is 6%. The predictive power of our activated CD4+ T cell V2G approach is comparable to that of the DICE eQTL approach (3.5%), but less precise than the Soskic eQTL (Fig. S6D). The proportion of total HIEI genes predicted by our dynamically regulated CD4 V2G gene set drops from 16% to 10%, but the precision of this set doubled to ∼6.2% (Fig. S6D), indicating that focusing the V2G set on those SNP-gene pairs that are dynamically regulated by T cell activation increases predictive power. These analyses indicate that chromosome capture-based V2G is a more sensitive approach for identifying true effector genes than eQTL approaches, but comes with additional predicted genes that represent either false positives or true effector genes for which monogenic LOF/GOF mutations have not yet been characterized in humans.

Functional validation of novel autoimmune V2G-predicted enhancers at the IL2 locus

This analysis specifically implicated potential dynamic, disease-associated regulatory elements in intergenic space at the IL2 locus. The IL2 gene encodes a cytokine with crucial, pleotropic roles in the immune system, and dysregulation of IL-2 and IL-2 receptor signaling leads to immunodeficiency and autoimmune disorders in mice and humans^64–67. Activation-induced transcription of IL2 involves an upstream regulatory region (URR) ∼375 bp from the TSS that has served as a paradigm of tissue-specific, inducible gene transcription for nearly four decades^68–70. However, the presence of evolutionarily conserved non-coding sequences (CNS) in the ∼150 kb of intergenic space 46, 51, 80, 83, 85, 96, 122, and 128 kb upstream of the TSS suggest that additional regulatory elements may have evolved to control IL2⁷¹ (Fig. 5A). The −51 kb CNS contains a SNP linked to T1D, IBD, PSO, CEL and allergy (rs72669153), while the −85 kb CNS contains a SNP linked to RA (rs6232750) and the −128 kb CNS contains two SNPs linked to T1D, JIA, and SLE (rs1512973 and rs12504008, Fig. 5A). In TCR/CD28-stimulated naïve CD4+ T cells, these CNS are remodeled to become highly accessible (Fig. 5A), and they loop to interact physically with the IL2 URR (Fig. 5B) at both time points. ChIP-seq analyses in human T cells⁷² show that the URR and all distal CNS except −85 are occupied by TF such as Jun/Fos (AP-1), NFAT, and NFkB that are known regulators of IL2 (Fig. S7), and the −85, −122, and −128 CNS are occupied by additional TF not previously thought to be direct regulators of IL2, such as MYC, BCL6, and STAT5 (Fig. S7). Recombinant reporter assays in primary activated human CD4+ T cells showed that the −46, −51, −83, and −128 CNS/OCR can enhance transcription from the URR (Fig. 5C). To determine whether the native elements contribute to the expression of IL2, we targeted each CNS/OCR individually in primary human CD4+ T cells or Jurkat T cells using CRISPR/CAS9 (Fig. 5D) and measured IL-2 secretion following TCR/CD28 stimulation (Fig. 5E).

Figure 5:

Deletion of the −46, −51, −83, −85, −122, and −128 kb elements in primary human CD4+ T cells each resulted in a ∼50% reduction in IL-2 production, while deletion of the −80 kb element had little effect. A very similar pattern of impact was observed when these elements were deleted individually in Jurkat T cells (Fig. 5E). The URR has a stronger contribution to IL-2 production than any individual intergenic element, as deletion of the URR almost completely abrogated activation-induced IL-2 production by both primary CD4+ or Jurkat T cells (Fig. 5E). To determine whether these intergenic enhancers exist in synergistic epistasis⁷³ necessary for IL2 transactivation, we generated Jurkat T cell clones in which the stretch of all 7 elements located −46 kb to −128 kb upstream of IL2 was deleted using CRISPR/CAS9 (Fig. 5F). Despite the URR and 46 kb of upstream sequence being intact in these clones, loss of the 81.3 kb stretch of intergenic enhancers renders these cells incapable of expressing IL2 at both the mRNA and protein level in response to stimulation (Fig. 5G). These results show that the URR is not sufficient for activation-induced expression of IL2, and that IL2 has a previously unappreciated, complex, and autoimmune disease-associated regulatory architecture that was accurately predicted by our 3D epigenomic V2G approach. Importantly, we find that the distal IL2 cRE are highly accessible in quiescent memory T cell subsets (Th1, Th2, Th17, Th1-17, Th22) isolated directly ex vivo from human blood, whereas naïve CD4+ T cells and non-IL-2-producing Treg showed little accessibility at these elements (Fig. S7). This suggests that stable remodeling of distal IL2 cRE can persist in vivo after TCR signals cease, and that this epigenetic imprinting contributes to the immediate, activation-induced production of IL-2 exhibited by memory, but not naïve or regulatory, CD4+ T cells^74–76.

Distal IL2 enhancers are evolutionarily conserved and impact in vivo T cell-mediated immunity in mice

The intergenic IL2 enhancers defined above are conserved at the sequence level between human and mouse (Fig. 6A). To test whether enhancer function is likewise evolutionarily conserved, we used zygotic CRISPR/CAS9 targeting to generate mice with a 583 bp deletion of the ortholog of the −128 kb human enhancer CNS/OCR situated 83 kb upstream from mouse Il2 (Il2-83-cRE-ko, C57BL6 background). Deletion of this genomic region did not discernably affect T lymphocyte development in Il2-83-cRE-ko mice. However, peripheral CD4⁺ T cells from Il2-83-cRE-ko mice produced substantially less IL-2 at both the protein and mRNA level, and exhibited reduced proliferation, in response to in vitro stimulation (Fig. 6B), confirming that the enhancer function of the orthologous −128 and −83 kb elements is conserved between human and mouse. The in vitro induction of Foxp3+ regulatory T cells (Treg) from conventional CD4+ T cell precursors in response to antigenic stimulation in the present of TGF-beta, a differentiation process highly dependent upon IL-2, was reduced 4-fold in Il2-83-cRE-ko mice compared to wild-type mice (Fig. 6C), but could be rescued by addition of exogenous IL-2 to the culture (Fig. 6C).

Figure 6:

To test whether the distal −83 kb Il2 enhancer contributes to physiologic immune responses in vivo, we immunized wild-type and Il2-83-cRE-ko mice with the model antigen chicken ovalbumin. Il2-83-cRE-ko animals showed a nominal increase in the differentiation of CD4+ T cells into CXCR5+PD-1^hi follicular helper T cells (Tfh) in the spleen compared to wild-type animals (Fig. 6D), a process known to be antagonized by IL-2, and generated significantly elevated levels of ovalbumin-specific IgG antibody following immunization (Fig. 6E). To determine whether the 83 kb Il2 enhancer contributes to auto-inflammatory disease susceptibility in vivo, we used an adoptive transfer model of IBD in which the development of colitis is determined by the balance between conventional and regulatory CD4+ T cell activities^77,78. Upon transfer into lymphocyte-deficient Rag1-ko recipients, wild-type CD4+ T cells respond to microbiota in the gut by inducing inflammatory colitis that leads to weight loss (Fig. 6F, WT Tconv), while co-transfer of wild-type Treg effectively controls inflammation (Fig. 6F, WT Tconv + WT Treg). Transfer of Il2-83-cRE-ko Tconv led to significantly less colitis (Fig. 6F, Il2-83-cRE-ko Tconv), and was associated with a nominal decrease in Il2-83-cRE-ko Tconv in the spleen, and a significant decrease in the accumulation of Il2-83-cRE-ko Tconv in the mesenteric lymph nodes (MLN) that drain the intestines (Fig. 6F inset). Treg require IL-2 from Tconv for their function and homeostasis⁷⁹, and despite their reduced numbers, Il2-83-cRE-ko Tconv were able to support the homeostasis and function of co-transferred wild-type Treg (Fig. 6F, Il2-83-cRE-ko Tconv + WT Treg). That the −83 kb ortholog of the human −128 kb IL2 enhancer contributes to physiologic immune responses and inflammatory disease susceptibility in vivo.

Impact of autoimmune risk-associated genetic variation on cis-regulatory element activity

The chromatin conformation approach used here employs GWAS variants as ‘signposts’ to identify disease-relevant regulatory elements and connect them to their target genes, but does not per se determine the effect of disease-associated genetic variation on enhancer activity or gene expression. We experimentally measured variant effects at the IL2 locus, where the −128 enhancer defined above contains two SNPs linked to T1D, JIA, and SLE (rs1512973 and rs12504008). Using a recombinant reporter assay in primary activated CD4+ T cells, we confirmed that disease-associated genetic variation influences intergenic IL2 enhancer activity at the −128 kb element, in that the risk allele contributes significantly less transcriptional activity than the reference allele (Fig. 7A).

Figure 7:

Autoimmune variants are likely to influence disease risk by altering the activity of cis-regulatory elements in T cells. At genome scale, we identified over 1000 cRE likely impacted by autoimmune disease-associated genetic variation, in that they contain autoimmune risk SNPs that are predicted to decrease or increase transcription factor binding affinity. Overall, 1370 autoimmune risk variants in open chromatin were predicted to influence the activity of 495 DNA binding factors (Table S17), including PLAG1, PRDM1, BACH2, MYC, TBX21, BHLHE40, LEF1, TCF7, BCL6, IRF, p53, STAT (Fig. S8A), and hundreds of NFkB, EGR, KLF, FKH/FOX, and FOS-JUN sites (Fig. 7B). For example, the T1D SNP rs3024505 (PP = 0.200) connected to the promoters of IL9 and FAIM3 (Fig. S8B) and the celiac SNP rs13010713 (PP = 0.154) contacting the ITGA4 promoter (Fig. S8C) are predicted to disrupt binding sites for MZF1 (Fig. S8D) and SOX4 (Fig. S8E). Similarly, the MS SNP rs1077667 contacting the promoters of GPR108 and TRIP10 (Fig. 4H) is predicted to reduce affinity for TP53, TP63, and OCT2/POU2F2 (Fig. S8F).

Functional validation of chromosome capture-based V2G-implicated effector genes

To determine whether genes identified via their physical interaction with autoimmune variants in CD4+ T cell chromatin contact maps tend to be directly involved in T cell activation and function, we compared the set of autoimmune genes implicated by chromatin contacts in this study to sets of genes identified in CRISPR-based screens that control aspects of CD4+ T cell activation like proliferation and expression of the inflammatory genes IFNG, CTLA4, IL2, IL2RA, and TNF^21–23. The set of all V2G-implicated genes was highly enriched for genes shown to regulate IL-2, IL-2 receptor, CTLA-4, and proliferation (Fig. 7C, Fig. S9A, Table S18). For example, 202 genes shown to regulate IL-2 production and 166 genes shown to regulate proliferation were also implicated in our autoimmune V2G set (Fig. S9A, Table S16). Genes implicated by V2G in activated CD4+ T cells were moderately enriched for genes known to control the production of IFN-G, but at the individual disease level, only genes connected to CRO- and ATD-associated variants were enriched for IFNG regulatory genes (Fig. 7C, Fig. S9A). Genes contacting CRO, PSO, RA, SLE, T1D and VIT variants were moderately enriched for TNF regulatory genes, but the set of all V2G genes was not enriched for TNF genes. We also queried the orthologs of our V2G-implicated genes from the international mouse phenotype consortium (IMPC) database and identified 97 genes that when knocked out give an immune phenotype and 126 V2G genes that result in a hematopoietic phenotype (Fig. 7D, Fig. S9B, Table S18). This frequency of observed immune/hematopoietic phenotypes represents a significant (adjP<0.05) ∼30% enrichment over expected. This gene set was also enriched for mortality, homeostasis/metabolism, growth/body size, skeleton, and embryonic phenotypes (Fig. 7D, Table S18).

An important application of this V2G approach is the identification of novel regulators of T cell activation and their potential as drug targets, as nearly 20% of implicated genes have at least one chemical modulator currently available (Fig 7E, Table S19). As shown above, this approach validates genes that are well-studied regulators of T cell function, however, a significant portion of implicated genes are not well-studied and are not currently known to regulate T cell activation (Fig. 7F). We observed a trend that genes expressed more highly in immune tissues (GTEX) have in general been better investigated, and identified several less-studied genes that could be novel targets, including the kinases GRK6, PTK6, SIK1, and MAP3K11, the G protein-coupled receptors OXER1, GPR183, GPR18, and KISS1R, the acetylcholine receptor CHRNB1, and the de novo purine pathway enzyme GART. To determine whether these V2G-implicated genes are novel regulators of T cell activation, we used commercially available pharmacologic modulators in dose-response assays of activation-induced T cell proliferation. Stimulation of T cells in the presence of ligands for CHRNB1, KISS1R and OXER1 did not significantly affect T cell proliferation, however, small molecules targeting GRK6, PTK6, MAP3K11, GPR183, GART, and SIK1 inhibited T cell activation in the nanomolar to micromolar range (Fig. 7G). Together, these data show that maps of dynamic, 3D variant-gene chromatin contacts in stimulated CD4+ T cells are able to identify genes with bona fide roles in T cell activation.

Discussion

By measuring dynamic changes in chromosome folding, chromatin accessibility, and gene expression in naïve CD4+ T cells as a function of TCR-CD28 costimulation, we identified the putative cis-regulatory landscape of autoimmune disease-associated genetic variation, and physically connected these elements to their putative effector genes. We conducted pharmacologic targeting experiments and compared our results with orthogonal eQTL and CRISPR-based studies to validate sets of effector genes with a confluence of evidence supporting their role in CD4+ T cell activation. This and prior chromosome capture-based studies show that most cRE and their variants interact with, and therefore have the potential to regulate, more than one gene (median 5 in this study), supporting a scenario in which multiple effector genes are operative at a GWAS locus. We also observe that the vast majority (87-95%) of GWAS effector genes predicted by chromosome capture-based approaches are not the genes nearest to the 614 sentinel SNPs queried in this study, while roughly one-third of eGenes predicted by eQTL approaches are the nearest to a sentinel. Of the roughly 2000 total genes implicated by both eQTL and our chromatin/GWAS-based V2G, only 41 were predicted by both approaches, which is consistent with the view that eQTL-based and GWAS-based approaches inherently implicate different types of genes. While eQTLs cluster strongly near transcription start sites of genes with simple regulatory structures and low enrichment for functional annotations, GWAS variants are generally far from the TSS of genes with complex regulatory landscapes⁸⁰. Moreover, eGene discovery by eQTL studies using scRNA-seq approaches are significantly limited by the low gene detection power inherent to these methods, particularly in rare cell types. Chromatin conformation mapping in disease-relevant cell types therefore brings a crucial added dimension to 1D GWAS data and eQTL findings for understanding the complex genetic and epigenetic mechanisms that regulate gene expression and for predicting novel therapeutic targets.

Why did our V2G approach not capture a larger proportion of genes from the human inborn errors in immunity ‘truth set’? Low recall/sensitivity could result from the fact that a substantial portion of the mutations in the full 449 HIEI gene set contains disease genes that do not result in an autoimmune phenotype. However, restricting the HIEI truth set to only those disease gene mutations that result in an autoimmune phenotype (143 genes) only increased recall from 16% to 17.5%, suggesting that this is not a major factor reducing sensitivity. Also, our method uses GWAS signals as an input, and unlike GWAS for height, BMI, blood traits, etc., most autoimmune GWAS signals are likely not saturated, which limits discoverability. Another reason for reduced sensitivity is the likelihood that many of the genes in the HIEI truth set operate in cells other than CD4+ T cells, and consistent with this, when the Javierre pcHiC V2G is extended to all immune cell types tested, recall increases from 17% to 27%). This observation emphasizes how limited inclusion cell types and states in a study can significantly limit the power to detect autoimmune effector genes. Why are so few of our V2G genes present in the HIEI truth set? Low precision or predictive power could be due to false positives in the V2G gene set; i.e., contacts between disease-associated cRE and genes that are not in fact involved in disease susceptibility. This does not argue per se against the biological relevance of the cRE or the gene, only that the linkage to disease susceptibility is not true. Alternatively, the absence of chromatin/GWAS-implicated genes in the truth set could be a false negative from the point of view of the truth set; i.e., monogenic, disease-causing mutations in these genes may exist in the human population, but have not yet been discovered. For example, in the year 2000 approximately 100 monogenic mutations causing human inborn errors of immunity were known, but this increased by ∼11 disease mutations per year until 300 human inborn errors of immunity had been identified by 2017. The advent of next-generation exome/genome sequencing and the COVID-19 pandemic resulted in an increase in the rate of HIEI discovery between 2018-2021 to ∼40 per year to the current recognized HIEI of ∼450. If we assume a conservative continued rate of HIEI discovery of 25 disease genes per year, the next 10 years will show us an additional ∼250 disease genes that are not currently contained in the truth set.

Many of the genes identified in this 3D epigenomic V2G screen have known roles in T cell activation and function. An example is IL2, and we used the resulting maps to identify and validate a stretch of previously unknown distal enhancers whose activity is required for IL2 expression and is influenced by autoimmune genetic variation. Another example is the phosphatase DUSP5 that regulates MAPK signaling during T cell activation^81,82. However, roles for many of the genes implicated here in T cell activation are not known. For example, one of the top implicated genes, PARK7, is a deglycase studied in the context of Parkinson’s disease, but has a recently been shown to modulate regulatory T cell function⁸³. The orphan G protein-coupled receptor GPR108 is another top gene uniquely implicated by our chromatin-based V2G that has not been studied in T cells, but was identified in a recent CRISPR screen for genes affecting IL-2 levels²³. Also co-implicated by our study and recent CRISPR screens are the cannabidiol receptor GPR18⁸⁴ and the purine biosynthetic enzyme GART⁸⁵. The GPR18 agonist arachidonyl glycine inhibited CD4+ T cell activation above 10 uM, while the GPR18 antagonist O-1918 slightly enhanced T cell activation. This GPR was implicated by both chromatin- and eQTL-based approaches. Antagonism of GART, an enzyme we previous identified as a V2G effector gene in COVID19 severity⁵⁸, with the FDA-approved drug lometrexol inhibited T cell activation in the 10 nM range. Antagonism of GRK6, a member of the G-coupled receptor kinase family associated with insulin secretion and T2D susceptibility⁸⁶, and PTK6, an oncogenic kinase studied in the context of cancer⁸⁷, led to inhibition of T cell activation in the nM to uM range. These targets were implicated by the other chromatin-based approaches but not by eQTL. Inhibition of MAP3K11, a kinase that facilitates signaling through JNK1 and IkappaB kinase in cancer^88,89 inhibited stimulation-induced CD4+ T cell proliferation in the 100 nM range, as did 25-hydroxycholesterol, a ligand of the G protein-coupled receptor GPR183 that was implicated by both chromatin- and eQTL-based approaches. SIK1 is a member of the salt-inducible kinase family uniquely implicated in our study that negatively regulates TORC activity⁶¹, and a small molecule SIK1 inhibitor potently antagonized stimulation-induced CD4+ T cell activation in the pM range.

Our integration of high-resolution Hi-C, ATAC-seq, RNA-seq and GWAS data in a single immune cell type across multiple activation states identified hundreds of autoimmune variant-gene pairs at ∼half of all GWAS loci studied, and application of this technique to additional immune cell types will likely identify effector genes at many of the remaining loci. This study highlights the value of chromosome conformation data as a powerful biological constraint for focusing variant-to-gene mapping efforts⁹⁰, and shows that dynamic changes in the spatial conformation of the genome that accompany cell state transitions alter gene expression by exposing promoters to a varying array of cis-regulatory elements, transcription factors, and genetic variants.

Materials and methods

T cell isolation and in vitro stimulation

Human primary CD4+ T cells were purified from the apheresis products obtained from healthy, screened human donors through University of Pennsylvania Human Immunology Core (HIC). Naïve CD4+ T cells were purified using EasySepTM human naïve CD4+ T cell isolation kit II (STEM cells Technologies, cat#17555) by immunomagnetic negative selection as per manufacturer’s protocol. Isolated untouched, highly purified (93-98%) naïve human CD4 T cells were activated using anti-CD3+anti-CD28 Dynabeads (1:1, Thermofisher scientific, cat # 11161D) for 8-24 hours. Cells were then used to prepare sequencing libraries. The human leukemic T cell line Jurkat was obtained from ATCC, cloned by limiting dilution, and clones with high activation-induced secretion of IL-2 were selected for further study. Single-cell mouse lymphocyte suspensions were prepared from spleen and lymph nodes isolated from 6-week-old female wild-type or Il2-83cRE-ko mice (C57BL6 background). Mouse CD4+ T cells were purified by negative selection using a CD4+ T cell isolation kit (Miltenyi Biotec, cat. # 130-104-454). For CD8 depletion, CD8+ T cells were positively stained using CD8a (Ly-2) microbeads (Miltenyi Biotec, cat. # 130-117-044) and the negative flow-through fraction was collected. Tregs were purified from the mouse lymphocytes using a CD4+CD25+ regulatory T cell isolation kit (Miltenyi Biotec, cat. # 130-091-041). The purity of the isolated cells was checked by flow cytometry, showing approximately 95% cell purity. For iTreg induction, CD4+ CD25-T cells (1x 10⁶) were activated in a 24-well plate pre-coated with anti-CD3 (1 µg/mL) and the cells were cultured for 72 hours in RPMI culture medium with soluble anti-CD3 (0.5 µg/mL), IL-2 (25 units/mL), TGF-beta (3 ng/mL), anti-IFN-gamma (5 µg/mL), and anti-IL-4 (5 µg/mL). iTreg induction cultures were also set up without adding IL-2. iTreg cultures were harvested after 72 hours, and cells were stained for Foxp3 expression.

Il2-83-cRE ko Mice

The CRISPR/CAS method was employed to delete the intergenic −83 CNS sequence between Il2 and Il21. CRISPR guide RNAs were designed (two guide RNAs upstream of −83CNS) and one guide RNA (downstream of −83CNS) using guide RNA design tools (http://crispr.mit.edu.guides). The pX335 plasmid (Addgene #42335) was used to generate a DNA template for sgRNA in vitro transcription. To in vitro transcribe the sgRNA, a T7 sequence was incorporated at the 5’ end of the guide RNA sequence, and a part of the sgRNA scaffold sequence from px335 was added at the 3’ end of the guide RNA. Oligonucleotide sequences containing the guide RNA (underlined) along with these added sequences were synthesized by IDT. The oligonucleotide sequences were as follows:

T7_guideRNA#1_scaffold#: TTAATACGACTCACTATAGGTTTTCCACGGATCTGCTCGGGTTTTAGAGCTAGAAATAGC

T7_guideRNA#2_scaffold#: TTAATACGACTCACTATAGGTGCTTTCTAGGTGAAGCCCCGTTTTAGAGCTAGAAATAGC

T7_guideRNA#3_scaffold#: TTAATACGACTCACTATAGGTCATTTGAGCCTAACTACTCGTTTTAGAGCTAGAAATAGC

Along with the above-cited sequences, a reverse primer sequence (AGCACCGACTCGGTGCCACT) from PX335 was used to amplify a PCR-generated template for sgRNA in vitro transcription. The PCR product (∼ 117 bp) was verified on a 2% agarose gel and then gel-purified using the QIAQuick gel extraction kit. The T7 sgRNA PCR product (500 ng) was used as a template for in vitro transcription with the T7 High Yield RNA synthesis kit (NEB, cat # E2040S). The reaction was incubated at 37°C for 4 hours, and then the sgRNA was purified using the MEGAclear kit (Life Technologies, cat # AM1908) according to the kit protocol. The sgRNA was eluted with elution buffer preheated to 95°C. The eluted sgRNA was centrifuged at 13,000 rpm for 20 minutes at 4°C, and the suspension was transferred into a new RNase-free tube. The sgRNA quality was checked by a bioanalyzer using an RNA nano Chip. The sgRNA was diluted to 500 ng/μl in injection buffer (sterile 10 mM Tris/0.1 mM EDTA, pH 7.5) and stored in a −80°C freezer until use. An injection mixture (final volume 30 μl) was prepared in injection buffer by mixing 500 ng/μl of each of the sgRNAs (left and right) with Cas9 mRNA (1 μg/μl, TriLink, cat # L-6125). Fertilized eggs collected from B6/129 mice were microinjected at the CHOP transgenic core and transferred into pseudo-pregnant B6 females. The pups were genotyped using primers flanking the targeted sequence (Forward primer: TTAGGACCTCACCCATCACAA and reverse primer: CATGCCCAGCTACTCTGACAT). The PCR product was cloned into the Promega PGEM T Easy TA cloning vector, and plasmid DNA was Sanger sequenced to determine the size of the deletion. The targeting resulted in mice with a 500 bp and 583 bp deletion at the targeted −83 CNS site and these mutant mice showed same phenotype. The Il2-83-cRE mutant heterozygous male mice were backcrossed with B6 females for 10 successive generations.

Elisa

Cell culture supernatants collected at various time intervals from in vitro stimulated T cells of mice or humans were analyzed for IL-2 by ELISA using kits purchased from ThermoFisher Scientific (mouse IL-2 ELISA kit: cat # 88-7024-88) and human IL-2 ELISA kit, cat # 88-7025-76). IL-2 ELISA was performed following the protocol provided by the vendor.

In vivo ovalbumin immunization

Eight week-old female WT and Il2-CNS-83 KO mice were injected intraperitoneally with 50 µg of ovalbumin (Sigma cat # A5503-5G) mixed with IFA (Sigma cat # F5506). Blood was collected from the immunized mice after 10 days, and serum was separated. The level of ovalbumin-specific IgG in the serum was determined by ELISA using an ovalbumin-specific IgG ELISA kit purchased from MyBiosource (cat # MBS763696). The immunized mice were sacrificed on day 10 of immunization, and spleen and lymph nodes were collected. Splenocytes and lymph node cells were stained with CD4 BV785, CD25 BV650, CD44 Percp Cy5.5, CD62L APC eFL780, CXCR5 BV421, PD-1 APC, Bcl-6 PE antibodies. TFH frequency was determined by flow cytometry analysis.

In vivo inflammatory colitis model

Conventional CD4+CD25-ve T cells and CD4+CD25+ Tregs were purified from spleens and lymph nodes of male wild-type or Il2-CNS-83 ko mice (6-8 weeks of age). T cells were transferred into Rag1-ko male mice (5 per group) by retro-orbital injection of 1 million wild-type or Il2-CNS-83 ko Tconv cells alone or along with (0.25 x 10⁶) wild-type Tregs. Experimental Rag1-ko recipients were weighed 3 times per week and scored for IBD-induced clinical symptoms. Mice were sacrificed 72 days post-transfer, and spleen, mesenteric lymph nodes, and colon were collected. Single-cell suspensions were prepared, cells were stained for CD4, CD8, CD25, CD44, and Foxp3, and analyzed by flow cytometry. The absolute T cell count was estimated using cell count and cell frequency derived from the flow cytometry analysis.

RNA-seq library generation and sequencing

RNA was isolated from ∼ 1 million of each cell stage using Trizol Re-agent (Invitrogen), purified using the Directzol RNA Miniprep Kit (Zymo Research), and depleted of contaminating genomic DNA using DNAse I. Purified RNA was checked for quality on a Bioanlayzer 2100 using the Nano RNA Chip and samples with RIN>7 were used for RNA-seq library preparation. RNA samples were depleted of rRNA using QIAseq Fastselect RNA removal kit (Qiagen). Samples were then processed for the preparation of libraries using the SMARTer Stranded Total RNA Sample Prep Kit (Takara Bio USA) according to the manufacturer’s instructions. Briefly, the purified first-strand cDNA is amplified into RNA-seq libraries using SeqAmp DNA Polymerase and the Forward and the Reverse PCR Primers from the Illumina Indexing Primer Set HT for Illumina. Quality and quantity of the libraries was assessed using the Agilent 2100 Bioanalyzer system and Qubit fluorometer (Life Technologies). Sequencing was performed on the NovaSeq 6000 platform at the CHOP Center for Spatial and Functional Genomics.

ATAC-seq library generation and sequencing

A total of 50,000 to 100,000 sorted cells were centrifuged at 550 g for 5 min at 4 °C. The cell pellet was washed with cold PBS and resuspended in 50 μL cold lysis buffer (10 mM Tris-HCl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40/IGEPAL CA-630) and immediately centrifuged at 550 g for 10 min at 4 °C. Nuclei were resuspended in the Nextera transposition reaction mix (25 μL 2x TD Buffer, 2.5 μL Nextera Tn5 transposase (Illumina Cat #FC-121-1030), and 22.5 μL nuclease free H2O) on ice, then incubated for 45 min at 37 °C. The tagmented DNA was then purified using the Qiagen MinElute kit eluted with 10.5 μL Elution Buffer (EB). Ten microliters of purified tagmented DNA was PCR amplified using Nextera primers for 12 cycles to generate each library. PCR reaction was subsequently cleaned up using 1.5x AMPureXP beads (Agencourt), and concentrations were measured by Qubit. Libraries were paired-end sequenced on the Illumina HiSeq 4000 platform (100 bp read length).

Hi-C library preparation

Hi-C library preparation on FACS-sorted CD4+ T cells was performed using the Arima-HiC kit (Arima Genomics Inc), according to the manufacturer’s protocols. Briefly, cells were crosslinked using formaldehyde. Crosslinked cells were then subject to the Arima-HiC protocol, which utilizes multiple restriction enzymes to digest chromatin. Arima-HiC sequencing libraries were prepared by first shearing purified proximally-ligated DNA and then size-selecting 200-600 bp DNA fragments using AmpureXP beads (Beckman Coulter). The size-selected fragments were then enriched using Enrichment Beads (provided in the Arima-HiC kit), and then converted into Illumina-compatible sequencing libraries with the Swift Accel-NGS 2SPlus DNA Library Kit (Swift, 21024) and Swift 2S Indexing Kit (Swift, 26148). The purified, PCR-amplified DNA underwent standard QC (qPCR, Bioanalyzer, and KAPA Library Quantification [Roche, KK4824]) and was sequenced with unique single indexes on the Illumina NovaSeq 6000 Sequencing System using 200 bp reads.

ATAC-seq data analysis

ATAC-seq peaks from libraries unstimulated and stimulated Naïve CD4+ T cells were called using the ENCODE ATAC-seq pipeline (https://www.encodeproject.org/atac-seq/). Briefly, pair-end reads from three biological replicates for each cell type were aligned to hg19 genome using bowtie2⁹¹, and duplicate reads were removed from the alignment. Narrow peaks were called independently for each replicate using macs2⁹² (-p 0.01 --nomodel --shift −75 --extsize 150 -B --SPMR --keep-dup all --call-summits) and ENCODE blacklist regions (ENCSR636HFF) were removed from peaks in individual replicates. Reproducible peaks, peaks called in at least two replicates, were used to generate a consensus peakset. Signal peaks were normalized using csaw⁹³ in 10kb bins background regions and low abundance peaks (CPM>1) were excluded from the analysis. Tests for differential accessibility was conducted with the glmQLFit approach implemented in edgeR⁹⁴ using the normalization factors calculated by csaw. OCRs with FDR <0.05 and abs(log₂FC > 1) between stages were considered differentially accessible.

Hi-C data analysis

Paired-end reads from two replicates were pre-processed using the HICUP pipeline v0.7.4⁹⁵, with bowtie as aligner and hg19 as the reference genome. The alignment .bam file were parsed to .pairs format using pairtools v0.3.0 (https://github.com/open2c/pairtools) and pairix v0.3.7 (https://github.com/4dn-dcic/pairix), and eventually converted to pre-binned Hi-C matrix in .cool format by cooler v0.8.10⁹⁶ with multiple resolutions (500bp, 1kbp, 2kbp, 2.5kbp, 4kbp, 5kbp, 10kbp, 25kbp, 40kbp, 50kbp, 100kbp, 250kbp, 500kbp, 1Mbp and 2.5Mbp) and normalized with ICE method⁹⁷. Replicate similarity was determined by HiCRep v1.12.2⁹⁶ at 10K resolution. For each sample, eigenvectors were determined from an ICE balanced Hi-C matrix with 40kb resolution using cooltools v0.3.2⁹⁸ and first principal components were used to determine A/B compartments with GC% of genome region as reference track to determine the sign. Differential TAD comparison was performed using TADcompare with the default settings for each chromosome (v1.4.0)⁹⁹. Finally, for each cell type, significant intra-chromosomal interaction loops were determined under multiple resolutions (1kb, 2kb and 4kb) using the Hi-C loop caller Fit-Hi-C2 v2.0.7¹⁰⁰ (FDR<1e-6) on merged replicates matrix. The consensus chromatin loops within resolution were identified by combining all three stages. These sets of loops were used as consensus for quantitative differential analysis explained below. The final consensus interaction loops for visualization were collected by merging loops from all the resolutions with preference to keep the highest resolution. Quantitative loop differential analysis across cell types was performed on fast lasso normalized interaction frequency (IF) implemented in multiCompareHiC v1.8.0¹⁰¹ for each chromosome at resolution 1kb, 2kb and 4kb independently. The contacts with zero interaction frequency (IF) among more than 80% of the samples and average IF less than 5 were excluded from differential analysis. The QLF test based on a generalized linear model was performed in cell type-pairwise comparisons, and p-values were corrected with FDR. The final differential loops were identified by overlapping differential IF contacts with consensus interaction loops.

Bulk RNA-seq data analysis

Bulk RNA-seq libraries were sequenced on an Illumina Novaseq 6000 instrument. The pair-end fastq files were mapped to the genome assembly hg19 by STAR (v2.6.0c) independently for each replicate. The GencodeV19 annotation was used for gene feature annotation and the raw read count for gene feature was calculated by htseq-count (v0.6.1)¹⁰² with parameter settings -f bam - r pos -s yes -t exon -m union. The gene features localized on chrM or annotated as rRNAs, small coding RNA, or pseudo genes were removed from the final sample-by-gene read count matrix.

Gene set variation analysis was performed using GSVA 1.42.0¹⁰³ with the MSigDB hallmark geneset¹⁰⁴, with resulting scores analyzed using limma (limma_3.50.3)¹⁰⁵. Low abundance peaks (CPM>1) were excluded from the analysis. Testing for differential expression was conducted with the glmQLFit approach implemented in edgeR⁹⁴. Genes with FDR<0.05 and abs(log₂FC>1) between stages were considered differentially expressed. Differential genes were then clustered using k-means clustering. The number of clusters was determined using the elbow method on the weighted sum of squares, where was set to k=5. Score for how similar each gene followed the clusters expression pattern was determined by calculating pearson correlation coefficients between each gene in the cluster and the cluster centroid.

Transcription factor footprinting and motif analysis

Transcription factor footprints were called using Regulatory Analysis Toolbox HINT-ATAC (v0.13.0) with pooled ATAC-seq data for each stage and consensus peak calls¹⁰⁶. The rgt-hint footprinting was run with parameters –atac-seq, --paired-end, and organism=hg19 set. The output footprint coordinates were subsequently matched using rgt-motifanalysis matching with parameters --organism hg19 and –pseudocount 0.8 set. The JASPAR2020 position weight matrix database was used to match footprints¹⁰⁷. Differential analysis of TF binding across conditions was performed using rgt-hint differential with parameters –organism hg19, --bc, --nc 24 using the motif matched transcription factor footprints. An activity score is then calculated based on the accessibility surrounding the footprint.

Partitioned heritability LD score regression enrichment analysis

Partitioned heritability LD score regression¹⁰⁸ (v1.0.0) was used to identify heritability enrichment with GWAS summary statistics and open chromatin regions annotated to genes. The baseline analysis was performed using LDSCORE data (https://data.broadinstitute.org/alkesgroup/LDSCORE) with LD scores, regression weights, and allele frequencies from the 1000G phase 1 data. The summary statistics were obtained from studies as described in Table S14 and harmonized with the munge_sumstats.py script. Annotations for Partitioned LD score regression were generated using the coordinates of open chromatin regions that contact gene promoters through Hi-C loops for each cell type. Finally, partitioned LD scores were compared to baseline LD scores to measure enrichment fold change and enrichment p-values, which were adjusted with FDR across all comparisons.

Variant-to-gene mapping using HiC-derived promoter contacts

95% credible sets were determined as previously described¹⁰⁹. Briefly, P values from GWAS summary statistics were converted to Bayes factors. Posterior probabilities were then calculated for each variant. The variants were ordered from highest to lowest posterior probabilities added to the credible set until the cumulative sum of posterior probabilities reached 95%. Variants in the 95% credible set were then intersected with the CD4+ T cell promoter interacting region OCRs from the three timepoints using the R package GenomicRanges (v1.46.1)¹¹⁰.

Genomic reference and visualizations

All analyses were performed using the hg19 reference genome using gencodeV19 as the gene reference. Genomic tracks were visualized with pyGenomeTracks v3.5¹¹¹. HiC matrices depict the log1p(balanced count) from the cooler count matrix. ATAC-seq tracks were generated from bigwig files that were normalized using deeptools¹¹².

Precision-recall analyses of HIEI genes

To benchmark our autoimmune effector gene predictions against prior eQTL and chromatin capture studies, we curated a list of 449 expert curated genes from Human Errors in Inborn Immunity 2022, where mutations have been reported to cause immune phenotypes in humans⁶³. Most of these genes were identified through rare loss of function mutations in the coding region.

In addition to this we also took a subset of 145 genes reported to specifically result in autoimmune phenotypes. These two sets of genes were treated as “gold standard” sets of genes to compare different approaches to predict GWAS effector genes. We curated the results of several other chromatin conformation or eQTL based methods of assigning variants to genes to compare with our study. For the chromatin confirmation studies, we obtained loop calls from the associated publications and intersected with the list variants in the 95% credible set from 15 GWAS used to predict effector genes using GenomicRanges (v1.46.1)¹¹⁰. For eQTL studies, we obtained the summary stats files and identified eGenes linked to any member of the credible set reported as an eQTL in the transcriptome wide association analysis (TWAS; p-value < 1e-4). Precision was calculated as the ratio of the number predicted genes in the truth set to the total number of predicted genes, while recall was calculated as the ratio of the number predicted genes in the truth set to the total number of genes in truth set.

Colocalized eQTL comparisons

In addition to comparisons with the eGenes identified by TWAS in the Soskic et al. study, we also compared overlap of gene nominations in our study with the eQTLs that colocalized with an autoimmune GWAS¹³. Enrichment of sentinel-gene assignments was conducted similarly as described previously⁵⁶. Briefly, a null distribution was constructed by randomly selecting genes within 1 Mb of the sentinel compared to the set of colocalized cis-eQTL¹³ found in through 10,000 iterations. The observed overlap reports the set of gene identified by both our HiC based approach with the set of colocalized eQTLs. We report the empirical P value of the observed value relative to null distribution.

International mouse phenotyping consortium comparisons

The set of HiC implicated genes were compared to the mouse international phenotyping consortium set of genes with reported phenotypes¹¹³. We converted the list of V2G implicated genes to mouse homologs using homologene. We tested for enrichment for each phenotype using a one-sided proportion test implicated in R prop.test with type set to “upper”.

Identification of pharmacological agents

We queried the Drug-Gene Interaction Database with the set of V2G implicated genes for chemicals using rDGIdb (v1.20.0)¹¹⁴. To identify the number of papers for each gene with at least one drug annotated to target it, we queried pubmed titles and abstracts using the R package RISmed (v2.3.0) with each gene’s name and either “autoimmune” and the list of autoimmune diseases (Table S17). A score to approximate expression specificity was computed using the sum GTEX median expression values (v8) for whole blood or spleen divided by other tissues¹¹⁵.

Lentiviral-based CRISPR/CAS9 targeting in Jurkat cells

LentiCRISPRv2-mCherry vectors encoding gRNA-CAS9 and the fluorescent reporter mCherry were used for Jurkat targeting. CRISPR guide RNAs (sgRNA) targeting human IL-2-21 intergenic −46, −51, −80, −83, −85, −122, −128 CNS regions were designed using http://crispr.tefor.net and cloned into lentiCRISPRv2-mCherry. Empty vector without gRNA insert was used as a control. Below is the list of CRISPR gRNA for causing deletion of human IL2-21 intergenic regions:

HEK 293T cells were grown in RPMI1640 complete medium (RPMI1640 + 1X P/S, 1x L-Glu, 10% FBS), 37C, 7%CO2. 293T cells were transfected with 10 ug of lenti-CRISPR-V2-CRE construct along with packaging plasmid 6 ug of PsPAX2 (Addgene, Cat #12260) and 3.5 ug of PmD2.G (Addgene, Cat #12259) using Lipofectamine 2000 transfection reagent (Invitrogen cat # 11668019). After 6 hours, the transfection medium was replaced with complete culture medium. Transfected cells were incubated at 37C for 48-72 hours in a cell culture incubator. and then the Lentiviral supernatants were harvested and spun at 300g for 5 minutes to remove cellular debris. Lentiviral supernatants were concentrated using Lenti-X^TM Concentrator (Takara Bio, Cat # 631232) and then centrifuged at 1500g for 30 minutes at 4°C and supernatant was discarded. The lentiviral pellet was resuspended at a ratio of 1:20 of the original volume using RPMI media and concentrated virus supernatant aliquots were prepared and stored until use at −80°C. To achieve high transduction efficiency, the viral supernatant was titrated in Jurkat cells through transduction using various dilutions of the viral supernatants and transduction efficiency was determined by mcherry expression analyzed through flow cytometry. Jurkat cells were seeded in a 24 well plate at 0.5 x106/well in culture media, viral supernatant with 8 ug/mL of polybrene was added to each well. Spinfection was performed for 90 min. at 2500 rpm, and transduced cells were equilibrated at 37C for ∼6 hrs, followed by incubation at 37C 5%CO2 for ∼72 hours culture. Transduced Jurkat Cells were then harvested and stimulated using PMA (15 ng/mL) + Ionomycin (1 uM) + human anti-CD28 (2 ug/ml), BioXcell cat # BE0248 in 96 well culture plates (TPP, cat # 92097) in triplicates. Cell culture supernatants were collected at the end of culture and analyzed for IL-2 by ELISA using a kit (cat # 88-7025-76) purchased from Thermofisher Scientific.

gRNA-CAS9 RNP-based targeting in primary human CD4+ T cells

Primary Human CD4 T cells derived from 5 normal healthy donors were obtained from Human Immunology core (University of Pennsylvania). Alt-R S.p. HiFi Cas9 Nuclease V3 cat # 1081061 CAS9 and following list of Alt-R® CRISPR-Cas9 sgRNA targeting IL-2-21 CRE were purchased from Integrated DNA Technologies, USA.

Primary Human CD4 T (5-10e6) were incubated with sgRNA and CAS9 protein complex and electroporation was done using P3 Primary Cell 4D-Nucleofector^TM X Kit L (Lonza, cat # V4XP-3024) and Lonza 4D nucleofector system. B2M gene was CRISPR targeted as a positive control. As per manufacturer’s protocol cells were electroporated pulse code Fl-115 in 100ul cuvette format. After nucleofection, cells were allowed to rest in the complete media for ∼2 days. Cells were then harvested, washed with PBS and aliquots of cells were used for further experimentation such as flow staining and cell activation. Primary human CD4 T Cells (0.1e6/well) were seeded in triplicates (for each experimental condition) in 96 well plate format in RPMI complete medium and stimulated using ImmunoCult™ Human CD3/CD28 T Cell Activator (STEM cells Technologies, cat # 10971). Cell culture supernatants were collected at 4h of stimulation and stored at −80C until assayed. IL-2 ELISA was performed using Thermofisher Scientific IL-2 Human Uncoated ELISA Kit with Plates, cat # 88-7025-76.

Recombinant reporter assays

The IL2 URR was cloned into Xho I and Hind III sites of PGL4 Luc vector (Promega) and then ∼500 bp individual sequence representing IL2-IL21 intergenic CNS at −46, −51, −80, −83, −85, −128 were cloned upstream to IL-2 URR at the Xho I site of the pGL4 vector. Primary human CD4 T cells obtained from 5 normal healthy donors were transfected with PGL4-cRE-URR constructs. Briefly, primary human CD4 T cells were activated with anti-CD3+ anti-CD28 Dynabeads (1:1) (Thermofisher scientific, cat # 11161D) + IL-2 overnight and then 1 million aliquots of cells (triplicates) were electroporated using Nucleofector 2b, human T cell Nuclefactor kit (Lonza VPA # 1002, program # T-020) with 2ug of PGL4 firefly vector constructs along with 0.2 ug of PGL4 Renilla vector; cells were allowed to rest ON in RPMI + IL-2 and then re-stimulated with plate-bound anti-CD3+ anti-CD28 (2 ug/ml each) for 5 hrs. Dual luciferase assay was performed with cell lysate prepared using Promega dual luciferase assay kit. Cell lysate was prepared in PLB as per manufacturer’s protocols and then firefly and renilla luciferase activities were analyzed by spectramax ID5 (Molecular Devices). Firefly luciferase activity was normalized against the internal control renilla luciferase activity.

Pharmacologic validation of novel T cell activation regulatory genes

The drugs lometrexol (GART antagonist), 25-hydroxycholesterol (GPR183 ligand), epinephrine & norepinephrine (CHRNB agonists), and arachidonoyl glycine (GPR18 agonist) were purchased from Sigma. The GPR18 antagonist O-1918 was purchased from ChemCruz. CEP1347 (MAP3K11 antagonist), kisspeptin 234 (KISSIR ligand) and the PTK6 antagonist tilfrinib were purchased from Tocris. The GRK6 antagonist GRK-IN-1 was purchased from DC Chemicals. The SIK1 antagonist HG-91-9-1 was purchased from Selleckchem. The OXER1 agonist 5-Oxo-ete was purchased from Cayman chemicals. The drugs were dissolved in DMSO or ethanol as suggested by the vendor and working stocking concentrations were prepared in RPMI1640 medium or PBS. The drug effect on T cell proliferation was assayed using murine lymphocytes cultured under TCR and CD28 activation conditions. Spleen and lymph nodes were collected from 6-8 weeks old female C57BL/7 mice and single cell lymphocyte cell suspensions were prepared in RPMI 1640 complete medium. 20 million lymphocytes were labeled with CFSE and re-suspended in RPMI 1640 medium. Cells (0.5 million labeled cells/well) were loaded into 48 well culture plate and activated with mouse anti-CD3 and anti-CD28 agonistic antibodies (1μg mL each). Drugs at the indicated concentrations were added in the culture medium and both untreated and drug treated cell cultures were incubated at 37°C for 72 hours in a cell culture incubator. Cells were harvested after 3 days of culture, washed with PBS and then stained with live-dead aqua dye. After washing with FACS buffer (PBS containing 2% FBS), cells were stained with fluorochrome conjugated antibodies CD4-APC, CD8-PB, CD44-Percp Cy5.5 and CD25-BV650. Stained cells were analyzed on a Beckman Coulter Cytoflex S flow cytometer. The division profile of CD4+ CFSE+ T cells were gated on live populations. The flow data was analyzed using Flowjo10 software and the number of divided CD4+ T cells were determined as described previously¹¹⁶.

Supplemental Tables

Table S1: Expression and clustering of differentially expressed genes.

Table S2: Pathway enrichment of differentially expressed RNA-seq genes

Table S3: Pathway enrichment of differentially expressed RNA-seq genes by cluster

Table S4: Accessible chromatin regions

Table S5: Differential accessible open chromatin regions

Table S6: A/B compartment calls

Table S7: Differential TAD boundaries

Table S8: Stripe calls

Table S9: Differential contact frequency

Table S10: Summary of TF footprinting

Table S11: TF target gene pathway enrichment

Table S12: List of all GWAS studies included

Table S13: Variant to gene mapping across all timepoints

Table S14: Partitioned LD score regression output

Table S15: Autoimmune variants in open gene promoters

Table S16: Comparison of V2G approaches

Table S17: Motifs predicted to disrupt transcription factor binding sites.

Table S18: V2G genes implicated by orthogonal data

Table S19: V2G target gene drug repurposing results