human follicular helper t cell promoter …...2019/12/20 · 1 1 human follicular helper t cell...

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Human follicular helper T cell promoter connectomes reveal novel genes and 1

regulatory elements at SLE GWAS loci 2

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Chun Su1*, Matthew E. Johnson1*, Annabel Torres2*, Rajan M. Thomas2*, Elisabetta 5

Manduchi1,3, Prabhat Sharma2, Carole Le Coz4, Michelle E. Leonard1, Sumei Lu1, 6

Kenyaita M. Hodge1, Alessandra Chesi1, James Pippin1, Neil Romberg4,5, Struan F. A. 7

Grant1,5,6,7* and Andrew D. Wells2,8* 8

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1Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA 10 2Department of Pathology, The Children’s Hospital of Philadelphia, Philadelphia, PA 11 3Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 12 4Division of Allergy and Immunology, The Children's Hospital of Philadelphia, Philadelphia, PA 13 5Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 14 6Division of Diabetes and Endocrinology, The Children’s Hospital of Philadelphia, Philadelphia, PA 15 7Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 16 8Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of 17

Pennsylvania, Philadelphia, PA 18

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*Authors contributed equally 20

Address correspondence to Andrew D. Wells, [email protected] 21

.CC-BY-NC-ND 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted December 21, 2019. ; https://doi.org/10.1101/2019.12.20.885426doi: bioRxiv preprint

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ABSTRACT 22

Systemic lupus erythematosus (SLE) is a complex inflammatory disease mediated by 23

autoreactive antibodies that damages multiple tissues in children and adults. Genome-24

wide association studies (GWAS) have statistically implicated hundreds of loci in the 25

susceptibility to human disease, including SLE, but the majority have failed to identify the 26

causal variants or the effector genes. As a physicochemical approach to detecting 27

functional variants and connecting them to target genes, we generated comprehensive, 28

high-resolution maps of SLE variant accessibility and gene connectivity in the context of 29

the three-dimensional chromosomal architecture of human tonsillar follicular helper T 30

cells (TFH), a cell type required for the production of anti-nuclear antibodies characteristic 31

of SLE. These spatial epigenomic maps identified a shortlist of over 400 potentially 32

functional variants across 48 GWAS-implicated SLE loci. Twenty percent of these 33

variants were located in open promoters of highly-expressed TFH genes, while 80% 34

reside in non-promoter genomic regions that are connected in 3D to genes that likewise 35

tend to be highly expressed in TFH. Importantly, we find that 90% of SLE-associated 36

variants exhibit spatial proximity to genes that are not nearby in the 1D sequence of the 37

genome, and over 60% of variants ‘skip’ the nearest gene to physically interact only with 38

the promoters of distant genes. Gene ontology confirmed that genes in spatial proximity 39

to SLE variants reside in highly SLE-relevant networks, including accessible variants that 40

loop 200-1000 kb to interact with the promoters of the canonical TFH genes BCL6 and 41

CXCR5. CRISPR-Cas9 genome editing confirmed that these variants reside in novel, 42

distal regulatory elements required for normal BCL6 and CXCR5 expression by T cells. 43

Furthermore, SLE-associated SNP-promoter interactomes implicated a set of novel 44



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genes with no known role in TFH or SLE disease biology, including the homeobox-45

interacting protein kinase HIPK1 and the Ste kinase homolog MINK1. Targeting these 46

kinases in primary human TFH cells inhibited production of IL-21, a requisite cytokine for 47

production of class-switched antibodies by B cells. This 3D-variant-to-gene mapping 48

approach gives mechanistic insight into the SLE-associated regulatory architecture of the 49

human genome. 50



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INTRODUCTION 51

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GWAS has been an important tool in understanding the genetic basis of complex, 53

heritable diseases and traits. However, GWAS is typically powered to identify relatively 54

large blocks of the genome containing dozens to hundreds of single nucleotide 55

polymorphisms (SNP) in linkage disequilibrium (LD), any one of which could be 56

responsible for the association of the entire locus with disease susceptibility. Moreover, 57

~90% of GWAS-implicated SNP are intergenic or intronic, and do not affect the coding 58

sequence of proteins. Therefore, the location of the GWAS signal per se does not identify 59

the culprit gene(s). Examples of this are the FTO GWAS signal in obesity1,2, and the 60

TCF7L2 GWAS signal in type 2 diabetes3, in which each suspected causal variant resides 61

in an intron of the local gene, but were shown instead to regulate expression of the distant 62

genes IRX3/5 and ACSL5, respectively. 63

Systemic lupus erythematosus (SLE) is a complex inflammatory disease mediated by 64

autoreactive antibodies that damage skin, joints, kidneys, brain and other tissues in 65

children and adults4. An important inflammatory leukocyte required for the development 66

of SLE is the follicular helper T cell (TFH). TFH differentiate from naïve CD4+ T cells in 67

the lymph nodes, spleen, and tonsil, where they license B cells to produce high affinity 68

protective or pathogenic antibodies5,6. Given the central role for TFH in the regulation of 69

humoral immune responses, genetic susceptibility to SLE is highly likely to manifest 70

functionally in this immune cell population. 71

GWAS has associated over 60 loci with SLE susceptibility to date7,8, but this 72

represents thousands of SNP in LD of which any could potentially contribute to disease. 73



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Given the paucity of immune cell eQTL data represented in GTEx, we mapped the open 74

chromatin landscape of TFH cells from human tonsil to identify potentially functional SLE 75

variants, and conducted a genome-wide, promoter-focused Capture-C analysis of 76

chromatin contacts at nearly all of the ~41,000 annotated human protein-coding and non-77

coding genes at ~270 bp resolution to map these variants to the genes they likely regulate 78

in this disease-relevant cell type. This approach leverages the power of existing SLE 79

genetic knowledge, using the location of common variants that have already been 80

strongly associated with SLE pathogenesis to identify (via assessment of linkage 81

disequilibrium) putative disease-associated regulatory elements. We then utilize high-82

resolution, promoter-focused Capture-C to physically connect these variants to their 83

target genes, in line with our previous, comparable approach to studying bone mineral 84

density loci14. This approach only requires three replicate samples to make valid 85

interaction calls, and it does not require material from SLE patients or genotyped 86

individuals. By design, we are not studying the effect of a variant in the system, but rather, 87

we are using reported variants as ‘signposts’ to identify potential enhancers in healthy 88

tissue. The variant connectomes in turn lead us to putative effector genes that warrant 89

further follow up. This study shows that the majority of accessible SLE-associated 90

variants do not interact with the nearest promoter, but are instead connected to more 91

distant genes, many of which have known roles in T cell and TFH biology. Using 92

CRISPR/CAS9 genome editing, we go on to validate several of these SLE-associated 93

open chromatin regions, revealing a requisite role in regulating their connected genes. 94

Finally, we experimentally verified that two novel kinases implicated by this variant-to-95

gene mapping approach are required for TFH differentiation and/or function, and 96



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therefore represent potential novel drug targets for SLE and other antibody-mediated 97

diseases. 98



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RESULTS 99

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Comparative open chromatin landscapes of naïve CD4+ T cells and TFH from 101

human tonsil 102

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The vast majority (>90%) of the human genome is packed tightly into cellular 104

chromatin and is not accessible to the nuclear machinery that regulates gene expression9. 105

Consequently, >95% of transcription factor and RNA polymerase occupancy is 106

concentrated at regions of open chromatin9, and thus a map of accessible chromatin in a 107

given cell type essentially defines its potential gene regulatory landscape. As a step 108

toward defining the disease-associated regulatory architecture of the complex heritable 109

autoimmune disease SLE, we focused on human follicular helper CD4+ T cells (TFH), 110

which are required for the production of pathogenic antibodies by autoreactive B cells 111

during the development of SLE4. Tonsillar TFH are derived from naïve CD4+ T cell 112

precursors, and represent a population of cells in healthy subjects that are actively in the 113

process of helping B cells to produce high-affinity, class-switched antibodies. We sorted 114

naive CD4+CD45RO- T cells and differentiated CD4+CD45RO+CD25-CXCR5hiPD1hi 115

TFH10 from human tonsil and generated open chromatin maps of both cell types from 116

three donors using ATAC-seq11. A binary peak calling approach identified a total of 91,222 117

open chromatin regions (OCR), 75,268 OCR in naïve CD4+ cells and 74,627 OCR in TFH 118

cells (Supplemental Table 1). Further quantitative analysis of the accessibility signal at 119

these OCR revealed a similar overall degree of genomic accessibility (~1.4%) in both cell 120

types (Supplemental Figure 1). However, the differentiation of naïve CD4+ T cells into 121

TFH is associated with remodeling of 22% of the T cell open chromatin landscape, with 122



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11,228 OCR becoming more accessible, and 8,804 OCR becoming less accessible, in 123

the TFH lineage (Figure 1A, Supplemental Table 1). Among all 20,032 differentially 124

accessible regions, 20.5% (4100) reside in promoters, and the genes driven by these 125

differentially accessible promoters tended to be differentially expressed between TFH and 126

naïve CD4+ cells as assessed by microarray (Figure 1B, GSEA enrichment p<0.05, 127

absNES>3.5, Supplemental Table 2). The functions of genes that are remodeled and 128

upregulated in TFH were significantly enriched for CD28 costimulatory, G-protein, Rho 129

GTPase, semaphorin, and TLR signaling pathways (hypergeometric test, FDR<0.05, 130

Supplemental Figure 2A). Conversely, gene promoters that become less accessible 131

upon TFH differentiation are enriched for pathways involved in chemokine and G protein-132

coupled signaling (Supplemental Figure 2B). These data show that global chromatin 133

remodeling dynamics faithfully reflect dynamic changes in gene expression during the 134

differentiation of follicular helper T cells from their naïve precursors. 135

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Open chromatin mapping of disease-relevant tissue implicates causal disease 137

variants 138

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Of the 7662 sentinel and proxy SNPs currently implicated by GWAS in SLE (r2>0.4)7,8, 140

432 (5.6%) reside in 246 open chromatin regions in either naïve CD4+ T cells or TFH 141

(Supplemental Table 3). Of these, 345 SNPs (80%) are in open chromatin shared by 142

both cell types, 39 are in naive-specific OCR, and 48 reside uniquely within the TFH open 143

chromatin landscape (Figure 2A). Altogether, 91% (393 of 432) of the accessible SLE 144

SNPs identified in this study reside in the open chromatin landscape of TFH cells. To 145

explore the potential significance of these open chromatin-implicated variants, we first 146



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focused on the 132 SLE proxy SNPs that reside in open promoters of protein-coding 147

genes in TFH. Of the 64 genes containing one or more open promoter variants, 62 are 148

expressed in TFH as assessed by microarray (Supplemental Table 2). Moreover, the 149

set of genes with accessible SLE variants in their promoters are expressed more highly 150

in TFH than the set of all genes, or compared to a random sample of genes with open 151

promoters in TFH (Figure 2B). Eighty-three SLE proxy SNPs reside in promoters of 36 152

genes in the top 75% expression quantile, and 43 SLE proxy SNPs reside in the 153

promoters of 18 genes in the 50-75% expression quantile. Thus, nearly 93% (123 of 132) 154

of open promoter SLE proxy variants are positioned at genes moderately to highly 155

expressed in TFH. Ingenuity pathway analysis (IPA) found that these TFH expressed 156

genes are enriched for factors involved in systemic lupus erythematosus (P=4.6x10-9), 157

systemic autoimmunity (p=6.0x10-8), and rheumatic disease (P=2.7x10-7) (Figure 2C), 158

including PTPRC (CD45), TCF7 (TCF1), IRF5, IFNLR1, TYK2, ELF1, IKZF2 and JAK2. 159

This set of highly-expressed TFH genes with SLE variants in their promoters also includes 160

DHCR7 and NADSYN1, enzymes involved in biogenesis of vitamin D, a process known 161

to play an important role in autoimmune disease susceptibility12. These results indicate 162

that open chromatin landscapes in disease-relevant cell types represent a highly specific 163

filter through which putatively functional SLE variants can be distinguished from the 164

thousands of proxy SNPs to the sentinels implicated by GWAS. 165

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Analysis of the three-dimensional promoter connectome structure in TFH cells 167

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It is relatively clear how genetic variation at a promoter might influence expression of 169

the downstream gene. However, ~70% of the accessible SLE SNPs in TFH cells are 170



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intronic, intergenic, or otherwise not in proximity to a promoter, so how these variants 171

regulate the expression of specific TFH genes is not clear from 1-dimensional open 172

chromatin mapping alone. To explore the role that accessible, non-coding SLE-173

associated variants play in the disease-related regulatory architecture of the human 174

genome, we derived genome-wide, three-dimensional promoter contact maps of naive 175

CD4+ T cells and differentiated follicular helper CD4+ T cells from human tonsil using 176

promoter-focused Capture-C technology. Our chromosome conformation capture (3C)-177

based approach is a high-resolution, large-scale modification of Capture-C13 that involves 178

massively parallel, hybridization-based enrichment of 41,970 targeted promoters 179

associated with 123,526 currently annotated transcripts (gencode v19) covering 89% of 180

protein-coding mRNA genes and 59% of non-coding (anti-sense RNA, snRNA, miRNA, 181

snoRNA and lincRNA) genes in the human genome14. As in standard capture-C 182

approaches, valid hybrid reads derived from ligation of distant fragments with bait 183

fragments were preprocessed using HiCUP15, and significant promoter-interacting 184

regions (PIR, score >5) were called using CHiCAGO16. Unlike promoter capture Hi-C17, 185

our method employs the 4-cutter DpnII to generate 3C libraries with a 270 bp median 186

resolution, ~9-fold higher than the 2300 bp median resolution of the HindIII-based 3C 187

libraries generated in HiC and capture-HiC approaches. This resolution allows mapping 188

of interactions between promoters and distal regulatory elements to within a span of two 189

nucleosomes. This precision comes at the expense of power, in that sequencing reads 190

are distributed across more fragments, leaving fewer reads available per fragment to call 191

significant promoter interactions. To circumvent this problem, we called promoter 192

interactions both at high resolution (single-fragment) and at lower resolution (four-193



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fragment) after an in silico fragment concatenation step. Combination of both sets of calls 194

allows this method to benefit from the precision of single DpnII fragment analysis and the 195

power of lower resolution analyses at farther distances to assemble comprehensive, 3D 196

promoter contact maps for the human genome (Figure 3A). 197

We detected a similar number of significant promoter interactions (CHiCAGO score 198

>5) in both cell types - 255,238 in naive CD4+ T cells and 224,263 in TFH - with the vast 199

majority (>99%) being intra-chromosomal (in cis). About 20% of total interactions were 200

between two promoters, while 80% of interactions were between a promoter and an 201

intergenic or intronic region (Supplemental Table 4). Ninety percent of promoters were 202

found engaged in at least one stable interaction with another genomic region. Of these 203

promoters, over 80% were connected to only one distal genomic region, indicating that 204

most promoters in these cell types exhibit very low spatial complexity. However, ~1% of 205

all promoters exhibited significant spatial complexity, interacting with four or more distal 206

regions, with some promoters engaging in as many as 70 interactions with distal regions. 207

The number of connections per promoter correlated with the level of gene expression in 208

both cell types, with the most interactive promoters belonging to highly-expressed genes 209

with known roles in TFH function (Figure 3B). Two examples are the IL21 and IFNG 210

promoters, which are expressed and show complex connectomes in TFH but not naïve 211

cells (Figures 3C and D). Promoter-interacting regions in both cell types were enriched 212

3-fold for open chromatin, and 2-fold for chromatin signatures associated with active 213

transcription, such as H3K27ac, H3K4me1, H3K4me3 (Figure 4A and Supplemental 214

Figure 3A). Conversely, PIR in both cell types were depleted of the silencing marks 215

H3K27me3 and H3K9me3 (Figure 4A and Supplement Figure 3A). Together, these 216



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trends indicate that the promoter contacts captured by this approach preferentially 217

represent the active regulatory architectures of the associated genes. 218

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The promoter-open chromatin connectome of TFH cells 220

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To further explore the regulatory nature of the spatial connections between promoters 222

and other genomic regions in the nucleus, we focused on contacts between promoters 223

and open chromatin regions, as the biochemical processes that regulate transcription 224

occur largely at accessible DNA9. Instead of using standard fragment-based 225

interactions13, we used a feature-based calling approach to define interactions between 226

promoters (-1500 to +500 from a TSS) and OCR, combining calls at both one- and four-227

fragment resolution to generate genome-wide, open chromatin-promoter interaction 228

landscapes in naïve and follicular helper CD4+ T cells (Figure 3A). In total, we detected 229

71,137 cis-interactions between accessible promoters and open chromatin among both 230

cell types, involving 34% of the total open chromatin landscape identified by our ATAC-231

seq analyses (Supplemental Table 5). We define these 31,404 promoter-interacting 232

OCR as iOCR. Roughly half of iOCR (15,109, 48%) are located in intergenic or intronic 233

regions relatively far from genes, while the other 16,295 promoter-interacting OCR (52%) 234

are located in the promoters of other distant genes. The distance between promoter and 235

promoter-interacting OCR pairs ranged from a few hundred base pairs to over a 236

megabase, with a median of ~112 kb for both categories. Remarkably, while OCR in 237

general are enriched for active chromatin marks9,11, we find that iOCR are even more 238

highly enriched for enhancer signatures compared to OCR that are not contacting a 239

promoter (14-fold for H3K4me1, 9-fold for H3K27ac, 7-fold for H3K4me3, Fisher test 240



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p<2x10-16, Figure 4B and Supplemental Figure 3B. Chromatin state modeling 241

(chromoHMM18) revealed that all iOCR are enriched at active promoters, bivalent 242

promoters, and active enhancers, as defined by histone modification ChIP-seq in both 243

naïve and TFH cells19–21 (Figure 4C and Supplemental Figure 3C). Open promoter 244

regions involved in promoter interactions (prOCR) were more specifically enriched with 245

active promoter signatures, while promoter-interacting OCR located in intergenic/intronic 246

space (nonprOCR) were more specifically enriched at poised and active enhancers 247

(Figure 4C). These results indicate that promoter-connected OCR are biochemically 248

distinct from OCR not connected to a promoter, and that this promoter-Capture-C 249

approach enriches for genomic elements that are actively engaged in gene regulation. 250

Using this open chromatin-promoter interaction landscape, we were able to connect 251

the promoters of 18,669 genes (associated with 79,330 transcripts) to their corresponding 252

putative regulatory elements, representing 145,568 distinct gene-OCR interactions. 253

Roughly half (47%, 68,229) of these interactions occur in both naive and TFH cells, while 254

24% (34,928) occur uniquely in naïve cells, and 29% (42,411) are only found in TFH 255

(Figure 5A). Overall, 91% of OCR-connected genes (17,021) exhibit at least one 256

differential promoter-OCR interaction in naïve vs. follicular helper T cells. The majority 257

(82%) of OCR-connected genes were incorporated into regulatory structures consisting 258

of more than one distal regulatory region in naïve and follicular helper T cells. On average, 259

each of these connected genes interact with 6 OCR in both naïve CD4+ T cells and 260

follicular helper T cells (4 in median, Supplemental Figure 4), with 10% of these genes 261

involved in 13 or more interactions with distal OCR. More interestingly, the degree of 262

spatial connectivity exhibited by a promoter tends to positively correlate with the level of 263



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gene expression in a lineage-specific manner (Figure 5B and C). The common, highly-264

connected promoters in both cell types drive the expression of genes involved in cell 265

cycle, DNA organization and repair, protein and RNA biogenesis and trafficking, and TCR 266

signaling (Supplemental Figure 5). In addition, highly interactive promoters in naïve cells 267

are involved in quiescence, signal transduction and immune function (e.g., FOXP1, 268

CCR7, IKZF1, CD3, FYN, GRB2, GRAP2, BIRC2/3; Figure 5B), while gene promoters 269

that exhibit complex regulatory architectures in TFH are highly expressed in TFH and are 270

involved in TFH and T cell differentiation, survival, homing, and function (e.g., BCL6, 271

CXCR5, CD40L, CTLA4, ICOS, CD2, CD3, CD28, CD69, TCF7, NFAT1, BATF, ITK, 272

IKZF2, IKZF3, IL21R, FAS; Figure 5C). An example is the CD28-CTLA4-ICOS multi-273

locus region. In naïve CD4+ T cells, which express CD28 but not CTLA4 or ICOS, the 274

CD28 promoter is engaged in multiple interactions with 8 downstream regions of open 275

chromatin (Figure 5D, blue), while the CTLA4 and ICOS promoters are much less 276

interactive. In TFH, which express all three genes, the CD28, CTLA4, and ICOS 277

promoters adopt extensive, de novo spatial conformations involving contacts with more 278

than two dozen putative regulatory elements within the TFH-specific open chromatin 279

landscape (Figure 5D, red). Together, these results reveal major restructuring of the T 280

cell gene regulatory architecture that occurs as naïve helper T cells differentiate into 281

follicular helper T cells, and indicate that complex, three-dimensional regulatory 282

architectures are a feature of highly expressed, lineage-specific genes involved in 283

specialized immune functions in this disease-relevant cell types. 284



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Disease-associated variant-to-gene mapping for SLE 285

The open chromatin landscape of follicular helper T cells from the three healthy tonsils 286

studied contained 393 accessible genomic regions that harbor SLE disease variants 287

(Supplemental Table 3), representing the TFH component of the potential cis-regulatory 288

landscape of SLE. While 33% of accessible variants (132 proxy SNPs) reside in 289

promoters, 67% of accessible SLE SNPs (N=261) are in non-promoter regions. 290

Therefore, the role these non-promoter variants play in gene transcription, and which 291

genes they control, is not clear from these one-dimensional epigenomic data. To 292

determine whether spatial proximity of a gene to an open SLE SNP in three dimensions 293

is a predictor of its role in TFH and/or SLE, we explored the 3D cis-regulatory architecture 294

of SLE genetic susceptibility based upon open chromatin region interaction landscape 295

generated in TFH cells, effectively mapping 256 open SLE variants (69 sentinels, r2>0.4) 296

to 330 potential target genes (1107 SNP-target gene pairs). This 3D variant-to-gene map 297

shows that only ~9% (22) of the SLE variants that reside in TFH open chromatin interact 298

exclusively with the nearest gene promoter (Supplemental Table 6). An example of this 299

category is rs35593987, a proxy to the SLE sentinel SNP rs11889341 and rs4274624 300

that resides in a TFH OCR and loops ~99 kb to interact with the STAT4 promoter (Figure 301

6A). Another ~30% (75) of open SLE variants interact with nearest promoter, but also 302

with the promoters of more distant genes (Figure 6A, Supplemental Table 6). An 303

example of this category is rs112677036, a proxy to the SLE sentinel SNP rs12938617 304

that resides in the first intron of IKZF3, interacts with nearby IKZF3 promoter, but also 305

interacts with promoters of two 157kb upstream genes PGAP3 and ERBB2 (Figure 6B). 306

Remarkably, over 60% of all open SLE variants (159) ‘skip’ the nearest gene to interact 307



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with at least one distant promoter (Supplemental Table 6). Examples of this most 308

abundant category are rs34631447, a proxy to the SLE sentinel rs6762714 SNP that 309

resides in open chromatin in the sixth intron of the LPP locus, and the intergenic 310

rs527619 and rs71041848 SNPs proxy to SLE sentinel rs4639966. Our 3D regulatory 311

map in TFH cells demonstrates that the ‘LPP’ variant in fact does not interact with the 312

LPP promoter, but instead is incorporated into a chromosomal loop structure spanning 313

over 1 Mb that positions it in direct, spatial proximity to the promoter of BCL6, the ‘master’ 314

transcription factor of follicular helper T cells22–26 (Figure 6C). Similarly, the OCR 315

containing the SLE proxies rs527619 and rs71041848 does not contact the nearby TREH 316

gene, but instead loops to interact with the promoter of the TFH-specific chemokine 317

receptor gene CXCR5, nearly 200 kb away (Figure 6D). Other relevant examples of this 318

class of SLE SNPs are rs3117582 and rs7769961, proxies to SLE sentinel SNP 319

rs1150757 and rs9462027, respectively. These SNPs in TFH open chromatin were found 320

in contact with LSM2 and SNRPC (Supplemental Figure 6), 35 to 150 kb away. Both of 321

these genes encode proteins that participate in the processing of nuclear precursor 322

messenger RNA splicing, and are frequently the targets of autoantibodies produced by 323

patients with SLE27,28. 324

Ontology of the set of genes found physically connected to open SLE variants showed 325

enrichment for pathways involved in dendritic cell maturation, T-B cell interactions, T 326

helper differentiation, NFkB signaling, and costimulation through CD28, ICOS, and CD40 327

(Figure 7A). The top three disease networks enriched in SLE SNP-connected genes are 328

systemic autoimmune disorders, rheumatic disease, and type 1 diabetes, all inflammatory 329

disorders involving autoantibody-mediated pathology (Figure 7B). At least 200 of these 330



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connected genes are differentially expressed between naïve and follicular helper T cells 331

(Supplemental Tables 2 and 6), and many have known roles in TFH and/or T cell 332

function (e.g., BCL6, CXCR5, TCF7, PRDM1, IKZF3, IKZF2, IRF8, ETS1, ELF1, EBI3, 333

PTPN22, PDL1, TET3, IL19, IL20). Similarly, SLE SNP-connected genes are highly 334

regulated (P<10-6) in a hierarchical manner by IFNg, IL-2, IL-21, IL-1, IL-27, CD40L, and 335

TCR/CD28 (Figure 7C). We also compared our list of genes found physically associated 336

with SLE SNPs in TFH with those found statistically associated with SLE variants through 337

eQTL studies in two distinct human subject cohorts. One study by Odhams et al. identified 338

97 gene-SNP eQTL in B-LCL lines29, while another by Bentham et al. used B-LCL lines 339

and undifferentiated leukocyte subsets from peripheral blood to identify 41 SLE eQTL7. 340

Over one-third (14/41) of the SNP-gene associations implicated by the Bentham study 341

were also identified by our promoter-Capture-C approach in tonsillar TFH cells from three 342

healthy donors (ANKS1A, C6orf106, RMI2, SOCS1, PXK, UHRF1BP1, LYST, 343

NADSYN1, DHCR7, C15orf39, MPI, CSK, ULK3, FAM219B; Supplemental Figure 7). 344

Similarly, 13% (13/97) of the genes implicated by Odhams et al. were found connected 345

to the same SLE SNPs in tonsillar TFH cells (LYST, NADSYN1, DHCR7, C15orf39, MPI, 346

CSK, ULK3, FAM219B, TNIP1, CCDC69, SPRED2, RP11, Supplemental Figure 7), for 347

a total of 16% (19/119) SNP-gene pairs overlapping between both studies. These results 348

indicate that a gene’s spatial proximity to an accessible, disease-associated SNP in 3D 349

is a strong predictor of its role in the context of both normal TFH biology and SLE disease 350

pathogenesis. 351



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Genomic regions identified by SLE GWAS and 3D epigenomics regulate major TFH 352

genes 353

354

To validate that genomic regions implicated by ATAC-seq, promoter-focused Capture-355

C, and SLE-associated genetic variation function as bona fide distal regulatory elements 356

for their connected promoters, we used CRISPR/CAS9 to specifically delete several iOCR 357

harboring SLE variants from the Jurkat T cell genome. We first targeted the intergenic 358

region near the TREH gene that harbors the rs527619 and rs71041848 proxies to the 359

rs4639966 SLE sentinel SNP, and was captured interacting with the CXCR5 promoter 360

(Supplemental Figure 8). Neither untargeted parental Jurkat cells nor a control-targeted 361

Jurkat line express CXCR5, but deletion of this region led to induction of CXCR5 in 362

approximately half of the cells (Figure 8A). Similarly, parental and control-targeted Jurkat 363

cells do not express IKZF1, which encodes the transcription factor Ikaros, but deletion of 364

the OCR containing the rs4385425 proxy SNP to the sentinel SLE SNP rs11185603 365

(Supplemental Figure 8) induced expression of Ikaros in nearly half of the cells (Figure 366

8B). We also targeted the TFH-specific open chromatin region in the sixth intron of the 367

LPP gene (Supplemental Figure 8) that harbors the rs34631447 and rs79044630 SNPs 368

proxy to sentinel rs6762714 SLE SNP, and was observed interacting with the promoter 369

of BCL6. BCL6 is not expressed by parental or control-targeted Jurkat cells, but is induced 370

by IFN-gamma (Figure 8C). However, inducible expression of BCL6 was completely 371

abrogated in Jurkat cells lacking the ~150 bp SLE-associated LPP OCR (Figure 8C). 372

These results confirm that these distal SLE-associated regions, which are located 373

hundreds to thousands of kilobases away in one dimension, interact with and act as 374



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crucial regulatory elements for the genes encoding the master TFH transcription factor 375

BCL6, the IKZF1 transcriptional repressor, and the TFH chemokine receptor CXCR5. 376

These results indicate that the 3D promoter connectomes detected in these cells reveal 377

bona fide gene regulatory architectures. 378

379

SLE-associated open chromatin-promoter connectomes implicate novel genes 380

involved in TFH function 381

382

From the set of 243 promoter-connected open SLE variants in TFH cells, we noted a 383

subset of variants that skipped nearby promoters to interact with genes that are 384

upregulated upon TFH differentiation, but have no known specific role in TFH biology. 385

These genes are enriched in canonical pathways such as mannose degradation (MPI), 386

epoxysqualene biosynthesis (FDFT1), di- and tri-acylglycerol biosynthesis (LCLAT1, 387

AGPAT1), cholesterol biosynthesis (DHCR7, FDFT1), oxidized GTP/dGTP detoxification 388

(DDX6), breast and lung carcinoma signaling (ERRBB2, HRAS, RASSF5, CDKN1B), 389

tRNA splicing (TSEN15, PDE4A), pentose phosphate pathway (TALDO1), acetyl-coA 390

biosynthesis (PDHB), dolichyl-diphosphooligosaccharide biosynthesis (DPAGT1), and 391

valine degradation (HIBADH). Two of these genes, HIPK1 and MINK1 (Figure 9A), 392

encode a homeobox-interacting kinase and a MAP3/4K homolog that each regulate gene 393

expression in other cell types30,31. Like many genes in this category, both HIPK1 and 394

MINK1 are upregulated in TFH, and their promoters interact with OCR that are genetically 395

associated with SLE risk, suggesting they are involved in TFH function. To test this, we 396

transduced TFH differentiated in vitro from naïve CD4+ T cells32 (Figure 9B and C) with 397



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a lentiviral vector expressing shRNA targeting the HIPK1 transcript to knock down HIPK1 398

expression (Figure 9D), or with scrambled or B2M shRNA as controls (Supplemental 399

Figure 9). GFP+ cells were sorted, restimulated with CD3/28 beads, and secretion of IL-400

21, the major cytokine required for T cell help for B cell antibody production, was 401

measured in the supernatant by ELISA. Remarkably, targeting of HIPK1 expression had 402

no effect on in vitro TFH differentiation as measured by induction of BCL6, PD-1 or 403

CXCR5 (Supplemental Figure 9), but resulted in a ~3-fold decrease in IL-21 production 404

(Figure 9E). To determine if pharmacologic targeting of HIPK1 can also modulate TFH 405

function, we treated in vitro differentiated TFH with the HIPK1/2 inhibitor A64. As with 406

genetic targeting, pharmacologic inhibition of HIPK activity resulted in a dose-dependent 407

reduction in IL-21 production by activated TFH cells (Figure 9F) without effecting 408

proliferation, viability, or differentiation (Supplemental Figure 9). As a further test of 409

whether SLE-associated promoter-OCR connectomes can reveal novel drug targets for 410

TFH function, we targeted MINK1 pharmacologically with the MAP3/4K antagonist 411

PF06260933. Treatment with this inhibitor resulted in a dose-dependent reduction in IL-412

21 secretion by TFH cells, with an ED50 of 5 nM (Figure 9G). Unlike the HIPK1 inhibitor, 413

this MINK1 inhibitor did impact T cell IL-2 production and proliferation, but with an ED50 414

8- to 10-fold higher than its effect on IL-21 production (Supplemental Figure 9). These 415

data show that integrated, 3-dimensional maps of disease-associated genetic variation, 416

open chromatin, and promoter connectomes can lead to bona fide novel drug targets that 417

control tissue-specific and SLE-relevant biology. 418



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DISCUSSION 419

420

In this study, we used systems-level integration of disease-associated genetic 421

variation and 3-dimensional epigenomic maps of the interactions between open 422

chromatin and promoters in a highly disease-relevant tissue to identify putative disease-423

associated regulatory regions and the genes they influence. Importantly, this study does 424

not aim to measure the impact of SLE-associated genetic variation on chromatin 425

accessibility or promoter interactions, and therefore does not require large sample sizes 426

from SLE patients and normal individuals. Rather, we use the location of reported, 427

potentially causal SLE variants as ‘signposts’ to identify long-range open chromatin 428

elements that may regulate SLE-relevant gene expression in the context of normal 429

follicular helper T cell biology. 430

Our one-dimensional open chromatin analyses demonstrated a strong correlation 431

between promoter accessibility and differential gene expression in human tonsillar naive 432

vs. follicular helper T cells. These analyses also showed that SLE-associated variants in 433

accessible promoters in TFH generally coincide with highly expressed genes enriched in 434

autoimmune disease pathways, suggesting that TFH open chromatin landscapes 435

represent a useful filter through which functional, systemic autoimmune disease-436

associated variants can be identified out of the thousands of sentinel and proxy SNPs to 437

the sentinel signals implicated by GWAS. 438

However, only 20% of OCR are located in promoter regions, while 80% of the open 439

chromatin regions in human naive and follicular helper T cells map to non-coding/non-440

promoter regions of the genome, making an interpretation of the potential regulatory role 441

of these regions challenging. To overcome this problem, we generated high-resolution, 442



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comprehensive maps of the open chromatin-promoter connectome in naive and TFH 443

cells, allowing physical assignment of non-coding OCR and SNPs to genes, and revealing 444

the potential regulatory architectures of nearly all coding genes and over half of non-445

coding genes in human immune cell types with crucial roles in humoral immunity and 446

systemic immunopathology. Similar to previous promoter interactome studies33, we found 447

that promoter-interacting regions are enriched for open chromatin and the chromatin-448

based signatures of enhancers. However, we also found that open chromatin regions that 449

interact with a promoter are enriched over 10-fold for enhancer marks compared to OCR 450

that are not connected to a promoter, suggesting that promoter-focused Capture-C 451

preferentially identifies non-coding regions with gene regulatory activity. We also 452

observed enrichment of enhancer marks at open promoters engaged in promoter-453

promoter interactions compared to promoters not connected to another promoter, 454

suggesting that promoters may synergize in three dimensions in an enhancer-like manner 455

to augment expression of their connected genes34. 456

Our study shows that, similar to previous estimates35, less than 10% of promoter 457

interactions exclusively involve the nearest genes. Over 90% of accessible disease 458

variants interact with distant genes, and that over 60% of open variants skip the nearest 459

gene altogether and exclusively interact only with distant genes. Importantly, we were 460

able to validate direct roles for several SLE-associated distal OCR in the regulation of 461

their connected genes (BCL6, CXCR5, IKZF1) using CRISPR/CAS9-mediated editing in 462

human T cells, suggesting that SLE-associated genetic variation at distant loci can 463

operate through effects on genes with known roles in TFH and/or SLE biology. A locus 464

control region ~130 kb upstream of the BCL6 gene has been defined previously in 465



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germinal center B cells36, and we also find evidence for usage of this region by human 466

TFH cells at the level of open chromatin, histone enhancer marks, and long-range 467

connectivity to the BCL6 promoter (Supplemental Figure 10). However, we also observe 468

a much more distant ‘stretch’ enhancer within the LPP gene in TFH cells, as evidenced 469

by extensive open chromatin, H3K27 acetylation, and H3K4 mono-methylation 470

(Supplemental Figure 10). This region shows extensive connectivity with BCL6 in the 471

3D architecture of the nucleus, and the 1 Mb distal SLE-associated BCL6 enhancer 472

validated by genome editing in this study is contained within this BCL6 stretch enhancer. 473

This enhancer region is occupied in lymphoid cell lines by NFkB/RelA and POU2F2, both 474

transcription factors known to positively regulate immunoglobulin and inflammatory gene 475

expression (ENCODE). Long-range regulatory elements for CXCR5 have not been 476

previously identified, and the -180 kb SLE-associated element in this study is the first 477

validated for CXCR5. Deletion of this element led to increased expression of CXCR5, 478

suggesting that unlike the distal BCL6-LPP enhancer, this element is a silencer in Jurkat 479

cells. Consistent with this finding, this region is occupied by the repressive transcription 480

factors YY1, BHLHE40 and BATF in lymphoid cells (ENCODE), but its function in primary 481

TFH cells remains to be determined. These distant SNP-gene regulatory pairs join 482

examples like the FTO and TCF7L2 loci1,3, in which GWAS data were interpreted to 483

implicate the nearest genes, while 3D epigenomics and functional follow-up showed that 484

the disease variants actually reside in elements that regulate the distant IRX3 gene (for 485

FTO) and the ACSL5 gene (for TCF7L2). Our results indicate that a gene’s spatial 486

proximity in three dimensions to a regulatory SLE SNP is a strong predictor of its function 487

in the context of TFH biology and SLE disease pathogenesis, and suggest that 488



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assumptions that a given genomic feature (e.g., SNP or TF binding motif) or epigenomic 489

feature (e.g., 5meCpG, 5hmCpG, or histone mark) identified by 1D mapping of the human 490

genome regulates the nearest gene are more likely to be incorrect than correct. These 491

data have important implications for the interpretation of all genetic and epigenomic 492

studies in all tissues. 493

Remarkably, our integrated open chromatin and promoter connectome mapping in 494

tonsillar TFH cells from three healthy individuals identified one-third of the SNP-gene 495

pairs identified by Bentham7, and 13% of the SNP-gene pairs identified by Odhams29. 496

These quantitative trait studies require samples from hundreds of individuals, and the 497

data are obtained from blood, B-LCL, or naïve mononuclear leukocytes. However, 498

immune responses do not take place in the blood, and the pathophysiologic aspects of 499

inflammatory disease are mediated by specialized, differentiated immune cell types that 500

are rare or not present in blood. Our approach utilized human follicular helper T cells from 501

tonsil that are ‘caught in the act’ of mediating coordinated in vivo T-B immune responses, 502

and is the same cell type involved in B cell help for autoantibody production in SLE. In 503

addition, this variant-to-gene mapping approach identified ~10-fold more SLE SNP-gene 504

association than current eQTL studies; further follow up work will determine how many of 505

these associations are valid vs. false positives. 506

In addition to revealing the previously unknown SLE-associated regulatory 507

architectures of known TFH/SLE genes, we show that the combination of GWAS and 3-508

dimensional epigenomics can identify genes with previously unappreciated roles in 509

disease biology through their connections with accessible disease SNPs. In a previous 510

study, we implicated the novel gene ING3 by virtue of its interaction with an accessible 511



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osteoporosis-associated SNP, and showed that this gene is required for osteoclast 512

differentiation in an in vitro model14. In this current study, approximately two dozen ‘novel’ 513

genes up- or down-regulated during differentiation of naive CD4+ T cells into TFH were 514

implicated through their connection to SLE SNPs. Among these are HIPK1, a nuclear 515

homeobox-interacting protein kinase that cooperates with homeobox, p53, and 516

TGFB/Wnt pathway transcription factors to regulate gene transcription30,37–39. A role for 517

HIPK1 in T-independent B cell responses has be identified in the mouse40, but no role for 518

this kinase has been previously established in TFH or SLE. Another gene implicated in 519

our study is MINK1, which encodes the misshapen-like kinase MAP4K6. This kinase 520

functions upstream of JNK and SMAD in neurons41,42, and has been shown to inhibit 521

TGFB-induced Th17 differentiation43. However, a role in TFH or SLE has likewise not 522

been previously appreciated. We show that genetic and/or pharmacologic targeting of 523

HIPK1 or MINK1 in human TFH cells inhibits their production of IL-21, a cytokine required 524

for T cell-mediated help for B cell antibody production44. While further work is required to 525

elucidate the role of these kinases in TFH biology and the pathogenesis of systemic 526

autoimmunity, these examples show the utility of this integrated approach in identifying 527

novel targets for drug repurposing or new compound development in complex heritable 528

diseases. 529



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METHODS 530

531

Purification of naïve and follicular helper T cells from human tonsil 532

Fresh tonsils were obtained from immune-competent children (n=10) undergoing 533

tonsillectomy to address airway obstruction or a history of recurrent tonsillitis. The mean 534

age of donors was 5.7 years (range 2-16 years) and 50% were male. Tonsillar 535

mononuclear cells were isolated from tissues by mechanical disruption (tonsils were 536

minced and pressed through a 70 micron cell screen) followed by Ficoll-Paque 537

centrifugation. CD19-positive cells were removed (StemCell) and CD4+ T cells were 538

enriched with magnetic beads (Biolegend) prior to sorting naïve T cells (CD4+CD45RO-) 539

and T follicular helper cells (CD4+CD45RO+CD25loCXCR5hiPD1hi) on a MoFlo Astrios EQ 540

(Beckman Coulter). 541

542

Cell fixation 543

We used standard methods for cell fixation14. Briefly, 107 TFH or naïve CD4+ T cells 544

were suspended in 10 mL RPMI + 10% FBS, followed by an additional 270uL of 37% 545

formaldehyde and incubation for 10 min at RT on a platform rocker. The fixation reaction 546

was quenched by the addition of 1.5 mL cold 1M glycine (4°C). Fixed cells were 547

centrifuged at 1000 rpm for 5 min at 4°C and supernatants were removed. The cell pellets 548

were washed in 10 ml cold PBS (4°C) followed by centrifugation as above. Cell pellets 549

were resuspended in 5 ml cold lysis buffer (10 mM Tris pH8, 10 mM NaCl, 0.2% NP-550

40/Igepal supplemented with a protease inhibitor cocktail). Resuspended cell pellets 551

were incubated for 20 minutes on ice, centrifuged at 1800 rpm, and lysis buffer was 552



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removed. Cell pellets were resuspended in 1 mL of fresh lysis buffer, transferred to 1.5 553

mL Eppendorf tubes, and snap frozen in ethanol/dry ice or liquid nitrogen. Frozen cell 554

pellets were stored at -80°C for 3C library generation. 555

556

3C library generation 557

We used standard methods for generation of 3C libraries14. For each library, 107 fixed 558

cells were thawed at 37ºC, followed by centrifugation at RT for 5 mins at 14,000rpm. The 559

cell pellet was resuspended in 1 mL of dH2O supplemented with 5 uL 200X protease 560

inhibitor cocktail, incubated on ice for 10 mins, then centrifuged. Cell pellet was 561

resuspended to a total volume of 650 uL in dH2O. 50 uL of cell suspension was set aside 562

for pre-digestion QC, and the remaining sample was divided into 3 tubes. Both pre-563

digestion controls and samples underwent a pre-digestion incubation in a Thermomixer 564

(BenchMark) with the addition of 0.3%SDS, 1x NEB DpnII restriction buffer, and dH2O 565

for 1hr at 37ºC shaking at 1,000rpm. A 1.7% solution of Triton X-100 was added to each 566

tube and shaking was continued for another hour. After pre-digestion incubation, 10 ul of 567

DpnII (NEB, 50 U/µL) was added to each sample tube only, and continued shaking along 568

with pre-digestion control until the end of the day. An additional 10 µL of DpnII was added 569

to each digestion reaction and digested overnight. The next day, a further 10 µL DpnII 570

was added and continue shaking for another 2-3 hours. 100 uL of each digestion reaction 571

was then removed, pooled into one 1.5 mL tube, and set aside for digestion efficiency 572

QC. The remaining samples were heat inactivated incubated at 1000 rpm in a MultiTherm 573

for 20 min at 65°C to inactivate the DpnII, and cooled on ice for 20 additional minutes. 574

Digested samples were ligated with 8 uL of T4 DNA ligase (HC ThermoFisher, 30 U/µL) 575



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and 1X ligase buffer at 1,000 rpm overnight at 16°C in a MultiTherm. The next day, an 576

additional 2 µL of T4 DNA ligase was spiked in to each sample and incubated for another 577

few hours. The ligated samples were then de-crosslinked overnight at 65°C with 578

Proteinase K (20 mg/mL, Denville Scientific) along with pre-digestion and digestion 579

control. The following morning, both controls and ligated samples were incubated for 30 580

min at 37°C with RNase A (Millipore), followed by phenol/chloroform extraction, ethanol 581

precipitation at -20°C, the 3C libraries were centrifuged at 3000 rpm for 45 min at 4°C to 582

pellet the samples. The controls were centrifuged at 14,000 rpm. The pellets were 583

resuspended in 70% ethanol and centrifuged as described above. The pellets of 3C 584

libraries and controls were resuspended in 300uL and 20μL dH2O, respectively, and 585

stored at −20°C. Sample concentrations were measured by Qubit. Digestion and ligation 586

efficiencies were assessed by gel electrophoresis on a 0.9% agarose gel and also by 587

quantitative PCR (SYBR green, Thermo Fisher). 588

589

Promoter-Capture-C design 590

Our promoter-Capture-C approach was designed to leverage the four-cutter restriction 591

enzyme DpnII in order to give high resolution restriction fragments of a median of 592

~250bp14. This approach also allows for scalable resolution through in silico fragment 593

concatenation (Supplemental Table 4). Custom capture baits were designed using 594

Agilent SureSelect RNA probes targeting both ends of the DpnII restriction fragments 595

containing promoters for coding mRNA, non-coding RNA, antisense RNA, snRNA, 596

miRNA, snoRNA, and lincRNA transcripts (UCSC lincRNA transcripts and sno/miRNA 597

under GRCh37/hg19 assembly) totaling 36,691 RNA baited fragments through the 598



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genome14. In this study, the capture library was re-annotated under gencodeV19 at both 599

1-fragment and 4-fragment resolution, and is successful in capturing 89% of all coding 600

genes and 57% of noncoding RNA gene types. The missing coding genes could not be 601

targeted due to duplication or highly repetitive DNA sequences in their promoter regions. 602

603

Promoter-Capture-C assay 604

Isolated DNA from 3C libraries was quantified using a Qubit fluorometer (Life 605

technologies), and 10 μg of each library was sheared in dH2O using a QSonica Q800R 606

to an average fragment size of 350bp. QSonica settings used were 60% amplitude, 30s 607

on, 30s off, 2 min intervals, for a total of 5 intervals at 4 °C. After shearing, DNA was 608

purified using AMPureXP beads (Agencourt). DNA size was assessed on a Bioanalyzer 609

2100 using a DNA 1000 Chip (Agilent) and DNA concentration was checked via Qubit. 610

SureSelect XT library prep kits (Agilent) were used to repair DNA ends and for adaptor 611

ligation following the manufacturer protocol. Excess adaptors were removed using 612

AMPureXP beads. Size and concentration were checked by Bioanalyzer using a DNA 613

1000 Chip and by Qubit fluorometer before hybridization. One microgram of adaptor-614

ligated library was used as input for the SureSelect XT capture kit using manufacturer 615

protocol and our custom-designed 41K promoter Capture-C library. The quantity and 616

quality of the captured library was assessed by Bioanalyzer using a high sensitivity DNA 617

Chip and by Qubit fluorometer. SureSelect XT libraries were then paired-end sequenced 618

on 8 lanes of Illumina Hiseq 4000 platform (100 bp read length). 619

620

ATAC-seq library generation 621



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A total of 50,000 to 100,000 sorted tonsillar naive or follicular helper T cells were 622

centrifuged at 550g for 5 min at 4°C. The cell pellet was washed with cold PBS and 623

resuspended in 50 μL cold lysis buffer (10 mM Tris-HCl, pH 7.4, 10 mM NaCl, 3 mM 624

MgCl2, 0.1% NP-40/IGEPAL CA-630) and immediately centrifuged at 550g for 10 min at 625

4°C. Nuclei were resuspended in the Nextera transposition reaction mix (25 ul 2x TD 626

Buffer, 2.5 uL Nextera Tn5 transposase (Illumina Cat #FC-121-1030), and 22.5 ul 627

nuclease free H2O) on ice, then incubated for 45 min at 37°C. The tagmented DNA was 628

then purified using the Qiagen MinElute kit eluted with 10.5 μL Elution Buffer (EB). Ten 629

microliters of purified tagmented DNA was PCR amplified using Nextera primers for 12 630

cycles to generate each library. PCR reaction was subsequently cleaned up using 1.5x 631

AMPureXP beads (Agencourt), and concentrations were measured by Qubit. Libraries 632

were paired-end sequenced on the Illumina HiSeq 4000 platform (100 bp read length). 633

634

ATAC-seq analysis 635

TFH and naïve ATAC-seq peaks were called using the ENCODE ATAC-seq pipeline 636

(https://www.encodeproject.org/atac-seq/). Briefly, pair-end reads from three biological 637

replicates for each cell type were aligned to hg19 genome using bowtie2, and duplicate 638

reads were removed from the alignment. Narrow peaks were called independently for 639

each replicate using macs2 (-p 0.01 --nomodel --shift -75 --extsize 150 -B --SPMR --keep-640

dup all --call-summits) and ENCODE blacklist regions (ENCSR636HFF) were removed 641

from peaks in individual replicates. Peaks from all replicates were merged by bedtools 642

(v2.25.0) within each cell type and the merged peaks present in less than two biological 643

replicates were removed from further analysis. Finally, ATAC-seq peaks from both cell 644



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types were merged to obtain reference open chromatin regions. To determine whether 645

an OCR is present in TFH and/or naïve cells, we first intersected peaks identified from 646

individual replicates in each cell type with reference OCRs. If any peaks from at least one 647

replicate overlapped with a given reference OCR, we consider that region is open in the 648

originating cell type. Quantitative comparisons of TFH and naïve open chromatin 649

landscapes were performed by evaluating read count differences against the reference 650

OCR set. De-duplicated read counts for OCR were calculated for each library and 651

normalized against background (10K bins of genome) using the R package csaw (v 1.8.1). 652

OCR peaks with less than 1.5 CPM (4.5 ~ 7.5 reads) support in the top 50% of samples 653

were removed from further differential analysis. Differential analysis was performed 654

independently using edgeR (v 3.16.5) and limmaVoom (v 3.30.13). Differential OCR 655

between cell types were called if FDR<0.05 and absolute log2 fold change >1 in both 656

methods. 657

658

Promoter-focused Capture-C analysis 659

Paired-end reads from three biological replicates for naïve and follicular helper T cells 660

were pre-processed using the HICUP pipeline (v0.5.9) {Wingett:2015}, with bowtie2 as 661

aligner and hg19 as the reference genome. We were able to detect non-hybrid reads from 662

all targeted promoters, validating the success of the promoter capture procedure. 663

Significant promoter interactions at 1-DpnII fragment resolution were called using 664

CHiCAGO (v1.1.8) {Cairns:2016} with default parameters except for binsize set to 2500. 665

Significant interactions at 4-DpnII fragment resolution were also called using CHiCAGO 666

with artificial .baitmap and .rmap files in which DpnII fragments were concatenated in 667



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silico into 4 consecutive fragments using default parameters except for removeAdjacent 668

set to False. The significant interactions (CHiCAGO score > 5) from both 1-fragment and 669

4-fragment resolutions were exported in .ibed format and merged into a single file using 670

custom a PERL script to remove redundant interactions and to keep the max CHiCAGO 671

score for each interaction. Open chromatin interaction landscapes were established by 672

projecting significant DpnII fragment interactions at merged 1- and 4-fragment resolutions 673

to reference OCR (Figure 3A). First, DpnII fragments involved in significant interactions 674

(both “bait” and “other end”) were intersected with reference OCR using bedtools 675

(v2.25.0). Interactions between bait and other end OCR pairs were called independently 676

for each cell type if their overlapped fragments interacted at either resolution and if both 677

OCR were called as “open” in the corresponding cell type. OCR involved in promoter 678

interactions (iOCR) were classified as promoter OCR (prOCR) or regulatory OCR 679

(nonprOCR) by comparing their genomic locations to pre-defined promoter regions (-680

1500bp ~ 500bp of TSS) of transcripts in GENCODE V19 and UCSC noncoding RNA 681

described above. If both ends of an OCR interaction failed to overlap a gene promoter, 682

that interaction was removed. OCR pair interactions were combined from both cell types 683

to obtain the reference open chromatin promoter-captured interaction landscapes. 684

685

Microarray analysis of gene expression 686

RNA from two biological naïve tonsillar CD4+ T cell replicates and four biological tonsillar 687

TFH replicates were hybridized to Affymetrix Human Clarion S arrays at the CHOP 688

Nucleic Acid and Protein Core. Data were pre-processed (RMA normalization), and 689

analyzed for differential expression (DE) using Transcriptome Analysis Console v 4.0 with 690



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a false discovery rate (FDR) threshold of 0.05 and a fold-change (FC) threshold of 2. Lists 691

of differentially expressed genes were generated and ranked by log2 fold change. The 692

log2 fold change of the genes with significantly differential accessibility at promoter 693

regions were compared to the pre-ranked gene expression data for GSEA enrichment 694

analysis. 695

696

Gene set enrichment and Ingenuity pathway analysis 697

Histone mark and CTCF ChIP-seq datasets for naïve and follicular helper T cells were 698

obtained from public resources19–21 and compared to promoter-interacting fragments or 699

promoter-interacting OCR. Enrichment of promoter-interacted fragments (PIR) for histone 700

marks and CTCF regions was determined independently in each cell type using the 701

function peakEnrichment4Features() in the CHiCAGO package, and feature enrichment 702

at promoter-interacting OCR were compared to enrichment at non-promoter-interacting 703

OCR using the feature enrichment R package LOLA (v1.4.0)45. Fisher’s exact tests were 704

performed and odd ratios were plotted for significant enrichment (pvalue<10-6) using 705

ggplot2. The chromatin states of promoter-interacting OCR were also determined using 706

ChromHMM (v1.17) on binarized bed file of histone marks ChIP-seq peaks with 15 states 707

for naïve T cells and 6 states for TFH cells. The annotation of chromatin states was 708

manually added with the reference to epigenome roadmap project20. Ingenuity pathway 709

analysis (IPA, QIAGEN) was used for all the pathway analysis. The top significantly 710

enriched canonical pathways were plotted using ggplot2 and networks with relevant 711

genes were directly exported from IPA. 712

713



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CRISPR/CAS9 genome editing 714

CRISPR guide RNAs (sgRNA) targeting rs34631447, rs79044630, rs527619, 715

rs71041848, and rs4385425 were designed using http://crispr.tefor.net and cloned into 716

lentiCRISPRv2-puro or lentiCRISPRv2-mCherry (Feng Zhang, Addgene plasmid #52961; 717

http://n2t.net/addgene:52961; RRID:Addgene_52961) by golden gate ligation using the 718

BsmB1 restriction enzyme (NEB). 293T cells were transfected in DMEM using 719

Lipofectamine 2000 (Invitrogen) with 6 ug PsPAX2 and 3.5 ug PmD2.G packaging 720

plasmids and 10 ug empty lentiCRISPRv2 or 10 ug sgRNA-encoding lentiCRISPRv2. 721

Viral supernatants were collected after 48 hrs for transduction into Jurkat leukemic T cells 722

maintained in RPMI 1640 with 10% fetal bovine serum, L-glutamine, 2-mercaptoethanol, 723

and penicillin/streptomycin. Cells were seeded in a 24 well plate at 0.5 x106 in 0.5 mL of 724

media per well, and 1 mL of viral supernatant with 8 ug/mL of polybrene was added to 725

each well. Spin-fection was performed for 90 min. at 2500 rpm and 25oC, and transduced 726

cells were equilibrated at 37C for 6 hrs. For rs34631447, rs79044630, and rs4385425, 727

1.2 ml of media was removed and replaced with 1 ml of fresh media containing 1 ug of 728

puromycin for 7 days of selection before use in experiments. Cells transduced with 729

sgRNAs targeting rs527619 and rs71041848 were sorted based on mCherry on a FACS 730

Jazz (BD Biosciences). Mutations were analyzed by PCR coupled with Sanger 731

sequencing at the CHOP Nucleic Acids and Protein Core. The following primers were 732

used for PCR: BLC6-F: CTCTGTGGTTGTGGGCAAGGC-733

R:CAGGTGGCGAATCAGGACAGG, CXCR5-F: GTCCCTGGTGATGGAAACTCAGGC-734

R: GCAGTGGCCTCCCTTACACAGG , IKZF1-F: CCTTCTCCATGCCCAGGTGACTC-R: 735

GGCCTCAGCTAGGCAAACCAGAG. Measurement of BCL-6 expression in targeted 736



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Jurkat lines was assessed by flow cytometry using anti-human APC-BCL-6 (Biolegend) 737

after treatment with human recombinant IFNg (5 ng/mL, R&D Systems) overnight and 738

stimulation with PMA (30 ng/mL) and ionomycin (1 uM, Sigma-Aldrich) for 4-6 hrs. 739

Expression of Ikaros and CXCR5 by targeted Jurkat lines was also assessed by flow 740

cytometry using anti-human APC-CXCR5 (Biolegend) and anti-human PE-Ikaros (BD 741

Biosciences). Fixation, permeabilization and intracellular staining for Ikaros and BCL-6 742

was performed using the Transcription Factor Buffer Set (BD Pharmingen). Cells were 743

analyzed on a CytoFLEX flow cytometer (Beckman Coulter). 744

745

Lentiviral shRNA-based gene targeting 746

A lentiviral shRNA-based approach was employed to silence the expression of HIPK1 as 747

well as B2M as a positive control. The lenti-shRNA vectors pGFP-C-shRNA-Lenti-Hipk1, 748

pGFP-C-shRNA-Lenti-B2M and pGFP-C-scrambled were purchased from Origene. The 749

packaging vectors PmD2G and PsPAX.2 were obtained from Addgene. Exponentially 750

growing 293T cells were split and seeded at 8 x 106 cells in 100 mm dishes in RPMI 1640 751

medium at 37C. The following day, cells were transfected in antibiotic- and serum-free 752

medium with lenti shRNA plus packaging vector DNA prepared in a complex with 753

Lipofectamine 2000. After 6 hrs of transfection, medium was replaced with complete 754

serum containing RPMI medium and cells were cultured at 37C for 2 days. Human 755

primary CD4+ T cells from healthy donors were obtained from the University of 756

Pennsylvania Human Immunology Core and stimulated overnight with human anti-CD3- 757

and anti-CD28-coated microbeads. Cells were harvested, de-beaded, washed with warm 758

RPMI medium, and aliquots of 106 activated CD4+T cells were infected with 1 ml of viral 759



https://doi.org/10.1101/2019.12.20.885426


36

supernatant collected from lenti-shRNA transfected 293T cell cultures. Polybrene was 760

added to the viral supernatant at 8 ug/ml, cells were spin-fected at 2500 rpm for 1.5 hrs, 761

cultured at 37C for 6 hrs, and restimulated with anti-CD3 and anti-CD28 beads, Activin A 762

(100 ng/ml), IL-12 (5 ng/ml), and anti-IL-2 (2 ug/ml) to induce in vitro TFH differentiation 763

{Locci;2016}. After 4 days of differentiation, transduced cells were FACS-sorted based on 764

GFP expression, and expression of B2M, BCL-6, CXCR5 and PD-1 was measured by 765

flow cytometry. In addition, sorted GFP+ in vitro TFH cells were restimulated with plate-766

bound human anti-CD3 and anti-CD28 (1 ug/ml each) in flat bottom 96 well plates, and 767

supernatants were collected at the indicated timepoints for assessment of IL-21 secretion 768

by ELISA. RNA was extracted from sorted GFP+ TFH cells using an RNeasy micro kit 769

(Qiagen), treated with DNase, and 500 ng of total RNA was reverse-transcribed using 770

iScript cDNA synthesis kit (Bio-Rad). qRT-PCR quantification of HIPK-1, B2M and 18s 771

rRNA transcripts was performed using Amplitaq Gold SYBR Master mix (ABI) on Applied 772

Biosytems step one plus real- time thermocycler. Specific mRNA levels were determined 773

as ratio of total 18s rRNA. The following primer sequences were used for qRT PCR: HIPK-774

1-F: CAGTCAGGAGTTCTCACGCA, HIPK-1-R: TGGCTACTTGAGGGTGGAGA, B2M-775

F: GCCGTGTGAACCATGTGACT, B2M-R: CATCCAATCCAAATGCGGCA, hu 18S-F: 776

CCTTTAACGAGGATCCATTGGA, hu 18S-R: CGCTATTGGAGCTGGAATTACC. 777

778

Pharmacologic inhibitors 779

The HIPK kinase family inhibitor A64 trifluoroacetate was purchased from Sigma, and the 780

MAP4K2 inhibitor PF06260933, which also inhibits MINK1 and TNIK, was purchased from 781

TOCRIS. Human primary CD4+ T cells were cultured under TFH condition for 5 days in 782

the presence of the indicated concentrations of each inhibitor (150 nM to 2500 nM for 783



https://doi.org/10.1101/2019.12.20.885426


37

A64, 3.7 nM to 100 nM for PF06260933). In addition, anti-CD3- and anti-CD28-stimulated 784

human CD4+ T cells (non-TFH) were cultured in the presence of inhibitors. After 5 days 785

of primary culture, cells were harvested and 106 cells were restimulated with plate-bound 786

human anti-CD3+ anti-CD28 (1 ug/ml each) in the presence of inhibitors. Culture 787

supernatants were collected at the indicated timepoints for measurement of IL-2 and IL-788

21 by ELISA (eBioscience). 789

790 791 Data availability 792

Our data are available from ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) with 793

accession numbers E-MTAB-6621 (promoter-Capture-C), E-MTAB-6617 (ATAC-seq), 794

and E-MTAB-6637(expression microarray) respectively. 795

796

SUPPLEMENTAL TABLE 1. Reference ATAC-seq peaks in naive and TFH cells. 797

798

SUPPLEMENTAL TABLE 2. Microarray-based gene expresssion analysis. 799

800

SUPPLEMENTAL TABLE 3. OCR harboring SLE proxy SNPs. 801

802

SUPPLEMENTAL TABLE 4. Summary of 1- and 4-fragment-based promoter interaction 803

calls. 804

805

SUPPLEMENTAL TABLE 5. Summary of OCR-based promoter interaction calls. 806

807



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38

SUPPLEMENTAL TABLE 6. SLE variant-to-gene mapping using promoter-OCR 808

connectomes in follicular helper T cells. 809



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39

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Acknowledgements 920 921 This work was supported by The Center for Spatial and Functional Genomics at The 922 Children’s Hospital of Philadelphia (ADW and SFG), the Daniel B. Burke Endowed Chair 923 for Diabetes Research (SFG), the Jeffrey Modell Foundation (NR), and NIH grants 924 AI123539 (ADW), DK122586 (ADW, SFG), HG010067 (SFG), AI146026 (NR), and 925 AI115001 (NR). 926 927 928 Author Contributions 929 930 C.S. assisted with preparation of the manuscript and conducted the epigenomic and 931 transcriptomic analyses with the help of E.M.; A.C. designed the custom Capture-C 932 promoter probe set; C.L.C. provided human tonsillar T cells, N.R. contributed to the 933 design of the immunologic studies and provided microarray data; M.E.J, M.E.L, S.L., 934 K.M.H, and J.P. generated and sequenced the epigenomic libraries; A.T., R.M.T. and 935 P.S. performed the CRISPR/CAS, shRNA and pharmacologic targeting experiments; 936 S.F.G. directed the genomic and epigenomic studies; A.D.W. directed the epigenomic 937 and immunologic studies and wrote the manuscript. C.S., M.E.J., A.T., R.M.T contributed 938 equally, and S.F.G and A.D.W. are co-senior authors. 939



https://doi.org/10.1101/2019.12.20.885426


BA

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FIGURE 1.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted December 21, 2019. ; https://doi.org/10.1101/2019.12.20.885426doi: bioRxiv preprint

Wells, Andrew

FIGURE 1. ATAC-seq analysis of open chromatin landscapes in naïve and follicular helper T cells from human tonsil. a. Quantitative differences between naive and follicular helper T cell open chromatin landscapes. A total of 91,222 OCR were used as reference for differential analysis of genome accessibility. The number of statistically up- (green) or down- (red) regulated OCR in TFH compared to naïve is shown as a Venn diagram and also plotted as a function of log2 fold change. b. GSEA enrichment analysis of genes with differential promoter accessibility at promoter regions. The log2 fold change in expression between naïve and follicular helper T cells was used to generate the pre-ranked list for GSEA.

https://doi.org/10.1101/2019.12.20.885426


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Wells, Andrew

FIGURE 2. Genes harboring accessible SLE variants in naïve and follicular helper T cells. a. Comparison of accessible SLE SNPs between TFH and naïve tonsillar T cells. b. Comparison of the expression levels of genes with accessible SLE SNPs in their promoters in TFH vs. all genes or a random sample of genes with no accessible SLE SNPs in their promoters. A Wilcoxon rank test was performed to evaluate the significance of differential expression between gene sets. c. Ingenuity disease network for the genes with accessible SLE variants at promoters. The color gradient represents the log2 fold change IN expression in TFH compared to naïve T cells.

https://doi.org/10.1101/2019.12.20.885426


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FIGURE 3 .CC-BY-NC-ND 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made


Wells, Andrew

FIGURE 3. High-resolution, fragment-based Capture-C analysis of promoter connectomes in naïve and follicular helper T cells. a. Cartoon depicting the approaches for 1 DpnII fragment promoter interaction analysis, 4 DpnII fragment promoter interaction analysis, and promoter-OCR interaction analysis. b. The relationship between the number of interactions per gene promoter and expression of the corresponding gene is shown. Gene expression was binned into the lowest 20th, 20-40th, 40-60th, 60-80th and >80th percentiles . Lower and upper boxplot hinges correspond to the first and third quartiles, and outliers were defined as > 1.5 * IQR from the hinge. WashU browser depictions of fragment-based promoter interactions and ATAC-seq accessibility at the IL21 (c) and IFNG (d) loci in TFH (red) and naive T cells (blue). Color gradients represent the CHiCAGO scores.

https://doi.org/10.1101/2019.12.20.885426


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https://doi.org/10.1101/2019.12.20.885426




Wells, Andrew

FIGURE 4. Enrichment of chromatin signatures at promoter interacting regions. a. PIR enrichment for genomic features compared with distance-matched random regions in naive T cells. Error bars show SD across 100 draws of non-significant interactions. b. Feature enrichment at promoter-interacting OCR (iOCR) compared to a random sample of non-promoter-interacting OCR in naive T cells. c. Enrichment of iOCR within chromHMM-defined chromatin states and TSS neighborhood in naive T cells. ChromHMM 15-state models defined on the basis of 5 histone modifications (H3K4me1, H3K4me3, H3K27me3, H3K27ac and H3K36me3) are shown in the middle panel, with blue color intensity representing the probability of observing the mark in each state. The heatmap to the left of the emission parameters displays the overlap fold enrichment for iOCR in promoters (prOCR) and non-promoter iOCR (nonprOCR), while the heatmap to the right shows the fold enrichment for each state within 2 kb around a set of TSS. Blue color intensity represents fold enrichment.

https://doi.org/10.1101/2019.12.20.885426


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https://doi.org/10.1101/2019.12.20.885426




Wells, Andrew

FIGURE 5. Analysis of promoter-open chromatin connectomes in naïve and follicular helper T cells. a. Comparison of promoter-OCR interactions between TFH and naïve T cells. Relationship between the number of promoter-OCR interactions at a gene and its corresponding expression level in naive T cells (b) and TFH (c) are depicted with genes with recognized functions in naive and TFH labeled. d. Promoter-OCR interactions and ATAC-seq accessibility at the CD28, CTLA4 and ICOS loci in TFH (red) and naive T cells (blue).

https://doi.org/10.1101/2019.12.20.885426


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TFH pCAP-C

TREH

TREHP1

AP002954.1AP002954.1

DDX6

AP004609.3AP004609.2

SETP16

CXCR5

AP004609.1BCL9L

rs527619, rs71041848

50 KbTFH OCR

TFH pCAP-C

BCL6AC068295.1

LINC01991LINC01991

AC092941.1

AC092941.2

AC022498.1

LPPFLJ42393

TFH OCR

TFH pCAP-C

rs34631447

50 Kb

C

D

SNP interacts only with nearest gene - 8.5%

SNP interacts with nearest and distant genes - 29%

SNP ‘skips’ nearest gene - 62.5%



https://doi.org/10.1101/2019.12.20.885426




Wells, Andrew

FIGURE 6. SLE variant-to-gene mapping through integration of GWAS and promoter-open chromatin connectomes in follicular helper T cells. Four categories of accessible SLE SNP-promoter interactions were detected. a. Accessible SLE SNP uniquely interacts with the nearest promoter (8.5%). An example is rs527619, which physically interacts only with the nearest gene STAT4. b. Accessible SLE SNP interacts with the nearest promoter and at least one distant promoter (29%). An example is rs112677036, which physically interacts with IKZF3 and the distant ERBB2 and PGAP3 genes. c. Accessible SLE SNP ‘skips’ the nearest promoter to interact exclusively with on or more distant promoters (62.5%). Examples are (c) rs34631447, which skips LPP to physically interact with BCL6, and (d) rs527619 and rs71041848, which interact with the distant CXCR5 gene instead of the nearest gene, TREH.�

https://doi.org/10.1101/2019.12.20.885426


FIGURE 7

B

C

HSPA1A/B

SYSTEMIC AUTOIMMUNE DISORDERS

TYPE 1 DIABETES MELLITUS RHEUMATIC DISEASE

ADC maturation

T helper differentiation

altered T-B signaling in RA

T cell exhaustion signaling

BAG2 signaling

Th1 signaling

CD28 costimulation

T1D signaling

B cell development

BCR signaling

Rac signaling

neuroinflammation signaling

Th1 and Th2 activation

GVHD signaling

TNFR2 signaling

DC-NK crosstalk

cdc42 signaling

ICOS-ICOSL signaling

NFkB signaling

CD40 signaling

IL-15 production

RhoA signaling

autoimmune thyroid disease

allograft rejection signaling

PKC signaling in T cells

Enrichment [-log(p-value)]



Wells, Andrew

FIGURE 7. Ontology and pathway analysis of genes implicated through integration of GWAS and promoter-open chromatin connectomes in follicular helper T cells from human tonsil. a. Enrichment of the top 25 canonical pathways (a) and 3 disease networks (b) among genes implicated through promoter-open chromatin connectomes in TFH. c. Regulatory hierarchical network from SLE-connectome-implicated genes. Color gradients in b and c represent log 2 expression changes between TFH and naïve T cells, with green indicating down-regulation and red indicating up-regulation in TFH. Blue nodes in c represent regulatory hubs for genes with no SLE-OCR connectome detected.

https://doi.org/10.1101/2019.12.20.885426


CTRL-gRNA IKZF1-OCR-gRNA+CAS9+CAS9

CTRL IKZF1-OCR

Rel

ativ

e ce

ll co

unt

MFI

15000

12000

9000

6000

3000

IKZF1

100

40

0

80

60

20

102 103 104 105

50

40

30

20Freq

uenc

y

10

Rel

ativ

e ce

ll co

unt

100

20

0

80

60

40

STIM +LPP-OCR-gRNA-CAS9UNSTIM- STIMULATED

UNSTIMSTIM-CTRL

STIM-LPP-OCR

+CTRL-gRNA+CAS9

MFI

14000

10000

8000

600

400

200

12000

BCL6102 103 104 105

120

90

60

30

Freq

uenc

y

CTRL-gRNA+CAS9

CTRL CXCR5-OCR

+CAS9CXCR5-OCR-gRNA

Rel

ativ

e ce

ll co

unt

MFI

7000

6000

5000

4000

3000

2000

1000

CXCR5

100

80

60

40

20

0102 103 104 105

Freq

uenc

y

40

30

20

10

FIGURE 8

BA C



Wells, Andrew

FIGURE 8. CRISPR/CAS9-based deletion of accessible, promoter-connected genomic regions harboring SLE variants influences TFH gene expression. a, CRISPR-CAS9 targeting of the 136 bp OCR near the TREH gene harboring the rs527619 and rs71041848 SLE proxy SNPs and captured interacting with the CXCR5 promoter leads to increased CXCR5 expression (blue histogram) by Jurkat cells compared to cells transduced with a CTRL-gRNA+CAS9 (pink histogram). b, CRISPR-CAS9 targeting of the 213 bp OCR containing the rs4385425 SLE proxy and captured interacting with the IKZF1 promoter leads to increased IKZF1 (Ikaros) expression (blue histogram) by Jurkat cells compared to cells transduced with a CTRL-gRNA+CAS9 (pink histogram). c, CRISPR-CAS9 targeting of the LPP SLE proxy SNPs rs34631447 and rs79044630 captured interacting with the BCL6 promoter abrogates IFNg-induced BCL-6 expression (orange histogram) compared to cells transduced with a CTRL-gRNA+CAS9 (blue histogram). The red histogram shows BCL-6 expression by unstimulated Jurkat cells. Bar graphs in a-c depict the mean fluorescence intensity (upper panels) and percent positive cells (lower panels) for CXCR5, Ikaros, and BCL-6 in control gRNA-tranduced vs. targeted cells. All data are representative of three independent experiments. See Supplemental Figure 7 for design and validation of CRISPR/CAS9-mediated deletion and mutation.

https://doi.org/10.1101/2019.12.20.885426


FIGURE 9

B C

0 500 1000 1500 20000

500

1000

1500

[HIPK inhibitor] (nM)

activated TFH

activated Naive

[IL-2

1] (p

g/m

L)

!"!!

!"#$

!"%!

!"&$

'()*

+#,-

.)/-

00(12,3

21/4

56(7-8,9

1,

#:;,/<

=>

?

CNTRLshRNA

HIPK1shRNA

HIP

K1 m

RN

A

A

CXCR5

PD

-1

NAIVE

T-act

TFH

0 10 20 30 400

500

1000

[Mink inhibitor] (nm)

Tstim

Tfh

[IL-2

1] (p

g/m

L)

GE

BCL6

PBMC

NAIVE

T-act

TFH

D

0 24 480

500

1000

1500

2000

2500

hours post stimulation

[IL-2

1] (p

g/m

L)

scrambled shRNA

HIPK1 shRNA

BCL2L15 AP4B1

10 Kb

rs11552449

TFH OCR

TFH pCAP-C

PTPN22 DCLRE1B HIPK1

AC233723.1PLD2 MINK1

10 Kb

TFH OCR

TFH pCAP-C

rs71368520rs71368521

F



Wells, Andrew

FIGURE 9. SLE variant-to-gene mapping implicates novel drug targets for modulation of TFH function. a. The interactomes of SLE proxy SNPs rs11552449, rs71368520, and rs71368521 implicate HIPK1 and MINK1. b. Purified naïve CD4+ T cells cultured under TFH-skewing conditions show increased expression of PD-1 and CXCR5, as well as BCL-6 (c). d. HIPK1 mRNA expression by in vitro-differentiated TFH cells transduced with scrambled Lenti-shRNA or Lenti-HIPK-1 shRNA. e. shRNA-mediated knock-down of HIPK1 inhibits IL-21 secretion by TFH cells. f. A HIPK inhibitory drug causes dose-dependent inhibition of IL-21 production by TFH cells. g. A MINK inhibitory drug causes dose-dependent inhibition of IL-21 production by TFH cells. All data are representative of 3-4 independent experiments.�

https://doi.org/10.1101/2019.12.20.885426


−5

0

5

0.0 0.1 0.2 0.3 0.4density

logF

C o

f TFH

vsN

aive

0.0

0.1

0.2

0.3

0.4

0 2 4 6 8mean_log2tpm

dens

ity

condition

NaiveTFH

SUPPLEMENTAL FIGURE 1

A B C



Wells, Andrew

SUPPLEMENTARY FIGURE 1. TFH and naive T cells show comparable genomic accessibility. Overall log2 fold changes in reference OCR accessibility (CPM) in TFH compared to naive T cells represented by density plot (a) or distribution plot (b). c. The accessibility signal was normalized by the counts per million method and mean p values across three replicates were used for comparison between TFH and naive T cells.

https://doi.org/10.1101/2019.12.20.885426



A

B

0.00 0.05 0.10 0.15

COLORECTAL_CANCER

GASTRIN_CREB_SIGNALLING_PATHWAY_VIA_PKC_AND_MAPK

G_ALPHA_Q_SIGNALLING_EVENTS

HEMOSTASIS

CXCR4_PATHWAY

SIGNALING_BY_GPCR

IMMUNE_SYSTEM

GPCR_DOWNSTREAM_SIGNALING

CHEMOKINE_SIGNALING_PATHWAY

HEMATOPOIETIC_CELL_LINEAGE

0 1 2 3

# Genes in Overlap / # Genes in Gene Set

log10FDR

−log10FDR

percentage

GENESET

0.0 0.1 0.2 0.3 0.4

REGULATION_OF_ACTIN_CYTOSKELETON

TOLL_RECEPTOR_CASCADES

SEMAPHORIN_INTERACTIONS

COSTIMULATION_BY_THE_CD28_FAMILY

SIGNALLING_BY_NGF

SIGNALING_BY_RHO_GTPASES

ADAPTIVE_IMMUNE_SYSTEM

PDGFRB_PATHWAY

G_ALPHA1213_SIGNALLING_EVENTS

IMMUNE_SYSTEM

0 2 4 6 8

# Genes in Overlap / # Genes in Gene Set

log10FDR

GENESET

−log10FDR

percentage

MORE OPEN/EXPRESSED IN TFH

MORE OPEN/EXPRESSED IN NAIVE



Wells, Andrew

SUPPLEMENTARY FIGURE 2. Canonical pathway enrichment for genes with accessible SLE SNPs in their promoters. The log2FDR (blue) and gene ratios (red) for the top 10 enriched Ingenuity canonical pathways is shown for TFH (a) and naive (b) cells.

https://doi.org/10.1101/2019.12.20.885426


active bivalent promoter


Polycomb-silenced

weak bivalent enhancer

actively transcribing promoter

poised promoter or enhancer

neutral/quiescent chromatin

poised intergenic enhancer

H3K

27ac

H3K

4me1

H3K

27m

e3

-200

0bp

0bp

+200

0bp

TSS distance

TSSenrichmentchromatin state PIR enrichment

markenrichment

iOC

R

prO

CR

nonp

rOC

R

geno

me

%

A

C

22127

17374

5153

4670

0.00 0.25 0.50 0.75 1.00

H3K27me3

CTCF

H3K27ac

H3K4me1

0 2 4 6

ratio of iOCR overlapping with given features

ODDS RATIO

logOddsRatio

percentage


ATAC−SeqCTCF H3K4me1

H3K27ac

promoter-interacting readsrandom reads

010

000

prom

oter

-inte

ract

ing

regi

on o

verla

ps20

000

3000

0

H3K27me3

TFH PIR FEATURE ENRICHMENT B



Wells, Andrew

SUPPLEMENTARY FIGURE 3. Enrichment of chromatin signatures at promoter interacting regions in TFH cells. a. PIR enrichment for genomic features compared with distance-matched random regions in TFH cells. Error bars show SD across 100 draws of non-significant interactions. b. Feature enrichment of promoter-interacting OCR (iOCR) compared to a random sample of non-promoter-interacting OCR in TFH. c. Enrichment of iOCR within chromHMM-defined chromatin states and TSS neighborhood in TFH. Roadmap Epigenomics 15-state models (middle panel) were defined on the basis of 5 histone modifications (H3K4me1, H3K4me3, H3K27me3, H3K27ac and H3K36me3). Blue color intensity represents the probability of observing the mark in the state. The heatmap to the left of the emission parameters displays the overlap fold enrichment for different categories of iOCR, while the heatmap to the right shows the fold enrichment for each state within 2 kb around a set of TSS. Blue color intensity represents fold enrichment.

https://doi.org/10.1101/2019.12.20.885426



0

2000

4000

6000

0 10 20 30 40 50number of PIR per gene

num

ber o

f gen

es

naive_OCR

TFH_OCR



Wells, Andrew

SUPPLEMENTARY FIGURE 4. Distribution of promoter-interacting OCR per gene in naïve T and TFH cells. The number of promoter-interacting OCR per gene is plotted for both naïve T (red) and TFH (blue) cells.

https://doi.org/10.1101/2019.12.20.885426



A Naive

B TFH

7520

interactions/promoter



https://doi.org/10.1101/2019.12.20.885426




Wells, Andrew

SUPPLEMENTARY FIGURE 5. Immune networks enriched among SLE SNP connectome implicated gene sets. The top 3 merged immune networks in naïve (a) and TFH (b) are depicted. Red color intensity represents the number of interactions detected per promoter for each gene in the network.

https://doi.org/10.1101/2019.12.20.885426



A

B

TFH OCR

TFH pCAP-C

BAG6APOM

C6orf47GPANK1

LY6G5C

ABHD16A

LY6G6FLY6G6E

LY6G6DMPIG6B

DDAH2

MSH5

VWA7

VARS

LSM2HSPA1A

50 Kb

rs3117582

TFH OCR

TFH pCAP-C

20 Kb

rs7769961

SNRPC UHRF1BP1



Wells, Andrew

SUPPLEMENTARY FIGURE 6. Interaction of open SLE variants with genes encoding nuclear proteins targeted by autoantibodies in SLE patients. a. The accessible SNP rs3117582 at the promoter of APOM physically interacts with the LSM2 promoter. b. The accessible SNP rs7769961 at the SNRPC promoter physically interacts with the UHRF1BP1 promoter.

https://doi.org/10.1101/2019.12.20.885426


73

16

11

437 5

68

captureC

Bentham_eQTL

Odhams_eQTL

rs9782955:LYSTrs3794060:NADSYN1rs3794060:DHCR7rs2289583:C15orf39rs2289583:MPIrs2289583:CSKrs2289583:ULK3rs2289583:FAM219B

rs10036748:TNIP1rs10036748:CCDC69rs268134:SPRED2rs3794060:RP11-660L16.2

rs9462027:ANKS1Ars9462027:C6orf106rs9652601:RMI2rs9652601:SOCS1rs9311676:PXKrs9462027:UHRF1BP1

SUPPLEMENTAL FIGURE 7.CC-BY-NC-ND 4.0 International licenseavailable under a


Wells, Andrew

SUPPLEMENTARY FIGURE 7. Comparison of SLE SNP-gene associations obtained by promoter-open chromatin connectomes vs. eQTL studies. Comparison of sentinel SNP-gene pairs implicated by the promoter-open chromatin connectomes in this study vs. sentinel SNP-gene pairs statistically associated in two SLE eQTL studies7,29. SNP-gene pairs shared by each group are detailed.

https://doi.org/10.1101/2019.12.20.885426



A

Scalechr3:

500 bases hg19188,471,800 188,471,900 188,472,000 188,472,100 188,472,200 188,472,300 188,472,400 188,472,500 188,472,600 188,472,700 188,472,800 188,472,900

Naive-TC59_rep2_PE.htd Total Tags = 4.60e+07, normalized to 1.00e+07

Tfh-TC59_rep2_PE.htd Total Tags = 3.01e+07, normalized to 1.00e+07

UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)

Your Sequence from Blat Search

Simple Nucleotide Polymorphisms (dbSNP 150)

LPPLPPLPPLPP

Deletion2sgRNA1Deletion1

sgRNA2 sgRNA5 sgRNA6

sgRNA4

rs142665120rs753716079rs547845495rs567685352rs536766203rs754770294rs549736572rs569717715rs115517957rs538904591rs558864403rs372526270rs773151136rs76579503

rs573622681rs191322276rs182999543rs542731898rs186626984rs576117809rs748086252rs190000439rs144819763rs868763955

rs777325242rs183352404

rs76335094rs561158149rs530091816

rs549870326rs187999111

rs151301420

rs538694758

rs552431520rs566278146rs977524126rs535346300rs554839212rs574982666rs192870447

rs556339041rs576083139rs545054094rs935557399rs564887212rs924655492

rs572217932rs936072353rs990124459rs546928740rs773885047rs915925198rs541320686

rs560878487

rs529938731rs543509640

rs185142513rs1533819

rs1046426148rs188390094rs907367516rs552118670rs1047895587

rs369492244rs1005278138

rs566090706rs577908781rs528735365rs776229139rs899538556rs996758743

rs548875175rs568553021rs537668077

rs1030027148rs746257468

rs999055707

rs556275720

rs569731176rs373370941

rs868638451

rs538759680rs990259824

rs558220238

rs572015935rs915894218

rs970500965rs540956408

rs34631447rs928848674rs192834734rs1047863541

rs563158334rs775904120rs941102213rs1038108911

rs140758303

rs79044630

rs996880711

rs764447562

rs184135640

rs868290836

rs1050785786

rs891013407rs201794579rs865785383

rs35065364rs559449813rs1011663287

rs188147382rs180953352

rs970177722

rs981813761rs774488738

rs150119674rs75523621

rs556406738

rs531243476

rs765449443rs909367577

rs184655176

rs571058988rs1039185422

rs920973139rs932436654

rs538695886rs190508909

rs576185441rs565536183rs890823326

rs765075330

rs752636642

rs182219536

Naive-TC59_rep2_PE.htd Total Tags = 4.60e+07, normalized to 1.00e+07

10 _

0 _


10 _

0 _

Scalechr11:

500 bases hg19118,562,700 118,562,800 118,562,900 118,563,000 118,563,100 118,563,200 118,563,300 118,563,400 118,563,500 118,563,600 118,563,700 118,563,800 118,563,900

UCSC annotations of RefSeq RNAs (NM_* and NR_*)UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)


Simple Nucleotide Polymorphisms (dbSNP 151)

sgRNA1sgRNA5

sgRNA2

sgRNA3 sgRNA4Deletion

sgRNA6sgRNA8

sgRNA7

rs530419317rs797040051rs548643234rs148529435

rs1784851rs1784852rs1784853

rs528063605

rs191973935rs571323579

rs538740178

rs667982rs582630

rs547671891

rs145410412

rs781972561rs369799318

rs533899782

rs782086031rs868970745

rs147699166

rs782398009

rs576879975rs117330495

rs551348110rs563089911rs569585274

rs574970012rs782173814

rs542429089

rs71041848rs781992552rs781795266

rs386757972

rs527475rs527561

rs527619

rs546570597rs182702632

rs142488942rs781799806

rs585039

rs371941716rs569011083

rs536034109rs781821642

rs534517690rs187644328

rs566390103

rs868974393rs144831714

rs200354386rs375394472

rs782509660

rs367671199rs558527738

rs576869835

rs200048264rs537936084

rs72525551

rs11455968

rs556411710rs574889469rs190041020

rs182703305rs572770619

rs782457713rs782569700

rs187342482


0 _

C

E

B

D

F

Scalechr7:

1 kb hg1950,306,500 50,307,000 50,307,500 50,308,000 50,308,500

UCSC annotations of RefSeq RNAs (NM_* and NR_*)UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)

NHGRI-EBI Catalog of Published Genome-Wide Association Studies


rs11185603 rs4385425rs4385425rs4385425

rs876036 rs876037rs876037

Deletion1sgRNA1

sgRNA2 sgRNA4 sgRNA5sgRNA3

Deletion2


12.72 _

0 _

deleted alleles

EV1 EV2rs527619OCR1

rs527619OCR2

EV rs4385425OCR1

rs4385425OCR2

EV3EV2EV1

rs34631447OCR2

rs34631447OCR1

rs34631447OCR3

deleted alleles

deleted alleles



https://doi.org/10.1101/2019.12.20.885426




Wells, Andrew

SUPPLEMENTARY FIGURE 8. CRISPR/CAS9-based editing of accessible genomic regions containing SLE GWAS proxy SNP in Jurkat T cells. a, UCSC genome browser track displaying intergenic TFH OCR near the TREH gene harboring the rs527619 and rs71041848 proxies to the rs4639966 SLE sentinel SNP that interacts with CXCR5 (chr11:118,563,185-118,563,321). Eight sgRNAs flanking the OCR and Sanger sequencing identifying several deletions present within the OCR are depicted. b, Electrophoresis gel analysis of PCR amplified regions encompassing the targeted region showing two distinct deletions present at 500bp and 350bp. c, Genomic region surrounding the TFH OCR containing the rs4385425 SNP proxy to the sentinel SLE SNP rs11185603 (chr7:50307234-50307447) that interacts with IKZF1, showing sgRNAs and deletions detected in targeted Jurkat lines. Note that this OCR was called by HOMER, but not by MACS2. d, Electrophoresis gel analysis detects three different deletions at 900bp, 400bp and 350bp. e, Intronic OCR (chr3:188,472,234-188,472,390) in the LPP locus harboring the rs34631447 and rs79044630 SNPs proxy to sentinel rs6762714 SLE SNP and found connected to BCL6. This region was targeted with five total sgRNAs surrounding the OCR and Sanger sequencing showed two distinct deletions. f, Electrophoresis gel analysis detects 1200bp and 821bp deletions. All experiments were performed in three biological replicates.

https://doi.org/10.1101/2019.12.20.885426



A

0 30 60 90 1200

2000

4000

6000

Mink inhibitor (nM)

IL-2

(pg/

ml)

Tstim

0 500 1000 1500 20000

5

10

15

Conc of A64 (nM)

IL-2

(ng/

ml)

TFH

MOCK SCRAMshRNA

HIPK1shRNA

MOCK SCRAMshRNA

B2MshRNA

HIPK

1/18

S RA

TIO

B2M

/18S

RAT

IO

B2M shRNA

B2M mRNA

HIPK1 shRNA

HIPK1 mRNA

B2M PROTEIN

C

D E

MOCK

B2MLENTIshRNA

HIPK1LENTIshRNA

TFH + 150 nM A64 TFH + 1 uM A64 TFH + 2.5 uM A64TFHT-act

B



https://doi.org/10.1101/2019.12.20.885426




Wells, Andrew

SUPPLEMENTARY FIGURE 9. Promoter-variant connectome-guided targeting of novel kinases for modulation of primary human TFH function. a, Lentiviral delivery of B2M shRNA and HIPK1 shRNA into in vitro differentiated TFH as assessed by GFP fluorescence by flow cytometry. b, Assessment of shRNA-mediated knock-down of B2M and HIPK1 in TFH by flow cytometry and qRT-PCR. Red histograms are TFH transduced with scrambled control shRNA, and blue histograms depict TFH transduced with specific B2M (left panel) or HIPK1 (right panel) shRNA. c, Effect of HIPK inhibitory drug treatment on TFH differentiation in vitro as measured by co-induction of PD-1 and CXCR5. d, The HIPK inhibitory drug A64 does not affect IL-2 secretion by TFH cells as measured by ELISA. e, A MINK inhibitory drug inhibits IL-2 secretion by activated T cells with an ED50 of ~50 nM. All data are representative of 3-4 replicate experiments.

https://doi.org/10.1101/2019.12.20.885426



TFH OCR

TFH H3K27ac

TFH H3K4me1

TFH pCAP-C

naive OCR

naive pCAP-C

187300K 187400K 187500K 187600K 187700K 187800K 187900K 188000K 188100K 188200K 188300K 188400K 188500K 188600K 188700K 188800K

SSTRTP2

AC072022.1AC072022.1

BCL6AC068295.1

LINC01991LINC01991AC092941.1AC092941.2

LPP TPRG1



Wells, Andrew

SUPPLEMENTARY FIGURE 10. 3D epigenomic map of promoter-Capture-C, ATAC-seq, H3K27ac, and H3K4me1 in the BCL6-LPP region in naïve (blue) and TFH cells (red).�

https://doi.org/10.1101/2019.12.20.885426


human follicular helper t cell promoter …...2019/12/20 · 1 1 human follicular helper t cell...

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