The early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic β cells. Here we show that exposure to proinflammatory cytokines reveals a marked plasticity of the β-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the β-cell transcriptome, proteome and three-dimensional chromatin structure. Our data indicate that the β-cell response to cytokines is mediated by the induction of new regulatory regions as well as the activation of primed regulatory elements prebound by islet-specific transcription factors. We find that T1D-associated loci are enriched with newly mapped cis-regulatory regions and identify T1D-associated variants disrupting cytokine-responsive enhancer activity in human β cells. Our study illustrates how β cells respond to a proinflammatory environment and implicate a role for stimulus response islet enhancers in T1D.
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Raw sequencing reads for the different high-throughput assays can be accessed at the Gene Expression Omnibus with the following identifiers: GSE123404 (ATAC-seq); GSE133135 (H3K27ac data); GSE137136 (RNA-seq); and GSE136865 (UMI-4C). Raw proteomics data can be accessed at the ProteomeXchange with the identifier PXD011902.
The code and scripts used in this study are available from the corresponding author upon reasonable request.
Todd, J. A. Etiology of type 1 diabetes. Immunity 32, 457–467 (2010).
Ziegler, A.-G. & Nepom, G. T. Prediction and pathogenesis in type 1 diabetes. Immunity 32, 468–478 (2010).
Eizirik, D. L., Colli, M. L. & Ortis, F. The role of inflammation in insulitis and β-cell loss in type 1 diabetes. Nat. Rev. Endocrinol. 5, 219–226 (2009).
Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).
Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
Ravassard, P. et al. A genetically engineered human pancreatic β cell line exhibiting glucose-inducible insulin secretion. J. Clin. Invest. 121, 3589–3597 (2011).
Ostuni, R. et al. Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171 (2013).
Gonzalez-Duque, S. et al. Conventional and neo-antigenic peptides presented by β cells are targeted by circulating naïve CD8+ T cells in type 1 diabetic and healthy donor.Cell Metab. 28, 946–960.e6 (2018).
Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).
Sung, M.-H., Guertin, M. J., Baek, S. & Hager, G. L. DNase footprint signatures are dictated by factor dynamics and DNA sequence. Mol. Cell 56, 275–285 (2014).
Bird, A. DNA methylation patterns and epigenetic memory. Genes Dev. 16, 6–21 (2002).
Hemberger, M., Dean, W. & Reik, W. Epigenetic dynamics of stem cells and cell lineage commitment: digging Waddington’s canal. Nat. Rev. Mol. Cell Biol. 10, 526–537 (2009).
Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).
Feinberg, A. P. Phenotypic plasticity and the epigenetics of human disease. Nature 447, 433–440 (2007).
Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).
Dekker, J., Marti-Renom, M. A. & Mirny, L. A. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14, 390–403 (2013).
Symmons, O. et al. Functional and topological characteristics of mammalian regulatory domains. Genome Res. 24, 390–400 (2014).
Krivega, I., Dale, R. K. & Dean, A. Role of LDB1 in the transition from chromatin looping to transcription activation. Genes Dev. 28, 1278–1290 (2014).
Yu, M. & Ren, B. The three-dimensional organization of mammalian genomes. Annu. Rev. Cell Dev. Biol. 33, 265–289 (2017).
Rowley, M. J. & Corces, V. G. Organizational principles of 3D genome architecture. Nat. Rev. Genet. 19, 789–800 (2018).
Schoenfelder, S. & Fraser, P. Long-range enhancer-promoter contacts in gene expression control. Nat. Rev. Genet. 20, 437–455 (2019).
Stadhouders, R., Filion, G. J. & Graf, T. Transcription factors and 3D genome conformation in cell-fate decisions. Nature 569, 345–354 (2019).
Miguel-Escalada, I. et al. Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nat. Genet. 51, 1137–1148 (2019).
Schwartzman, O. et al. UMI-4C for quantitative and targeted chromosomal contact profiling. Nat. Methods 13, 685–691 (2016).
MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).
Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 17921–17926 (2013).
Cooper, N. J. et al. Type 1 diabetes genome-wide association analysis with imputation identifies five new risk regions. Preprint at bioRxiv https://doi.org/10.1101/120022 (2017).
Fortune, M. D. et al. Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat. Genet. 47, 839–846 (2015).
Ray, A., Basu, S., Williams, C. B., Salzman, N. H. & Dittel, B. N. A novel IL-10-independent regulatory role for B cells in suppressing autoimmunity by maintenance of regulatory T cells via GITR ligand. J. Immunol. 188, 3188–3198 (2012).
Davison, L. J. et al. Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene. Hum. Mol. Genet. 21, 322–333 (2012).
Dos Santos, R. S. et al. DEXI, a candidate gene for type 1 diabetes, modulates rat and human pancreatic beta cell inflammation via regulation of the type I IFN/STAT signalling pathway. Diabetologia 62, 459–472 (2019).
Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018).
Heinz, S. et al. Effect of natural genetic variation on enhancer selection and function. Nature 503, 487–492 (2013).
Vandenbon, A., Kumagai, Y., Lin, M., Suzuki, Y. & Nakai, K. Waves of chromatin modifications in mouse dendritic cells in response to LPS stimulation. Genome Biol. 19, 138 (2018).
Phanstiel, D. H. et al. Static and dynamic DNA loops form AP-1-bound activation hubs during macrophage development. Mol. Cell 67, 1037–1048.e6 (2017).
Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).
Taberlay, P. C. et al. Three-dimensional disorganization of the cancer genome occurs coincident with long-range genetic and epigenetic alterations. Genome Res. 26, 719–731 (2016).
Barutcu, A. R. et al. Chromatin interaction analysis reveals changes in small chromosome and telomere clustering between epithelial and breast cancer cells. Genome Biol. 16, 214 (2015).
Chandra, T. et al. Global reorganization of the nuclear landscape in senescent cells. Cell Rep. 10, 471–483 (2015).
Criscione, S. W. et al. Reorganization of chromosome architecture in replicative cellular senescence. Sci. Adv. 2, e1500882 (2016).
Le Dily, F. et al. Distinct structural transitions of chromatin topological domains correlate with coordinated hormone-induced gene regulation. Genes Dev. 28, 2151–2162 (2014).
Op de Beeck, A. & Eizirik, D. L. Viral infections in type 1 diabetes mellitus: why the β cells? Nat. Rev. Endocrinol. 12, 263–273 (2016).
Kim-Hellmuth, S. et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nat. Commun. 8, 266 (2017).
Bucher, P. et al. Assessment of a novel two-component enzyme preparation for human islet isolation and transplantation. Transplantation 79, 91–97 (2005).
Melzi, R. et al. Role of CCL2/MCP-1 in islet transplantation. Cell Transplant. 19, 1031–1046 (2010).
Eizirik, D. L. et al. The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines. PLoS Genet. 8, e1002552 (2012).
Colli, M. L., Moore, F., Gurzov, E. N., Ortis, F. & Eizirik, D. L. MDA5 and PTPN2, two candidate genes for type 1 diabetes, modify pancreatic β-cell responses to the viral by-product double-stranded RNA. Hum. Mol. Genet. 19, 135–146 (2010).
Ortis, F. et al. Cytokine-induced proapoptotic gene expression in insulin-producing cells is related to rapid, sustained, and nonoscillatory nuclear factor-κB activation. Mol. Endocrinol. 20, 1867–1879 (2006).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).
Raurell-Vila H., Ramos-Rodríguez M., & Pasquali L. Assay for transposase accessible chromatin (ATAC-Seq) to chart the open chromatin landscape of human pancreatic islets. Methods Mol. Biol. 1766, 197–208 (2018).
Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods 12, 963–965 (2015).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Juan-Mateu, J. et al. SRp55 regulates a splicing network that controls human pancreatic β-cell function and survival. Diabetes 67, 423–436 (2018).
Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).
Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
Anders, S., Pyl, P. T. & Huber, W. HTSeq: a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Nakayasu, E. S. et al. MPLEx: a robust and universal protocol for single-sample integrative proteomic, metabolomic, and lipidomic analyses. mSystems 1, e00043-16 (2016).
Mayampurath, A. M. et al. DeconMSn: a software tool for accurate parent ion monoisotopic mass determination for tandem mass spectra. Bioinformatics 24, 1021–1023 (2008).
Petyuk, V. A. et al. DtaRefinery, a software tool for elimination of systematic errors from parent ion mass measurements in tandem mass spectra data sets. Mol. Cell. Proteomics 9, 486–496 (2010).
Kim, S. & Pevzner, P. A. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat. Commun. 5, 5277 (2014).
Webb-Robertson, B.-J. M. et al. Bayesian proteoform modeling improves protein quantification of global proteomic measurements. Mol. Cell. Proteomics 13, 3639–3646 (2014).
Li, T. et al. GeNets: a unified web platform for network-based genomic analyses. Nat. Methods 15, 543–546 (2018).
Li, T. et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2017).
Herwig, R., Hardt, C., Lienhard, M. & Kamburov, A. Analyzing and interpreting genome data at the network level with ConsensusPathDB. Nat. Protoc. 11, 1889–1907 (2016).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).
Moran, S., Arribas, C. & Esteller, M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics 8, 389–399 (2016).
Assenov, Y. et al. Comprehensive analysis of DNA methylation data with RnBeads. Nat. Methods 11, 1138–1140 (2014).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Du, P. et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11, 587 (2010).
Aronesty, E. Comparison of sequencing utility programs. Open Bioinforma. J. 7, 1–8 (2013).
Ahmed, M. et al. Variant Set Enrichment: an R package to identify disease-associated functional genomic regions. BioData Min. 10, 9 (2017).
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Jordà, M. et al. Upregulation of MMP-9 in MDCK epithelial cell line in response to expression of the Snail transcription factor. J. Cell Sci. 118, 3371–3385 (2005).
Brozzi, F. et al. Cytokines induce endoplasmic reticulum stress in human, rat and mouse beta cells via different mechanisms. Diabetologia 58, 2307–2316 (2015).
Mularoni, L., Ramos-Rodríguez, M. & Pasquali, L. The pancreatic Islet Regulome Browser. Front. Genet. 8, 13 (2017).
L.Pasquali was supported by grants from the Spanish Ministry of Economy and Competitiveness (nos. BFU2014-58150-R and SAF2017-86242-R), Marató TV3 (no. 201624.10) and a young investigator award from the Spanish Society of Diabetes (Ayuda SED a Proyectos de Investigación, no. 2017-SED). L.Pasquali is a recipient of a Ramon y Cajal contract from the Spanish Ministry of Economy and Competitiveness (no. RYC-2013-12864). The Pasquali lab is further supported by Instituto de Salud Carlos III (no. PIE16/00011). M.R. is supported by an FI Agència de Gestió d’Ajuts Universitaris i de Recerca PhD fellowship (no. 2019FI_B100203). J.J. was supported by a Marie Skłodowska-Curie Actions fellowship grant from the Horizons 2020 European Union (EU) Programme (project no. 660449). M.I.A. was supported by a FRIA fellowship from the Fonds de la Recherche Scientifique (FNRS) (no. 26410496). Human islets were provided through the European Consortium for Islet Transplantation distribution program for basic research supported by JDRF (no. 31-2008-416). D.L.E. was supported by the Walloon Region through the FRFS-WELBIO Fund for Strategic Fundamental research (no. CR-2015A-06s and CR-2019C-04) and by grants from the Fonds National de la Recherche Scientifique (no. T003613F), the Horizon 2020 Programme (project T2Dsystems, no. GA667191), Brussels Capital Region Innoviris (project DiaType, no. 2017-PFS-24), Dutch Diabetes Research Fundation (project Innovate2CureType1, DDRF; no. 2018.10.002) and the Innovative Medicines Initiative 2 Joint Undertaking (project INNODIA, no. 115797). This Innovative Medicines Initiative 2 Joint Undertaking receives support from the EU’s Horizon 2020 Research and Innovation Programme and European Federation of Pharmaceutical Industries and Associations, JDRF and the Leona M. and Harry B. Helmsley Charitable Trust (project INNODIA, no. 115797). T.O.M. and D.L.E. were supported by a grant from the National Institutes of Health-National Institute of Diabetes and Digestive and Kidney Diseases-Human Islet Research Network Consortium (no. 1UC4DK104166-01). Part of the work was performed at the Environmental Molecular Sciences Laboratory, a US Department of Energy national scientific user facility located at Pacific Northwest National Laboratory. Battelle operates the Pacific Northwest National Laboratory for the Department of Energy (contract no. DE-AC05-76RLO01830). We thank J. Mercader (Broad Institute) and M. Guindo Martínez (Barcelona Supercomputing Center) for helpful discussions regarding GWAS enrichment analyses and A. Castela (Université Libre de Bruxelles Center for Diabetes Research) for helping with the glucose-stimulated insulin secretion experiments.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Chromatin characterization of human pancreatic β cells exposed to pro-inflammatory cytokines.
a, Pearson correlation values between replicates in different assays and conditions (see Supplementary Note 2). b, Volcano plots of ATAC-seq (left) and H3K27ac ChIP-seq (right) changes obtained after exposure of EndoC-βH1 to IFN-γ and IL-1β; green and red dots correspond to sites with absolute log2 fold change > 1 and FDR adjusted P < 0.05 as calculated by fitting a negative binomial model in DESeq2. Chromatin changes are classified as ‘gained’ and ‘lost’ chromatin sites whereas non-significant changes are defined as ‘stable’. c, Chromatin accessibility and H3K27ac enrichment changes observed in EndoC-βH1 are largely replicated in human pancreatic islets as illustrated by the distribution of log2 fold change at regions as classified in b in EndoC-βH1. Dotted lines indicate log2 fold change thresholds (absolute log2 fold change > 1). Box plot limits show upper and lower quartiles, whiskers extend to 1.5 times the interquartile range and the notch represents the confidence interval around the median. d, Hierarchical clustering using normalized ATAC-seq and H3K27ac read counts at EndoC-βH1 IREs shows that samples cluster according to treatment, suggesting that the differences caused by the proinflammatory cytokines are greater than those derived by the sample heterogeneity. HI = Human pancreatic islets, EndoC = EndoC-βH1 e, Distribution of distances to nearest TSS for the different types of regulatory elements, showing that IREs, compared with stable regulatory elements (SREs), are preferentially located distally to TSS. f, Mean sequence conservation score of IREs and a randomized set of IREs in placental mammals. Peaks were extended from the center 1 kb to each direction and mean score was calculated in 50 bp windows. g, Sequence composition analysis of IREs (n = 3,009) illustrating the top identified de novo motifs. Colors for matched genes correspond to RNA-seq (name) or protein (underlined) status (red = down-regulated, blue = equal-regulated, green = up-regulated, black/no line = not expressed/detected).
Extended Data Fig. 2 Exposure to pro-inflammatory cytokines drives changes in the transcriptome and proteome of pancreatic β cells.
a, Volcano plot of RNA-seq genes, showing up-regulated genes (green) and down-regulated genes (red) upon exposure of EndoC-βH1 to cytokines. Vertical lines indicate the log2 fold change threshold (absolute log2 fold change > 1) and horizontal line indicates the FDR adjusted P cutoff for significance (FDR adjusted P < 0.05) calculated by fitting a negative binomial model in DESeq2. b, Distribution of RNA-seq counts in human islet samples in the genes previously classified as up, down or equal-regulated in EndoC-βH1 cells. Boxplot limits show upper and lower quartiles, whiskers extend to 1.5 times the interquartile range and the notch represents the confidence interval around the median. c, Volcano plot for multiplex proteomics, showing in green the up-regulated proteins and in red the down-regulated, which have a Q-value < 0.1 and absolute log2 fold change > 0.58. Vertical lines indicate the log2 fold change thresholds. d, Protein-protein Interaction (PPI) network generated from up-regulated proteins after cytokine exposure. Node color indicates belonging to same interacting community and background corresponds to specific pathway enrichment. e, Proportion of up, equal or down-regulated proteins encoded by genes located <15 kb from IREs or SREs. *** Chi-squared test P < 0.001. f, An additive effect on gene up-regulation was observed for multiple IREs located at <40 kb of a gene. Box plot limits show upper and lower quartiles, whiskers extend to 1.5 times the interquartile range and the notch represents the confidence interval around the median. ANOVA P < 2.2 × 10−16. g, View of the LY6E locus, whose expression is induced after cytokine exposure and is coupled with chromatin changes in the vicinity.
a, Genes associated to different classes of IREs (classified as in Fig. 2a) show cytokine-induced expression in EndoC-βH1. CYT = cytokine exposed, CTRL = control. Boxplot limits show upper and lower quartiles, whiskers extend to 1.5 times the interquartile range and notch represents the median confidence interval. ***Wilcoxon test P < 0.001. b, Sequence conservation score of IREs and a corresponding randomized set used as control. c, Distribution of distances to nearest TSS of the different classes of open chromatin sites. Line indicates the threshold used to classify them as ‘promoters’. d, Number of IREs overlapping regions annotated as ‘Strong’ or ‘Weak’ enhancers by ENCODE ChromHMM. *Chi-squared P < 2 × 10−16. e, f, Top hits for de novo motif analysis in opening (e) and primed enhancers (f). Colors for matched genes correspond to RNA-seq (name) or protein (underlined) status (red = down-regulated, blue = equal-regulated, green = up-regulated, black/no-line = not-expressed/detected). g, Diagram showing the percentage of colocalization between the TF binding sites identified by de novo motif analysis in SRE and primed enhancers (that is excluding sites < 2Kb from a TSS). Label size indicates number of regions containing the TF binding sites and line width/intensity percentage of regions in which two motifs colocalize. h, Odds-ratio for finding a motif pair in the same enhancer in primed vs. SRE. Only significant pairs (FDR-adjusted Fisher’s Exact test P < 0.001) are shown. Immune and islet-specific TF motifs colocalize more often in primed compared to SRE chromatin sites. i, Percentage of overlap between EndoC-βH1 different classes of open chromatin and islet-specific TFs obtained by ChIP-seq in untreated human islets. j, Volcano plot showing differentially methylated sites (depicted in red) in EndoC-βH1 exposed or not to cytokines. Dotted lines indicate the threshold for methylation differences or significance using limma moderated t-test. k, Distribution of demethylated and stable CpGs according to different classes of open chromatin.
a, Gene Regulatory Network (GRN) derived from IREs and their putative target genes. Squares represent the IREs inferred TF binding sites (motifs logos and TF matches are shown on the right side) and the ellipses represent their putative target genes (see Methods). The size of the squares reflects the number of connections (edge count) while the gene node size reflects the log2 fold change of RNA expression after cytokine exposure. The resulting GRN is an interconnected scale-free network composed of 648 nodes and 3,589 edges. Genes regulated exclusively by primed IREs are represented in blue while green depicts opening IREs regulated genes. Red denotes genes regulated by both types of IREs. In each of these three groups the representation of the hierarchy is based on the principle of network centrality where authoritative nodes are located more proximal to the core. b, Comparison between the degree distribution of the observed GRN (black triangles) and a random generated network (blue squares) having the same number of nodes and edges. The bell-shaped degree distribution of random graph denotes a statistically homogeneity in the degree of small and large nodes. In contrast, the observed network showed a high frequency of small degree nodes and a low frequency of highly connected nodes as is typical of a scale-free network. c, Bar plot of gene ontology biological process enrichment analysis. Gene-ontology analysis was performed using all target genes in the GRN. Functional enrichment analysis was performed by Metascape (http://metascape.org). Only terms with P < 0.001 and with at least 3 enriched genes were considered as significant. Color is proportional to their P values.
Extended Data Fig. 5 3D chromatin changes induced by exposure of human islets to pro-inflammatory cytokines.
a, Violin plots showing the distribution of CHiCAGO scores of contacts, detected by promoter capture HiC experiments in untreated human islets24, between stable and induced enhancers and their target genes. SREs engage chromatin contacts with higher interaction scores compared to those detected for IREs. *** Wilcoxon test P < 0.001. b, c, d, Views of the 3D chromatin contacts of CIITA (b), SOCS1 (c) and RSAD2 (d) promoters obtained by UMI-4C performed in islets exposed or not to pro-inflammatory cytokines. In yellow we highlight those IREs that gain contacts with the up-regulated gene promoter. A heatmap under the 4C track represents the log10 odds ratio (OR) of the UMI-4C contacts difference in cytokine vs. control and a small black diamond on top of the contact heatmap indicates a significant difference in contacts between cytokine-treated and control samples (Chi-squared P < 0.05). ATAC-seq peaks are represented by rectangles, shaded from gray to green proportionally to the cytokine-induced H2K27ac log2 fold change observed at that site.
Extended Data Fig. 6 Cytokine-induced islet regulatory elements are enriched in T1D associated variants.
a, EndoC-βH1 cytokine-induced regulatory elements (IREs) overlap more often than expected T1D associated variants while the opposite is true for T2D. EndoC-βH1 cytokine-invariant regulatory elements (SREs) are instead enriched for T2D, but not T1D associated variants. Each dot denotes the Varian Set Enrichment (VSE) score in IREs or SREs regions. Boxplot shows the enrichment distribution of the matched null permutated data sets. Red dots indicate that the difference is statistically significant as determined by VSE (Bonferroni adjusted P < 0.05). Box plot limits show upper and lower quartiles, whiskers extend to 1.5 times the interquartile range and the notch represents the confidence interval around the median. b-f, Representative regional plots of different T1D risk loci containing T1D variants overlapping IREs and up-regulated genes. R2 values are based on 1KG phase 3 EUR and the leading SNPs in the locus is represented by a diamond. If different leading variants are present in the same locus, their proxies are depicted in different colors. Yellow squares highlight those variants that overlap a human islet IRE. IREs are depicted as boxes, with the filling color corresponding to the H3K27ac log2 fold change. g, The IRE bearing the T1D associated variant rs78037977 is marked by the ENCODE ChromHMM classification as a ‘strong enhancer’ (orange) in other non β-cell lines (left). ENCODE ChromHMM classification in non β-cell lines for the IRE bearing the T1D associated variant rs193778. h, i, Allele-specific luciferase experiments for rs78037977 (h) and rs193778 (i) in untreated EndoC-βH1. ANOVA followed by Bonferroni correction * P < 0.05; ** P < 0.01. Bars represent mean ± sd.
GSIS was assessed, in pancreatic human islets (a) and EndoC-βH1 cells (b). In the case of EndoC-βH1 cells, the experiments were performed upon exposure or not to IFNγ (1000 U/ml) +IL1β (50U/ml) for 48 h. Data are mean plus range of four to eight independent experiments, and are expressed as the ratio between glucose stimulated and basal insulin secretion. *P < 0.05, **P < 0.01, ***P < 0.001, for the indicated comparisons (paired t test (a) or ANOVA followed by Bonferroni correction (b)). NT = Non treated.
a, Agilent TapeStation profiles obtained by chromatin tagmentation of human islets and EndoC-βH1 samples showing the laddering pattern of ATAC-seq libraries. The band sizes correspond to the expected nucleosomal pattern. *Notice that samples HI-19 CTRL and CYT were used as examples to illustrate the expected fragment distribution pattern in ATAC-seq experiments in Raurell-Vila et al.52. b, Summary of per-replicate sequencing metrics, showing total library sizes, percentage of aligned reads, percentage of mitochondrial aligned reads, normalized strand cross-correlation coefficient (NSC, values significantly lower than 1.1 (<1.05) tend to have low signal to noise or few peaks) and relative strand cross-correlation coefficient (RSC, values significantly lower than 1 (<0.8) tend to have low signal to noise). c, TSS enrichment over a 4 kb window centered on genes TSS compared to a set of genes randomized along the genome, showing the expected pattern of open chromatin centered on the TSS. d, Percentage of total reads found at called open chromatin peaks classified as distal (>2 kb from TSS) or promoters (≤2 kb from TSS) compared to a randomized set of peaks. e, UCSC views at islet-specific loci (NKX6.1, PDX1 and NEUROD1) showing the high reproducibility of ATAC-seq profiles among independent replicates.
Supplementary Notes 1–15 and Tables 3, 5, 8, 9 and 10
Supplementary Tables 1, 2, 4, 6 and 7
ATAC-seq peaks in EndoC-βH1
ATAC-seq peaks in human islets
H3K27ac ChIP-seq peaks in EndoC-βH1
H3K27ac ChIP-seq peaks in human islets
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Ramos-Rodríguez, M., Raurell-Vila, H., Colli, M.L. et al. The impact of proinflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabetes. Nat Genet 51, 1588–1595 (2019) doi:10.1038/s41588-019-0524-6