Genetic regulation of RNA splicing in human pancreatic islets

Background Non-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown. Results We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3. Conclusions These data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit.This research was supported by Ministerio de Ciencia e Innovación (BFU2014-54284-R, RTI2018-095666-B-I00), Medical Research Council (MR/L02036X/1), a Wellcome Trust Senior Investigator Award (WT101033), European Research Council Advanced Grant (789055), EU Horizon 2020 TDSystems (667191), ESPACE (874710), and Marie Sklodowska-Curie (643062, ZENCODE). S.B.G was supported by a Juan de la Cierva postdoctoral fellowship (MINECO; FJCI-2017-32090). M.C.A was supported by a Boehringer Ingelheim Fonds PhD fellowship. Work in CRG was supported by the CERCA Programme, Generalitat de Catalunya, Centro de Excelencia Severo Ochoa (CEX2020-001049), and support of the Spanish Ministry of Science and Innovation to the EMBL partnership. Work in Imperial College was supported by NIHR Imperial Biomedical Research Centre. M.I. was supported by a European Research Council consolidator award (101002275). D.J.M.C. and J.A.T. were supported by JDRF grants 9-2011-253, 5-SRA-2015-130-A-N, 4- SRA-2017-473-A-N, and Wellcome grants 091157/Z/10/Z and 107212/Z/15/Z, to the Diabetes and Inflammation Laboratory, Oxford, as well as the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and NIHR Oxford Biomedical Research Centre, and Wellcome Trust Core Award grant 203141/Z/16/Z. D.M.J.C analysis with the UK Biobank Resource was conducted under Application 31295. A.L.G. is a Wellcome Senior Fellow in Basic Biomedical Science and was supported by the Wellcome Trust (095101, 200837, 106130, 203141), the NIDDK (U01DK105535 and UM1 DK126185), and the Oxford NIHR Biomedical Research Centre.Peer Reviewed"Article signat per 20 autors/es: Goutham Atla, Silvia Bonàs-Guarch, Mirabai Cuenca-Ardura, Anthony Beucher, Daniel J. M. Crouch, Javier Garcia-Hurtado, Ignasi Moran, the T2DSystems Consortium, Manuel Irimia, Rashmi B. Prasad, Anna L. Gloyn, Lorella Marselli, Mara Suleiman, Thierry Berney, Eelco J. P. de Koning, Julie Kerr-Conte, Francois Pattou, John A. Todd, Lorenzo Piemonti & Jorge Ferrer"Postprint (published version

The impact of non-additive genetic associations on age-related complex diseases

Genome-wide association studies (GWAS) are not fully comprehensive, as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implement an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association. We apply this methodology to 62,281 subjects across 22 age-related diseases and identify 94 genome-wide associated loci, including 26 previously unreported. Moreover, we observe that 27.7% of the 94 loci are missed if we use standard imputation strategies with a single reference panel, such as HRC, and only test the additive model. Among the new findings, we identify three novel low-frequency recessive variants with odds ratios larger than 4, which need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases. Most genome-wide association studies assume an additive model, exclude the X chromosome, and use one reference panel. Here, the authors implement a strategy including non-additive models and find that the number of loci for age-related traits increases as compared to the additive model alone.Peer reviewe

Pancreatic microexons regulate islet function and glucose homeostasis

Pancreatic islets control glucose homeostasis by the balanced secretion of insulin and other hormones, and their abnormal function causes diabetes or hypoglycaemia. Here we uncover a conserved programme of alternative microexons included in mRNAs of islet cells, particularly in genes involved in vesicle transport and exocytosis. Islet microexons (IsletMICs) are regulated by the RNA binding protein SRRM3 and represent a subset of the larger neural programme that are particularly sensitive to SRRM3 levels. Both SRRM3 and IsletMICs are induced by elevated glucose levels, and depletion of SRRM3 in human and rat beta cell lines and mouse islets, or repression of particular IsletMICs using antisense oligonucleotides, leads to inappropriate insulin secretion. Consistently, mice harbouring mutations in Srrm3 display defects in islet cell identity and function, leading to hyperinsulinaemic hypoglycaemia. Importantly, human genetic variants that influence SRRM3 expression and IsletMIC inclusion in islets are associated with fasting glucose variation and type 2 diabetes risk. Taken together, our data identify a conserved microexon programme that regulates glucose homeostasis.We thank B. Banfi (University of Iowa) for kindly sharing the Srrm3 gene-trapped mouse line with us; M. Ángel Maestro for excellent technical advice on multiple protocols related to the study of Srrm3 mutant mice; J. Permanyer and C. Rodriguez for help with mouse genotyping; D. Balboa, I. Miguel-Escalada and E. Bernardo, as well as members of the M.I. and J.V. groups for constant scientific discussion; A. Gohr for assistance on bioinformatic analyses; S. Taylor (University of Manchester) for kindly sharing the HeLa Flp-In T-Rex cell line with us; and CRG Genomics and Advanced Light Microscopy Units for the RNA-seq and microscopy services. The research has been funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC-StG-LS2-637591 and ERCCoG-LS2-101002275 to M.I., ERC-AdG-LS2-670146 to J.V., and ERC-AdG-LS4-789055 to J.F.), EU Horizon 2020 TDSystems (667191) to J.F., la Caixa Foundation (ID 100010434), under the agreement LCF/PR/HR20/52400008 to M.I., an EFSD award supported by EFSD/Lilly European Diabetes Research Programme, the Spanish Ministry of Science and Innovation (BFU-2017-89308-P to J.V., BFU-2017-89201-P to M.I. and RTI2018-095666-B-I00 to J.F.) and the ‘Centro de Excelencia Severo Ochoa’ (CEX2020-001049). G.A. was supported by the Marie Skłodowska-Curie project ZENCODE-ITN (No. 643062). S.B.-G. was supported by a Juan de la Cierva postdoctoral fellowship (MINECO; FJCI-2017-32090). J.J.-M. was supported by the Beatriu de Pinós Programme and the Ministry of Research and Universities of the Government of Catalonia, and a Marie Skłodowska-Curie Individual Fellowship from the European Union’s Horizon 2020 research and innovation programme (MSCA-IF-2019-841758; http://ec.europa.eu/)

Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals.This research was supported by the National Institute for Health Research Imperial Biomedical Research Centre. Work was funded by grants from the Wellcome Trust (nos. WT101033 to J.F. and WT205915 to I.P.), Horizon 2020 (Research and Innovation Programme nos. 667191, to J.F., 633595, to I.P., and 676556, to M.A.M.-R.; Marie Sklodowska-Curie 658145, to I.M.-E., and 43062 ZENCODE, to G.A.), European Research Council (nos. 789055, to J.F., and 609989, to M.A.M.-R.). Marató TV3 (no. 201611, to J.F. and M.A.M.-R.), Ministerio de Ciencia Innovación y Universidades (nos. BFU2014-54284-R, RTI2018-095666, to J.F., BFU2017-85926-P, to M.A.M.-R., IJCI-2015-23352, to I.F.), AGAUR (to M.A.M.-R.). UK Medical Research Council (no. MR/L007150/1, to P.F., MR/L02036X/1 to J.F.), World Cancer Research Fund (WCRF UK, to I.P.) and World Cancer Research Fund International (no. 2017/1641 to I.P.), Biobanking and Biomolecular Resources Research Infrastructure (nos. BBMRI-NL, NWO 184.021.007, to I.O.F.). Work in IDIBAPS, CRG and CNAG was supported by the CERCA Programme, Generalitat de Catalunya and Centros de Excelencia Severo Ochoa (no. SEV-2012-0208). Human islets were provided through the European islet distribution program for basic research supported by JDRF (no. 3-RSC-2016-160-I-X). We thank N. Ruiz-Gomez for technical assistance; R. L. Fernandes, T. Thorne (University of Reading) and A. Perdones-Montero (Imperial College London) for helpful discussions regarding Machine Learning approaches; B. Lenhard and M. Merkenschlager (London Institute of Medical Sciences, Imperial College London), F. Müller (University of Birmingham) and J. L. Gómez-Skarmeta (Centro Andaluz de Biología del Desarrollo) for critical comments on the draft; the CRG Genomics Unit; and the Imperial College High Performance Computing Service

CRISPR-based genome editing in primary human pancreatic islet cells

Gene targeting studies in primary human islets could advance our understanding of mechanisms driving diabetes pathogenesis. Here, we demonstrate successful genome editing in primary human islets using clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9). CRISPR-based targeting efficiently mutated protein-coding exons, resulting in acute loss of islet β-cell regulators, like the transcription factor PDX1 and the KATP channel subunit KIR6.2, accompanied by impaired β-cell regulation and function. CRISPR targeting of non-coding DNA harboring type 2 diabetes (T2D) risk variants revealed changes in ABCC8, SIX2 and SIX3 expression, and impaired β-cell function, thereby linking regulatory elements in these target genes to T2D genetic susceptibility. Advances here establish a paradigm for genetic studies in human islet cells, and reveal regulatory and genetic mechanisms linking non-coding variants to human diabetes risk.We gratefully acknowledge organ donors and their families, Canadian organ procurement organizations, particularly the Human Organ Procurement and Exchange (HOPE) program and the Trillium Gift of Life Network, and islet procurement through the Alberta Diabetes Institute Islet Core, Integrated Islet Distribution Program (U.S. NIH UC4 DK098085). R.J.B. was supported by a postdoctoral fellowship from JDRF (3-PDF-2018-584-A-N) and is on leave from the Animal Biotechnology Laboratory, Facultad de Agronomía, Universidad de Buenos Aires/INPA CONICET, CABA, Argentina. Work in the CRG and ICL was funded by the Wellcome Trust (WT101033), Medical Research Council (MR/L02036X/1), European Research Council Advanced Grant (789055), Ministerio de Ciencia e Innovación (RTI2018-095666-B-I00) and Marató TV3 #201611. Work in the University of Alberta was supported by a Foundation Grant from the Canadian Institutes of Health Research (CIHR: 148451, MacDonald). Work in the Kim lab was supported by NIH awards (R01 DK107507; R01 DK108817; U01 DK123743 to S.K.K.), and JDRF Northern California Center of Excellence (to S.K.K. and M. Hebrok). Work here was also supported by NIH grant P30 DK116074 (S.K.K.

Genetic regulation of RNA splicing in human pancreatic islets

Background: Non-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown. Results: We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3. Conclusions: These data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit.This research was supported by Ministerio de Ciencia e Innovación (BFU2014-54284-R, RTI2018-095666-B-I00), Medical Research Council (MR/L02036X/1), a Wellcome Trust Senior Investigator Award (WT101033), European Research Council Advanced Grant (789055), EU Horizon 2020 TDSystems (667191), ESPACE (874710), and Marie Sklodowska-Curie (643062, ZENCODE). S.B.G was supported by a Juan de la Cierva postdoctoral fellowship (MINECO; FJCI-2017-32090). M.C.A was supported by a Boehringer Ingelheim Fonds PhD fellowship. Work in CRG was supported by the CERCA Programme, Generalitat de Catalunya, Centro de Excelencia Severo Ochoa (CEX2020-001049), and support of the Spanish Ministry of Science and Innovation to the EMBL partnership. Work in Imperial College was supported by NIHR Imperial Biomedical Research Centre. M.I. was supported by a European Research Council consolidator award (101002275). D.J.M.C. and J.A.T. were supported by JDRF grants 9-2011-253, 5-SRA-2015-130-A-N, 4- SRA-2017-473-A-N, and Wellcome grants 091157/Z/10/Z and 107212/Z/15/Z, to the Diabetes and Inflammation Laboratory, Oxford, as well as the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and NIHR Oxford Biomedical Research Centre, and Wellcome Trust Core Award grant 203141/Z/16/Z. D.M.J.C analysis with the UK Biobank Resource was conducted under Application 31295. A.L.G. is a Wellcome Senior Fellow in Basic Biomedical Science and was supported by the Wellcome Trust (095101, 200837, 106130, 203141), the NIDDK (U01DK105535 and UM1 DK126185), and the Oxford NIHR Biomedical Research Centre

Genome-wide association study meta-analysis identifies five new loci for systemic lupus erythematosus

Background: Systemic lupus erythematosus (SLE) is a common systemic autoimmune disease with a complex genetic inheritance. Genome-wide association studies (GWAS) have significantly increased the number of significant loci associated with SLE risk. To date, however, established loci account for less than 30% of the disease heritability and additional risk variants have yet to be identified. Here we performed a GWAS followed by a meta-analysis to identify new genome-wide significant loci for SLE. Methods: We genotyped a cohort of 907 patients with SLE (cases) and 1524 healthy controls from Spain and performed imputation using the 1000 Genomes reference data. We tested for association using logistic regression with correction for the principal components of variation. Meta-analysis of the association results was subsequently performed on 7,110,321 variants using genetic data from a large cohort of 4036 patients with SLE and 6959 controls of Northern European ancestry. Genetic association was also tested at the pathway level after removing the effect of known risk loci using PASCAL software. Results: We identified five new loci associated with SLE at the genome-wide level of significance (p < 5 × 10− 8): GRB2, SMYD3, ST8SIA4, LAT2 and ARHGAP27. Pathway analysis revealed several biological processes significantly associated with SLE risk: B cell receptor signaling (p = 5.28 × 10− 6), CTLA4 co-stimulation during T cell activation (p = 3.06 × 10− 5), interleukin-4 signaling (p = 3.97 × 10− 5) and cell surface interactions at the vascular wall (p = 4.63 × 10− 5). Conclusions: Our results identify five novel loci for SLE susceptibility, and biologic pathways associated via multiple low-effect-size loci.This study was funded by the Spanish Ministry of Economy and Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36). This work has been also sponsored by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. This work was supported by an EFSD/Lilly research fellowship. Josep M. Mercader was supported by Sara Borrell Fellowship from the Instituto Carlos III. Sílvia Bonàs was awarded an FI-DGR Fellowship from FI-DGR 2013 from Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya)

Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals

Additional file 2: of Genome-wide association study meta-analysis identifies five new loci for systemic lupus erythematosus

Table S3. Association results for the 52 previously known SLE risk loci in the Spain GWAS. Table S4. Pathway association results after combining the two SLE cohorts (combined raw p value <0.05). Table S5. List of biological pathways significantly associated with ST8SIA4 gene network. Table S6. List of biological pathways significantly associated with ARHGAP27 gene network. (XLSX 48 kb