1
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Robertson CC, Elgamal RM, Henry-Kanarek BA, Arvan P, Chen S, Dhawan S, Eizirik DL, Kaddis JS, Vahedi G, Parker SCJ, Gaulton KJ, Soleimanpour SA. Untangling the genetics of beta cell dysfunction and death in type 1 diabetes. Mol Metab 2024; 86:101973. [PMID: 38914291 PMCID: PMC11283044 DOI: 10.1016/j.molmet.2024.101973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Type 1 diabetes (T1D) is a complex multi-system disease which arises from both environmental and genetic factors, resulting in the destruction of insulin-producing pancreatic beta cells. Over the past two decades, human genetic studies have provided new insight into the etiology of T1D, including an appreciation for the role of beta cells in their own demise. SCOPE OF REVIEW Here, we outline models supported by human genetic data for the role of beta cell dysfunction and death in T1D. We highlight the importance of strong evidence linking T1D genetic associations to bona fide candidate genes for mechanistic and therapeutic consideration. To guide rigorous interpretation of genetic associations, we describe molecular profiling approaches, genomic resources, and disease models that may be used to construct variant-to-gene links and to investigate candidate genes and their role in T1D. MAJOR CONCLUSIONS We profile advances in understanding the genetic causes of beta cell dysfunction and death at individual T1D risk loci. We discuss how genetic risk prediction models can be used to address disease heterogeneity. Further, we present areas where investment will be critical for the future use of genetics to address open questions in the development of new treatment and prevention strategies for T1D.
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Affiliation(s)
- Catherine C Robertson
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Ruth M Elgamal
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Belle A Henry-Kanarek
- Department of Internal Medicine and Division of Metabolism, Endocrinology, and Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Peter Arvan
- Department of Internal Medicine and Division of Metabolism, Endocrinology, and Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Shuibing Chen
- Department of Surgery, Weill Cornell Medicine, New York, NY, USA; Center for Genomic Health, Weill Cornell Medicine, New York, NY, USA
| | - Sangeeta Dhawan
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA, USA
| | - Decio L Eizirik
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, Belgium
| | - John S Kaddis
- Department of Diabetes and Cancer Discovery Science, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
| | - Kyle J Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
| | - Scott A Soleimanpour
- Department of Internal Medicine and Division of Metabolism, Endocrinology, and Diabetes, University of Michigan, Ann Arbor, MI, USA.
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2
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Mandla R, Lorenz K, Yin X, Bocher O, Huerta-Chagoya A, Arruda AL, Piron A, Horn S, Suzuki K, Hatzikotoulas K, Southam L, Taylor H, Yang K, Hrovatin K, Tong Y, Lytrivi M, Rayner NW, Meigs JB, McCarthy MI, Mahajan A, Udler MS, Spracklen CN, Boehnke M, Vujkovic M, Rotter JI, Eizirik DL, Cnop M, Lickert H, Morris AP, Zeggini E, Voight BF, Mercader JM. Multi-omics characterization of type 2 diabetes associated genetic variation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.15.24310282. [PMID: 39072045 PMCID: PMC11275663 DOI: 10.1101/2024.07.15.24310282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.
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Affiliation(s)
- Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Kim Lorenz
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA
| | - Xianyong Yin
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Graduate School of Experimental Medicine, Technical University of Munich, Munich, Germany
| | - Anthony Piron
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels (IB2), Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
- Diabetes and Inflammation Laboratory, Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Susanne Horn
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Kaiyuan Yang
- Institute of Diabetes and Regeneration Research (IDR), Helmholtz Munich, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Karin Hrovatin
- Institute of Computational Biology (ICB), Helmholtz Munich, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Yue Tong
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Maria Lytrivi
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Division of Endocrinology, Erasmus Hospital, Universite Libre de Bruxelles, Brussels, Belgium
| | - Nigel W. Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - James B. Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Miriam S. Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Cassandra N. Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Decio L. Eizirik
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Miriam Cnop
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
- Division of Endocrinology, Erasmus Hospital, Universite Libre de Bruxelles, Brussels, Belgium
- WEL Research Institute, Wavre, Belgium
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research (IDR), Helmholtz Munich, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Andrew P. Morris
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
| | - Benjamin F. Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
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3
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Besin V, Humardani FM, Yulianti T, Putra SED, Triana R, Justyn M. The Apo gene's genetic variants: hidden role in Asian vascular risk. Neurogenetics 2024; 25:157-164. [PMID: 38625441 DOI: 10.1007/s10048-024-00757-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/05/2024] [Indexed: 04/17/2024]
Abstract
Vascular risk factors, including diabetes, hypertension, hyperlipidemia, and obesity, pose significant health threats with implications extending to neuropsychiatric disorders such as stroke and Alzheimer's disease. The Asian population, in particular, appears to be disproportionately affected due to unique genetic predispositions, as well as epigenetic factors such as dietary patterns and lifestyle habits. Existing management strategies often fall short of addressing these specific needs, leading to greater challenges in prevention and treatment. This review highlights a significant gap in our understanding of the impact of genetic screening in the early detection and tailored treatment of vascular risk factors among the Asian population. Apolipoprotein, a key player in cholesterol metabolism, is primarily associated with dyslipidemia, yet emerging evidence suggests its involvement in conditions such as diabetes, hypertension, and obesity. While genetic variants of vascular risk are ethnic-dependent, current evidence indicates that epigenetics also exhibits ethnic specificity. Understanding the interplay between Apolipoprotein and genetics, particularly within diverse ethnic backgrounds, has the potential to refine risk stratification and enhance precision in management. For Caucasian carrying the APOA5 rs662799 C variant, pharmacological interventions are recommended, as dietary interventions may not be sufficient. In contrast, for Asian populations with the same genetic variant, dietary modifications are initially advised. Should dyslipidemia persist, the consideration of pharmaceutical agents such as statins is recommended.
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Affiliation(s)
| | - Farizky Martriano Humardani
- Faculty of Medicine, Universitas Surabaya, Surabaya, Indonesia.
- Magister in Biomedical Science Program, Faculty of Medicine, Universitas Brawijaya, Malang, Indonesia.
- Bioinformatics Research Center, Indonesian Bioinformatics and Biomolecular, Malang, Indonesia.
| | - Trilis Yulianti
- Prodia Education and Research Institute, Jakarta, Indonesia
- Faculty of Medicine, Universitas Sumatera Utara, Medan, Indonesia
| | | | - Rina Triana
- Prodia Clinical Laboratories, Jakarta, Indonesia
| | - Matthew Justyn
- Faculty of Pharmacy, Universitas Padjajaran, Jatinangor, Indonesia
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4
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Ewald JD, Lu Y, Ellis CE, Worton J, Kolic J, Sasaki S, Zhang D, dos Santos T, Spigelman AF, Bautista A, Dai XQ, Lyon JG, Smith NP, Wong JM, Rajesh V, Sun H, Sharp SA, Rogalski JC, Moravcova R, Cen HH, Manning Fox JE, Atlas E, Bruin JE, Mulvihill EE, Verchere CB, Foster LJ, Gloyn AL, Johnson JD, Pepper AR, Lynn FC, Xia J, MacDonald PE. HumanIslets: An integrated platform for human islet data access and analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599613. [PMID: 38948734 PMCID: PMC11212983 DOI: 10.1101/2024.06.19.599613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.
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Affiliation(s)
- Jessica D. Ewald
- Institute of Parasitology, McGill University, Montreal, QC
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yao Lu
- Institute of Parasitology, McGill University, Montreal, QC
| | - Cara E. Ellis
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Jessica Worton
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Jelena Kolic
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Shugo Sasaki
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Dahai Zhang
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Theodore dos Santos
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Aliya F. Spigelman
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Austin Bautista
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
| | - Xiao-Qing Dai
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - James G. Lyon
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
| | - Nancy P. Smith
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Jordan M. Wong
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Varsha Rajesh
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Han Sun
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Seth A. Sharp
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Jason C. Rogalski
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Renata Moravcova
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Haoning H Cen
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Jocelyn E. Manning Fox
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | | | - Ella Atlas
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON
| | - Jennifer E. Bruin
- Department of Biology & Institute of Biochemistry, Carleton University, Ottawa, ON
| | - Erin E. Mulvihill
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, ON
- University of Ottawa Heart Institute, Ottawa, ON
| | - C. Bruce Verchere
- Department of Surgery, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, BC
- Department of Pathology and Laboratory Medicine, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, BC
- Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC
| | - Leonard J. Foster
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Anna L. Gloyn
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - James D. Johnson
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Andrew R. Pepper
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Francis C. Lynn
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, QC
| | - Patrick E. MacDonald
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
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5
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Hoedjes KM, Grath S, Posnien N, Ritchie MG, Schlötterer C, Abbott JK, Almudi I, Coronado-Zamora M, Durmaz Mitchell E, Flatt T, Fricke C, Glaser-Schmitt A, González J, Holman L, Kankare M, Lenhart B, Orengo DJ, Snook RR, Yılmaz VM, Yusuf L. From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology. Mol Ecol 2024:e17382. [PMID: 38856653 DOI: 10.1111/mec.17382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/09/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
RNA sequencing (RNAseq) methodology has experienced a burst of technological developments in the last decade, which has opened up opportunities for studying the mechanisms of adaptation to environmental factors at both the organismal and cellular level. Selecting the most suitable experimental approach for specific research questions and model systems can, however, be a challenge and researchers in ecology and evolution are commonly faced with the choice of whether to study gene expression variation in whole bodies, specific tissues, and/or single cells. A wide range of sometimes polarised opinions exists over which approach is best. Here, we highlight the advantages and disadvantages of each of these approaches to provide a guide to help researchers make informed decisions and maximise the power of their study. Using illustrative examples of various ecological and evolutionary research questions, we guide the readers through the different RNAseq approaches and help them identify the most suitable design for their own projects.
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Affiliation(s)
- Katja M Hoedjes
- Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sonja Grath
- Division of Evolutionary Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Nico Posnien
- Department of Developmental Biology, Göttingen Center for Molecular Biosciences (GZMB), University of Göttingen, Göttingen, Germany
| | - Michael G Ritchie
- Centre for Biological Diversity, University of St Andrews, St Andrews, UK
| | | | | | - Isabel Almudi
- Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | | | - Esra Durmaz Mitchell
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Functional Genomics and Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Thomas Flatt
- Department of Biology, University of Fribourg, Fribourg, Switzerland
| | - Claudia Fricke
- Institute for Zoology/Animal Ecology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | | | - Josefa González
- Institute of Evolutionary Biology, CSIC, UPF, Barcelona, Spain
| | - Luke Holman
- School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK
| | - Maaria Kankare
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
| | - Benedict Lenhart
- Department of Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Dorcas J Orengo
- Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | - Rhonda R Snook
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Vera M Yılmaz
- Division of Evolutionary Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Leeban Yusuf
- Centre for Biological Diversity, University of St Andrews, St Andrews, UK
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6
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Muñoz F, Fex M, Moritz T, Mulder H, Cataldo LR. Unique features of β-cell metabolism are lost in type 2 diabetes. Acta Physiol (Oxf) 2024; 240:e14148. [PMID: 38656044 DOI: 10.1111/apha.14148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/28/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
Pancreatic β cells play an essential role in the control of systemic glucose homeostasis as they sense blood glucose levels and respond by secreting insulin. Upon stimulating glucose uptake in insulin-sensitive tissues post-prandially, this anabolic hormone restores blood glucose levels to pre-prandial levels. Maintaining physiological glucose levels thus relies on proper β-cell function. To fulfill this highly specialized nutrient sensor role, β cells have evolved a unique genetic program that shapes its distinct cellular metabolism. In this review, the unique genetic and metabolic features of β cells will be outlined, including their alterations in type 2 diabetes (T2D). β cells selectively express a set of genes in a cell type-specific manner; for instance, the glucose activating hexokinase IV enzyme or Glucokinase (GCK), whereas other genes are selectively "disallowed", including lactate dehydrogenase A (LDHA) and monocarboxylate transporter 1 (MCT1). This selective gene program equips β cells with a unique metabolic apparatus to ensure that nutrient metabolism is coupled to appropriate insulin secretion, thereby avoiding hyperglycemia, as well as life-threatening hypoglycemia. Unlike most cell types, β cells exhibit specialized bioenergetic features, including supply-driven rather than demand-driven metabolism and a high basal mitochondrial proton leak respiration. The understanding of these unique genetically programmed metabolic features and their alterations that lead to β-cell dysfunction is crucial for a comprehensive understanding of T2D pathophysiology and the development of innovative therapeutic approaches for T2D patients.
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Affiliation(s)
- Felipe Muñoz
- Clinical Research Center, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund, Sweden
| | - Malin Fex
- Clinical Research Center, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund, Sweden
| | - Thomas Moritz
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hindrik Mulder
- Clinical Research Center, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund, Sweden
| | - Luis Rodrigo Cataldo
- Clinical Research Center, Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund, Sweden
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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7
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Hu M, Kim I, Morán I, Peng W, Sun O, Bonnefond A, Khamis A, Bonàs-Guarch S, Froguel P, Rutter GA. Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function. FASEB J 2024; 38:e23610. [PMID: 38661000 PMCID: PMC11108099 DOI: 10.1096/fj.202301700rr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 03/22/2024] [Accepted: 04/01/2024] [Indexed: 04/26/2024]
Abstract
Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, we examined multiple variants that influence SLC30A8 allele-specific expression. Epigenomic mapping has previously identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighboring genes. Here, we show that deletion of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowers the expression of SLC30A8 and several neighboring genes and improves glucose-stimulated insulin secretion. While downregulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21, or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.
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Affiliation(s)
- Ming Hu
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Innah Kim
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Ignasi Morán
- Life Sciences Department, Barcelona Supercomputing Center (BSC-CNS), 08034 Barcelona, Spain
| | - Weicong Peng
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Orien Sun
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Amélie Bonnefond
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Amna Khamis
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Sílvia Bonàs-Guarch
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Center for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Spain
| | - Philippe Froguel
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Guy A. Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
- Centre de Recherche du CHUM, Faculté de Médicine, Université de Montréal, Montréal, QC, Canada
- Lee Kong Chian Imperial Medical School, Nanyang Technological University, Singapore
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8
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Arthur TD, Nguyen JP, D'Antonio-Chronowska A, Jaureguy J, Silva N, Henson B, Panopoulos AD, Belmonte JCI, D'Antonio M, McVicker G, Frazer KA. Multi-omic QTL mapping in early developmental tissues reveals phenotypic and temporal complexity of regulatory variants underlying GWAS loci. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588874. [PMID: 38645112 PMCID: PMC11030419 DOI: 10.1101/2024.04.10.588874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Most GWAS loci are presumed to affect gene regulation, however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we identify eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental (EDev) tissues. Through colocalization, we annotate 586 GWAS loci for 17 traits by QTL complexity, QTL phenotype, and QTL temporal specificity. We show that GWAS loci are highly enriched for colocalization with complex QTL modules that affect multiple elements (genes and/or peaks). We also demonstrate that caQTLs and haQTLs capture regulatory variations not associated with eQTLs and explain ∼49% of the functionally annotated GWAS loci. Additionally, we show that EDev-unique QTLs are strongly depleted for colocalizing with GWAS loci. By conducting one of the largest multi-omic QTL studies to date, we demonstrate that many GWAS loci exhibit phenotypic complexity and therefore, are missed by traditional eQTL analyses.
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9
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Arunagiri A, Alam M, Haataja L, Draz H, Alasad B, Samy P, Sadique N, Tong Y, Cai Y, Shakeri H, Fantuzzi F, Ibrahim H, Jang I, Sidarala V, Soleimanpour SA, Satin LS, Otonkoski T, Cnop M, Itkin‐Ansari P, Kaufman RJ, Liu M, Arvan P. Proinsulin folding and trafficking defects trigger a common pathological disturbance of endoplasmic reticulum homeostasis. Protein Sci 2024; 33:e4949. [PMID: 38511500 PMCID: PMC10955614 DOI: 10.1002/pro.4949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/23/2024] [Accepted: 02/14/2024] [Indexed: 03/22/2024]
Abstract
Primary defects in folding of mutant proinsulin can cause dominant-negative proinsulin accumulation in the endoplasmic reticulum (ER), impaired anterograde proinsulin trafficking, perturbed ER homeostasis, diminished insulin production, and β-cell dysfunction. Conversely, if primary impairment of ER-to-Golgi trafficking (which also perturbs ER homeostasis) drives misfolding of nonmutant proinsulin-this might suggest bi-directional entry into a common pathological phenotype (proinsulin misfolding, perturbed ER homeostasis, and deficient ER export of proinsulin) that can culminate in diminished insulin storage and diabetes. Here, we've challenged β-cells with conditions that impair ER-to-Golgi trafficking, and devised an accurate means to assess the relative abundance of distinct folded/misfolded forms of proinsulin using a novel nonreducing SDS-PAGE/immunoblotting protocol. We confirm abundant proinsulin misfolding upon introduction of a diabetogenic INS mutation, or in the islets of db/db mice. Whereas blockade of proinsulin trafficking in Golgi/post-Golgi compartments results in intracellular accumulation of properly-folded proinsulin (bearing native disulfide bonds), impairment of ER-to-Golgi trafficking (regardless whether such impairment is achieved by genetic or pharmacologic means) results in decreased native proinsulin with more misfolded proinsulin. Remarkably, reversible ER-to-Golgi transport defects (such as treatment with brefeldin A or cellular energy depletion) upon reversal quickly restore the ER folding environment, resulting in the disappearance of pre-existing misfolded proinsulin while preserving proinsulin bearing native disulfide bonds. Thus, proper homeostatic balance of ER-to-Golgi trafficking is linked to a more favorable proinsulin folding (as well as trafficking) outcome.
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Affiliation(s)
- Anoop Arunagiri
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Maroof Alam
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Leena Haataja
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Hassan Draz
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Bashiyer Alasad
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Praveen Samy
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Nadeed Sadique
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Yue Tong
- ULB Center for Diabetes Research, Medical Faculty; and Division of EndocrinologyErasmus Hospital, Universite Libre de BruxellesBrusselsBelgium
| | - Ying Cai
- ULB Center for Diabetes Research, Medical Faculty; and Division of EndocrinologyErasmus Hospital, Universite Libre de BruxellesBrusselsBelgium
| | - Hadis Shakeri
- ULB Center for Diabetes Research, Medical Faculty; and Division of EndocrinologyErasmus Hospital, Universite Libre de BruxellesBrusselsBelgium
| | - Federica Fantuzzi
- ULB Center for Diabetes Research, Medical Faculty; and Division of EndocrinologyErasmus Hospital, Universite Libre de BruxellesBrusselsBelgium
| | - Hazem Ibrahim
- Stem Cells and Metabolism Research Program, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
| | - Insook Jang
- Degenerative Diseases ProgramCenter for Genetic Disorders and Aging Research, Sanford Burnham Prebys Medical Discovery InstituteLa JollaCaliforniaUSA
| | - Vaibhav Sidarala
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Scott A. Soleimanpour
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
| | - Leslie S. Satin
- Department of PharmacologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Timo Otonkoski
- Stem Cells and Metabolism Research Program, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
| | - Miriam Cnop
- ULB Center for Diabetes Research, Medical Faculty; and Division of EndocrinologyErasmus Hospital, Universite Libre de BruxellesBrusselsBelgium
| | - Pamela Itkin‐Ansari
- Development, Aging and Regeneration ProgramCenter for Genetic Disorders and Aging Research, Sanford Burnham Prebys Medical Discovery InstituteLa JollaCaliforniaUSA
| | - Randal J. Kaufman
- Degenerative Diseases ProgramCenter for Genetic Disorders and Aging Research, Sanford Burnham Prebys Medical Discovery InstituteLa JollaCaliforniaUSA
| | - Ming Liu
- Department of Endocrinology and MetabolismTianjin Medical University General HospitalTianjinChina
| | - Peter Arvan
- Division of Metabolism, Endocrinology & DiabetesUniversity of Michigan Medical CenterAnn ArborMichiganUSA
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10
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Jia K, Shen J. Transcriptome-wide association studies associated with Crohn's disease: challenges and perspectives. Cell Biosci 2024; 14:29. [PMID: 38403629 PMCID: PMC10895848 DOI: 10.1186/s13578-024-01204-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 02/04/2024] [Indexed: 02/27/2024] Open
Abstract
Crohn's disease (CD) is regarded as a lifelong progressive disease affecting all segments of the intestinal tract and multiple organs. Based on genome-wide association studies (GWAS) and gene expression data, transcriptome-wide association studies (TWAS) can help identify susceptibility genes associated with pathogenesis and disease behavior. In this review, we overview seven reported TWASs of CD, summarize their study designs, and discuss the key methods and steps used in TWAS, which affect the prioritization of susceptibility genes. This article summarized the screening of tissue-specific susceptibility genes for CD, and discussed the reported potential pathological mechanisms of overlapping susceptibility genes related to CD in a certain tissue type. We observed that ileal lipid-related metabolism and colonic extracellular vesicles may be involved in the pathogenesis of CD by performing GO pathway enrichment analysis for susceptibility genes. We further pointed the low reproducibility of TWAS associated with CD and discussed the reasons for these issues, strategies for solving them. In the future, more TWAS are needed to be designed into large-scale, unified cohorts, unified analysis pipelines, and fully classified databases of expression trait loci.
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Affiliation(s)
- Keyu Jia
- Laboratory of Medicine, Baoshan Branch, Ren Ji Hospital, School of Medicine, Nephrology department, Shanghai Jiao Tong University, 1058 Huanzhen Northroad, Shanghai, 200444, China
| | - Jun Shen
- Laboratory of Medicine, Baoshan Branch, Ren Ji Hospital, School of Medicine, Nephrology department, Shanghai Jiao Tong University, 1058 Huanzhen Northroad, Shanghai, 200444, China.
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Research Center, Ren Ji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai Jiao Tong University, Shanghai, China.
- NHC Key Laboratory of Digestive Diseases, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
- Division of Gastroenterology and Hepatology, Baoshan Branch, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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11
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Arruda AL, Khandaker GM, Morris AP, Smith GD, Huckins LM, Zeggini E. Genomic insights into the comorbidity between type 2 diabetes and schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:22. [PMID: 38383672 PMCID: PMC10881980 DOI: 10.1038/s41537-024-00445-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/31/2024] [Indexed: 02/23/2024]
Abstract
Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify putative effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity.
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Affiliation(s)
- Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Munich School for Data Science, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Technical University of Munich (TUM), TUM School of Medicine and Health, Graduate School of Experimental Medicine, Munich, 81675, Germany
| | - Golam M Khandaker
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, M13 9PT, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Laura M Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany.
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, 81675, Germany.
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12
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Bevacqua RJ, Zhao W, Merheb E, Kim SH, Marson A, Gloyn AL, Kim SK. Multiplexed CRISPR gene editing in primary human islet cells with Cas9 ribonucleoprotein. iScience 2024; 27:108693. [PMID: 38205242 PMCID: PMC10777115 DOI: 10.1016/j.isci.2023.108693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/27/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
Successful genome editing in primary human islets could reveal features of the genetic regulatory landscape underlying β cell function and diabetes risk. Here, we describe a CRISPR-based strategy to interrogate functions of predicted regulatory DNA elements using electroporation of a complex of Cas9 ribonucleoprotein (Cas9 RNP) and guide RNAs into primary human islet cells. We successfully targeted coding regions including the PDX1 exon 1, and non-coding DNA linked to diabetes susceptibility. CRISPR-Cas9 RNP approaches revealed genetic targets of regulation by DNA elements containing candidate diabetes risk SNPs, including an in vivo enhancer of the MPHOSPH9 gene. CRISPR-Cas9 RNP multiplexed targeting of two cis-regulatory elements linked to diabetes risk in PCSK1, which encodes an endoprotease crucial for Insulin processing, also demonstrated efficient simultaneous editing of PCSK1 regulatory elements, resulting in impaired β cell PCSK1 regulation and Insulin secretion. Multiplex CRISPR-Cas9 RNP provides powerful approaches to investigate and elucidate human islet cell gene regulation in health and diabetes.
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Affiliation(s)
- Romina J Bevacqua
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Diabetes, Obesity and Metabolism Institute (DOMI), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Weichen Zhao
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Emilio Merheb
- Diabetes, Obesity and Metabolism Institute (DOMI), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Seung Hyun Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology and Northern California JDRF Center of Excellence, University of California at San Francisco, San Francisco, CA 94158, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
- Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Anna L Gloyn
- Department of Pediatrics (Endocrinology) and of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Seung K Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA
- Northern California JDRF Center of Excellence, Stanford University School of Medicine, Stanford, CA 94305, USA
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13
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Pan S, Kang H, Liu X, Li S, Yang P, Wu M, Yuan N, Lin S, Zheng Q, Jia P. COLOCdb: a comprehensive resource for multi-model colocalization of complex traits. Nucleic Acids Res 2024; 52:D871-D881. [PMID: 37941154 PMCID: PMC10767919 DOI: 10.1093/nar/gkad939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/01/2023] [Accepted: 10/12/2023] [Indexed: 11/10/2023] Open
Abstract
Large-scale genome-wide association studies (GWAS) have provided profound insights into complex traits and diseases. Yet, deciphering the fine-scale molecular mechanisms of how genetic variants manifest to cause the phenotypes remains a daunting task. Here, we present COLOCdb (https://ngdc.cncb.ac.cn/colocdb), a comprehensive genetic colocalization database by integrating more than 3000 GWAS summary statistics and 13 types of xQTL to date. By employing two representative approaches for the colocalization analysis, COLOCdb deposits results from three key components: (i) GWAS-xQTL, pair-wise colocalization between GWAS loci and different types of xQTL, (ii) GWAS-GWAS, pair-wise colocalization between the trait-associated genetic loci from GWASs and (iii) xQTL-xQTL, pair-wise colocalization between the genetic loci associated with molecular phenotypes in xQTLs. These results together represent the most comprehensive colocalization analysis, which also greatly expands the list of shared variants with genetic pleiotropy. We expect that COLOCdb can serve as a unique and useful resource in advancing the discovery of new biological mechanisms and benefit future functional studies.
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Affiliation(s)
- Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Shuhua Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Peng Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Mingqiu Wu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Na Yuan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Shiqi Lin
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Qiwen Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
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14
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Walker JT, Saunders DC, Rai V, Chen HH, Orchard P, Dai C, Pettway YD, Hopkirk AL, Reihsmann CV, Tao Y, Fan S, Shrestha S, Varshney A, Petty LE, Wright JJ, Ventresca C, Agarwala S, Aramandla R, Poffenberger G, Jenkins R, Mei S, Hart NJ, Phillips S, Kang H, Greiner DL, Shultz LD, Bottino R, Liu J, Below JE, Parker SCJ, Powers AC, Brissova M. Genetic risk converges on regulatory networks mediating early type 2 diabetes. Nature 2023; 624:621-629. [PMID: 38049589 DOI: 10.1038/s41586-023-06693-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 09/28/2023] [Indexed: 12/06/2023]
Abstract
Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet β cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and β cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging3-5. Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by β cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the β cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by β cells. RFX6 perturbation in primary human islet cells alters β cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data.
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Affiliation(s)
- John T Walker
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Diane C Saunders
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Vivek Rai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chunhua Dai
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yasminye D Pettway
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alexander L Hopkirk
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Conrad V Reihsmann
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yicheng Tao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Simin Fan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Shristi Shrestha
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan J Wright
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christa Ventresca
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Samir Agarwala
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Radhika Aramandla
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Greg Poffenberger
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Regina Jenkins
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shaojun Mei
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nathaniel J Hart
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sharon Phillips
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dale L Greiner
- Department of Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA
| | | | - Rita Bottino
- Imagine Pharma, Devon, PA, USA
- Institute of Cellular Therapeutics, Allegheny-Singer Research Institute, Allegheny Health Network, Pittsburgh, PA, USA
| | - Jie Liu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
| | - Alvin C Powers
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- VA Tennessee Valley Healthcare System, Nashville, TN, USA.
| | - Marcela Brissova
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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15
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Xue D, Narisu N, Taylor DL, Zhang M, Grenko C, Taylor HJ, Yan T, Tang X, Sinha N, Zhu J, Vandana JJ, Nok Chong AC, Lee A, Mansell EC, Swift AJ, Erdos MR, Zhong A, Bonnycastle LL, Zhou T, Chen S, Collins FS. Functional interrogation of twenty type 2 diabetes-associated genes using isogenic human embryonic stem cell-derived β-like cells. Cell Metab 2023; 35:1897-1914.e11. [PMID: 37858332 PMCID: PMC10841752 DOI: 10.1016/j.cmet.2023.09.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/26/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023]
Abstract
Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional roles of many loci remain unexplored. Here, we engineered isogenic knockout human embryonic stem cell lines for 20 genes associated with T2D risk. We examined the impacts of each knockout on β cell differentiation, functions, and survival. We generated gene expression and chromatin accessibility profiles on β cells derived from each knockout line. Analyses of T2D-association signals overlapping HNF4A-dependent ATAC peaks identified a likely causal variant at the FAIM2 T2D-association signal. Additionally, the integrative association analyses identified four genes (CP, RNASE1, PCSK1N, and GSTA2) associated with insulin production, and two genes (TAGLN3 and DHRS2) associated with β cell sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental line and identified a single likely functional variant at each of 23 T2D-association signals.
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Affiliation(s)
- Dongxiang Xue
- Department of Surgery, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Center for Genomic Health, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - D Leland Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Meili Zhang
- Department of Surgery, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - Caleb Grenko
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Henry J Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA; Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN Cambridge, UK
| | - Tingfen Yan
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xuming Tang
- Department of Surgery, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Center for Genomic Health, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - Neelam Sinha
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jiajun Zhu
- Department of Surgery, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Center for Genomic Health, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - J Jeya Vandana
- Department of Surgery, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Center for Genomic Health, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medicine, The Rockefeller University, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Angie Chi Nok Chong
- Department of Surgery, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Center for Genomic Health, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - Angela Lee
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Erin C Mansell
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Amy J Swift
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael R Erdos
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Aaron Zhong
- Stem Cell Research Facility, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Lori L Bonnycastle
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ting Zhou
- Stem Cell Research Facility, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Shuibing Chen
- Department of Surgery, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Center for Genomic Health, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA.
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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16
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Nguyen JP, Arthur TD, Fujita K, Salgado BM, Donovan MKR, Matsui H, Kim JH, D'Antonio-Chronowska A, D'Antonio M, Frazer KA. eQTL mapping in fetal-like pancreatic progenitor cells reveals early developmental insights into diabetes risk. Nat Commun 2023; 14:6928. [PMID: 37903777 PMCID: PMC10616100 DOI: 10.1038/s41467-023-42560-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 10/13/2023] [Indexed: 11/01/2023] Open
Abstract
The impact of genetic regulatory variation active in early pancreatic development on adult pancreatic disease and traits is not well understood. Here, we generate a panel of 107 fetal-like iPSC-derived pancreatic progenitor cells (iPSC-PPCs) from whole genome-sequenced individuals and identify 4065 genes and 4016 isoforms whose expression and/or alternative splicing are affected by regulatory variation. We integrate eQTLs identified in adult islets and whole pancreas samples, which reveal 1805 eQTL associations that are unique to the fetal-like iPSC-PPCs and 1043 eQTLs that exhibit regulatory plasticity across the fetal-like and adult pancreas tissues. Colocalization with GWAS risk loci for pancreatic diseases and traits show that some putative causal regulatory variants are active only in the fetal-like iPSC-PPCs and likely influence disease by modulating expression of disease-associated genes in early development, while others with regulatory plasticity likely exert their effects in both the fetal and adult pancreas by modulating expression of different disease genes in the two developmental stages.
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Affiliation(s)
- Jennifer P Nguyen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Timothy D Arthur
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Kyohei Fujita
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Bianca M Salgado
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Margaret K R Donovan
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Ji Hyun Kim
- Department of Pediatrics, Dongguk University Ilsan Hospital, Goyang, South Korea
| | | | - Matteo D'Antonio
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Kelly A Frazer
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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17
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Hu M, Kim I, Morán I, Peng W, Sun O, Bonnefond A, Khamis A, Bonas-Guarch S, Froguel P, Rutter GA. Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548906. [PMID: 37502937 PMCID: PMC10369998 DOI: 10.1101/2023.07.13.548906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, combined allele-specific expression (cASE) analysis in human islets revealed multiple variants that influence SLC30A8 expression. Epigenomic mapping identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighbouring genes. Deletions of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowered the expression of SLC30A8 and several neighbouring genes, and improved insulin secretion. Whilst down-regulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21 or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.
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Affiliation(s)
- Ming Hu
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Innah Kim
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Ignasi Morán
- Life Sciences Department, Barcelona Supercomputing Center (BSC-CNS), 08034 Barcelona, Spain
| | - Weicong Peng
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Orien Sun
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Amélie Bonnefond
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Amna Khamis
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Silvia Bonas-Guarch
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Center for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Spain
| | - Philippe Froguel
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Guy A. Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
- Centre de Recherche du CHUM, Faculté de Médicine, Université de Montréal, Montréal, QC, Canada
- Lee Kong Chian Imperial Medical School, Nanyang Technological University, Singapore
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18
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Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, Metzger BE, Nathan DM, Kirkman MS. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Diabetes Care 2023; 46:e151-e199. [PMID: 37471273 PMCID: PMC10516260 DOI: 10.2337/dci23-0036] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/11/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.
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Affiliation(s)
- David B. Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD
| | - Mark Arnold
- Department of Chemistry, University of Iowa, Iowa City, IA
| | - George L. Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, Section of Endocrinology, Diabetes and Metabolism, University of Chicago Medicine, Chicago, IL
| | - David E. Bruns
- Department of Pathology, University of Virginia Medical School, Charlottesville, VA
| | - Andrea R. Horvath
- New South Wales Health Pathology Department of Chemical Pathology, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skane University Hospital Malmö, Malmö, Sweden
| | - Boyd E. Metzger
- Division of Endocrinology, Northwestern University, The Feinberg School of Medicine, Chicago, IL
| | - David M. Nathan
- Massachusetts General Hospital Diabetes Center and Harvard Medical School, Boston, MA
| | - M. Sue Kirkman
- Department of Medicine, University of North Carolina, Chapel Hill, NC
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19
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Bevacqua RJ, Zhao W, Merheb E, Kim SH, Marson A, Gloyn AL, Kim SK. Multiplexed CRISPR gene editing in primary human islet cells with Cas9 ribonucleoprotein. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.16.558090. [PMID: 37745551 PMCID: PMC10516051 DOI: 10.1101/2023.09.16.558090] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Successful genome editing in primary human islets could reveal features of the genetic regulatory landscape underlying β cell function and diabetes risk. Here, we describe a CRISPR-based strategy to interrogate functions of predicted regulatory DNA elements using electroporation of a complex of Cas9 ribonucleoprotein (Cas9 RNP) and guide RNAs into primary human islet cells. We successfully targeted coding regions including the PDX1 exon 1, and non-coding DNA linked to diabetes susceptibility. CRISPR/Cas9 RNP approaches revealed genetic targets of regulation by DNA elements containing candidate diabetes risk SNPs, including an in vivo enhancer of the MPHOSPH9 gene. CRISPR/Cas9 RNP multiplexed targeting of two cis-regulatory elements linked to diabetes risk in PCSK1, which encodes an endoprotease crucial for insulin processing, also demonstrated efficient simultaneous editing of PCSK1 regulatory elements, resulting in impaired β cell PCSK1 regulation and insulin secretion. Multiplex CRISPR/Cas9 RNP provides powerful approaches to investigate and elucidate human islet cell gene regulation in health and diabetes.
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Affiliation(s)
- Romina J. Bevacqua
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Diabetes, Obesity and Metabolism Institute (DOMI), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mount Sinai Regenerative Biology and Stem Cell Institute, New York, NY, United States
| | - Weichen Zhao
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Emilio Merheb
- Diabetes, Obesity and Metabolism Institute (DOMI), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Seung Hyun Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology and Northern California JDRF Center of Excellence, University of California at San Francisco, CA, 94158, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
- Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Anna L. Gloyn
- Department of Pediatrics (Endocrinology) and of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Seung K. Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Northern California JDRF Center of Excellence, Stanford University School of Medicine, Stanford, CA, 94305, USA
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20
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Hrovatin K, Bastidas-Ponce A, Bakhti M, Zappia L, Büttner M, Salinno C, Sterr M, Böttcher A, Migliorini A, Lickert H, Theis FJ. Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas. Nat Metab 2023; 5:1615-1637. [PMID: 37697055 PMCID: PMC10513934 DOI: 10.1038/s42255-023-00876-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 07/26/2023] [Indexed: 09/13/2023]
Abstract
Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin β-cell ablation model. The β-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that β-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD β-cells. We also report pathways that are shared between β-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation and demise.
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Affiliation(s)
- Karin Hrovatin
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Aimée Bastidas-Ponce
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Mostafa Bakhti
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Luke Zappia
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Maren Büttner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Ciro Salinno
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Michael Sterr
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Anika Böttcher
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Adriana Migliorini
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- McEwen Stem Cell Institute, University Health Network (UHN), Toronto, Ontario, Canada
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
- Medical Faculty, Technical University of Munich, Munich, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- Department of Mathematics, Technical University of Munich, Garching, Germany.
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21
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Hudaiberdiev S, Taylor DL, Song W, Narisu N, Bhuiyan RM, Taylor HJ, Tang X, Yan T, Swift AJ, Bonnycastle LL, Consortium DIAMANTE, Chen S, Stitzel ML, Erdos MR, Ovcharenko I, Collins FS. Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits. Proc Natl Acad Sci U S A 2023; 120:e2206612120. [PMID: 37603758 PMCID: PMC10469333 DOI: 10.1073/pnas.2206612120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/19/2023] [Indexed: 08/23/2023] Open
Abstract
Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.
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Affiliation(s)
- Sanjarbek Hudaiberdiev
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD20892
| | - D. Leland Taylor
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Wei Song
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD20892
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Redwan M. Bhuiyan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT06032
- Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT06032
| | - Henry J. Taylor
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, CambridgeCB1 8RN, UK
| | - Xuming Tang
- Department of Surgery, Weill Cornell Medicine, New York, NY10065
- Center for Genomic Health, Weill Cornell Medicine, New York, NY10065
| | - Tingfen Yan
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Amy J. Swift
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Lori L. Bonnycastle
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - DIAMANTE Consortium
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD20892
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
- The Jackson Laboratory for Genomic Medicine, Farmington, CT06032
- Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT06032
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, CambridgeCB1 8RN, UK
- Department of Surgery, Weill Cornell Medicine, New York, NY10065
- Center for Genomic Health, Weill Cornell Medicine, New York, NY10065
- Institute of Systems Genomics, University of Connecticut, Farmington, CT06032
| | - Shuibing Chen
- Department of Surgery, Weill Cornell Medicine, New York, NY10065
- Center for Genomic Health, Weill Cornell Medicine, New York, NY10065
| | - Michael L. Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, CT06032
- Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT06032
- Institute of Systems Genomics, University of Connecticut, Farmington, CT06032
| | - Michael R. Erdos
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Ivan Ovcharenko
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD20892
| | - Francis S. Collins
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
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22
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Brown AA, Fernandez-Tajes JJ, Hong MG, Brorsson CA, Koivula RW, Davtian D, Dupuis T, Sartori A, Michalettou TD, Forgie IM, Adam J, Allin KH, Caiazzo R, Cederberg H, De Masi F, Elders PJM, Giordano GN, Haid M, Hansen T, Hansen TH, Hattersley AT, Heggie AJ, Howald C, Jones AG, Kokkola T, Laakso M, Mahajan A, Mari A, McDonald TJ, McEvoy D, Mourby M, Musholt PB, Nilsson B, Pattou F, Penet D, Raverdy V, Ridderstråle M, Romano L, Rutters F, Sharma S, Teare H, 't Hart L, Tsirigos KD, Vangipurapu J, Vestergaard H, Brunak S, Franks PW, Frost G, Grallert H, Jablonka B, McCarthy MI, Pavo I, Pedersen O, Ruetten H, Walker M, Adamski J, Schwenk JM, Pearson ER, Dermitzakis ET, Viñuela A. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits. Nat Commun 2023; 14:5062. [PMID: 37604891 PMCID: PMC10442420 DOI: 10.1038/s41467-023-40569-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.
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Affiliation(s)
- Andrew A Brown
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Juan J Fernandez-Tajes
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Mun-Gwan Hong
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Caroline A Brorsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert W Koivula
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, United Kingdom
| | - David Davtian
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Théo Dupuis
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Ambra Sartori
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Theodora-Dafni Michalettou
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom
| | - Ian M Forgie
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Jonathan Adam
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Kristine H Allin
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert Caiazzo
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Henna Cederberg
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Federico De Masi
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Petra J M Elders
- Department of General Practice, Amsterdam UMC- location Vumc, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Giuseppe N Giordano
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Mark Haid
- Metabolomics and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Torben Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Tue H Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Alison J Heggie
- Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Cédric Howald
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Tarja Kokkola
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Andrea Mari
- Institute of Neuroscience, National Research Council, Padova, 35127, Italy
| | - Timothy J McDonald
- Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, EX2 5DW, United Kingdom
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Miranda Mourby
- Nuffield Department of Population Health, Centre for Health, Law and Emerging Technologies (HeLEX), University of Oxford, Oxford, OX2 7DD, United Kingdom
| | - Petra B Musholt
- Global Development, Sanofi-Aventis Deutschland GmbH, Hoechst Industrial Park, Frankfurt am Main, 65926, Germany
| | - Birgitte Nilsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Francois Pattou
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Deborah Penet
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Violeta Raverdy
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | | | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Femke Rutters
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
| | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Food Chemistry and Molecular and Sensory Science, Technical University of Munich, München, Germany
| | - Harriet Teare
- Centre for Health Law and Emerging Technologies, Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, United Kingdom
| | - Leen 't Hart
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Molecular Epidemiology section, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Jagadish Vangipurapu
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Henrik Vestergaard
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
- Steno Diabetes Center Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Paul W Franks
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Gary Frost
- Nutrition and Dietetics Research Group, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Bernd Jablonka
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
- GENENTECH, 1 DNA Way, San Francisco, CA, 94080, USA
| | - Imre Pavo
- Eli Lilly Regional Operations Ges.m.b.H, Vienna, 1030, Austria
| | - Oluf Pedersen
- Center for Clinical Metabolic Research, Herlev and Gentofte University Hospital, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Hartmut Ruetten
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, United Kingdom
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Institute of Experimental Genetics, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Ewan R Pearson
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland.
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland.
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland.
| | - Ana Viñuela
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom.
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23
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Torres JM, Sun H, Nylander V, Downes DJ, van de Bunt M, McCarthy MI, Hughes JR, Gloyn AL. Inferring causal genes at type 2 diabetes GWAS loci through chromosome interactions in islet cells. Wellcome Open Res 2023; 8:165. [PMID: 37736013 PMCID: PMC10509606 DOI: 10.12688/wellcomeopenres.18653.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/04/2023] [Indexed: 09/23/2023] Open
Abstract
Background: Resolving causal genes for type 2 diabetes at loci implicated by genome-wide association studies (GWAS) requires integrating functional genomic data from relevant cell types. Chromatin features in endocrine cells of the pancreatic islet are particularly informative and recent studies leveraging chromosome conformation capture (3C) with Hi-C based methods have elucidated regulatory mechanisms in human islets. However, these genome-wide approaches are less sensitive and afford lower resolution than methods that target specific loci. Methods: To gauge the extent to which targeted 3C further resolves chromatin-mediated regulatory mechanisms at GWAS loci, we generated interaction profiles at 23 loci using next-generation (NG) capture-C in a human beta cell model (EndoC-βH1) and contrasted these maps with Hi-C maps in EndoC-βH1 cells and human islets and a promoter capture Hi-C map in human islets. Results: We found improvements in assay sensitivity of up to 33-fold and resolved ~3.6X more chromatin interactions. At a subset of 18 loci with 25 co-localised GWAS and eQTL signals, NG Capture-C interactions implicated effector transcripts at five additional genetic signals relative to promoter capture Hi-C through physical contact with gene promoters. Conclusions: High resolution chromatin interaction profiles at selectively targeted loci can complement genome- and promoter-wide maps.
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Affiliation(s)
- Jason M. Torres
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, England, OX3 7BN, UK
| | - Han Sun
- Department of Pediatrics, Division of Endocrinology and Diabetes, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Vibe Nylander
- Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, Oxford, England, OX3 7L3, UK
| | - Damien J. Downes
- Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9D2, UK
| | - Martijn van de Bunt
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, England, OX3 7BN, UK
- Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, Oxford, England, OX3 7L3, UK
- Present address: Cytoki Pharma ApS, Tuborg Boulevard 12, Hellerup, DK-2900, Denmark
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, England, OX3 7BN, UK
- Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, Oxford, England, OX3 7L3, UK
- Present address: OMNI Human Genetics, Genentech, 1 DNA Way, South San Francisco, CA, 94080, USA
| | - Jim R. Hughes
- Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9D2, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9D2, UK
| | - Anna L. Gloyn
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, England, OX3 7BN, UK
- Department of Pediatrics, Division of Endocrinology and Diabetes, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, Oxford, England, OX3 7L3, UK
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24
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Rouskas K, Katsareli EA, Amerikanou C, Dimopoulos AC, Glentis S, Kalantzi A, Skoulakis A, Panousis N, Ongen H, Bielser D, Planchon A, Romano L, Harokopos V, Reczko M, Moulos P, Griniatsos I, Diamantis T, Dermitzakis ET, Ragoussis J, Dedoussis G, Dimas AS. Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population. BMC Genomics 2023; 24:442. [PMID: 37543566 PMCID: PMC10403965 DOI: 10.1186/s12864-023-09532-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource. RESULTS We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue. CONCLUSIONS By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.
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Affiliation(s)
- Konstantinos Rouskas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, Greece
| | - Efthymia A Katsareli
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Charalampia Amerikanou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Alexandros C Dimopoulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Hellenic Naval Academy, Hatzikyriakou Avenue, Pireaus, Greece
| | - Stavros Glentis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Pediatric Hematology/Oncology Unit (POHemU), First Department of Pediatrics, University of Athens, Aghia Sophia Children's Hospital, Athens, Greece
| | - Alexandra Kalantzi
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Anargyros Skoulakis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | | | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Swiss Institute of Bioinformatics, University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Deborah Bielser
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Alexandra Planchon
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Vaggelis Harokopos
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Martin Reczko
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Panagiotis Moulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Center of New Biotechnologies & Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Griniatsos
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Theodoros Diamantis
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
- Department of Bioengineering, McGill University, Montréal, QC, Canada
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece.
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25
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Arruda AL, Hartley A, Katsoula G, Smith GD, Morris AP, Zeggini E. Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. Am J Hum Genet 2023; 110:1304-1318. [PMID: 37433298 PMCID: PMC10432145 DOI: 10.1016/j.ajhg.2023.06.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/13/2023] Open
Abstract
Multimorbidity is a rising public health challenge with important implications for health management and policy. The most common multimorbidity pattern is the combination of cardiometabolic and osteoarticular diseases. Here, we study the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. We find genome-wide genetic correlation between the two diseases and robust evidence for association-signal colocalization at 18 genomic regions. We integrate multi-omics and functional information to resolve the colocalizing signals and identify high-confidence effector genes, including FTO and IRX3, which provide proof-of-concept insights into the epidemiologic link between obesity and both diseases. We find enrichment for lipid metabolism and skeletal formation pathways for signals underpinning the knee and hip osteoarthritis comorbidities with type 2 diabetes, respectively. Causal inference analysis identifies complex effects of tissue-specific gene expression on comorbidity outcomes. Our findings provide insights into the biological basis for the type 2 diabetes-osteoarthritis disease co-occurrence.
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Affiliation(s)
- Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Munich School of Data Science, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich (TUM), School of Medicine, Graduate School of Experimental Medicine, 81675 Munich, Germany
| | - April Hartley
- MRC Integrative Epidemiology Unit, University of Bristol, BS8 2BN Bristol, UK
| | - Georgia Katsoula
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich (TUM), School of Medicine, Graduate School of Experimental Medicine, 81675 Munich, Germany
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, BS8 2BN Bristol, UK
| | - Andrew P Morris
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, M13 9PT Manchester, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; TUM School of Medicine, Technical University Munich and Klinikum Rechts der Isar, 81675 Munich, Germany.
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26
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Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, Metzger BE, Nathan DM, Kirkman MS. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Clin Chem 2023:hvad080. [PMID: 37473453 DOI: 10.1093/clinchem/hvad080] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/12/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.
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Affiliation(s)
- David B Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD, United States
| | - Mark Arnold
- Department of Chemistry, University of Iowa, Iowa City, IA, United States
| | - George L Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, Section of Endocrinology, Diabetes and Metabolism, University of Chicago Medicine, Chicago, ILUnited States
| | - David E Bruns
- Department of Pathology, University of Virginia Medical School, Charlottesville, VA, United States
| | - Andrea R Horvath
- New South Wales Health Pathology Department of Chemical Pathology, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skane University Hospital Malmö, Malmö, Sweden
| | - Boyd E Metzger
- Division of Endocrinology, Northwestern University, The Feinberg School of Medicine, Chicago, IL, United States
| | - David M Nathan
- Massachusetts General Hospital Diabetes Center and Harvard Medical School, Boston, MA, United States
| | - M Sue Kirkman
- Department of Medicine, University of North Carolina, Chapel Hill, NC, United States
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27
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Schlosser P, Zhang J, Liu H, Surapaneni AL, Rhee EP, Arking DE, Yu B, Boerwinkle E, Welling PA, Chatterjee N, Susztak K, Coresh J, Grams ME. Transcriptome- and proteome-wide association studies nominate determinants of kidney function and damage. Genome Biol 2023; 24:150. [PMID: 37365616 DOI: 10.1186/s13059-023-02993-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 06/15/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND The pathophysiological causes of kidney disease are not fully understood. Here we show that the integration of genome-wide genetic, transcriptomic, and proteomic association studies can nominate causal determinants of kidney function and damage. RESULTS Through transcriptome-wide association studies (TWAS) in kidney cortex, kidney tubule, liver, and whole blood and proteome-wide association studies (PWAS) in plasma, we assess for effects of 12,893 genes and 1342 proteins on kidney filtration (glomerular filtration rate (GFR) estimated by creatinine; GFR estimated by cystatin C; and blood urea nitrogen) and kidney damage (albuminuria). We find 1561 associations distributed among 260 genomic regions that are supported as putatively causal. We then prioritize 153 of these genomic regions using additional colocalization analyses. Our genome-wide findings are supported by existing knowledge (animal models for MANBA, DACH1, SH3YL1, INHBB), exceed the underlying GWAS signals (28 region-trait combinations without significant GWAS hit), identify independent gene/protein-trait associations within the same genomic region (INHBC, SPRYD4), nominate tissues underlying the associations (tubule expression of NRBP1), and distinguish markers of kidney filtration from those with a role in creatinine and cystatin C metabolism. Furthermore, we follow up on members of the TGF-beta superfamily of proteins and find a prognostic value of INHBC for kidney disease progression even after adjustment for measured glomerular filtration rate (GFR). CONCLUSION In summary, this study combines multimodal, genome-wide association studies to generate a catalog of putatively causal target genes and proteins relevant to kidney function and damage which can guide follow-up studies in physiology, basic science, and clinical medicine.
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Affiliation(s)
- Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hongbo Liu
- Department of Medicine and Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aditya L Surapaneni
- Welch Center for Prevention Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Eugene P Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Bing Yu
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Paul A Welling
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Katalin Susztak
- Department of Medicine and Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY, USA
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28
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Gagnon E, Mitchell PL, Arsenault BJ. Body Fat Distribution, Fasting Insulin Levels, and Insulin Secretion: A Bidirectional Mendelian Randomization Study. J Clin Endocrinol Metab 2023; 108:1308-1317. [PMID: 36585897 DOI: 10.1210/clinem/dgac758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 12/07/2022] [Accepted: 12/28/2022] [Indexed: 01/01/2023]
Abstract
CONTEXT Hyperinsulinemia and adiposity are associated with one another, but the directionality of this relation is debated. OBJECTIVE Here, we tested the direction of the causal effects of fasting insulin (FI) levels and body fat accumulation/distribution using 2-sample bidirectional Mendelian randomization (MR). METHODS We included summary statistics from large-scale genome-wide association studies for body mass index (BMI, n = 806 834), waist to hip ratio adjusted for BMI (WHRadjBMI, n = 694 649), abdominal subcutaneous, visceral and gluteofemoral adipose tissue (n = 38 965), FI levels (n = 98 210), pancreatic islets gene expression (n = 420), and hypothalamus gene expression (n = 155). We used inverse variance-weighted and robust MR methods that relied on statistically and biologically driven genetic instruments. RESULTS Both BMI and WHRadjBMI were positively associated with FI. Results were consistent across all robust MR methods and when variants mapped to the hypothalamus (presumably associated with food behavior) were included. In multivariable MR analyses, when waist circumference and BMI were mutually adjusted, the direct effect of waist circumference on FI was 2.43 times larger than the effect of BMI on FI. FI was not associated with adiposity. By contrast, using genetic instruments mapped to gene expression in pancreatic islets (presumably more specific to insulin secretion), insulin was positively associated with BMI and abdominal subcutaneous and gluteofemoral adipose tissue, but not with visceral adipose tissue. CONCLUSION Although these results will need to be supported by experimental investigations, results of this MR study suggest that abdominal adiposity may be a key determinant of circulating insulin levels. Alternatively, insulin secretion may promote peripheral adipose tissue accumulation.
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Affiliation(s)
- Eloi Gagnon
- Quebec Heart and Lung Institute, Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC G1V 4G5, Canada
| | - Patricia L Mitchell
- Quebec Heart and Lung Institute, Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC G1V 4G5, Canada
| | - Benoit J Arsenault
- Quebec Heart and Lung Institute, Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC G1V 4G5, Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC G1V 5C3, Canada
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29
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Xue D, Narisu N, Taylor DL, Zhang M, Grenko C, Taylor HJ, Yan T, Tang X, Sinha N, Zhu J, Vandana JJ, Chong ACN, Lee A, Mansell EC, Swift AJ, Erdos MR, Zhou T, Bonnycastle LL, Zhong A, Chen S, Collins FS. Functional interrogation of twenty type 2 diabetes-associated genes using isogenic hESC-derived β-like cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.07.539774. [PMID: 37214922 PMCID: PMC10197532 DOI: 10.1101/2023.05.07.539774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional role of many loci has remained unexplored. In this study, we engineered isogenic knockout human embryonic stem cell (hESC) lines for 20 genes associated with T2D risk. We systematically examined β-cell differentiation, insulin production and secretion, and survival. We performed RNA-seq and ATAC-seq on hESC-β cells from each knockout line. Analyses of T2D GWAS signals overlapping with HNF4A-dependent ATAC peaks identified a specific SNP as a likely causal variant. In addition, we performed integrative association analyses and identified four genes ( CP, RNASE1, PCSK1N and GSTA2 ) associated with insulin production, and two genes ( TAGLN3 and DHRS2 ) associated with sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental hESC line, to identify a single likely functional variant at each of 23 T2D GWAS signals.
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30
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Jeon YG, Kim YY, Lee G, Kim JB. Physiological and pathological roles of lipogenesis. Nat Metab 2023; 5:735-759. [PMID: 37142787 DOI: 10.1038/s42255-023-00786-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 03/15/2023] [Indexed: 05/06/2023]
Abstract
Lipids are essential metabolites, which function as energy sources, structural components and signalling mediators. Most cells are able to convert carbohydrates into fatty acids, which are often converted into neutral lipids for storage in the form of lipid droplets. Accumulating evidence suggests that lipogenesis plays a crucial role not only in metabolic tissues for systemic energy homoeostasis but also in immune and nervous systems for their proliferation, differentiation and even pathophysiological roles. Thus, excessive or insufficient lipogenesis is closely associated with aberrations in lipid homoeostasis, potentially leading to pathological consequences, such as dyslipidaemia, diabetes, fatty liver, autoimmune diseases, neurodegenerative diseases and cancers. For systemic energy homoeostasis, multiple enzymes involved in lipogenesis are tightly controlled by transcriptional and post-translational modifications. In this Review, we discuss recent findings regarding the regulatory mechanisms, physiological roles and pathological importance of lipogenesis in multiple tissues such as adipose tissue and the liver, as well as the immune and nervous systems. Furthermore, we briefly introduce the therapeutic implications of lipogenesis modulation.
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Affiliation(s)
- Yong Geun Jeon
- Center for Adipocyte Structure and Function, Institute of Molecular Biology and Genetics, School of Biological Sciences, Seoul National University, Seoul, South Korea
| | - Ye Young Kim
- Center for Adipocyte Structure and Function, Institute of Molecular Biology and Genetics, School of Biological Sciences, Seoul National University, Seoul, South Korea
| | - Gung Lee
- Center for Adipocyte Structure and Function, Institute of Molecular Biology and Genetics, School of Biological Sciences, Seoul National University, Seoul, South Korea
| | - Jae Bum Kim
- Center for Adipocyte Structure and Function, Institute of Molecular Biology and Genetics, School of Biological Sciences, Seoul National University, Seoul, South Korea.
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31
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Hemerich D, Smit RAJ, Preuss M, Stalbow L, van der Laan SW, Asselbergs FW, van Setten J, Tragante V. Effect of tissue-grouped regulatory variants associated to type 2 diabetes in related secondary outcomes. Sci Rep 2023; 13:3579. [PMID: 36864090 PMCID: PMC9981672 DOI: 10.1038/s41598-023-30369-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 02/21/2023] [Indexed: 03/04/2023] Open
Abstract
Genome-wide association studies have identified over five hundred loci that contribute to variation in type 2 diabetes (T2D), an established risk factor for many diseases. However, the mechanisms and extent through which these loci contribute to subsequent outcomes remain elusive. We hypothesized that combinations of T2D-associated variants acting on tissue-specific regulatory elements might account for greater risk for tissue-specific outcomes, leading to diversity in T2D disease progression. We searched for T2D-associated variants acting on regulatory elements and expression quantitative trait loci (eQTLs) in nine tissues. We used T2D tissue-grouped variant sets as genetic instruments to conduct 2-Sample Mendelian Randomization (MR) in ten related outcomes whose risk is increased by T2D using the FinnGen cohort. We performed PheWAS analysis to investigate whether the T2D tissue-grouped variant sets had specific predicted disease signatures. We identified an average of 176 variants acting in nine tissues implicated in T2D, and an average of 30 variants acting on regulatory elements that are unique to the nine tissues of interest. In 2-Sample MR analyses, all subsets of regulatory variants acting in different tissues were associated with increased risk of the ten secondary outcomes studied on similar levels. No tissue-grouped variant set was associated with an outcome significantly more than other tissue-grouped variant sets. We did not identify different disease progression profiles based on tissue-specific regulatory and transcriptome information. Bigger sample sizes and other layers of regulatory information in critical tissues may help identify subsets of T2D variants that are implicated in certain secondary outcomes, uncovering system-specific disease progression.
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Affiliation(s)
- Daiane Hemerich
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Roelof A J Smit
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren Stalbow
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Jessica van Setten
- Department of Cardiology, UMC Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Vinicius Tragante
- Department of Cardiology, UMC Utrecht, Utrecht University, Utrecht, The Netherlands.
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32
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D'Antonio M, Nguyen JP, Arthur TD, Matsui H, D'Antonio-Chronowska A, Frazer KA. Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease. Nat Commun 2023; 14:1132. [PMID: 36854752 PMCID: PMC9975214 DOI: 10.1038/s41467-023-36638-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 02/10/2023] [Indexed: 03/02/2023] Open
Abstract
The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high posterior probabilities for being causal in 210 GWAS loci. Pulse pressure GWAS loci are enriched for colocalization with fetal- and smooth muscle- eQTLs; pulse rate with adult- and cardiac muscle- eQTLs; and atrial fibrillation with cardiac muscle- eQTLs. Fine mapping identifies 79 credible sets with five or fewer SNPs, of which 15 were associated with spatiotemporal eQTLs. Our study shows that many cardiac GWAS variants impact traits and disease in a developmental stage-, tissue- and/or cell type-specific fashion.
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Affiliation(s)
- Matteo D'Antonio
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
| | - Jennifer P Nguyen
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Timothy D Arthur
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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33
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Bacos K, Perfilyev A, Karagiannopoulos A, Cowan E, Ofori JK, Bertonnier-Brouty L, Rönn T, Lindqvist A, Luan C, Ruhrmann S, Ngara M, Nilsson Å, Gheibi S, Lyons CL, Lagerstedt JO, Barghouth M, Esguerra JL, Volkov P, Fex M, Mulder H, Wierup N, Krus U, Artner I, Eliasson L, Prasad RB, Cataldo LR, Ling C. Type 2 diabetes candidate genes, including PAX5, cause impaired insulin secretion in human pancreatic islets. J Clin Invest 2023; 133:163612. [PMID: 36656641 PMCID: PMC9927941 DOI: 10.1172/jci163612] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023] Open
Abstract
Type 2 diabetes (T2D) is caused by insufficient insulin secretion from pancreatic β cells. To identify candidate genes contributing to T2D pathophysiology, we studied human pancreatic islets from approximately 300 individuals. We found 395 differentially expressed genes (DEGs) in islets from individuals with T2D, including, to our knowledge, novel (OPRD1, PAX5, TET1) and previously identified (CHL1, GLRA1, IAPP) candidates. A third of the identified expression changes in islets may predispose to diabetes, as expression of these genes associated with HbA1c in individuals not previously diagnosed with T2D. Most DEGs were expressed in human β cells, based on single-cell RNA-Seq data. Additionally, DEGs displayed alterations in open chromatin and associated with T2D SNPs. Mouse KO strains demonstrated that the identified T2D-associated candidate genes regulate glucose homeostasis and body composition in vivo. Functional validation showed that mimicking T2D-associated changes for OPRD1, PAX5, and SLC2A2 impaired insulin secretion. Impairments in Pax5-overexpressing β cells were due to severe mitochondrial dysfunction. Finally, we discovered PAX5 as a potential transcriptional regulator of many T2D-associated DEGs in human islets. Overall, we have identified molecular alterations in human pancreatic islets that contribute to β cell dysfunction in T2D pathophysiology.
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Affiliation(s)
- Karl Bacos
- Epigenetics and Diabetes Unit, Department of Clinical Sciences and
| | | | - Alexandros Karagiannopoulos
- Unit of Islet Cell Exocytosis, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Malmö, Scania, Sweden
| | - Elaine Cowan
- Unit of Islet Cell Exocytosis, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Malmö, Scania, Sweden
| | - Jones K. Ofori
- Epigenetics and Diabetes Unit, Department of Clinical Sciences and
| | - Ludivine Bertonnier-Brouty
- Endocrine Cell Differentiation, Department of Laboratory Medicine, Lund Stem Cell Center, Malmö, Scania, Sweden
| | - Tina Rönn
- Epigenetics and Diabetes Unit, Department of Clinical Sciences and
| | - Andreas Lindqvist
- Neuroendocrine Cell Biology, Department of Experimental Medical Science
| | - Cheng Luan
- Unit of Islet Pathophysiology, Department of Clinical Sciences
| | - Sabrina Ruhrmann
- Epigenetics and Diabetes Unit, Department of Clinical Sciences and
| | - Mtakai Ngara
- Neuroendocrine Cell Biology, Department of Experimental Medical Science
| | - Åsa Nilsson
- Human Tissue Lab, Department of Clinical Sciences
| | - Sevda Gheibi
- Molecular Metabolism Unit, Department of Clinical Sciences, and
| | - Claire L. Lyons
- Molecular Metabolism Unit, Department of Clinical Sciences, and
| | - Jens O. Lagerstedt
- Unit of Islet Cell Exocytosis, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Malmö, Scania, Sweden
| | | | - Jonathan L.S. Esguerra
- Unit of Islet Cell Exocytosis, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Malmö, Scania, Sweden
| | - Petr Volkov
- Epigenetics and Diabetes Unit, Department of Clinical Sciences and
| | - Malin Fex
- Molecular Metabolism Unit, Department of Clinical Sciences, and
| | - Hindrik Mulder
- Molecular Metabolism Unit, Department of Clinical Sciences, and
| | - Nils Wierup
- Neuroendocrine Cell Biology, Department of Experimental Medical Science
| | - Ulrika Krus
- Human Tissue Lab, Department of Clinical Sciences
| | - Isabella Artner
- Endocrine Cell Differentiation, Department of Laboratory Medicine, Lund Stem Cell Center, Malmö, Scania, Sweden
| | - Lena Eliasson
- Unit of Islet Cell Exocytosis, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Malmö, Scania, Sweden
| | - Rashmi B. Prasad
- Genomics, Diabetes and Endocrinology, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Malmö, Scania, Sweden.,Institute of Molecular Medicine (FIMM), Helsinki University, Helsinki, Finland
| | - Luis Rodrigo Cataldo
- Molecular Metabolism Unit, Department of Clinical Sciences, and,The Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences and
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34
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Taylor HJ, Hung YH, Narisu N, Erdos MR, Kanke M, Yan T, Grenko CM, Swift AJ, Bonnycastle LL, Sethupathy P, Collins FS, Taylor DL. Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis. Proc Natl Acad Sci U S A 2023; 120:e2206797120. [PMID: 36757889 PMCID: PMC9963967 DOI: 10.1073/pnas.2206797120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 01/11/2023] [Indexed: 02/10/2023] Open
Abstract
Genetic studies have identified ≥240 loci associated with the risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterized the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into cis- and trans-acting genetic components and mapping cis-acting loci associated with miRNA expression [miRNA-expression quantitative trait loci (eQTLs)]. We found i) 84 heritable miRNAs, primarily regulated by trans-acting genetic effects, and ii) 5 miRNA-eQTLs. We also used several different strategies to identify T2D-associated miRNAs. First, we colocalized miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersected miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and found 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we performed differential expression analysis and identified 14 miRNAs associated with T2D status-including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p-and 4 miRNAs associated with a polygenic score for HbA1c levels-miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.
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Affiliation(s)
- Henry J. Taylor
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, CambridgeCB2 0BB, UK
- Heart and Lung Research Institute, University of Cambridge, CambridgeCB2 0BB, UK
| | - Yu-Han Hung
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY14853
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Michael R. Erdos
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Matthew Kanke
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY14853
| | - Tingfen Yan
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Caleb M. Grenko
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Amy J. Swift
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Lori L. Bonnycastle
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Praveen Sethupathy
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY14853
| | - Francis S. Collins
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - D. Leland Taylor
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD20892
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35
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Broadaway KA, Yin X, Williamson A, Parsons VA, Wilson EP, Moxley AH, Vadlamudi S, Varshney A, Jackson AU, Ahuja V, Bornstein SR, Corbin LJ, Delgado GE, Dwivedi OP, Fernandes Silva L, Frayling TM, Grallert H, Gustafsson S, Hakaste L, Hammar U, Herder C, Herrmann S, Højlund K, Hughes DA, Kleber ME, Lindgren CM, Liu CT, Luan J, Malmberg A, Moissl AP, Morris AP, Perakakis N, Peters A, Petrie JR, Roden M, Schwarz PEH, Sharma S, Silveira A, Strawbridge RJ, Tuomi T, Wood AR, Wu P, Zethelius B, Baldassarre D, Eriksson JG, Fall T, Florez JC, Fritsche A, Gigante B, Hamsten A, Kajantie E, Laakso M, Lahti J, Lawlor DA, Lind L, März W, Meigs JB, Sundström J, Timpson NJ, Wagner R, Walker M, Wareham NJ, Watkins H, Barroso I, O'Rahilly S, Grarup N, Parker SC, Boehnke M, Langenberg C, Wheeler E, Mohlke KL. Loci for insulin processing and secretion provide insight into type 2 diabetes risk. Am J Hum Genet 2023; 110:284-299. [PMID: 36693378 PMCID: PMC9943750 DOI: 10.1016/j.ajhg.2023.01.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 01/03/2023] [Indexed: 01/25/2023] Open
Abstract
Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.
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Affiliation(s)
- K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Victoria A Parsons
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Emma P Wilson
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Anne H Moxley
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | | | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Anne U Jackson
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Vasudha Ahuja
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Stefan R Bornstein
- Department of Internal Medicine, Metabolic and Vascular Medicine, MedicCal Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Laura J Corbin
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Om P Dwivedi
- University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland
| | | | | | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Stefan Gustafsson
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Liisa Hakaste
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Ulf Hammar
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Christian Herder
- German Center for Diabetes Research, Neuherberg, Germany; Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sandra Herrmann
- Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany
| | | | - David A Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marcus E Kleber
- Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany; SYNLAB MVZ Humangenetik Mannheim, Mannheim, BW, Germany
| | - Cecilia M Lindgren
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK; Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK; Broad Institute, Cambridge, MA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Anni Malmberg
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Angela P Moissl
- Institute of Nutritional Sciences, Friedrich-Schiller-University, Jena, Germany; Competence Cluster for Nutrition and Cardiovascular Health, Halle-Jena-Leipzig, Germany; Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Nikolaos Perakakis
- Department of Internal Medicine, Metabolic and Vascular Medicine, MedicCal Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - John R Petrie
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Peter E H Schwarz
- Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Sapna Sharma
- German Center for Diabetes Research, Neuherberg, Germany; Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Chair of Food Chemistry and Molecular Sensory Science, Technische Universität München, Freising, Germany
| | - Angela Silveira
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden; Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rona J Strawbridge
- Institute of Health and Wellbeing, Mental Health and Wellbeing, University of Glasgow, Glasgow, UK; Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland; Abdominal Center, Endocrinology, Helsinki University Hospital, Helsinki, Finland
| | - Andrew R Wood
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Björn Zethelius
- Department of Geriatrics, Uppsala University, Uppsala, Sweden
| | - Damiano Baldassarre
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy; Cardiovascular Prevention Area, Centro Cardiologico Monzino I.R.C.C.S., Milan, Italy
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Folkhälsan Research Centre, Helsinki, Finland; Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andreas Fritsche
- Department of Internal Medicine, Diabetology, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Bruna Gigante
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hamsten
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Eero Kajantie
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lars Lind
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Winfried März
- Synlab Academy, SYNLAB Holding Deutschland GmbH, Mannheim, BW, Germany; Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - James B Meigs
- Department of Medicine, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Johan Sundström
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robert Wagner
- Department of Internal Medicine, Diabetology, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Mark Walker
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; Health Data Research UK, Gibbs Building, London, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research, Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Stephen O'Rahilly
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephen Cj Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
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36
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Qiao Z, Sidorenko J, Revez JA, Xue A, Lu X, Pärna K, Snieder H, Visscher PM, Wray NR, Yengo L. Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose. Nat Commun 2023; 14:451. [PMID: 36707517 PMCID: PMC9883484 DOI: 10.1038/s41467-023-36013-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 01/12/2023] [Indexed: 01/29/2023] Open
Abstract
The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h2 = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.
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Affiliation(s)
- Zhen Qiao
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Joana A Revez
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Angli Xue
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Xueling Lu
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, Guangdong, China
| | - Katri Pärna
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
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37
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Insights from multi-omics integration in complex disease primary tissues. Trends Genet 2023; 39:46-58. [PMID: 36137835 DOI: 10.1016/j.tig.2022.08.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 11/23/2022]
Abstract
Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer's disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations.
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38
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Rottner AK, Ye Y, Navarro-Guerrero E, Rajesh V, Pollner A, Bevacqua RJ, Yang J, Spigelman AF, Baronio R, Bautista A, Thomsen SK, Lyon J, Nawaz S, Smith N, Wesolowska-Andersen A, Fox JEM, Sun H, Kim SK, Ebner D, MacDonald PE, Gloyn AL. A genome-wide CRISPR screen identifies CALCOCO2 as a regulator of beta cell function influencing type 2 diabetes risk. Nat Genet 2023; 55:54-65. [PMID: 36543916 PMCID: PMC9839450 DOI: 10.1038/s41588-022-01261-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 10/26/2022] [Indexed: 12/24/2022]
Abstract
Identification of the genes and processes mediating genetic association signals for complex diseases represents a major challenge. As many of the genetic signals for type 2 diabetes (T2D) exert their effects through pancreatic islet-cell dysfunction, we performed a genome-wide pooled CRISPR loss-of-function screen in a human pancreatic beta cell line. We assessed the regulation of insulin content as a disease-relevant readout of beta cell function and identified 580 genes influencing this phenotype. Integration with genetic and genomic data provided experimental support for 20 candidate T2D effector transcripts including the autophagy receptor CALCOCO2. Loss of CALCOCO2 was associated with distorted mitochondria, less proinsulin-containing immature granules and accumulation of autophagosomes upon inhibition of late-stage autophagy. Carriers of T2D-associated variants at the CALCOCO2 locus further displayed altered insulin secretion. Our study highlights how cellular screens can augment existing multi-omic efforts to support mechanistic understanding and provide evidence for causal effects at genome-wide association studies loci.
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Affiliation(s)
- Antje K Rottner
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Yingying Ye
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Elena Navarro-Guerrero
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Varsha Rajesh
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Alina Pollner
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Romina J Bevacqua
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Centre, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Jing Yang
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Aliya F Spigelman
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Roberta Baronio
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Austin Bautista
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Soren K Thomsen
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - James Lyon
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Sameena Nawaz
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Nancy Smith
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | | | - Jocelyn E Manning Fox
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Han Sun
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Seung K Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Centre, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Daniel Ebner
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Patrick E MacDonald
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA, USA.
- Stanford Diabetes Research Centre, Stanford School of Medicine, Stanford University, Stanford, CA, USA.
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK.
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
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39
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García-Pérez R, Ramirez JM, Ripoll-Cladellas A, Chazarra-Gil R, Oliveros W, Soldatkina O, Bosio M, Rognon PJ, Capella-Gutierrez S, Calvo M, Reverter F, Guigó R, Aguet F, Ferreira PG, Ardlie KG, Melé M. The landscape of expression and alternative splicing variation across human traits. CELL GENOMICS 2022; 3:100244. [PMID: 36777183 PMCID: PMC9903719 DOI: 10.1016/j.xgen.2022.100244] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/08/2022] [Accepted: 12/07/2022] [Indexed: 12/31/2022]
Abstract
Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease.
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Affiliation(s)
- Raquel García-Pérez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Jose Miguel Ramirez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Aida Ripoll-Cladellas
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Ruben Chazarra-Gil
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Winona Oliveros
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Oleksandra Soldatkina
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Mattia Bosio
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Paul Joris Rognon
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain,Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Catalonia 08005, Spain,Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Catalonia 08034, Spain
| | - Salvador Capella-Gutierrez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Miquel Calvo
- Statistics Section, Faculty of Biology, Universitat de Barcelona (UB), Barcelona, Catalonia 08028, Spain
| | - Ferran Reverter
- Statistics Section, Faculty of Biology, Universitat de Barcelona (UB), Barcelona, Catalonia 08028, Spain
| | - Roderic Guigó
- Bioinformatics and Genomics, Center for Genomic Regulation, Barcelona, Catalonia 08003, Spain
| | | | - Pedro G. Ferreira
- Department of Computer Science, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal,Laboratory of Artificial Intelligence and Decision Support, INESC TEC, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal,Institute of Molecular Pathology and Immunology of the University of Porto, Institute for Research and Innovation in Health (i3s), R. Alfredo Allen 208, 4200-135 Porto, Portugal
| | | | - Marta Melé
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain,Corresponding author
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40
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Benaglio P, Zhu H, Okino ML, Yan J, Elgamal R, Nariai N, Beebe E, Korgaonkar K, Qiu Y, Donovan MK, Chiou J, Wang G, Newsome J, Kaur J, Miller M, Preissl S, Corban S, Aylward A, Taipale J, Ren B, Frazer KA, Sander M, Gaulton KJ. Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines. CELL GENOMICS 2022; 2:100214. [PMID: 36778047 PMCID: PMC9903835 DOI: 10.1016/j.xgen.2022.100214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 06/17/2022] [Accepted: 10/15/2022] [Indexed: 11/13/2022]
Abstract
We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate cis-regulatory elements (cCREs) in beta cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells, we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation, and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with SOCS1, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta cells during inflammation that modulate T1D risk.
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Affiliation(s)
- Paola Benaglio
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Han Zhu
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Mei-Lin Okino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jian Yan
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- School of Medicine, Northwest University, Xi’an, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden
| | - Ruth Elgamal
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Naoki Nariai
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Elisha Beebe
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Katha Korgaonkar
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Yunjiang Qiu
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | | | - Joshua Chiou
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Gaowei Wang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jacklyn Newsome
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jaspreet Kaur
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA
| | - Sierra Corban
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Anthony Aylward
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jussi Taipale
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Genome-Scale Biology Program, University of Helsinki, Helsinki, Finland
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA
| | - Kelly A. Frazer
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Maike Sander
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
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The human batokine EPDR1 regulates β-cell metabolism and function. Mol Metab 2022; 66:101629. [PMID: 36343918 PMCID: PMC9663883 DOI: 10.1016/j.molmet.2022.101629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 10/14/2022] [Accepted: 11/01/2022] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE Ependymin-Related Protein 1 (EPDR1) was recently identified as a secreted human batokine regulating mitochondrial respiration linked to thermogenesis in brown fat. Despite that EPDR1 is expressed in human pancreatic β-cells and that glucose-stimulated mitochondrial metabolism is critical for stimulus-secretion coupling in β-cells, the role of EPDR1 in β-cell metabolism and function has not been investigated. METHODS EPDR1 mRNA levels in human pancreatic islets from non-diabetic (ND) and type 2 diabetes (T2D) subjects were assessed. Human islets, EndoC-βH1 and INS1 832/13 cells were transfected with scramble (control) and EPDR1 siRNAs (EPDR1-KD) or treated with human EPDR1 protein, and glucose-stimulated insulin secretion (GSIS) assessed by ELISA. Mitochondrial metabolism was investigated by extracellular flux analyzer, confocal microscopy and mass spectrometry-based metabolomics analysis. RESULTS EPDR1 mRNA expression was upregulated in human islets from T2D and obese donors and positively correlated to BMI of donors. In T2D donors, EPDR1 mRNA levels negatively correlated with HbA1c and positively correlated with GSIS. EPDR1 silencing in human islets and β-cell lines reduced GSIS whereas treatment with human EPDR1 protein increased GSIS. Epdr1 silencing in INS1 832/13 cells reduced glucose- and pyruvate- but not K+-stimulated insulin secretion. Metabolomics analysis in Epdr1-KD INS1 832/13 cells suggests diversion of glucose-derived pyruvate to lactate production and decreased malate-aspartate shuttle and the tricarboxylic acid (TCA) cycle activity. The glucose-stimulated rise in mitochondrial respiration and ATP/ADP-ratio was impaired in Epdr1-deficient cells. CONCLUSION These results suggests that to maintain glucose homeostasis in obese people, upregulation of EPDR1 may improve β-cell function via channelling glycolysis-derived pyruvate to the mitochondrial TCA cycle.
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Alghamdi TA, Krentz NA, Smith N, Spigelman AF, Rajesh V, Jha A, Ferdaoussi M, Suzuki K, Yang J, Manning Fox JE, Sun H, Sun Z, Gloyn AL, MacDonald PE. Zmiz1 is required for mature β-cell function and mass expansion upon high fat feeding. Mol Metab 2022; 66:101621. [PMID: 36307047 PMCID: PMC9643564 DOI: 10.1016/j.molmet.2022.101621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/15/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVE Identifying the transcripts which mediate genetic association signals for type 2 diabetes (T2D) is critical to understand disease mechanisms. Studies in pancreatic islets support the transcription factor ZMIZ1 as a transcript underlying a T2D GWAS signal, but how it influences T2D risk is unknown. METHODS β-Cell-specific Zmiz1 knockout (Zmiz1βKO) mice were generated and phenotypically characterised. Glucose homeostasis was assessed in Zmiz1βKO mice and their control littermates on chow diet (CD) and high fat diet (HFD). Islet morphology and function were examined by immunohistochemistry and in vitro islet function was assessed by dynamic insulin secretion assay. Transcript and protein expression were assessed by RNA sequencing and Western blotting. In islets isolated from genotyped human donors, we assessed glucose-dependent insulin secretion and islet insulin content by static incubation assay. RESULTS Male and female Zmiz1βKO mice were glucose intolerant with impaired insulin secretion, compared with control littermates. Transcriptomic profiling of Zmiz1βKO islets identified over 500 differentially expressed genes including those involved in β-cell function and maturity, which we confirmed at the protein level. Upon HFD, Zmiz1βKO mice fail to expand β-cell mass and become severely diabetic. Human islets from carriers of the ZMIZ1-linked T2D-risk alleles have reduced islet insulin content and glucose-stimulated insulin secretion. CONCLUSIONS β-Cell Zmiz1 is required for normal glucose homeostasis. Genetic variation at the ZMIZ1 locus may influence T2D-risk by reducing islet mass expansion upon metabolic stress and the ability to maintain a mature β-cell state.
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Affiliation(s)
- Tamadher A. Alghamdi
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, T6G2R3, Canada
| | - Nicole A.J. Krentz
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Nancy Smith
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, T6G2R3, Canada
| | - Aliya F. Spigelman
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, T6G2R3, Canada
| | - Varsha Rajesh
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Alokkumar Jha
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Mourad Ferdaoussi
- Department of Pediatrics, University of Alberta, Edmonton AB, T6G2R3, Canada
| | - Kunimasa Suzuki
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, T6G2R3, Canada
| | - Jing Yang
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jocelyn E. Manning Fox
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, T6G2R3, Canada
| | - Han Sun
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Zijie Sun
- Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Anna L. Gloyn
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA,Stanford Diabetes Research Centre, Stanford University, Stanford, CA, USA,Oxford Centre for Diabetes Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, UK,Corresponding author. Center for Academic Medicine, Division of Endocrinology & Diabetes, Department of Pediatrics, 453 Quarry Road, Palo Alto CA, 94304, USA. http://www.bcell.org
| | - Patrick E. MacDonald
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, T6G2R3, Canada,Corresponding author. Alberta Diabetes Institute, LKS Centre, Rm. 6-126, Edmonton, AB, T6G 2R3, Canada.
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Zeng W, Liu Q, Yin Q, Jiang R, Wong WH. HiChIPdb: a comprehensive database of HiChIP regulatory interactions. Nucleic Acids Res 2022; 51:D159-D166. [PMID: 36215037 PMCID: PMC9825415 DOI: 10.1093/nar/gkac859] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/19/2022] [Accepted: 09/27/2022] [Indexed: 01/29/2023] Open
Abstract
Elucidating the role of 3D architecture of DNA in gene regulation is crucial for understanding cell differentiation, tissue homeostasis and disease development. Among various chromatin conformation capture methods, HiChIP has received increasing attention for its significant improvement over other methods in profiling of regulatory (e.g. H3K27ac) and structural (e.g. cohesin) interactions. To facilitate the studies of 3D regulatory interactions, we developed a HiChIP interactions database, HiChIPdb (http://health.tsinghua.edu.cn/hichipdb/). The current version of HiChIPdb contains ∼262M annotated HiChIP interactions from 200 high-throughput HiChIP samples across 108 cell types. The functionalities of HiChIPdb include: (i) standardized categorization of HiChIP interactions in a hierarchical structure based on organ, tissue and cell line and (ii) comprehensive annotations of HiChIP interactions with regulatory genes and GWAS Catalog SNPs. To the best of our knowledge, HiChIPdb is the first comprehensive database that utilizes a unified pipeline to map the functional interactions across diverse cell types and tissues in different resolutions. We believe this database has the potential to advance cutting-edge research in regulatory mechanisms in development and disease by removing the barrier in data aggregation, preprocessing, and analysis.
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Affiliation(s)
| | | | | | - Rui Jiang
- Correspondence may also be addressed to Rui Jiang. Tel: +86 10 6279 5578;
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Atla G, Bonàs-Guarch S, Cuenca-Ardura M, Beucher A, Crouch DJM, Garcia-Hurtado J, Moran I, Irimia M, Prasad RB, Gloyn AL, Marselli L, Suleiman M, Berney T, de Koning EJP, Kerr-Conte J, Pattou F, Todd JA, Piemonti L, Ferrer J. Genetic regulation of RNA splicing in human pancreatic islets. Genome Biol 2022; 23:196. [PMID: 36109769 PMCID: PMC9479353 DOI: 10.1186/s13059-022-02757-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 08/23/2022] [Indexed: 12/30/2022] Open
Abstract
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.
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Affiliation(s)
- Goutham Atla
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Silvia Bonàs-Guarch
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain.
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
| | - Mirabai Cuenca-Ardura
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain
| | - Anthony Beucher
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Daniel J M Crouch
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Javier Garcia-Hurtado
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain
| | - Ignasi Moran
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Present Address: Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034, Barcelona, Spain
| | - Manuel Irimia
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Rashmi B Prasad
- Lund University Diabetes Centre, Clinical Research Center, Malmö, Sweden
- Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Lorella Marselli
- Department of Clinical and Experimental Medicine, AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Mara Suleiman
- Department of Clinical and Experimental Medicine, AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Thierry Berney
- Cell Isolation and Transplantation Center, University of Geneva, Geneva, Switzerland
| | - Eelco J P de Koning
- Department of Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Hubrecht Institute/KNAW, Utrecht, the Netherlands
| | - Julie Kerr-Conte
- University of Lille, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Lille (CHU Lille), Institute Pasteur Lille, U1190 -European Genomic Institute for Diabetes (EGID), F59000, Lille, France
| | - Francois Pattou
- University of Lille, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Lille (CHU Lille), Institute Pasteur Lille, U1190 -European Genomic Institute for Diabetes (EGID), F59000, Lille, France
| | - John A Todd
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Lorenzo Piemonti
- Diabetes Research Institute, IRCCS Ospedale San Raffaele and Università Vita-Salute San Raffaele, Milan, Italy
| | - Jorge Ferrer
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain.
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
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Asplund O, Storm P, Chandra V, Hatem G, Ottosson-Laakso E, Mansour-Aly D, Krus U, Ibrahim H, Ahlqvist E, Tuomi T, Renström E, Korsgren O, Wierup N, Ibberson M, Solimena M, Marchetti P, Wollheim C, Artner I, Mulder H, Hansson O, Otonkoski T, Groop L, Prasad RB. Islet Gene View-a tool to facilitate islet research. Life Sci Alliance 2022; 5:e202201376. [PMID: 35948367 PMCID: PMC9366203 DOI: 10.26508/lsa.202201376] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 07/18/2022] [Accepted: 07/18/2022] [Indexed: 01/27/2023] Open
Abstract
Characterization of gene expression in pancreatic islets and its alteration in type 2 diabetes (T2D) are vital in understanding islet function and T2D pathogenesis. We leveraged RNA sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. Expression data were related to islet phenotypes, diabetes status, other islet-expressed genes, islet hormone-encoding genes and for expression in insulin target tissues. The IGW web application produces output graphs for a particular gene of interest. In IGW, 284 differentially expressed genes (DEGs) were identified in T2D donor islets compared with controls. Forty percent of DEGs showed cell-type enrichment and a large proportion significantly co-expressed with islet hormone-encoding genes; glucagon (<i>GCG</i>, 56%), amylin (<i>IAPP</i>, 52%), insulin (<i>INS</i>, 44%), and somatostatin (<i>SST</i>, 24%). Inhibition of two DEGs, <i>UNC5D</i> and <i>SERPINE2</i>, impaired glucose-stimulated insulin secretion and impacted cell survival in a human β-cell model. The exploratory use of IGW could help designing more comprehensive functional follow-up studies and serve to identify therapeutic targets in T2D.
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Affiliation(s)
- Olof Asplund
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Petter Storm
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
- Department of Experimental Medical Science, Developmental and Regenerative Neurobiology, Wallenberg Neuroscience Center, Lund, Sweden
| | - Vikash Chandra
- Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Gad Hatem
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Emilia Ottosson-Laakso
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Dina Mansour-Aly
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Ulrika Krus
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Hazem Ibrahim
- Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Emma Ahlqvist
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Tiinamaija Tuomi
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
- Department of Endocrinology, Abdominal Centre, Helsinki University Hospital, Folkhalsan Research Center, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Erik Renström
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Olle Korsgren
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Nils Wierup
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michele Solimena
- Paul Langerhans Institute Dresden of the Helmholtz Center, Munich at University Hospital Carl Gustav Carus and Faculty of Medicine, TU Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, (MPI-CBG), Dresden, Germany
| | - Piero Marchetti
- Department of Clinical and Experimental Medicine, Cisanello, University Hospital, University of Pisa, Pisa, Italy
| | - Claes Wollheim
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
- Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Isabella Artner
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Hindrik Mulder
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
| | - Ola Hansson
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Timo Otonkoski
- Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Children's Hospital, Helsinki University Hospital, Helsinki, Finland
| | - Leif Groop
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Rashmi B Prasad
- Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
- Lund University Diabetes Centre (LUDC), Lund, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Human Tissue Laboratory at Lund University Diabetes Centre, Lund, Sweden
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Verma M, Loh NY, Sabaratnam R, Vasan SK, van Dam AD, Todorčević M, Neville MJ, Toledo E, Karpe F, Christodoulides C. TCF7L2 plays a complex role in human adipose progenitor biology, which might contribute to genetic susceptibility to type 2 diabetes. Metabolism 2022; 133:155240. [PMID: 35697299 DOI: 10.1016/j.metabol.2022.155240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 05/31/2022] [Accepted: 06/04/2022] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Non-coding genetic variation at TCF7L2 is the strongest genetic determinant of type 2 diabetes (T2D) risk in humans. TCF7L2 encodes a transcription factor mediating the nuclear effects of WNT signaling in adipose tissue (AT). In vivo studies in transgenic mice have highlighted important roles for TCF7L2 in adipose tissue biology and systemic metabolism. OBJECTIVE To map the expression of TCF7L2 in human AT, examine its role in human adipose cell biology in vitro, and investigate the effects of the fine-mapped T2D-risk allele at rs7903146 on AT morphology and TCF7L2 expression. METHODS Ex vivo gene expression studies of TCF7L2 in whole and fractionated human AT. In vitro TCF7L2 gain- and/or loss-of-function studies in primary and immortalized human adipose progenitor cells (APCs) and mature adipocytes (mADs). AT phenotyping of rs7903146 T2D-risk variant carriers and matched controls. RESULTS Adipose progenitors (APs) exhibited the highest TCF7L2 mRNA abundance compared to mature adipocytes and adipose-derived endothelial cells. Obesity was associated with reduced TCF7L2 transcript levels in whole subcutaneous abdominal AT but paradoxically increased expression in APs. In functional studies, TCF7L2 knockdown (KD) in abdominal APs led to dose-dependent activation of WNT/β-catenin signaling, impaired proliferation and dose-dependent effects on adipogenesis. Whilst partial KD enhanced adipocyte differentiation, near-total KD impaired lipid accumulation and adipogenic gene expression. Over-expression of TCF7L2 accelerated adipogenesis. In contrast, TCF7L2-KD in gluteal APs dose-dependently enhanced lipid accumulation. Transcriptome-wide profiling revealed that TCF7L2 might modulate multiple aspects of AP biology including extracellular matrix secretion, immune signaling and apoptosis. The T2D-risk allele at rs7903146 was associated with reduced AP TCF7L2 expression and enhanced AT insulin sensitivity. CONCLUSIONS TCF7L2 plays a complex role in AP biology and has both dose- and depot-dependent effects on adipogenesis. In addition to regulating pancreatic insulin secretion, genetic variation at TCF7L2 might also influence T2D risk by modulating AP function.
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Affiliation(s)
- Manu Verma
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK
| | - Nellie Y Loh
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK
| | - Rugivan Sabaratnam
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK; Steno Diabetes Center Odense, Odense University Hospital, DK-5000 Odense, Denmark; Department of Clinical Research, University of Southern Denmark, DK-5000 Odense, Denmark
| | - Senthil K Vasan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK
| | - Andrea D van Dam
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK
| | - Marijana Todorčević
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK
| | - Matthew J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK
| | - Enrique Toledo
- Department of Computational Biology, Novo Nordisk Research Centre Oxford, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK; NIHR Oxford Biomedical Research Centre, OUH Foundation Trust, Oxford OX3 7LE, UK
| | - Constantinos Christodoulides
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK; NIHR Oxford Biomedical Research Centre, OUH Foundation Trust, Oxford OX3 7LE, UK.
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Gloyn AL, Ibberson M, Marchetti P, Powers AC, Rorsman P, Sander M, Solimena M. Every islet matters: improving the impact of human islet research. Nat Metab 2022; 4:970-977. [PMID: 35953581 PMCID: PMC11135339 DOI: 10.1038/s42255-022-00607-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/14/2022] [Indexed: 11/10/2022]
Abstract
Detailed characterization of human pancreatic islets is key to elucidating the pathophysiology of all forms of diabetes, especially type 2 diabetes. However, access to human pancreatic islets is limited. Pancreatic tissue for islet retrieval can be obtained from brain-dead organ donors or from individuals undergoing pancreatectomy, often referred to as 'living donors'. Different protocols for human islet procurement can substantially impact islet function. This variability, coupled with heterogeneity between individuals and islets, results in analytical challenges to separate genuine disease pathology or differences between human donors from experimental noise. There are currently no international guidelines for human donor phenotyping, islet procurement and functional characterization. This lack of standardization means that substantial investments from multiple international efforts towards improved understanding of diabetes pathology cannot be fully leveraged. In this Perspective, we overview the status of the field of human islet research, highlight the challenges and propose actions that could accelerate research progress and increase understanding of type 2 diabetes to slow its pandemic spreading.
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Affiliation(s)
- Anna L Gloyn
- Department of Pediatrics, Division of Endocrinology & Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA.
| | - Mark Ibberson
- Vital-IT, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Piero Marchetti
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Alvin C Powers
- Vanderbilt University Medical Center, Nashville, TN, USA
- VA Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Patrik Rorsman
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Metabolic Physiology Unit, Institute of Neuroscience and Physiology, University of Göteborg, Göteborg, Sweden
| | - Maike Sander
- Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, San Diego, CA, USA
| | - Michele Solimena
- Department of Molecular Diabetology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
- Paul Langerhans Institute Dresden and German Center for Diabetes Resaerch (DZD e.V.), Helmholtz Center Munich at University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
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DiCorpo D, Gaynor SM, Russell EM, Westerman KE, Raffield LM, Majarian TD, Wu P, Sarnowski C, Highland HM, Jackson A, Hasbani NR, de Vries PS, Brody JA, Hidalgo B, Guo X, Perry JA, O'Connell JR, Lent S, Montasser ME, Cade BE, Jain D, Wang H, D'Oliveira Albanus R, Varshney A, Yanek LR, Lange L, Palmer ND, Almeida M, Peralta JM, Aslibekyan S, Baldridge AS, Bertoni AG, Bielak LF, Chen CS, Chen YDI, Choi WJ, Goodarzi MO, Floyd JS, Irvin MR, Kalyani RR, Kelly TN, Lee S, Liu CT, Loesch D, Manson JE, Minster RL, Naseri T, Pankow JS, Rasmussen-Torvik LJ, Reiner AP, Reupena MS, Selvin E, Smith JA, Weeks DE, Xu H, Yao J, Zhao W, Parker S, Alonso A, Arnett DK, Blangero J, Boerwinkle E, Correa A, Cupples LA, Curran JE, Duggirala R, He J, Heckbert SR, Kardia SLR, Kim RW, Kooperberg C, Liu S, Mathias RA, McGarvey ST, Mitchell BD, Morrison AC, Peyser PA, Psaty BM, Redline S, Shuldiner AR, Taylor KD, Vasan RS, Viaud-Martinez KA, Florez JC, Wilson JG, Sladek R, Rich SS, Rotter JI, Lin X, Dupuis J, Meigs JB, Wessel J, Manning AK. Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program. Commun Biol 2022; 5:756. [PMID: 35902682 PMCID: PMC9334637 DOI: 10.1038/s42003-022-03702-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 07/12/2022] [Indexed: 01/04/2023] Open
Abstract
The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.
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Affiliation(s)
- Daniel DiCorpo
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Sheila M Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Emily M Russell
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Kenneth E Westerman
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, 02114, USA
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Timothy D Majarian
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Anne Jackson
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Natalie R Hasbani
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98101, USA
- Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Bertha Hidalgo
- Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - James A Perry
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Jeffrey R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Samantha Lent
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
| | - Ricardo D'Oliveira Albanus
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Arushi Varshney
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Leslie Lange
- Department of Medicine, Anschutz Medical Campus, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Marcio Almeida
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville and Edinburg, TX, 78539, USA
| | - Juan M Peralta
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville and Edinburg, TX, 78539, USA
| | | | - Abigail S Baldridge
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Alain G Bertoni
- Department of Epidemiology & Prevention, Wake Forest School of Medicine, Winston-, Salem, NC, 27157, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Chung-Shiuan Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | | | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98195, USA
- Department of Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Marguerite R Irvin
- Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Rita R Kalyani
- GeneSTAR Research Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
| | | | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Douglas Loesch
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - JoAnn E Manson
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Ryan L Minster
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, 55454, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
| | | | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21287, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Daniel E Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Stephen Parker
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY, 40506, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville and Edinburg, TX, 78539, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39211, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
- National Heart Lung and Blood Institute and Boston University's Framingham Heart Study, Framingham, MA, 01702, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville and Edinburg, TX, 78539, USA
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville and Edinburg, TX, 78539, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98195, USA
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ryan W Kim
- Psomagen, Inc, Rockville, MD, 20850, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Simin Liu
- Center for Global Cardiometabolic Health (CGCH), Boston, MA, 02215, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Stephen T McGarvey
- International Health Institute and Department of Epidemiology, Brown University School of Public Health, Providence, RI, 02912, USA
| | - Braxton D Mitchell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Geriatrics Research and Education Clinical Center, Baltimore VA Medical Center, Baltimore, MD, 21201, USA
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98101, USA
- Department of Medicine, University of Washington, Seattle, WA, 98101, USA
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
- Department of Health Services, University of Washington, Seattle, WA, 98101, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
| | - Alan R Shuldiner
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, 21231, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Ramachandran S Vasan
- National Heart Lung and Blood Institute and Boston University's Framingham Heart Study, Framingham, MA, 01702, USA
- Evans Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Evans Department of Medicine, Whitaker Cardiovascular Institute and Cardiology Section, Boston University School of Medicine, Boston, MA, 02118, USA
| | | | - Jose C Florez
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
| | - Robert Sladek
- Department of Human Genetics, McGill University, Montreal, Montreal, Quebec, H3A 0G1, Canada
- Department of Medicine, McGill University, Montreal, Montreal, Quebec, H3A 0G1, Canada
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public Health, Indiana University, IN, 46202, USA.
- Department of Medicine, School of Medicine, Indiana University, IN, 46202, USA.
- Diabetes Translational Research Center, Indiana University, IN, 46202, USA.
| | - Alisa K Manning
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA.
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Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther 2022; 7:216. [PMID: 35794109 PMCID: PMC9259665 DOI: 10.1038/s41392-022-01073-0] [Citation(s) in RCA: 141] [Impact Index Per Article: 70.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/15/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
The centenary of insulin discovery represents an important opportunity to transform diabetes from a fatal diagnosis into a medically manageable chronic condition. Insulin is a key peptide hormone and mediates the systemic glucose metabolism in different tissues. Insulin resistance (IR) is a disordered biological response for insulin stimulation through the disruption of different molecular pathways in target tissues. Acquired conditions and genetic factors have been implicated in IR. Recent genetic and biochemical studies suggest that the dysregulated metabolic mediators released by adipose tissue including adipokines, cytokines, chemokines, excess lipids and toxic lipid metabolites promote IR in other tissues. IR is associated with several groups of abnormal syndromes that include obesity, diabetes, metabolic dysfunction-associated fatty liver disease (MAFLD), cardiovascular disease, polycystic ovary syndrome (PCOS), and other abnormalities. Although no medication is specifically approved to treat IR, we summarized the lifestyle changes and pharmacological medications that have been used as efficient intervention to improve insulin sensitivity. Ultimately, the systematic discussion of complex mechanism will help to identify potential new targets and treat the closely associated metabolic syndrome of IR.
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Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization. Am J Hum Genet 2022; 109:838-856. [PMID: 35460606 DOI: 10.1016/j.ajhg.2022.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/04/2022] [Indexed: 11/22/2022] Open
Abstract
Isolating the causal genes from numerous genetic association signals in genome-wide association studies (GWASs) of complex phenotypes remains an open and challenging question. In the present study, we proposed a statistical approach, the effective-median-based Mendelian randomization (MR) framework, for inferring the causal genes of complex phenotypes with the GWAS summary statistics (named EMIC). The effective-median method solved the high false-positive issue in the existing MR methods due to either correlation among instrumental variables or noises in approximated linkage disequilibrium (LD). EMIC can further perform a pleiotropy fine-mapping analysis to remove possible false-positive estimates. With the usage of multiple cis-expression quantitative trait loci (eQTLs), EMIC was also more powerful than the alternative methods for the causal gene inference in the simulated datasets. Furthermore, EMIC rediscovered many known causal genes of complex phenotypes (schizophrenia, bipolar disorder, and total cholesterol) and reported many new and promising candidate causal genes. In sum, this study provided an efficient solution to discriminate the candidate causal genes from vast amounts of GWAS signals with eQTLs. EMIC has been implemented in our integrative software platform KGGSEE.
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