1
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de Oliveira EF, Garg P, Hjerling-Leffler J, Batista-Brito R, Sjulson L. Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.08.607264. [PMID: 39149388 PMCID: PMC11326262 DOI: 10.1101/2024.08.08.607264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.
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Affiliation(s)
| | - Pranjal Garg
- All India Institute of Medical Sciences, Rishikesh, India
| | - Jens Hjerling-Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Renata Batista-Brito
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY
| | - Lucas Sjulson
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY
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2
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Mummey HM, Elison W, Korgaonkar K, Elgamal RM, Kudtarkar P, Griffin E, Benaglio P, Miller M, Jha A, Fox JEM, McCarthy MI, Preissl S, Gloyn AL, MacDonald PE, Gaulton KJ. Single cell multiome profiling of pancreatic islets reveals physiological changes in cell type-specific regulation associated with diabetes risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.03.606460. [PMID: 39149326 PMCID: PMC11326183 DOI: 10.1101/2024.08.03.606460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.
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Affiliation(s)
- Hannah M Mummey
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA
| | - Weston Elison
- Biomedical Sciences Program, University of California San Diego, La Jolla CA, USA
| | - Katha Korgaonkar
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Ruth M Elgamal
- Biomedical Sciences Program, University of California San Diego, La Jolla CA, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Emily Griffin
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Paola Benaglio
- 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
| | - Alokkumar Jha
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford CA, USA
| | - Jocelyn E Manning Fox
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Mark I McCarthy
- Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK*
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford CA, USA
| | - Anna L Gloyn
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA
| | - Patrick E MacDonald
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
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3
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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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4
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Laver TW, Wakeling MN, Caswell RC, Bunce B, Yau D, Männistö JME, Houghton JAL, Hopkins JJ, Weedon MN, Saraff V, Kershaw M, Honey EM, Murphy N, Giri D, Nath S, Tangari Saredo A, Banerjee I, Hussain K, Owens NDL, Flanagan SE. Chromosome 20p11.2 deletions cause congenital hyperinsulinism via the loss of FOXA2 or its regulatory elements. Eur J Hum Genet 2024; 32:813-818. [PMID: 38605124 PMCID: PMC11220097 DOI: 10.1038/s41431-024-01593-z] [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: 09/14/2023] [Revised: 02/20/2024] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Persistent congenital hyperinsulinism (HI) is a rare genetically heterogeneous condition characterised by dysregulated insulin secretion leading to life-threatening hypoglycaemia. For up to 50% of affected individuals screening of the known HI genes does not identify a disease-causing variant. Large deletions have previously been used to identify novel regulatory regions causing HI. Here, we used genome sequencing to search for novel large (>1 Mb) deletions in 180 probands with HI of unknown cause and replicated our findings in a large cohort of 883 genetically unsolved individuals with HI using off-target copy number variant calling from targeted gene panels. We identified overlapping heterozygous deletions in five individuals (range 3-8 Mb) spanning chromosome 20p11.2. The pancreatic beta-cell transcription factor gene, FOXA2, a known cause of HI was deleted in two of the five individuals. In the remaining three, we found a minimal deleted region of 2.4 Mb adjacent to FOXA2 that encompasses multiple non-coding regulatory elements that are in conformational contact with FOXA2. Our data suggests that the deletions in these three children may cause disease through the dysregulation of FOXA2 expression. These findings provide new insights into the regulation of FOXA2 in the beta-cell and confirm an aetiological role for chromosome 20p11.2 deletions in syndromic HI.
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Affiliation(s)
- Thomas W Laver
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
| | - Matthew N Wakeling
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
| | - Richard C Caswell
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
| | - Benjamin Bunce
- The Genomics Laboratory, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Daphne Yau
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
- Department of Paediatric Endocrinology, Royal Manchester Children's Hospital, Manchester, UK
| | - Jonna M E Männistö
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
- Department of Health Sciences, School of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jayne A L Houghton
- The Genomics Laboratory, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Jasmin J Hopkins
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
| | - Michael N Weedon
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
| | - Vrinda Saraff
- Department of Paediatric Endocrinology and Diabetes, Birmingham Women's and Children's Hospital, Birmingham, UK
| | - Melanie Kershaw
- Department of Paediatric Endocrinology and Diabetes, Birmingham Women's and Children's Hospital, Birmingham, UK
| | - Engela M Honey
- Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa
| | - Nuala Murphy
- Department of Paediatric Endocrinology, Children's University Hospital, Dublin, Ireland
| | - Dinesh Giri
- Department of Paediatric Endocrinology, Bristol Royal Hospital for Children, Bristol, UK
| | | | | | - Indraneel Banerjee
- Department of Paediatric Endocrinology, Royal Manchester Children's Hospital, Manchester, UK
| | - Khalid Hussain
- Department of Paediatrics, Division of Endocrinology, Sidra Medicine, Doha, Qatar
| | - Nick D L Owens
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
| | - Sarah E Flanagan
- Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK.
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5
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Patra M, Klochendler A, Condiotti R, Kaffe B, Elgavish S, Drawshy Z, Avrahami D, Narita M, Hofree M, Drier Y, Meshorer E, Dor Y, Ben-Porath I. Senescence of human pancreatic beta cells enhances functional maturation through chromatin reorganization and promotes interferon responsiveness. Nucleic Acids Res 2024; 52:6298-6316. [PMID: 38682582 PMCID: PMC11194086 DOI: 10.1093/nar/gkae313] [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: 03/07/2024] [Revised: 04/02/2024] [Accepted: 04/11/2024] [Indexed: 05/01/2024] Open
Abstract
Senescent cells can influence the function of tissues in which they reside, and their propensity for disease. A portion of adult human pancreatic beta cells express the senescence marker p16, yet it is unclear whether they are in a senescent state, and how this affects insulin secretion. We analyzed single-cell transcriptome datasets of adult human beta cells, and found that p16-positive cells express senescence gene signatures, as well as elevated levels of beta-cell maturation genes, consistent with enhanced functionality. Senescent human beta-like cells in culture undergo chromatin reorganization that leads to activation of enhancers regulating functional maturation genes and acquisition of glucose-stimulated insulin secretion capacity. Strikingly, Interferon-stimulated genes are elevated in senescent human beta cells, but genes encoding senescence-associated secretory phenotype (SASP) cytokines are not. Senescent beta cells in culture and in human tissue show elevated levels of cytoplasmic DNA, contributing to their increased interferon responsiveness. Human beta-cell senescence thus involves chromatin-driven upregulation of a functional-maturation program, and increased responsiveness of interferon-stimulated genes, changes that could increase both insulin secretion and immune reactivity.
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Affiliation(s)
- Milan Patra
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Agnes Klochendler
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Reba Condiotti
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Binyamin Kaffe
- Department of Genetics, the Institute of Life Sciences and the Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sharona Elgavish
- Info-CORE, Bioinformatics Unit of the I-CORE at the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Zeina Drawshy
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dana Avrahami
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Masashi Narita
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | - Matan Hofree
- The Lautenberg Center for Immunology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yotam Drier
- The Lautenberg Center for Immunology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Eran Meshorer
- Department of Genetics, the Institute of Life Sciences and the Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Dor
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ittai Ben-Porath
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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6
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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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7
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Wu CH, Zhou X, Chen M. The curses of performing differential expression analysis using single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596315. [PMID: 38853843 PMCID: PMC11160624 DOI: 10.1101/2024.05.28.596315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Differential expression analysis is pivotal in single-cell transcriptomics for unraveling cell-type- specific responses to stimuli. While numerous methods are available to identify differentially expressed genes in single-cell data, recent evaluations of both single-cell-specific methods and methods adapted from bulk studies have revealed significant shortcomings in performance. In this paper, we dissect the four major challenges in single-cell DE analysis: normalization, excessive zeros, donor effects, and cumulative biases. These "curses" underscore the limitations and conceptual pitfalls in existing workflows. In response, we introduce a novel paradigm addressing several of these issues.
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8
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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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9
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Keller MP, Hawes EM, Schueler KL, Stapleton DS, Mitok KA, Simonett SP, Oeser JK, Sampson LL, Attie AD, Magnuson MA, O’Brien RM. An Enhancer Within Abcb11 Regulates G6pc2 in C57BL/6 Mouse Pancreatic Islets. Diabetes 2023; 72:1621-1628. [PMID: 37552875 PMCID: PMC10588275 DOI: 10.2337/db23-0215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 08/01/2023] [Indexed: 08/10/2023]
Abstract
G6PC2 is predominantly expressed in pancreatic islet β-cells where it encodes a glucose-6-phosphatase catalytic subunit that modulates the sensitivity of insulin secretion to glucose by opposing the action of glucokinase, thereby regulating fasting blood glucose (FBG). Prior studies have shown that the G6pc2 promoter alone is unable to confer sustained islet-specific gene expression in mice, suggesting the existence of distal enhancers that regulate G6pc2 expression. Using information from both mice and humans and knowledge that single nucleotide polymorphisms (SNPs) both within and near G6PC2 are associated with variations in FBG in humans, we identified several putative enhancers 3' of G6pc2. One region, herein referred to as enhancer I, resides in the 25th intron of Abcb11 and binds multiple islet-enriched transcription factors. CRISPR-mediated deletion of enhancer I in C57BL/6 mice had selective effects on the expression of genes near the G6pc2 locus. In isolated islets, G6pc2 and Spc25 expression were reduced ∼50%, and Gm13613 expression was abolished, whereas Cers6 and nostrin expression were unaffected. This partial reduction in G6pc2 expression enhanced islet insulin secretion at basal glucose concentrations but did not affect FBG or glucose tolerance in vivo, consistent with the absence of a phenotype in G6pc2 heterozygous C57BL/6 mice. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Mark P. Keller
- Department of Biochemistry, University of Wisconsin–Madison, Madison, WI
| | - Emily M. Hawes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
| | | | | | - Kelly A. Mitok
- Department of Biochemistry, University of Wisconsin–Madison, Madison, WI
| | - Shane P. Simonett
- Department of Biochemistry, University of Wisconsin–Madison, Madison, WI
| | - James K. Oeser
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
| | - Leesa L. Sampson
- Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN
| | - Alan D. Attie
- Department of Biochemistry, University of Wisconsin–Madison, Madison, WI
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI
- Department of Medicine, University of Wisconsin–Madison, Madison, WI
| | - Mark A. Magnuson
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
- Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN
| | - Richard M. O’Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
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10
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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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11
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Lyu X, Rowley MJ, Kulik MJ, Dalton S, Corces VG. Regulation of CTCF loop formation during pancreatic cell differentiation. Nat Commun 2023; 14:6314. [PMID: 37813869 PMCID: PMC10562423 DOI: 10.1038/s41467-023-41964-6] [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: 04/13/2023] [Accepted: 09/22/2023] [Indexed: 10/11/2023] Open
Abstract
Transcription reprogramming during cell differentiation involves targeting enhancers to genes responsible for establishment of cell fates. To understand the contribution of CTCF-mediated chromatin organization to cell lineage commitment, we analyzed 3D chromatin architecture during the differentiation of human embryonic stem cells into pancreatic islet organoids. We find that CTCF loops are formed and disassembled at different stages of the differentiation process by either recruitment of CTCF to new anchor sites or use of pre-existing sites not previously involved in loop formation. Recruitment of CTCF to new sites in the genome involves demethylation of H3K9me3 to H3K9me2, demethylation of DNA, recruitment of pioneer factors, and positioning of nucleosomes flanking the new CTCF sites. Existing CTCF sites not involved in loop formation become functional loop anchors via the establishment of new cohesin loading sites containing NIPBL and YY1 at sites between the new anchors. In both cases, formation of new CTCF loops leads to strengthening of enhancer promoter interactions and increased transcription of genes adjacent to loop anchors. These results suggest an important role for CTCF and cohesin in controlling gene expression during cell differentiation.
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Affiliation(s)
- Xiaowen Lyu
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
- State Key Laboratory of Cellular Stress Biology, Fujian Provincial Key Laboratory of Reproductive Health Research, School of Medicine, Faculty of Medicine and Life Sciences, Xiamen University, 361102, Xiamen, China.
- Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Faculty of Medicine and Life Sciences, Xiamen University, 361102, Xiamen, China.
| | - M Jordan Rowley
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Michael J Kulik
- Department of Biochemistry and Molecular Biology, The University of Georgia, Athens, GA, 30602, USA
- Center for Molecular Medicine, The University of Georgia, Athens, GA, 30602, USA
| | - Stephen Dalton
- Department of Biochemistry and Molecular Biology, The University of Georgia, Athens, GA, 30602, USA
- Center for Molecular Medicine, The University of Georgia, Athens, GA, 30602, USA
- School of Biomedical Sciences, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Victor G Corces
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
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12
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Stathopoulou A, Wang P, Thellier C, Kelly RG, Zheng D, Scambler PJ. CHARGE syndrome-associated CHD7 acts at ISL1-regulated enhancers to modulate second heart field gene expression. Cardiovasc Res 2023; 119:2089-2105. [PMID: 37052590 PMCID: PMC10478754 DOI: 10.1093/cvr/cvad059] [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: 08/22/2022] [Revised: 01/20/2022] [Accepted: 04/12/2023] [Indexed: 04/14/2023] Open
Abstract
AIMS Haploinsufficiency of the chromo-domain protein CHD7 underlies most cases of CHARGE syndrome, a multisystem birth defect including congenital heart malformation. Context specific roles for CHD7 in various stem, progenitor, and differentiated cell lineages have been reported. Previously, we showed severe defects when Chd7 is absent from cardiopharyngeal mesoderm (CPM). Here, we investigate altered gene expression in the CPM and identify specific CHD7-bound target genes with known roles in the morphogenesis of affected structures. METHODS AND RESULTS We generated conditional KO of Chd7 in CPM and analysed cardiac progenitor cells using transcriptomic and epigenomic analyses, in vivo expression analysis, and bioinformatic comparisons with existing datasets. We show CHD7 is required for correct expression of several genes established as major players in cardiac development, especially within the second heart field (SHF). We identified CHD7 binding sites in cardiac progenitor cells and found strong association with histone marks suggestive of dynamically regulated enhancers during the mesodermal to cardiac progenitor transition of mESC differentiation. Moreover, CHD7 shares a subset of its target sites with ISL1, a pioneer transcription factor in the cardiogenic gene regulatory network, including one enhancer modulating Fgf10 expression in SHF progenitor cells vs. differentiating cardiomyocytes. CONCLUSION We show that CHD7 interacts with ISL1, binds ISL1-regulated cardiac enhancers, and modulates gene expression across the mesodermal heart fields during cardiac morphogenesis.
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Affiliation(s)
- Athanasia Stathopoulou
- Developmental Biology of Birth Defects, UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK
| | - Ping Wang
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | | | - Robert G Kelly
- Aix-Marseille University, CNRS UMR 7288, IBDM, Marseille, France
| | - Deyou Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Departments of Neurology and Neurosciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Peter J Scambler
- Developmental Biology of Birth Defects, UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK
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13
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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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14
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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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15
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Tan WX, Sim X, Khoo CM, Teo AKK. Prioritization of genes associated with type 2 diabetes mellitus for functional studies. Nat Rev Endocrinol 2023:10.1038/s41574-023-00836-1. [PMID: 37169822 DOI: 10.1038/s41574-023-00836-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 05/13/2023]
Abstract
Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell 'village' cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.
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Affiliation(s)
- Wei Xuan Tan
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Chin Meng Khoo
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Adrian K K Teo
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Precision Medicine Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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16
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Mawla AM, van der Meulen T, Huising MO. Chromatin accessibility differences between alpha, beta, and delta cells identifies common and cell type-specific enhancers. BMC Genomics 2023; 24:202. [PMID: 37069576 PMCID: PMC10108528 DOI: 10.1186/s12864-023-09293-6] [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: 09/22/2022] [Accepted: 04/03/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND High throughput sequencing has enabled the interrogation of the transcriptomic landscape of glucagon-secreting alpha cells, insulin-secreting beta cells, and somatostatin-secreting delta cells. These approaches have furthered our understanding of expression patterns that define healthy or diseased islet cell types and helped explicate some of the intricacies between major islet cell crosstalk and glucose regulation. All three endocrine cell types derive from a common pancreatic progenitor, yet alpha and beta cells have partially opposing functions, and delta cells modulate and control insulin and glucagon release. While gene expression signatures that define and maintain cellular identity have been widely explored, the underlying epigenetic components are incompletely characterized and understood. However, chromatin accessibility and remodeling is a dynamic attribute that plays a critical role to determine and maintain cellular identity. RESULTS Here, we compare and contrast the chromatin landscape between mouse alpha, beta, and delta cells using ATAC-Seq to evaluate the significant differences in chromatin accessibility. The similarities and differences in chromatin accessibility between these related islet endocrine cells help define their fate in support of their distinct functional roles. We identify patterns that suggest that both alpha and delta cells are poised, but repressed, from becoming beta-like. We also identify patterns in differentially enriched chromatin that have transcription factor motifs preferentially associated with different regions of the genome. Finally, we not only confirm and visualize previously discovered common endocrine- and cell specific- enhancer regions across differentially enriched chromatin, but identify novel regions as well. We compiled our chromatin accessibility data in a freely accessible database of common endocrine- and cell specific-enhancer regions that can be navigated with minimal bioinformatics expertise. CONCLUSIONS Both alpha and delta cells appear poised, but repressed, from becoming beta cells in murine pancreatic islets. These data broadly support earlier findings on the plasticity in identity of non-beta cells under certain circumstances. Furthermore, differential chromatin accessibility shows preferentially enriched distal-intergenic regions in beta cells, when compared to either alpha or delta cells.
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Affiliation(s)
- Alex M Mawla
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California, One Shields Avenue, Davis, CA, 95616, USA
| | - Talitha van der Meulen
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California, One Shields Avenue, Davis, CA, 95616, USA
| | - Mark O Huising
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California, One Shields Avenue, Davis, CA, 95616, USA.
- Department of Physiology and Membrane Biology, School of Medicine, University of California, One Shields Avenue, Davis, CA, 95616, USA.
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17
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Cheng J, Cao X, Wang X, Wang J, Yue B, Sun W, Huang Y, Lan X, Ren G, Lei C, Chen H. Dynamic chromatin architectures provide insights into the genetics of cattle myogenesis. J Anim Sci Biotechnol 2023; 14:59. [PMID: 37055796 PMCID: PMC10103417 DOI: 10.1186/s40104-023-00855-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 02/16/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND Sharply increased beef consumption is propelling the genetic improvement projects of beef cattle in China. Three-dimensional genome structure is confirmed to be an important layer of transcription regulation. Although genome-wide interaction data of several livestock species have already been produced, the genome structure states and its regulatory rules in cattle muscle are still limited. RESULTS Here we present the first 3D genome data in Longissimus dorsi muscle of fetal and adult cattle (Bos taurus). We showed that compartments, topologically associating domains (TADs), and loop undergo re-organization and the structure dynamics were consistent with transcriptomic divergence during muscle development. Furthermore, we annotated cis-regulatory elements in cattle genome during myogenesis and demonstrated the enrichments of promoter and enhancer in selection sweeps. We further validated the regulatory function of one HMGA2 intronic enhancer near a strong sweep region on primary bovine myoblast proliferation. CONCLUSIONS Our data provide key insights of the regulatory function of high order chromatin structure and cattle myogenic biology, which will benefit the progress of genetic improvement of beef cattle.
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Affiliation(s)
- Jie Cheng
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Xiukai Cao
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, 225009, China
| | - Xiaogang Wang
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Jian Wang
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Binglin Yue
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Southwest Minzu University, Chengdu, 610225, China
| | - Wei Sun
- College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, China
| | - Yongzhen Huang
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Xianyong Lan
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Gang Ren
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Chuzhao Lei
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China
| | - Hong Chen
- College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling district, Yangling, Shaanxi province, 712100, China.
- College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
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18
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Dong C, Shen S, Keleş S. AdaLiftOver: high-resolution identification of orthologous regulatory elements with Adaptive liftOver. Bioinformatics 2023; 39:btad149. [PMID: 37004197 PMCID: PMC10085516 DOI: 10.1093/bioinformatics/btad149] [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: 05/11/2022] [Revised: 03/02/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
MOTIVATION Elucidating functionally similar orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from genome-wide association studies (GWAS). Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues. RESULTS We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying functionally similar orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets across a wide range of model organisms and GWAS SNPs, yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs. AVAILABILITY AND IMPLEMENTATION The R package and the data for AdaLiftOver is available from https://github.com/keleslab/AdaLiftOver.
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Affiliation(s)
- Chenyang Dong
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, USA
| | - Siqi Shen
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI 53706, USA
| | - Sündüz Keleş
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI 53706, USA
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19
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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: 29] [Impact Index Per Article: 29.0] [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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20
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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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21
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Regué L, Wang W, Ji F, Avruch J, Wang H, Dai N. Human T2D-Associated Gene IMP2/IGF2BP2 Promotes the Commitment of Mesenchymal Stem Cells Into Adipogenic Lineage. Diabetes 2023; 72:33-44. [PMID: 36219823 PMCID: PMC9797317 DOI: 10.2337/db21-1087] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 10/06/2022] [Indexed: 01/19/2023]
Abstract
Excessive adiposity is the main cause of obesity and type two diabetes (T2D). Variants in human IMP2/IGF2BP2 gene are associated with increased risk of T2D. However, little is known about its role in adipogenesis and in insulin resistance. Here, we investigate the function of IMP2 during adipocyte development. Mice with Imp2 deletion in mesenchymal stem cells (MSC) are resistant to diet-induced obesity without glucose and insulin tolerance affected. Imp2 is essential for the early commitment of adipocyte-derived stem cells (ADSC) into preadipocytes, but the deletion of Imp2 in MSC is not required for the proliferation and terminal differentiation of committed preadipocytes. Mechanistically, Imp2 binds Wnt receptor Fzd8 mRNA and promotes its degradation by recruiting CCR4-NOT deadenylase complex in an mTOR-dependent manner. Our data demonstrate that Imp2 is required for maintaining white adipose tissue homeostasis through controlling mRNA stability in ADSC. However, the contribution of IMP2 to insulin resistance, a main risk of T2D, is not evident.
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Affiliation(s)
- Laura Regué
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
- Diabetes Unit of the Medical Services, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - William Wang
- The Lundquist Institute, Torrance, CA
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Fei Ji
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Joseph Avruch
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
- Diabetes Unit of the Medical Services, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Hua Wang
- The Lundquist Institute, Torrance, CA
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Ning Dai
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
- Diabetes Unit of the Medical Services, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
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22
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Hao RH, Guo Y, Wang C, Chen F, Di CX, Dong SS, Cao QL, Guo J, Rong Y, Yao S, Zhu DL, Chen YX, Chen H, Yang TL. Lineage-specific rearrangement of chromatin loops and epigenomic features during adipocytes and osteoblasts commitment. Cell Death Differ 2022; 29:2503-2518. [PMID: 35906483 PMCID: PMC9751090 DOI: 10.1038/s41418-022-01035-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 06/09/2022] [Accepted: 06/13/2022] [Indexed: 01/31/2023] Open
Abstract
Human mesenchymal stem cells (hMSCs) can be differentiated into adipocytes and osteoblasts. The processes are driven by the rewiring of chromatin architectures and transcriptomic/epigenomic changes. Here, we induced hMSCs to adipogenic and osteogenic differentiation, and performed 2 kb resolution Hi-C experiments for chromatin loops detection. We also generated matched RNA-seq, ChIP-seq and ATAC-seq data for integrative analysis. After comprehensively comparing adipogenesis and osteogenesis, we quantitatively identified lineage-specific loops and screened out lineage-specific enhancers and open chromatin. We reveal that lineage-specific loops can activate gene expression and facilitate cell commitment through combining enhancers and accessible chromatin in a lineage-specific manner. We finally proposed loop-mediated regulatory networks and identified the controlling factors for adipocytes and osteoblasts determination. Functional experiments validated the lineage-specific regulation networks towards IRS2 and RUNX2 that are associated with adipogenesis and osteogenesis, respectively. These results are expected to help better understand the chromatin conformation determinants of hMSCs fate commitment.
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Affiliation(s)
- Ruo-Han Hao
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Yan Guo
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Chen Wang
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Fei Chen
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Chen-Xi Di
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Shan-Shan Dong
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Qi-Long Cao
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
- Research and Development Department, Qingdao Haier Biotech Co. Ltd, Qingdao, Shandong, 266109, P. R. China
| | - Jing Guo
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Yu Rong
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Shi Yao
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710004, P. R. China
| | - Dong-Li Zhu
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Yi-Xiao Chen
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710004, P. R. China
| | - Hao Chen
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China
| | - Tie-Lin Yang
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China.
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710004, P. R. China.
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23
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Non-coding variants disrupting a tissue-specific regulatory element in HK1 cause congenital hyperinsulinism. Nat Genet 2022; 54:1615-1620. [PMID: 36333503 PMCID: PMC7614032 DOI: 10.1038/s41588-022-01204-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 09/16/2022] [Indexed: 11/06/2022]
Abstract
Gene expression is tightly regulated, with many genes exhibiting cell-specific silencing when their protein product would disrupt normal cellular function1. This silencing is largely controlled by non-coding elements, and their disruption might cause human disease2. We performed gene-agnostic screening of the non-coding regions to discover new molecular causes of congenital hyperinsulinism. This identified 14 non-coding de novo variants affecting a 42-bp conserved region encompassed by a regulatory element in intron 2 of the hexokinase 1 gene (HK1). HK1 is widely expressed across all tissues except in the liver and pancreatic beta cells and is thus termed a 'disallowed gene' in these specific tissues. We demonstrated that the variants result in a loss of repression of HK1 in pancreatic beta cells, thereby causing insulin secretion and congenital hyperinsulinism. Using epigenomic data accessed from public repositories, we demonstrated that these variants reside within a regulatory region that we determine to be critical for cell-specific silencing. Importantly, this has revealed a disease mechanism for non-coding variants that cause inappropriate expression of a disallowed gene.
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24
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Wang D, Wu X, Jiang G, Yang J, Yu Z, Yang Y, Yang W, Niu X, Tang K, Gong J. Systematic analysis of the effects of genetic variants on chromatin accessibility to decipher functional variants in non-coding regions. Front Oncol 2022; 12:1035855. [PMID: 36330496 PMCID: PMC9623183 DOI: 10.3389/fonc.2022.1035855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 10/03/2022] [Indexed: 11/18/2022] Open
Abstract
Genome-wide association study (GWAS) has identified thousands of single nucleotide polymorphisms (SNPs) associated with complex diseases and traits. However, deciphering the functions of these SNPs still faces challenges. Recent studies have shown that SNPs could alter chromatin accessibility and result in differences in tumor susceptibility between individuals. Therefore, systematically analyzing the effects of SNPs on chromatin accessibility could help decipher the functions of SNPs, especially those in non-coding regions. Using data from The Cancer Genome Atlas (TCGA), chromatin accessibility quantitative trait locus (caQTL) analysis was conducted to estimate the associations between genetic variants and chromatin accessibility. We analyzed caQTLs in 23 human cancer types and identified 9,478 caQTLs in breast carcinoma (BRCA). In BRCA, these caQTLs tend to alter the binding affinity of transcription factors, and open chromatin regions regulated by these caQTLs are enriched in regulatory elements. By integrating with eQTL data, we identified 141 caQTLs showing a strong signal for colocalization with eQTLs. We also identified 173 caQTLs in genome-wide association studies (GWAS) loci and inferred several possible target genes of these caQTLs. By performing survival analysis, we found that ~10% caQTLs potentially influence the prognosis of patients. To facilitate access to relevant data, we developed a user-friendly data portal, BCaQTL (http://gong_lab.hzau.edu.cn/caqtl_database), for data searching and downloading. Our work may facilitate fine-map regulatory mechanisms underlying risk loci of cancer and discover the biomarkers or therapeutic targets for cancer prognosis. The BCaQTL database will be an important resource for genetic and epigenetic studies.
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Affiliation(s)
- Dongyang Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xiaohong Wu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Guanghui Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Jianye Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Zhanhui Yu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yanbo Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Wenqian Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xiaohui Niu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Ke Tang
- Department of Biochemistry and Molecular Biology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Jing Gong, ; Ke Tang,
| | - Jing Gong
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
- College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, China
- *Correspondence: Jing Gong, ; Ke Tang,
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25
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Zhang K, Wang C, Sun L, Zheng J. Prediction of gene co-expression from chromatin contacts with graph attention network. Bioinformatics 2022; 38:4457-4465. [PMID: 35929807 PMCID: PMC9525008 DOI: 10.1093/bioinformatics/btac535] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 07/12/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The technology of high-throughput chromatin conformation capture (Hi-C) allows genome-wide measurement of chromatin interactions. Several studies have shown statistically significant relationships between gene-gene spatial contacts and their co-expression. It is desirable to uncover epigenetic mechanisms of transcriptional regulation behind such relationships using computational modeling. Existing methods for predicting gene co-expression from Hi-C data use manual feature engineering or unsupervised learning, which either limits the prediction accuracy or lacks interpretability. RESULTS To address these issues, we propose HiCoEx (Hi-C predicts gene co-expression), a novel end-to-end framework for explainable prediction of gene co-expression from Hi-C data based on graph neural network. We apply graph attention mechanism to a gene contact network inferred from Hi-C data to distinguish the importance among different neighboring genes of each gene, and learn the gene representation to predict co-expression in a supervised and task-specific manner. Then, from the trained model, we extract the learned gene embeddings as a model interpretation to distill biological insights. Experimental results show that HiCoEx can learn gene representation from 3D genomics signals automatically to improve prediction accuracy, and make the black box model explainable by capturing some biologically meaningful patterns, e.g., in a gene contact network, the common neighbors of two central genes might contribute to the co-expression of the two central genes through sharing enhancers. AVAILABILITY AND IMPLEMENTATION The source code is freely available at https://github.com/JieZheng-ShanghaiTech/HiCoEx. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ke Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Chenxi Wang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Liping Sun
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Jie Zheng
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Engineering Research Center of Intelligent Vision and Imaging, ShanghaiTech University, Shanghai 201210, China
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26
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Su C, Gao L, May CL, Pippin JA, Boehm K, Lee M, Liu C, Pahl MC, Golson ML, Naji A, Grant SFA, Wells AD, Kaestner KH. 3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk. Cell Metab 2022; 34:1394-1409.e4. [PMID: 36070683 PMCID: PMC9664375 DOI: 10.1016/j.cmet.2022.08.014] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/03/2022] [Accepted: 08/17/2022] [Indexed: 12/20/2022]
Abstract
Three-dimensional (3D) chromatin organization maps help dissect cell-type-specific gene regulatory programs. Furthermore, 3D chromatin maps contribute to elucidating the pathogenesis of complex genetic diseases by connecting distal regulatory regions and genetic risk variants to their respective target genes. To understand the cell-type-specific regulatory architecture of diabetes risk, we generated transcriptomic and 3D epigenomic profiles of human pancreatic acinar, alpha, and beta cells using single-cell RNA-seq, single-cell ATAC-seq, and high-resolution Hi-C of sorted cells. Comparisons of these profiles revealed differential A/B (open/closed) chromatin compartmentalization, chromatin looping, and transcriptional factor-mediated control of cell-type-specific gene regulatory programs. We identified a total of 4,750 putative causal-variant-to-target-gene pairs at 194 type 2 diabetes GWAS signals using pancreatic 3D chromatin maps. We found that the connections between candidate causal variants and their putative target effector genes are cell-type stratified and emphasize previously underappreciated roles for alpha and acinar cells in diabetes pathogenesis.
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Affiliation(s)
- Chun Su
- Division of Human Genetics and Endocrinology & Diabetes, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Long Gao
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Catherine L May
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - James A Pippin
- Division of Human Genetics and Endocrinology & Diabetes, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Keith Boehm
- Division of Human Genetics and Endocrinology & Diabetes, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Michelle Lee
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Chengyang Liu
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew C Pahl
- Division of Human Genetics and Endocrinology & Diabetes, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maria L Golson
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Ali Naji
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Struan F A Grant
- Division of Human Genetics and Endocrinology & Diabetes, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Institute for Immunology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
| | - Klaus H Kaestner
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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Kupai K, Várkonyi T, Török S, Gáti V, Czimmerer Z, Puskás LG, Szebeni GJ. Recent Progress in the Diagnosis and Management of Type 2 Diabetes Mellitus in the Era of COVID-19 and Single Cell Multi-Omics Technologies. Life (Basel) 2022; 12:1205. [PMID: 36013384 PMCID: PMC9409806 DOI: 10.3390/life12081205] [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: 05/26/2022] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 11/17/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is one of the world's leading causes of death and life-threatening conditions. Therefore, we review the complex vicious circle of causes responsible for T2DM and risk factors such as the western diet, obesity, genetic predisposition, environmental factors, and SARS-CoV-2 infection. The prevalence and economic burden of T2DM on societal and healthcare systems are dissected. Recent progress on the diagnosis and clinical management of T2DM, including both non-pharmacological and latest pharmacological treatment regimens, are summarized. The treatment of T2DM is becoming more complex as new medications are approved. This review is focused on the non-insulin treatments of T2DM to reach optimal therapy beyond glycemic management. We review experimental and clinical findings of SARS-CoV-2 risks that are attributable to T2DM patients. Finally, we shed light on the recent single-cell-based technologies and multi-omics approaches that have reached breakthroughs in the understanding of the pathomechanism of T2DM.
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Affiliation(s)
- Krisztina Kupai
- Department of Physiology, Anatomy and Neuroscience, Faculty of Science and Informatics, University of Szeged, Közép fasor 52, 6726 Szeged, Hungary
- Department of Internal Medicine, University of Szeged, Korányi fasor 8, 6720 Szeged, Hungary
| | - Tamás Várkonyi
- Department of Internal Medicine, University of Szeged, Korányi fasor 8, 6720 Szeged, Hungary
| | - Szilvia Török
- Department of Physiology, Anatomy and Neuroscience, Faculty of Science and Informatics, University of Szeged, Közép fasor 52, 6726 Szeged, Hungary
| | - Viktória Gáti
- Laboratory of Functional Genomics, Biological Research Centre, Temesvári krt. 62, 6726 Szeged, Hungary
| | - Zsolt Czimmerer
- Laboratory of Functional Genomics, Biological Research Centre, Temesvári krt. 62, 6726 Szeged, Hungary
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Life Science Building, Egyetem tér 1, 4032 Debrecen, Hungary
| | - László G. Puskás
- Laboratory of Functional Genomics, Biological Research Centre, Temesvári krt. 62, 6726 Szeged, Hungary
- Avidin Ltd., Alsó kikötő sor 11/D, 6726 Szeged, Hungary
| | - Gábor J. Szebeni
- Department of Physiology, Anatomy and Neuroscience, Faculty of Science and Informatics, University of Szeged, Közép fasor 52, 6726 Szeged, Hungary
- Laboratory of Functional Genomics, Biological Research Centre, Temesvári krt. 62, 6726 Szeged, Hungary
- CS-Smartlab Devices Ltd., Ady E. u. 14, 7761 Kozármisleny, Hungary
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RNA-Binding Proteins in the Regulation of Adipogenesis and Adipose Function. Cells 2022; 11:cells11152357. [PMID: 35954201 PMCID: PMC9367552 DOI: 10.3390/cells11152357] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 01/27/2023] Open
Abstract
The obesity epidemic represents a critical public health issue worldwide, as it is a vital risk factor for many diseases, including type 2 diabetes (T2D) and cardiovascular disease. Obesity is a complex disease involving excessive fat accumulation. Proper adipose tissue accumulation and function are highly transcriptional and regulated by many genes. Recent studies have discovered that post-transcriptional regulation, mainly mediated by RNA-binding proteins (RBPs), also plays a crucial role. In the lifetime of RNA, it is bound by various RBPs that determine every step of RNA metabolism, from RNA processing to alternative splicing, nucleus export, rate of translation, and finally decay. In humans, it is predicted that RBPs account for more than 10% of proteins based on the presence of RNA-binding domains. However, only very few RBPs have been studied in adipose tissue. The primary aim of this paper is to provide an overview of RBPs in adipogenesis and adipose function. Specifically, the following best-characterized RBPs will be discussed, including HuR, PSPC1, Sam68, RBM4, Ybx1, Ybx2, IGF2BP2, and KSRP. Characterization of these proteins will increase our understanding of the regulatory mechanisms of RBPs in adipogenesis and provide clues for the etiology and pathology of adipose-tissue-related diseases.
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29
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Xu X, Shen HR, Zhang JR, Li XL. The role of insulin-like growth factor 2 mRNA binding proteins in female reproductive pathophysiology. Reprod Biol Endocrinol 2022; 20:89. [PMID: 35706003 PMCID: PMC9199150 DOI: 10.1186/s12958-022-00960-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 06/10/2022] [Indexed: 11/10/2022] Open
Abstract
Insulin-like growth factor 2 (IGF2) mRNA binding proteins (IMPs) family belongs to a highly conserved family of RNA-binding proteins (RBPs) and is responsible for regulating RNA processing including localization, translation and stability. Mammalian IMPs (IMP1-3) take part in development, metabolism and tumorigenesis, where they are believed to play a major role in cell growth, metabolism, migration and invasion. IMPs have been identified that are expressed in ovary, placenta and embryo. The up-to-date evidence suggest that IMPs are involved in folliculogenesis, oocyte maturation, embryogenesis, implantation, and placentation. The dysregulation of IMPs not only contributes to carcinogenesis but also disturbs the female reproduction, and may participate in the pathogenesis of reproductive diseases and obstetric syndromes, such as polycystic ovary syndrome (PCOS), pre-eclampsia (PE), gestational diabetes mellitus (GDM) and gynecological tumors. In this review, we summarize the role of IMPs in female reproductive pathophysiology, and hope to provide new insights into the identification of potential therapeutic targets.
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Affiliation(s)
- Xiao Xu
- Department of Obstetrics and Gynecology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Hao-Ran Shen
- Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, People's Republic of China
| | - Jia-Rong Zhang
- Department of Obstetrics and Gynecology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Xue-Lian Li
- Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, People's Republic of China.
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30
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Grandi FC, Modi H, Kampman L, Corces MR. Chromatin accessibility profiling by ATAC-seq. Nat Protoc 2022; 17:1518-1552. [PMID: 35478247 PMCID: PMC9189070 DOI: 10.1038/s41596-022-00692-9] [Citation(s) in RCA: 112] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 02/22/2022] [Indexed: 12/13/2022]
Abstract
The assay for transposase-accessible chromatin using sequencing (ATAC-seq) provides a simple and scalable way to detect the unique chromatin landscape associated with a cell type and how it may be altered by perturbation or disease. ATAC-seq requires a relatively small number of input cells and does not require a priori knowledge of the epigenetic marks or transcription factors governing the dynamics of the system. Here we describe an updated and optimized protocol for ATAC-seq, called Omni-ATAC, that is applicable across a broad range of cell and tissue types. The ATAC-seq workflow has five main steps: sample preparation, transposition, library preparation, sequencing and data analysis. This protocol details the steps to generate and sequence ATAC-seq libraries, with recommendations for sample preparation and downstream bioinformatic analysis. ATAC-seq libraries for roughly 12 samples can be generated in 10 h by someone familiar with basic molecular biology, and downstream sequencing analysis can be implemented using benchmarked pipelines by someone with basic bioinformatics skills and with access to a high-performance computing environment.
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Affiliation(s)
- Fiorella C Grandi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Hailey Modi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Lucas Kampman
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA.
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
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31
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Falih Z, Khodair BAW, Mohammed NI, Mohammed TK. Insulin-like Growth Factor-2 Binding Protein-2 Gene Polymorphisms in Iraqi Patients with Type 2 Diabetes Mellitus. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.9754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Background: Diabetes mellitus type2 (T2DM) represent a hyperglycemia causing metabolic disease which exists in the peripheral tissues due to incomplete pancreatic insulin secretion or insulin resistance. IGF2BP2 is a protein that is involved in embryogenesis and pancreatic development. Genetic association researches had suggested that the single nucleotide polymorphisms (SNP) spanning IGF2BP2 gene are associated with the progression as well as development of the T2DM.
Aim: This study aims to evaluate the association of IGF2BP2 gene polymorphisms (rs4402960 & rs1470579) with T2DM in a sample of Iraqi individuals.
Methods: A case-control study has been conducted on 800 participants, they were divided to two equal groups, which are a healthy control group (400) and type 2 diabetic patients (400). Fast blood sugar (FBS), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and HbA1c] measured suitable for both participant groups. IGF2BP2 gene has been genotyped for polymorphisms; rs4402960 and rs1470579 by using the PCR-RFLP technique.
Results: There is significant changes in the biochemical parameters in patients group when compared to the control group.The SNP rs4402960 show minor allele frequency of T allele considerably different between the two participating groups (p 0.0013) with 33.6 % in T2DM group. Homo-variant TT shows a significant p <0.0001) odd ratio (4.5) as codominant type. Similarly, dominant and recessive models exert significant (0.02 & <0.0001 respectively) adjusted odd ratio (1.45 & 4.14 respectively). The rs1470579 SNP show a significant (0.024) risk (1.28) of C allele in the patients group than in A allele. The CC genotype in codominant and recessive models show significant (0.03) odd ratio differences (2.03 & 1.96 respectively. The rs1470579 SNP exerts significant differences as codominant model in biochemical features of BMI, FBG, Tgs, VLDL-C, insulin and HOMA-IR. The study power of rs4402960 is 69.5% and rs1470579 is 34.1%.
Conclusion: This study confirmed the association of rs4402960 as codominant, dominant and recessive with T2DM significantly. However, rs1470579 is associate as recessive model with T2DM in Iraqi population.
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32
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An effector index to predict target genes at GWAS loci. Hum Genet 2022; 141:1431-1447. [PMID: 35147782 DOI: 10.1007/s00439-022-02434-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 01/15/2022] [Indexed: 11/04/2022]
Abstract
Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single-nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript-altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes.
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33
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Lien YC, Pinney SE, Lu XM, Simmons RA. Identification of Novel Regulatory Regions Induced by Intrauterine Growth Restriction in Rat Islets. Endocrinology 2022; 163:6459683. [PMID: 34894232 PMCID: PMC8743043 DOI: 10.1210/endocr/bqab251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Indexed: 01/05/2023]
Abstract
Intrauterine growth restriction (IUGR) leads to the development of type 2 diabetes in adulthood, and the permanent alterations in gene expression implicate an epigenetic mechanism. Using a rat model of IUGR, we performed TrueSeq-HELP Tagging to assess the association of DNA methylation changes and gene dysregulation in islets. We identified 511 differentially methylated regions (DMRs) and 4377 significantly altered single CpG sites. Integrating the methylome and our published transcriptome data sets resulted in the identification of pathways critical for islet function. The identified DMRs were enriched with transcription factor binding motifs, such as Elk1, Etv1, Foxa1, Foxa2, Pax7, Stat3, Hnf1, and AR. In silico analysis of 3-dimensional chromosomal interactions using human pancreas and islet Hi-C data sets identified interactions between 14 highly conserved DMRs and 35 genes with significant expression changes at an early age, many of which persisted in adult islets. In adult islets, there were far more interactions between DMRs and genes with significant expression changes identified with Hi-C, and most of them were critical to islet metabolism and insulin secretion. The methylome was integrated with our published genome-wide histone modification data sets from IUGR islets, resulting in further characterization of important regulatory regions of the genome altered by IUGR containing both significant changes in DNA methylation and specific histone marks. We identified novel regulatory regions in islets after exposure to IUGR, suggesting that epigenetic changes at key transcription factor binding motifs and other gene regulatory regions may contribute to gene dysregulation and an abnormal islet phenotype in IUGR rats.
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Affiliation(s)
- Yu-Chin Lien
- Center for Research on Reproduction and Women’s Health, Perelman School of Medicine, the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Division of Neonatology, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA
| | - Sara E Pinney
- Center for Research on Reproduction and Women’s Health, Perelman School of Medicine, the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Division Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA
- Perlman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Xueqing Maggie Lu
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Rebecca A Simmons
- Center for Research on Reproduction and Women’s Health, Perelman School of Medicine, the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Division of Neonatology, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA
- Perlman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Correspondence: Rebecca A. Simmons, MD, Center for Research on Reproduction and Women’s Health, Perelman School of Medicine, the University of Pennsylvania, BRB II/III, 13th Fl, Rm 1308, 421 Curie Blvd, Philadelphia, PA 19104, USA.
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Zhang X, Li TY, Xiao HM, Ehrlich KC, Shen H, Deng HW, Ehrlich M. Epigenomic and Transcriptomic Prioritization of Candidate Obesity-Risk Regulatory GWAS SNPs. Int J Mol Sci 2022; 23:1271. [PMID: 35163195 PMCID: PMC8836216 DOI: 10.3390/ijms23031271] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023] Open
Abstract
Concern about rising rates of obesity has prompted searches for obesity-related single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS). Identifying plausible regulatory SNPs is very difficult partially because of linkage disequilibrium. We used an unusual epigenomic and transcriptomic analysis of obesity GWAS-derived SNPs in adipose versus heterologous tissues. From 50 GWAS and 121,064 expanded SNPs, we prioritized 47 potential causal regulatory SNPs (Tier-1 SNPs) for 14 gene loci. A detailed examination of seven loci revealed that four (CABLES1, PC, PEMT, and FAM13A) had Tier-1 SNPs positioned so that they could regulate use of alternative transcription start sites, resulting in different polypeptides being generated or different amounts of an intronic microRNA gene being expressed. HOXA11 and long noncoding RNA gene RP11-392O17.1 had Tier-1 SNPs in their 3' or promoter region, respectively, and strong preferences for expression in subcutaneous versus visceral adipose tissue. ZBED3-AS1 had two intragenic Tier-1 SNPs, each of which could contribute to mediating obesity risk through modulating long-distance chromatin interactions. Our approach not only revealed especially credible novel regulatory SNPs, but also helped evaluate previously highlighted obesity GWAS SNPs that were candidates for transcription regulation.
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Affiliation(s)
- Xiao Zhang
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
| | - Tian-Ying Li
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (T.-Y.L.); (H.-M.X.)
| | - Hong-Mei Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (T.-Y.L.); (H.-M.X.)
| | - Kenneth C. Ehrlich
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
| | - Melanie Ehrlich
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
- Tulane Cancer Center and Hayward Genetics Center, Tulane University, New Orleans, LA 70112, USA
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35
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Alvarez-Dominguez JR, Winther S, Hansen JB, Lodish HF, Knoll M. An adipose lncRAP2-Igf2bp2 complex enhances adipogenesis and energy expenditure by stabilizing target mRNAs. iScience 2022; 25:103680. [PMID: 35036870 PMCID: PMC8749451 DOI: 10.1016/j.isci.2021.103680] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 07/06/2021] [Accepted: 12/20/2021] [Indexed: 02/09/2023] Open
Abstract
lncRAP2 is a conserved cytoplasmic lncRNA enriched in adipose tissue and required for adipogenesis. Using purification and in vivo interactome analyses, we show that lncRAP2 forms complexes with proteins that stabilize mRNAs and modulate translation, among them Igf2bp2. Surveying transcriptome-wide Igf2bp2 client mRNAs in white adipocytes reveals selective binding to mRNAs encoding adipogenic regulators and energy expenditure effectors, including adiponectin. These same target proteins are downregulated when either Igf2bp2 or lncRAP2 is downregulated, hindering adipocyte lipolysis. Proteomics and ribosome profiling show this occurs predominantly through mRNA accumulation, as lncRAP2-Igf2bp2 complex binding does not impact translation efficiency. Phenome-wide association studies reveal specific associations of genetic variants within both lncRAP2 and Igf2bp2 with body mass and type 2 diabetes, and both lncRAP2 and Igf2bp2 are suppressed in adipose depots of obese and diabetic individuals. Thus, the lncRAP2-Igf2bp2 complex potentiates adipose development and energy expenditure and is associated with susceptibility to obesity-linked diabetes.
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Affiliation(s)
- Juan R. Alvarez-Dominguez
- Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA19104, USA
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA19104, USA
| | - Sally Winther
- Department of Biology, University of Copenhagen, Universitetsparken 13, DK2100, Copenhagen, Denmark
| | - Jacob B. Hansen
- Department of Biology, University of Copenhagen, Universitetsparken 13, DK2100, Copenhagen, Denmark
| | - Harvey F. Lodish
- Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA
- Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, 21Ames Street, Cambridge, MA02142, USA
| | - Marko Knoll
- Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA
- Institute for Diabetes Research, Helmholtz Zentrum München, Heidemannstrasse 1, 80939München, Germany
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36
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Bartolomé A. Stem Cell-Derived β Cells: A Versatile Research Platform to Interrogate the Genetic Basis of β Cell Dysfunction. Int J Mol Sci 2022; 23:501. [PMID: 35008927 PMCID: PMC8745644 DOI: 10.3390/ijms23010501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 02/07/2023] Open
Abstract
Pancreatic β cell dysfunction is a central component of diabetes progression. During the last decades, the genetic basis of several monogenic forms of diabetes has been recognized. Genome-wide association studies (GWAS) have also facilitated the identification of common genetic variants associated with an increased risk of diabetes. These studies highlight the importance of impaired β cell function in all forms of diabetes. However, how most of these risk variants confer disease risk, remains unanswered. Understanding the specific contribution of genetic variants and the precise role of their molecular effectors is the next step toward developing treatments that target β cell dysfunction in the era of personalized medicine. Protocols that allow derivation of β cells from pluripotent stem cells, represent a powerful research tool that allows modeling of human development and versatile experimental designs that can be used to shed some light on diabetes pathophysiology. This article reviews different models to study the genetic basis of β cell dysfunction, focusing on the recent advances made possible by stem cell applications in the field of diabetes research.
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Affiliation(s)
- Alberto Bartolomé
- Instituto de Investigaciones Biomédicas Alberto Sols, CSIC-UAM, 28029 Madrid, Spain
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Cui C, Li T, Xie Y, Yang J, Fu C, Qiu Y, Shen L, Ni Q, Wang Q, Nie A, Ning G, Wang W, Gu Y. Enhancing Acsl4 in absence of mTORC2/Rictor drove β-cell dedifferentiation via inhibiting FoxO1 and promoting ROS production. Biochim Biophys Acta Mol Basis Dis 2021; 1867:166261. [PMID: 34455055 DOI: 10.1016/j.bbadis.2021.166261] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 08/16/2021] [Accepted: 08/24/2021] [Indexed: 02/07/2023]
Abstract
Rapamycin insensitive companion of mechanistic target of Rapamycin (Rictor), the key component of mTOR complex 2 (mTORC2), controls both β-cell proliferation and function. We sought to study whether long chain acyl-CoA synthetase 4 (Acsl4) worked downstream of Rictor/mTORC2 to maintain β-cell functional mass. We found Acsl4 was positively regulated by Rictor at transcriptional and posttranslational levels in mouse β-cell. Infecting adenovirus expressing Acsl4 in β-cell-specific-Rictor-knockout (βRicKO) islets and Min6 cells knocking down Rictor with lentivirus-expressing siRNA-oligos targeting Rictor(siRic), recovered the β-cell dysplasia but not dysfunction. Cell bioenergetic experiment performed with Seahorse XF showed that Acsl4 could not rescue the dampened glucose oxidation in Rictor-lacking β-cell, but further promoted lipid oxidation. Transposase-Accessible Chromatin (ATAC) and H3K27Ac chromatin immunoprecipitation (ChIP) sequencing studies reflected the epigenetic elevated molecular signature for β-cell dedifferentiation and mitigated oxidative defense/response. These results were confirmed by the observations of elevated acetylation and ubiquitination of FoxO1, increased protein levels of Gpx1 and Hif1an, excessive reactive oxygen species (ROS) production and diminished MafA in Acsl4 overexpressed Rictor-lacking β-cells. In these cells, antioxidant treatment significantly recovered MafA level and insulin content. Inducing lipid oxidation alone could not mimic the effect of Acsl4 in Rictor lacking β-cell. Our study suggested that Acsl4 function in β-cell was context dependent and might facilitate β-cell dedifferentiation with attenuated Rictor/mTORC2 activity or insulin signaling via posttranslational inhibiting FoxO1 and epigenetically enhancing ROS induced MafA degradation.
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Affiliation(s)
- Canqi Cui
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tingting Li
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Xie
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Yang
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chenyang Fu
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yixuan Qiu
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linyan Shen
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qicheng Ni
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qidi Wang
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Aifang Nie
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Ning
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Weiqing Wang
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanyun Gu
- Shanghai National Research Centre for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Simonett SP, Shin S, Herring JA, Bacher R, Smith LA, Dong C, Rabaglia ME, Stapleton DS, Schueler KL, Choi J, Bernstein MN, Turkewitz DR, Perez-Cervantes C, Spaeth J, Stein R, Tessem JS, Kendziorski C, Keleş S, Moskowitz IP, Keller MP, Attie AD. Identification of direct transcriptional targets of NFATC2 that promote β cell proliferation. J Clin Invest 2021; 131:e144833. [PMID: 34491912 PMCID: PMC8553569 DOI: 10.1172/jci144833] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 09/02/2021] [Indexed: 12/13/2022] Open
Abstract
The transcription factor NFATC2 induces β cell proliferation in mouse and human islets. However, the genomic targets that mediate these effects have not been identified. We expressed active forms of Nfatc2 and Nfatc1 in human islets. By integrating changes in gene expression with genomic binding sites for NFATC2, we identified approximately 2200 transcriptional targets of NFATC2. Genes induced by NFATC2 were enriched for transcripts that regulate the cell cycle and for DNA motifs associated with the transcription factor FOXP. Islets from an endocrine-specific Foxp1, Foxp2, and Foxp4 triple-knockout mouse were less responsive to NFATC2-induced β cell proliferation, suggesting the FOXP family works to regulate β cell proliferation in concert with NFATC2. NFATC2 induced β cell proliferation in both mouse and human islets, whereas NFATC1 did so only in human islets. Exploiting this species difference, we identified approximately 250 direct transcriptional targets of NFAT in human islets. This gene set enriches for cell cycle-associated transcripts and includes Nr4a1. Deletion of Nr4a1 reduced the capacity of NFATC2 to induce β cell proliferation, suggesting that much of the effect of NFATC2 occurs through its induction of Nr4a1. Integration of noncoding RNA expression, chromatin accessibility, and NFATC2 binding sites enabled us to identify NFATC2-dependent enhancer loci that mediate β cell proliferation.
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Affiliation(s)
- Shane P. Simonett
- Biochemistry Department, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Sunyoung Shin
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Jacob A. Herring
- Nutrition, Dietetics and Food Science Department, Brigham Young University, Provo, Utah, USA
| | - Rhonda Bacher
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Linsin A. Smith
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Chenyang Dong
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Mary E. Rabaglia
- Biochemistry Department, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Donnie S. Stapleton
- Biochemistry Department, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Kathryn L. Schueler
- Biochemistry Department, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Jeea Choi
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | | | - Daniel R. Turkewitz
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Carlos Perez-Cervantes
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Jason Spaeth
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Roland Stein
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Jeffery S. Tessem
- Nutrition, Dietetics and Food Science Department, Brigham Young University, Provo, Utah, USA
| | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Sündüz Keleş
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Ivan P. Moskowitz
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Mark P. Keller
- Biochemistry Department, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Alan D. Attie
- Biochemistry Department, University of Wisconsin–Madison, Madison, Wisconsin, USA
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Kahn SE, Chen YC, Esser N, Taylor AJ, van Raalte DH, Zraika S, Verchere CB. The β Cell in Diabetes: Integrating Biomarkers With Functional Measures. Endocr Rev 2021; 42:528-583. [PMID: 34180979 PMCID: PMC9115372 DOI: 10.1210/endrev/bnab021] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Indexed: 02/08/2023]
Abstract
The pathogenesis of hyperglycemia observed in most forms of diabetes is intimately tied to the islet β cell. Impairments in propeptide processing and secretory function, along with the loss of these vital cells, is demonstrable not only in those in whom the diagnosis is established but typically also in individuals who are at increased risk of developing the disease. Biomarkers are used to inform on the state of a biological process, pathological condition, or response to an intervention and are increasingly being used for predicting, diagnosing, and prognosticating disease. They are also proving to be of use in the different forms of diabetes in both research and clinical settings. This review focuses on the β cell, addressing the potential utility of genetic markers, circulating molecules, immune cell phenotyping, and imaging approaches as biomarkers of cellular function and loss of this critical cell. Further, we consider how these biomarkers complement the more long-established, dynamic, and often complex measurements of β-cell secretory function that themselves could be considered biomarkers.
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Affiliation(s)
- Steven E Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, 98108 WA, USA
| | - Yi-Chun Chen
- BC Children's Hospital Research Institute and Centre for Molecular Medicine and Therapeutics, Vancouver, BC, V5Z 4H4, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada.,Department of Surgery, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Nathalie Esser
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, 98108 WA, USA
| | - Austin J Taylor
- BC Children's Hospital Research Institute and Centre for Molecular Medicine and Therapeutics, Vancouver, BC, V5Z 4H4, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada.,Department of Surgery, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Daniël H van Raalte
- Department of Internal Medicine, Amsterdam University Medical Center (UMC), Vrije Universiteit (VU) University Medical Center, 1007 MB Amsterdam, The Netherlands.,Department of Experimental Vascular Medicine, Amsterdam University Medical Center (UMC), Academic Medical Center, 1007 MB Amsterdam, The Netherlands
| | - Sakeneh Zraika
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, 98108 WA, USA
| | - C Bruce Verchere
- BC Children's Hospital Research Institute and Centre for Molecular Medicine and Therapeutics, Vancouver, BC, V5Z 4H4, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada.,Department of Surgery, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
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Cao J, Yan W, Ma X, Huang H, Yan H. Insulin-like Growth Factor 2 mRNA-Binding Protein 2-a Potential Link Between Type 2 Diabetes Mellitus and Cancer. J Clin Endocrinol Metab 2021; 106:2807-2818. [PMID: 34061963 PMCID: PMC8475209 DOI: 10.1210/clinem/dgab391] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Indexed: 12/12/2022]
Abstract
CONTEXT Type 2 diabetes mellitus (T2DM) and cancer share a variety of risk factors and pathophysiological features. It is becoming increasingly accepted that the 2 diseases are related, and that T2DM increases the risk of certain malignancies. OBJECTIVE This review summarizes recent advancements in the elucidation of functions of insulin-like growth factor 2 (IGF-2) messenger RNA (mRNA)-binding protein 2 (IGF2BP2) in T2DM and cancer. METHODS A PubMed review of the literature was conducted, and search terms included IGF2BP2, IMP2, or p62 in combination with cancer or T2DM. Additional sources were identified through manual searches of reference lists. The increased risk of multiple malignancies and cancer-associated mortality in patients with T2DM is believed to be driven by insulin resistance, hyperinsulinemia, hyperglycemia, chronic inflammation, and dysregulation of adipokines and sex hormones. Furthermore, IGF-2 is oncogenic, and its loss-of-function splice variant is protective against T2DM, which highlights the pivotal role of this growth factor in the pathogenesis of these 2 diseases. IGF-2 mRNA-binding proteins, particularly IGF2BP2, are also involved in T2DM and cancer, and single-nucleotide variations (formerly single-nucleotide polymorphisms) of IGF2BP2 are associated with both diseases. Deletion of the IGF2BP2 gene in mice improves their glucose tolerance and insulin sensitivity, and mice with transgenic p62, a splice variant of IGF2BP2, are prone to diet-induced fatty liver disease and hepatocellular carcinoma, suggesting the biological significance of IGF2BP2 in T2DM and cancer. CONCLUSION Accumulating evidence has revealed that IGF2BP2 mediates the pathogenesis of T2DM and cancer by regulating glucose metabolism, insulin sensitivity, and tumorigenesis. This review provides insight into the potential involvement of this RNA binding protein in the link between T2DM and cancer.
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Affiliation(s)
- Junguo Cao
- Shaanxi Eye Hospital (Xi’an People’s Hospital), Affiliated Guangren Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an 71004, Shaanxi Province, China
- Division of Experimental Neurosurgery, Department of Neurosurgery, University of Heidelberg, Heidelberg 69120, Germany
| | - Weijia Yan
- Shaanxi Eye Hospital (Xi’an People’s Hospital), Affiliated Guangren Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an 71004, Shaanxi Province, China
- Department of Ophthalmology, University of Heidelberg, Heidelberg 69120, Germany
| | - Xiujian Ma
- Division of Molecular Neurogenetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Haiyan Huang
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun 130000, China
| | - Hong Yan
- Shaanxi Eye Hospital (Xi’an People’s Hospital), Affiliated Guangren Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an 71004, Shaanxi Province, China
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Long J, Long T, Li Y, Yuan P, Liu K, Li J, Cheng L. A Functional Polymorphism in Accessible Chromatin Region Confers Risk of Non-Small Cell Lung Cancer in Chinese Population. Front Oncol 2021; 11:698993. [PMID: 34552866 PMCID: PMC8450516 DOI: 10.3389/fonc.2021.698993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/17/2021] [Indexed: 11/21/2022] Open
Abstract
Background The disease-associated non-coding variants identified by genome-wide association studies (GWASs) were enriched in open chromatin regions (OCRs) and implicated in gene regulation. Genetic variants in OCRs thus may exert regulatory functions and contribute to non-small cell lung cancer (NSCLC) susceptibility. Objective To fine map potential functional variants in GWAS loci that contribute to NSCLC predisposition using chromatin accessibility and histone modification data and explore their functions by population study and biochemical experimental analyses. Methods We mapped the chromatin accessible regions of lung tissues using data of assay for transposase-accessible chromatin using sequencing (ATAC-seq) in The Cancer Genome Atlas (TCGA) and prioritized potential regulatory variants within lung cancer GWAS loci by aligning with histone signatures using data of chromatin immunoprecipitation assays followed by sequencing (ChIP-seq) in the Encyclopedia of DNA Elements (ENCODE). A two-stage case–control study with 1,830 cases and 2,001 controls was conducted to explore the associations between candidate variants and NSCLC risk in Chinese population. Bioinformatic annotations and biochemical experiments were performed to further reveal the potential functions of significant variants. Results Sixteen potential functional single-nucleotide polymorphisms (SNPs) were selected as candidates from bioinformatics analyses. Three variants out of the 16 candidate SNPs survived after genotyping in stage 1 case–control study, and only the results of SNP rs13064999 were successfully validated in the analyses of stage 2 case–control study. In combined analyses, rs13064999 was significantly associated with NSCLC risk [additive model; odds ratio (OR) = 1.17; 95%CI, 1.07–1.29; p = 0.001]. Functional annotations indicated its potential enhancer bioactivity, and dual-luciferase reporter assays revealed a significant increase in luciferase activity for the reconstructed plasmid with rs13064999 A allele, when compared to the one with wild-type G allele (pA549 < 0.001, pSK-MES-1 = 0.004). Further electrophoretic mobility shift assays (EMSA) and super-shift assays confirmed a stronger affinity of HP1γ for the binding motif containing SNP rs13064999 A allele. Conclusion These findings suggested that the functional variant rs13064999, identified by the integration of ATAC-seq and ChIP-seq data, contributes to the susceptibility of NSCLC by affecting HP1γ binding, while the exact biological mechanism awaits further exploration.
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Affiliation(s)
- Jieyi Long
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Long
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Li
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peihong Yuan
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ke Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaoyuan Li
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liming Cheng
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Khetan S, Kales S, Kursawe R, Jillette A, Ulirsch JC, Reilly SK, Ucar D, Tewhey R, Stitzel ML. Functional characterization of T2D-associated SNP effects on baseline and ER stress-responsive β cell transcriptional activation. Nat Commun 2021; 12:5242. [PMID: 34475398 PMCID: PMC8413311 DOI: 10.1038/s41467-021-25514-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 08/10/2021] [Indexed: 11/08/2022] Open
Abstract
Genome-wide association studies (GWAS) have linked single nucleotide polymorphisms (SNPs) at >250 loci in the human genome to type 2 diabetes (T2D) risk. For each locus, identifying the functional variant(s) among multiple SNPs in high linkage disequilibrium is critical to understand molecular mechanisms underlying T2D genetic risk. Using massively parallel reporter assays (MPRA), we test the cis-regulatory effects of SNPs associated with T2D and altered in vivo islet chromatin accessibility in MIN6 β cells under steady state and pathophysiologic endoplasmic reticulum (ER) stress conditions. We identify 1,982/6,621 (29.9%) SNP-containing elements that activate transcription in MIN6 and 879 SNP alleles that modulate MPRA activity. Multiple T2D-associated SNPs alter the activity of short interspersed nuclear element (SINE)-containing elements that are strongly induced by ER stress. We identify 220 functional variants at 104 T2D association signals, narrowing 54 signals to a single candidate SNP. Together, this study identifies elements driving β cell steady state and ER stress-responsive transcriptional activation, nominates causal T2D SNPs, and uncovers potential roles for repetitive elements in β cell transcriptional stress response and T2D genetics.
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Affiliation(s)
- Shubham Khetan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT, USA
| | - Susan Kales
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | - Romy Kursawe
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - Jacob C Ulirsch
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Duygu Ucar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT, USA
- Institute of Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Ryan Tewhey
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA.
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA.
- Tufts University School of Medicine, Boston, MA, USA.
| | - Michael L Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
- Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT, USA.
- Institute of Systems Genomics, University of Connecticut, Farmington, CT, USA.
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Liu N, Low WY, Alinejad-Rokny H, Pederson S, Sadlon T, Barry S, Breen J. Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C. Epigenetics Chromatin 2021; 14:41. [PMID: 34454581 PMCID: PMC8399707 DOI: 10.1186/s13072-021-00417-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/19/2021] [Indexed: 11/30/2022] Open
Abstract
Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome.
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Affiliation(s)
- Ning Liu
- Computational & Systems Biology, Precision Medicine Theme, South Australian Health & Medical Research Institute, SA, 5000, Adelaide, Australia
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
| | - Wai Yee Low
- The Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA, 5371, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, The University of New South Wales, NSW, 2052, Sydney, Australia
- Core Member of UNSW Data Science Hub, The University of New South Wales, 2052, Sydney, Australia
| | - Stephen Pederson
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
- Dame Roma Mitchell Cancer Research Laboratories (DRMCRL), Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
| | - Timothy Sadlon
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Women's & Children's Health Network, SA, 5006, North Adelaide, Australia
| | - Simon Barry
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Core Member of UNSW Data Science Hub, The University of New South Wales, 2052, Sydney, Australia
- Women's & Children's Health Network, SA, 5006, North Adelaide, Australia
| | - James Breen
- Computational & Systems Biology, Precision Medicine Theme, South Australian Health & Medical Research Institute, SA, 5000, Adelaide, Australia.
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia.
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia.
- South Australian Genomics Centre (SAGC), South Australian Health & Medical Research Institute (SAHMRI), SA, 5000, Adelaide, Australia.
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Dong C, Simonett SP, Shin S, Stapleton DS, Schueler KL, Churchill GA, Lu L, Liu X, Jin F, Li Y, Attie AD, Keller MP, Keleş S. INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants. Genome Biol 2021; 22:241. [PMID: 34425882 PMCID: PMC8381555 DOI: 10.1186/s13059-021-02450-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 08/02/2021] [Indexed: 11/24/2022] Open
Abstract
Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA's superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/ .
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Affiliation(s)
- Chenyang Dong
- Department of Statistics, University of Wisconsin-Madison, Madison, WI USA
| | - Shane P. Simonett
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Sunyoung Shin
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX USA
| | - Donnie S. Stapleton
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Kathryn L. Schueler
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI USA
| | | | - Leina Lu
- Case Western University, Cleveland, OH USA
| | | | - Fulai Jin
- Case Western University, Cleveland, OH USA
| | - Yan Li
- Case Western University, Cleveland, OH USA
| | - Alan D. Attie
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Mark P. Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Sündüz Keleş
- Department of Statistics, University of Wisconsin-Madison, Madison, WI USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI USA
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46
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Golson ML. Islet Epigenetic Impacts on β-Cell Identity and Function. Compr Physiol 2021; 11:1961-1978. [PMID: 34061978 DOI: 10.1002/cphy.c200004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The development and maintenance of differentiation is vital to the function of mature cells. Terminal differentiation is achieved by locking in the expression of genes essential for the function of those cells. Gene expression and its memory through generations of cell division is controlled by transcription factors and a host of epigenetic marks. In type 2 diabetes, β cells have altered gene expression compared to controls, accompanied by altered chromatin marks. Mutations, diet, and environment can all disrupt the implementation and preservation of the distinctive β-cell transcriptional signature. Understanding of the full complement of genomic control in β cells is still nascent. This article describes the known effects of histone marks and variants, DNA methylation, how they are regulated in the β cell, and how they affect cell-fate specification, maintenance, and lineage propagation. © 2021 American Physiological Society. Compr Physiol 11:1-18, 2021.
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Affiliation(s)
- Maria L Golson
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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Aylward A, Okino ML, Benaglio P, Chiou J, Beebe E, Padilla JA, Diep S, Gaulton KJ. Glucocorticoid signaling in pancreatic islets modulates gene regulatory programs and genetic risk of type 2 diabetes. PLoS Genet 2021; 17:e1009531. [PMID: 33983929 PMCID: PMC8183998 DOI: 10.1371/journal.pgen.1009531] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/07/2021] [Accepted: 04/06/2021] [Indexed: 02/08/2023] Open
Abstract
Glucocorticoids are key regulators of glucose homeostasis and pancreatic islet function, but the gene regulatory programs driving responses to glucocorticoid signaling in islets and the contribution of these programs to diabetes risk are unknown. In this study we used ATAC-seq and RNA-seq to map chromatin accessibility and gene expression from eleven primary human islet samples cultured in vitro with the glucocorticoid dexamethasone at multiple doses and durations. We identified thousands of accessible chromatin sites and genes with significant changes in activity in response to glucocorticoids. Chromatin sites up-regulated in glucocorticoid signaling were prominently enriched for glucocorticoid receptor binding sites and up-regulated genes were enriched for ion transport and lipid metabolism, whereas down-regulated chromatin sites and genes were enriched for inflammatory, stress response and proliferative processes. Genetic variants associated with glucose levels and T2D risk were enriched in glucocorticoid-responsive chromatin sites, including fine-mapped variants at 51 known signals. Among fine-mapped variants in glucocorticoid-responsive chromatin, a likely casual variant at the 2p21 locus had glucocorticoid-dependent allelic effects on beta cell enhancer activity and affected SIX2 and SIX3 expression. Our results provide a comprehensive map of islet regulatory programs in response to glucocorticoids through which we uncover a role for islet glucocorticoid signaling in mediating genetic risk of T2D.
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Affiliation(s)
- Anthony Aylward
- Bioinformatics and Systems Biology graduate program, University of California San Diego, La Jolla, California, United States of America
| | - Mei-Lin Okino
- Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America
| | - Paola Benaglio
- Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America
| | - Joshua Chiou
- Biomedical Sciences graduate program, University of California San Diego, La Jolla, California, United States of America
| | - Elisha Beebe
- Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America
| | - Jose Andres Padilla
- Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America
| | - Sharlene Diep
- Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America
- Institute for Genomic Medicine, University of California San Diego, La Jolla, California, United States of America
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48
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Scrt1, a transcriptional regulator of β-cell proliferation identified by differential chromatin accessibility during islet maturation. Sci Rep 2021; 11:8800. [PMID: 33888791 PMCID: PMC8062533 DOI: 10.1038/s41598-021-88003-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 03/25/2021] [Indexed: 12/12/2022] Open
Abstract
Glucose-induced insulin secretion, a hallmark of mature β-cells, is achieved after birth and is preceded by a phase of intense proliferation. These events occurring in the neonatal period are decisive for establishing an appropriate functional β-cell mass that provides the required insulin throughout life. However, key regulators of gene expression involved in functional maturation of β-cells remain to be elucidated. Here, we addressed this issue by mapping open chromatin regions in newborn versus adult rat islets using the ATAC-seq assay. We obtained a genome-wide picture of chromatin accessible sites (~ 100,000) among which 20% were differentially accessible during maturation. An enrichment analysis of transcription factor binding sites identified a group of transcription factors that could explain these changes. Among them, Scrt1 was found to act as a transcriptional repressor and to control β-cell proliferation. Interestingly, Scrt1 expression was controlled by the transcriptional repressor RE-1 silencing transcription factor (REST) and was increased in an in vitro reprogramming system of pancreatic exocrine cells to β-like cells. Overall, this study led to the identification of several known and unforeseen key transcriptional events occurring during β-cell maturation. These findings will help defining new strategies to induce the functional maturation of surrogate insulin-producing cells.
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Gong H, Yang Y, Zhang S, Li M, Zhang X. Application of Hi-C and other omics data analysis in human cancer and cell differentiation research. Comput Struct Biotechnol J 2021; 19:2070-2083. [PMID: 33995903 PMCID: PMC8086027 DOI: 10.1016/j.csbj.2021.04.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/04/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023] Open
Abstract
With the development of 3C (chromosome conformation capture) and its derivative technology Hi-C (High-throughput chromosome conformation capture) research, the study of the spatial structure of the genomic sequence in the nucleus helps researchers understand the functions of biological processes such as gene transcription, replication, repair, and regulation. In this paper, we first introduce the research background and purpose of Hi-C data visualization analysis. After that, we discuss the Hi-C data analysis methods from genome 3D structure, A/B compartment, TADs (topologically associated domain), and loop detection. We also discuss how to apply genome visualization technologies to the identification of chromosome feature structures. We continue with a review of correlation analysis differences among multi-omics data, and how to apply Hi-C and other omics data analysis into cancer and cell differentiation research. Finally, we summarize the various problems in joint analyses based on Hi-C and other multi-omics data. We believe this review can help researchers better understand the progress and applications of 3D genome technology.
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Affiliation(s)
- Haiyan Gong
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
| | - Yi Yang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Sichen Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Minghong Li
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiaotong Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
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Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk. Nat Genet 2021; 53:455-466. [PMID: 33795864 PMCID: PMC9037575 DOI: 10.1038/s41588-021-00823-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 02/18/2021] [Indexed: 02/06/2023]
Abstract
Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. We observed state-specific enrichment of fasting glucose and T2D genome-wide association studies for beta cells and enrichment for other endocrine cell types. At T2D signals localized to islet-accessible chromatin, we prioritized variants with predicted regulatory function and co-accessibility with target genes. A causal T2D variant rs231361 at the KCNQ1 locus had predicted effects on a beta cell enhancer co-accessible with INS and genome editing in embryonic stem cell-derived beta cells affected INS levels. Together our findings demonstrate the power of single-cell epigenomics for interpreting complex disease genetics.
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