1
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Chen G, Wang Y, Wang X. Insulin-related traits and prostate cancer: A Mendelian randomization study. Comput Struct Biotechnol J 2024; 23:2337-2344. [PMID: 38867724 PMCID: PMC11167198 DOI: 10.1016/j.csbj.2024.05.034] [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: 11/21/2023] [Revised: 05/17/2024] [Accepted: 05/17/2024] [Indexed: 06/14/2024] Open
Abstract
Investigating the causal relationship between insulin secretion and prostate cancer (PCa) development is challenging due to the multifactorial nature of PCa, which complicates the isolation of the specific impact of insulin-related factors. We conducted a Mendelian randomization (MR) study to investigate the associations between insulin secretion-related traits and PCa. We used 36, 60, 56, 23, 48, and 49 single nucleotide polymorphisms (SNPs) as instrumental variables for fasting insulin, insulin sensitivity, proinsulin, and proinsulin in nondiabetic individuals, individuals with diabetes, and individuals receiving exogenous insulin, respectively. These SNPs were selected from various genome-wide association studies. To clarify the causal relationship between insulin-related traits and PCa, we utilized a multivariable MR analysis to adjust for obesity and body fat percentage. Additionally, two-step Mendelian randomization was conducted to assess the role of insulin-like growth factor 1 (IGF-1) in the relationship between proinsulin and PCa. Two-sample MR analysis revealed strong associations between genetically predicted fasting insulin, insulin sensitivity, proinsulin, and proinsulin in nondiabetic individuals and the development of PCa. After adjustment for obesity and body fat percentage using multivariable MR analysis, proinsulin remained significantly associated with PCa, whereas other factors were not. Furthermore, two-step MR analysis demonstrated that proinsulin acts as a negative factor in prostate carcinogenesis, largely independent of IGF-1. This study provides evidence suggesting that proinsulin may act as a negative factor contributing to the development of PCa. Novel therapies targeting proinsulin may have potential benefits for PCa patients, potentially reducing the need for unnecessary surgical treatments.
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Affiliation(s)
- Guihua Chen
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Yi Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
- Department of Urology, Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Xiang Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
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2
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Tanshee RR, Mahmud Z, Nabi AHMN, Sayem M. A comprehensive in silico investigation into the pathogenic SNPs in the RTEL1 gene and their biological consequences. PLoS One 2024; 19:e0309713. [PMID: 39240887 PMCID: PMC11379182 DOI: 10.1371/journal.pone.0309713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 08/16/2024] [Indexed: 09/08/2024] Open
Abstract
The Regulator of Telomere Helicase 1 (RTEL1) gene encodes a critical DNA helicase intricately involved in the maintenance of telomeric structures and the preservation of genomic stability. Germline mutations in the RTEL1 gene have been clinically associated with Hoyeraal-Hreidarsson syndrome, a more severe version of Dyskeratosis Congenita. Although various research has sought to link RTEL1 mutations to specific disorders, no comprehensive investigation has yet been conducted on missense mutations. In this study, we attempted to investigate the functionally and structurally deleterious coding and non-coding SNPs of the RTEL1 gene using an in silico approach. Initially, out of 1392 nsSNPs, 43 nsSNPs were filtered out through ten web-based bioinformatics tools. With subsequent analysis using nine in silico tools, these 43 nsSNPs were further shortened to 11 most deleterious nsSNPs. Furthermore, analyses of mutated protein structures, evolutionary conservancy, surface accessibility, domains & PTM sites, cancer susceptibility, and interatomic interaction revealed the detrimental effect of these 11 nsSNPs on RTEL1 protein. An in-depth investigation through molecular docking with the DNA binding sequence demonstrated a striking change in the interaction pattern for F15L, M25V, and G706R mutant proteins, suggesting the more severe consequences of these mutations on protein structure and functionality. Among the non-coding variants, two had the highest likelihood of being regulatory variants, whereas one variant was predicted to affect the target region of a miRNA. Thus, this study lays the groundwork for extensive analysis of RTEL1 gene variants in the future, along with the advancement of precision medicine and other treatment modalities.
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Affiliation(s)
- Rifah Rownak Tanshee
- Department of Mathematics and Natural Sciences, BRAC University, Badda, Dhaka, Bangladesh
| | - Zimam Mahmud
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - A H M Nurun Nabi
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Mohammad Sayem
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
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3
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Ray D, Loomis SJ, Venkataraghavan S, Zhang J, Tin A, Yu B, Chatterjee N, Selvin E, Duggal P. Characterizing Common and Rare Variations in Nontraditional Glycemic Biomarkers Using Multivariate Approaches on Multiancestry ARIC Study. Diabetes 2024; 73:1537-1550. [PMID: 38869630 PMCID: PMC11333373 DOI: 10.2337/db23-0318] [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: 06/14/2023] [Accepted: 06/05/2024] [Indexed: 06/14/2024]
Abstract
Genetic studies of nontraditional glycemic biomarkers, glycated albumin and fructosamine, can shed light on unknown aspects of type 2 diabetes genetics and biology. We performed a multiphenotype genome-wide association study of glycated albumin and fructosamine from 7,395 White and 2,016 Black participants in the Atherosclerosis Risk in Communities (ARIC) study on common variants from genotyped/imputed data. We discovered two genome-wide significant loci, one mapping to a known type 2 diabetes gene (ARAP1/STARD10) and another mapping to a novel region (UGT1A complex of genes), using multiomics gene-mapping strategies in diabetes-relevant tissues. We identified additional loci that were ancestry- and sex-specific (e.g., PRKCA in African ancestry, FCGRT in European ancestry, TEX29 in males). Further, we implemented multiphenotype gene-burden tests on whole-exome sequence data from 6,590 White and 2,309 Black ARIC participants. Ten variant sets annotated to genes across different variant aggregation strategies were exome-wide significant only in multiancestry analysis, of which CD1D, EGFL7/AGPAT2, and MIR126 had notable enrichment of rare predicted loss of function variants in African ancestry despite smaller sample sizes. Overall, 8 of 14 discovered loci and genes were implicated to influence these biomarkers via glycemic pathways, and most of them were not previously implicated in studies of type 2 diabetes. This study illustrates improved locus discovery and potential effector gene discovery by leveraging joint patterns of related biomarkers across the entire allele frequency spectrum in multiancestry analysis. Future investigation of the loci and genes potentially acting through glycemic pathways may help us better understand the risk of developing type 2 diabetes. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | | | - Sowmya Venkataraghavan
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Jiachen Zhang
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Adrienne Tin
- School of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Elizabeth Selvin
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
- Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins University, Baltimore, MD
| | - Priya Duggal
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
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4
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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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5
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Malfait J, Wan J, Spicuglia S. Epromoters are new players in the regulatory landscape with potential pleiotropic roles. Bioessays 2023; 45:e2300012. [PMID: 37246247 DOI: 10.1002/bies.202300012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
Precise spatiotemporal control of gene expression during normal development and cell differentiation is achieved by the combined action of proximal (promoters) and distal (enhancers) cis-regulatory elements. Recent studies have reported that a subset of promoters, termed Epromoters, works also as enhancers to regulate distal genes. This new paradigm opened novel questions regarding the complexity of our genome and raises the possibility that genetic variation within Epromoters has pleiotropic effects on various physiological and pathological traits by differentially impacting multiple proximal and distal genes. Here, we discuss the different observations pointing to an important role of Epromoters in the regulatory landscape and summarize the evidence supporting a pleiotropic impact of these elements in disease. We further hypothesize that Epromoter might represent a major contributor to phenotypic variation and disease.
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Affiliation(s)
- Juliette Malfait
- Aix-Marseille University, Inserm, TAGC, UMR1090, Marseille, France
- Equipe Labélisée Ligue Contre le Cancer, LIGUE, Marseille, France
| | - Jing Wan
- Aix-Marseille University, Inserm, TAGC, UMR1090, Marseille, France
- Equipe Labélisée Ligue Contre le Cancer, LIGUE, Marseille, France
| | - Salvatore Spicuglia
- Aix-Marseille University, Inserm, TAGC, UMR1090, Marseille, France
- Equipe Labélisée Ligue Contre le Cancer, LIGUE, Marseille, France
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6
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Powis G, Meuillet EJ, Indarte M, Booher G, Kirkpatrick L. Pleckstrin Homology [PH] domain, structure, mechanism, and contribution to human disease. Biomed Pharmacother 2023; 165:115024. [PMID: 37399719 DOI: 10.1016/j.biopha.2023.115024] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/14/2023] [Indexed: 07/05/2023] Open
Abstract
The pleckstrin homology [PH] domain is a structural fold found in more than 250 proteins making it the 11th most common domain in the human proteome. 25% of family members have more than one PH domain and some PH domains are split by one, or several other, protein domains although still folding to give functioning PH domains. We review mechanisms of PH domain activity, the role PH domain mutation plays in human disease including cancer, hyperproliferation, neurodegeneration, inflammation, and infection, and discuss pharmacotherapeutic approaches to regulate PH domain activity for the treatment of human disease. Almost half PH domain family members bind phosphatidylinositols [PIs] that attach the host protein to cell membranes where they interact with other membrane proteins to give signaling complexes or cytoskeleton scaffold platforms. A PH domain in its native state may fold over other protein domains thereby preventing substrate access to a catalytic site or binding with other proteins. The resulting autoinhibition can be released by PI binding to the PH domain, or by protein phosphorylation thus providing fine tuning of the cellular control of PH domain protein activity. For many years the PH domain was thought to be undruggable until high-resolution structures of human PH domains allowed structure-based design of novel inhibitors that selectively bind the PH domain. Allosteric inhibitors of the Akt1 PH domain have already been tested in cancer patients and for proteus syndrome, with several other PH domain inhibitors in preclinical development for treatment of other human diseases.
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Affiliation(s)
- Garth Powis
- PHusis Therapeutics Inc., 6019 Folsom Drive, La Jolla, CA 92037, USA.
| | | | - Martin Indarte
- PHusis Therapeutics Inc., 6019 Folsom Drive, La Jolla, CA 92037, USA
| | - Garrett Booher
- PHusis Therapeutics Inc., 6019 Folsom Drive, La Jolla, CA 92037, USA
| | - Lynn Kirkpatrick
- PHusis Therapeutics Inc., 6019 Folsom Drive, La Jolla, CA 92037, USA
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7
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Ray D, Loomis SJ, Venkataraghavan S, Tin A, Yu B, Chatterjee N, Selvin E, Duggal P. Characterizing common and rare variations in non-traditional glycemic biomarkers using multivariate approaches on multi-ancestry ARIC study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.13.23289200. [PMID: 37398180 PMCID: PMC10312851 DOI: 10.1101/2023.06.13.23289200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Glycated hemoglobin, fasting glucose, glycated albumin, and fructosamine are biomarkers that reflect different aspects of the glycemic process. Genetic studies of these glycemic biomarkers can shed light on unknown aspects of type 2 diabetes genetics and biology. While there exists several GWAS of glycated hemoglobin and fasting glucose, very few GWAS have focused on glycated albumin or fructosamine. We performed a multi-phenotype GWAS of glycated albumin and fructosamine from 7,395 White and 2,016 Black participants in the Atherosclerosis Risk in Communities (ARIC) study on the common variants from genotyped/imputed data. We found 2 genome-wide significant loci, one mapping to known type 2 diabetes gene (ARAP1/STARD10, p = 2.8 × 10-8) and another mapping to a novel gene (UGT1A, p = 1.4 × 10-8) using multi-omics gene mapping strategies in diabetes-relevant tissues. We identified additional loci that were ancestry-specific (e.g., PRKCA from African ancestry individuals, p = 1.7 × 10-8) and sex-specific (TEX29 locus in males only, p = 3.0 × 10-8). Further, we implemented multi-phenotype gene-burden tests on whole-exome sequence data from 6,590 White and 2,309 Black ARIC participants. Eleven genes across different rare variant aggregation strategies were exome-wide significant only in multi-ancestry analysis. Four out of 11 genes had notable enrichment of rare predicted loss of function variants in African ancestry participants despite smaller sample size. Overall, 8 out of 15 loci/genes were implicated to influence these biomarkers via glycemic pathways. This study illustrates improved locus discovery and potential effector gene discovery by leveraging joint patterns of related biomarkers across entire allele frequency spectrum in multi-ancestry analyses. Most of the loci/genes we identified have not been previously implicated in studies of type 2 diabetes, and future investigation of the loci/genes potentially acting through glycemic pathways may help us better understand risk of developing type 2 diabetes.
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Affiliation(s)
- Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | | | - Sowmya Venkataraghavan
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Adrienne Tin
- School of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Bing Yu
- Department of Epidemiology, UTHealth School of Public Health, Houston, TX
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Elizabeth Selvin
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
- Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins University, Baltimore, MD
| | - Priya Duggal
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
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8
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Ganekal P, Vastrad B, Kavatagimath S, Vastrad C, Kotrashetti S. Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59020309. [PMID: 36837510 PMCID: PMC9967176 DOI: 10.3390/medicina59020309] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/19/2023] [Accepted: 01/29/2023] [Indexed: 02/10/2023]
Abstract
Background and Objectives: A subject with diabetes and obesity is a class of the metabolic disorder. The current investigation aimed to elucidate the potential biomarker and prognostic targets in subjects with diabetes and obesity. Materials and Methods: The next-generation sequencing (NGS) data of GSE132831 was downloaded from Gene Expression Omnibus (GEO) database. Functional enrichment analysis of DEGs was conducted with ToppGene. The protein-protein interactions network, module analysis, target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed. Furthermore, hub genes were validated by receiver operating characteristic (ROC) analysis. A total of 872 DEGs, including 439 up-regulated genes and 433 down-regulated genes were observed. Results: Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for subjects with diabetes and obesity. The hub genes were validated in subjects with diabetes and obesity. Conclusion: This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for subjects with diabetes and obesity.
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Affiliation(s)
- Prashanth Ganekal
- Department of General Medicine, Basaveshwara Medical College, Chitradurga 577501, Karnataka, India
| | - Basavaraj Vastrad
- Department of Pharmaceutical Chemistry, K.L.E. College of Pharmacy, Gadag 582101, Karnataka, India
| | - Satish Kavatagimath
- Department of Pharmacognosy, K.L.E. College of Pharmacy, Belagavi 590010, Karnataka, India
| | - Chanabasayya Vastrad
- Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India
- Correspondence: ; Tel.: +91-9480073398
| | - Shivakumar Kotrashetti
- Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India
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9
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Zlobin AS, Volkova NA, Zinovieva NA, Iolchiev BS, Bagirov VA, Borodin PM, Axenovich TI, Tsepilov YA. Loci Associated with Negative Heterosis for Viability and Meat Productivity in Interspecific Sheep Hybrids. Animals (Basel) 2023; 13:ani13010184. [PMID: 36611792 PMCID: PMC9817718 DOI: 10.3390/ani13010184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/05/2023] Open
Abstract
Negative heterosis can occur on different economically important traits, but the exact biological mechanisms of this phenomenon are still unknown. The present study focuses on determining the genetic factors associated with negative heterosis in interspecific hybrids between domestic sheep (Ovis aries) and argali (Ovis ammon). One locus (rs417431015) associated with viability and two loci (rs413302370, rs402808951) associated with meat productivity were identified. One gene (ARAP2) was prioritized for viability and three for meat productivity (PDE2A, ARAP1, and PCDH15). The loci associated with meat productivity were demonstrated to fit the overdominant inheritance model and could potentially be involved int negative heterosis mechanisms.
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Affiliation(s)
- Alexander S. Zlobin
- Kurchatov Genomic Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences SB RAS, 630090 Novosibirsk, Russia
| | - Natalia A. Volkova
- L.K. Ernst Federal Science Center for Animal Husbandry, 101000 Moscow, Russia
| | | | - Baylar S. Iolchiev
- L.K. Ernst Federal Science Center for Animal Husbandry, 101000 Moscow, Russia
| | - Vugar A. Bagirov
- L.K. Ernst Federal Science Center for Animal Husbandry, 101000 Moscow, Russia
| | - Pavel M. Borodin
- Institute of Cytology and Genetics, SB RAS, 630090 Novosibirsk, Russia
| | | | - Yakov A. Tsepilov
- Kurchatov Genomic Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences SB RAS, 630090 Novosibirsk, Russia
- Correspondence:
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10
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Benaglio P, Zhu H, Okino ML, Yan J, Elgamal R, Nariai N, Beebe E, Korgaonkar K, Qiu Y, Donovan MK, Chiou J, Wang G, Newsome J, Kaur J, Miller M, Preissl S, Corban S, Aylward A, Taipale J, Ren B, Frazer KA, Sander M, Gaulton KJ. Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines. CELL GENOMICS 2022; 2:100214. [PMID: 36778047 PMCID: PMC9903835 DOI: 10.1016/j.xgen.2022.100214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 06/17/2022] [Accepted: 10/15/2022] [Indexed: 11/13/2022]
Abstract
We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate cis-regulatory elements (cCREs) in beta cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells, we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation, and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with SOCS1, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta cells during inflammation that modulate T1D risk.
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Affiliation(s)
- Paola Benaglio
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Han Zhu
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Mei-Lin Okino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jian Yan
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- School of Medicine, Northwest University, Xi’an, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden
| | - Ruth Elgamal
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Naoki Nariai
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Elisha Beebe
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Katha Korgaonkar
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Yunjiang Qiu
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | | | - Joshua Chiou
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Gaowei Wang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jacklyn Newsome
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jaspreet Kaur
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA
| | - Sierra Corban
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Anthony Aylward
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jussi Taipale
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Genome-Scale Biology Program, University of Helsinki, Helsinki, Finland
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA
| | - Kelly A. Frazer
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Maike Sander
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
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11
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Muhtaseb AW, Duan J. Modeling common and rare genetic risk factors of neuropsychiatric disorders in human induced pluripotent stem cells. Schizophr Res 2022:S0920-9964(22)00156-6. [PMID: 35459617 PMCID: PMC9735430 DOI: 10.1016/j.schres.2022.04.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 12/13/2022]
Abstract
Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders.
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Affiliation(s)
- Abdurrahman W Muhtaseb
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, United States of America; Department of Human Genetics, The University of Chicago, Chicago, IL 60637, United States of America
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, United States of America; Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL 60637, United States of America.
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12
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Alsheikh AJ, Wollenhaupt S, King EA, Reeb J, Ghosh S, Stolzenburg LR, Tamim S, Lazar J, Davis JW, Jacob HJ. The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases. BMC Med Genomics 2022; 15:74. [PMID: 35365203 PMCID: PMC8973751 DOI: 10.1186/s12920-022-01216-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 03/17/2022] [Indexed: 02/08/2023] Open
Abstract
Background The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. Methods To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles. Results We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33). Conclusions This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01216-w.
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Affiliation(s)
- Ammar J Alsheikh
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA.
| | - Sabrina Wollenhaupt
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Emily A King
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Jonas Reeb
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Sujana Ghosh
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | | | - Saleh Tamim
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Jozef Lazar
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - J Wade Davis
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Howard J Jacob
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
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13
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Chen D, Wu H, Wang X, Huang T, Jia J. Shared Genetic Basis and Causal Relationship Between Television Watching, Breakfast Skipping and Type 2 Diabetes: Evidence From a Comprehensive Genetic Analysis. Front Endocrinol (Lausanne) 2022; 13:836023. [PMID: 35399945 PMCID: PMC8988136 DOI: 10.3389/fendo.2022.836023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Epidemiological investigations have established unhealthy lifestyles, such as excessive leisurely sedentary behavior (especially TV/television watching) and breakfast skipping, increase the risk of type 2 diabetes (T2D), but the causal relationship is unclear. We aimed to understand how single nucleotide variants contribute to the co-occurrence of unhealthy lifestyles and T2D, thereby providing meaningful insights into disease mechanisms. Methods Combining summary statistics from genome-wide association studies (GWAS) on TV watching (N = 422218), breakfast skipping (N = 193860) and T2D (N = 159208) in European pedigrees, we conducted comprehensive pairwise genetic analysis, including high-definition likelihood (HDL-method), cross-phenotype association studies (CPASSOC), GWAS-eQTL colocalization analysis and transcriptome-wide association studies (TWAS), to understand the genetic overlap between them. We also performed bidirectional two-sample Mendelian randomization (MR) analysis for causal inference using genetic instrumental variables, and two-step MR mediation analysis was used to assess any effects explained by body mass index, lipid traits and glycemic traits. Results HDL-method showed that T2D shared a strong genetic correlation with TV watching (rg = 0.26; P = 1.63×10-29) and skipping breakfast (rg = 0.15; P =2.02×10-6). CPASSOC identifies eight independent SNPs shared between T2D and TV watching, including one novel shared locus. TWAS and CPASSOC showed that shared genes were enriched in lung, esophageal, adipose, and thyroid tissues and highlighted potential shared regulatory pathways for lipoprotein metabolism, pancreatic β-cell function, cellular senescence and multi-mediator factors. MR showed TV watching had a causal effect on T2D (βIVW = 0.629, PIVW = 1.80×10-10), but no significant results were observed between breakfast skipping and T2D. Mediation analysis provided evidence that body mass index, fasting glucose, hemoglobin A1c and high-density lipoprotein are potential factors that mediate the causal relationship between TV and T2D. Conclusions Our findings provide strong evidence of shared genetics and causation between TV watching and T2D and facilitate our identification of common genetic architectures shared between them.
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Affiliation(s)
- Dongze Chen
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Hanyu Wu
- Department of Bioinformatics, School of Life Science, Peking University, Beijing, China
| | - Xinpei Wang
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China
| | - Jinzhu Jia
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Statistical Science, Peking University, Beijing, China
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14
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Eslamimehr S, Jones AD, Anthony TM, Arshad SH, Holloway JW, Ewart S, Luo R, Mukherjee N, Kheirkhah Rahimabad P, Chen S, Karmaus W. Association of prenatal acetaminophen use and acetaminophen metabolites with DNA methylation of newborns: analysis of two consecutive generations of the Isle of Wight birth cohort. ENVIRONMENTAL EPIGENETICS 2022; 8:dvac002. [PMID: 35317219 PMCID: PMC8933617 DOI: 10.1093/eep/dvac002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/04/2022] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
Acetaminophen is used by nearly two-thirds of pregnant women. Although considered safe, studies have demonstrated associations between prenatal acetaminophen use and adverse health outcomes in offspring. Since DNA methylation (DNAm) at birth may act as an early indicator of later health, assessments on whether DNAm of newborns is associated with gestational acetaminophen use or its metabolites are needed. Using data from three consecutive generations of the Isle of Wight cohort (F0-grandmothers, F1-mothers, and F2-offspring) we investigated associations between acetaminophen metabolites in F0 serum at delivery with epigenome-wide DNAm in F1 (Guthrie cards) and between acetaminophen use of F1 and F2-cord-serum levels with F2 cord blood DNAm. In epigenome-wide screening, we eliminated non-informative DNAm sites followed by linear regression of informative sites. Based on repeated pregnancies, indication bias analyses tested whether acetaminophen indicated maternal diseases or has a risk in its own right. Considering that individuals with similar intake process acetaminophen differently, metabolites were clustered to distinguish metabolic exposures. Finally, metabolite clusters from F1-maternal and F2-cord sera were tested for their associations with newborn DNAm (F1 and F2). Twenty-one differential DNAm sites in cord blood were associated with reported maternal acetaminophen intake in the F2 generation. For 11 of these cytosine-phosphate-guanine (CpG) sites, an indication bias was excluded and five were replicated in F2 with metabolite clusters. In addition, metabolite clusters showed associations with 25 CpGs in the F0-F1 discovery analysis, of which five CpGs were replicated in the F2-generation. Our results suggest that prenatal acetaminophen use, measured as metabolites, may influence DNAm in newborns.
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Affiliation(s)
- Shakiba Eslamimehr
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Robison Hall 3825 DeSoto Avenue Memphis, TN 38152, USA
| | - A Daniel Jones
- Department of Biochemistry & Molecular Biology, Michigan State University, 603 Wilson Rd Rm 212, East Lansing, MI 48823, USA
| | - Thilani M Anthony
- Department of Biochemistry & Molecular Biology, Michigan State University, 603 Wilson Rd Rm 212, East Lansing, MI 48823, USA
| | - S Hasan Arshad
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Hartley Library B12, University Rd, Highfield, Southampton SO17 1BJ, UK
- The David Hide Asthma and Allergy Research Centre, Hartley Library B12, University Rd, Highfield, Southampton, Isle of Wight SO17 1BJ, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Hartley Library B12, University Rd, Highfield, Southampton SO17 1BJ, UK
| | - John W Holloway
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Hartley Library B12, University Rd, Highfield, Southampton SO17 1BJ, UK
- Human Development and Health, Faculty of Medicine, University of Southampton, Hartley Library B12, University Rd, Highfield, Southampton SO17 1BJ, UK
| | - Susan Ewart
- Department of Large Animal Clinical Sciences, Michigan State University, 736 Wilson Road, D202 East Lansing, MI 48824, USA
| | - Rui Luo
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Robison Hall 3825 DeSoto Avenue Memphis, TN 38152, USA
| | - Nandini Mukherjee
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Robison Hall 3825 DeSoto Avenue Memphis, TN 38152, USA
| | - Parnian Kheirkhah Rahimabad
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Robison Hall 3825 DeSoto Avenue Memphis, TN 38152, USA
| | - Su Chen
- Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
| | - Wilfried Karmaus
- **Correspondence address. School of Public Health, University of Memphis, Robison Hall, Memphis, TN 38152, USA. Tel: 803-767-8425; Fax: 9010678-1715; E-mail:
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15
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Bailetti D, Sentinelli F, Prudente S, Cimini FA, Barchetta I, Totaro M, Di Costanzo A, Barbonetti A, Leonetti F, Cavallo MG, Baroni MG. Deep Resequencing of 9 Candidate Genes Identifies a Role for ARAP1 and IGF2BP2 in Modulating Insulin Secretion Adjusted for Insulin Resistance in Obese Southern Europeans. Int J Mol Sci 2022; 23:ijms23031221. [PMID: 35163144 PMCID: PMC8835579 DOI: 10.3390/ijms23031221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/18/2022] [Accepted: 01/19/2022] [Indexed: 02/07/2023] Open
Abstract
Type 2 diabetes is characterized by impairment in insulin secretion, with an established genetic contribution. We aimed to evaluate common and low-frequency (1–5%) variants in nine genes strongly associated with insulin secretion by targeted sequencing in subjects selected from the extremes of insulin release measured by the disposition index. Collapsing data by gene and/or function, the association between disposition index and nonsense variants were significant, also after adjustment for confounding factors (OR = 0.25, 95% CI = 0.11–0.59, p = 0.001). Evaluating variants individually, three novel variants in ARAP1, IGF2BP2 and GCK, out of eight reaching significance singularly, remained associated after adjustment. Constructing a genetic risk model combining the effects of the three variants, only carriers of the ARAP1 and IGF2BP2 variants were significantly associated with a reduced probability to be in the lower, worst, extreme of insulin secretion (OR = 0.223, 95% CI = 0.105–0.473, p < 0.001). Observing a high number of normal glucose tolerance between carriers, a regression posthoc analysis was performed. Carriers of genetic risk model variants had higher probability to be normoglycemic, also after adjustment (OR = 2.411, 95% CI = 1.136–5.116, p = 0.022). Thus, in our southern European cohort, nonsense variants in all nine candidate genes showed association with better insulin secretion adjusted for insulin resistance, and we established the role of ARAP1 and IGF2BP2 in modulating insulin secretion.
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Affiliation(s)
- Diego Bailetti
- Department of Clinical Medicine, Public Health, Life and Environmental Sciences (MeSVA), University of L’Aquila, 67100 L’Aquila, Italy; (F.S.); (M.T.); (A.B.)
- Correspondence: (D.B.); (M.G.B.); Tel.: +39-862-433327 (M.G.B.)
| | - Federica Sentinelli
- Department of Clinical Medicine, Public Health, Life and Environmental Sciences (MeSVA), University of L’Aquila, 67100 L’Aquila, Italy; (F.S.); (M.T.); (A.B.)
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy; (F.A.C.); (I.B.); (M.G.C.)
| | - Sabrina Prudente
- Research Unit of Metabolic and Cardiovascular Diseases, Fondazione IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Italy;
| | - Flavia Agata Cimini
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy; (F.A.C.); (I.B.); (M.G.C.)
| | - Ilaria Barchetta
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy; (F.A.C.); (I.B.); (M.G.C.)
| | - Maria Totaro
- Department of Clinical Medicine, Public Health, Life and Environmental Sciences (MeSVA), University of L’Aquila, 67100 L’Aquila, Italy; (F.S.); (M.T.); (A.B.)
| | - Alessia Di Costanzo
- Department of Translational and Precision Medicine, Sapienza University of Rome, 00185 Rome, Italy;
| | - Arcangelo Barbonetti
- Department of Clinical Medicine, Public Health, Life and Environmental Sciences (MeSVA), University of L’Aquila, 67100 L’Aquila, Italy; (F.S.); (M.T.); (A.B.)
| | - Frida Leonetti
- Diabetes Unit, Department of Medical-Surgical Sciences and Biotechnologies, Santa Maria Goretti Hospital, Sapienza University of Rome, 04100 Latina, Italy;
| | - Maria Gisella Cavallo
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy; (F.A.C.); (I.B.); (M.G.C.)
| | - Marco Giorgio Baroni
- Department of Clinical Medicine, Public Health, Life and Environmental Sciences (MeSVA), University of L’Aquila, 67100 L’Aquila, Italy; (F.S.); (M.T.); (A.B.)
- Neuroendocrinology and Metabolic Diseases, IRCCS Neuromed, 86077 Pozzilli, Italy
- Correspondence: (D.B.); (M.G.B.); Tel.: +39-862-433327 (M.G.B.)
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16
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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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17
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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: 13] [Impact Index Per Article: 4.3] [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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18
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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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19
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Robinson EK, Jagannatha P, Covarrubias S, Cattle M, Smaliy V, Safavi R, Shapleigh B, Abu-Shumays R, Jain M, Cloonan SM, Akeson M, Brooks AN, Carpenter S. Inflammation drives alternative first exon usage to regulate immune genes including a novel iron-regulated isoform of Aim2. eLife 2021; 10:69431. [PMID: 34047695 PMCID: PMC8260223 DOI: 10.7554/elife.69431] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 05/21/2021] [Indexed: 12/11/2022] Open
Abstract
Determining the layers of gene regulation within the innate immune response is critical to our understanding of the cellular responses to infection and dysregulation in disease. We identified a conserved mechanism of gene regulation in human and mouse via changes in alternative first exon (AFE) usage following inflammation, resulting in changes to the isoforms produced. Of these AFE events, we identified 95 unannotated transcription start sites in mice using a de novo transcriptome generated by long-read native RNA-sequencing, one of which is in the cytosolic receptor for dsDNA and known inflammatory inducible gene, Aim2. We show that this unannotated AFE isoform of Aim2 is the predominant isoform expressed during inflammation and contains an iron-responsive element in its 5′UTR enabling mRNA translation to be regulated by iron levels. This work highlights the importance of examining alternative isoform changes and translational regulation in the innate immune response and uncovers novel regulatory mechanisms of Aim2.
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Affiliation(s)
- Elektra K Robinson
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, United States
| | - Pratibha Jagannatha
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, United States.,Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, United States
| | - Sergio Covarrubias
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, United States
| | - Matthew Cattle
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, United States
| | - Valeriya Smaliy
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, United States
| | - Rojin Safavi
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, United States
| | - Barbara Shapleigh
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, United States
| | - Robin Abu-Shumays
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, United States
| | - Miten Jain
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, United States
| | - Suzanne M Cloonan
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, United States
| | - Mark Akeson
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, United States
| | - Angela N Brooks
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, United States
| | - Susan Carpenter
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, United States
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20
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Huang LO, Rauch A, Mazzaferro E, Preuss M, Carobbio S, Bayrak CS, Chami N, Wang Z, Schick UM, Yang N, Itan Y, Vidal-Puig A, den Hoed M, Mandrup S, Kilpeläinen TO, Loos RJF. Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities. Nat Metab 2021; 3:228-243. [PMID: 33619380 DOI: 10.1038/s42255-021-00346-2] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 01/14/2021] [Indexed: 01/31/2023]
Abstract
Obesity is a major risk factor for cardiometabolic diseases. Nevertheless, a substantial proportion of individuals with obesity do not suffer cardiometabolic comorbidities. The mechanisms that uncouple adiposity from its cardiometabolic complications are not fully understood. Here, we identify 62 loci of which the same allele is significantly associated with both higher adiposity and lower cardiometabolic risk. Functional analyses show that the 62 loci are enriched for genes expressed in adipose tissue, and for regulatory variants that influence nearby genes that affect adipocyte differentiation. Genes prioritized in each locus support a key role of fat distribution (FAM13A, IRS1 and PPARG) and adipocyte function (ALDH2, CCDC92, DNAH10, ESR1, FAM13A, MTOR, PIK3R1 and VEGFB). Several additional mechanisms are involved as well, such as insulin-glucose signalling (ADCY5, ARAP1, CREBBP, FAM13A, MTOR, PEPD, RAC1 and SH2B3), energy expenditure and fatty acid oxidation (IGF2BP2), browning of white adipose tissue (CSK, VEGFA, VEGFB and SLC22A3) and inflammation (SH2B3, DAGLB and ADCY9). Some of these genes may represent therapeutic targets to reduce cardiometabolic risk linked to excess adiposity.
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Affiliation(s)
- Lam O Huang
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Alexander Rauch
- Functional Genomics & Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
- Molecular Endocrinology & Stem Cell Research Unit, Department of Endocrinology and Metabolism, Odense University Hospital and Steno Diabetes Center Odense and Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Eugenia Mazzaferro
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Stefania Carobbio
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Cigdem S Bayrak
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Ursula M Schick
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Nancy Yang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Yuval Itan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Antonio Vidal-Puig
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Marcel den Hoed
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Susanne Mandrup
- Functional Genomics & Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA.
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA.
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA.
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21
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Li X, Ma TK, Wen S, Li LL, Xu L, Zhu XW, Zhang CX, Liu N, Wang X, Fan QL. LncRNA ARAP1-AS2 promotes high glucose-induced human proximal tubular cell injury via persistent transactivation of the EGFR by interacting with ARAP1. J Cell Mol Med 2020; 24:12994-13009. [PMID: 32969198 PMCID: PMC7701572 DOI: 10.1111/jcmm.15897] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 08/25/2020] [Indexed: 12/12/2022] Open
Abstract
The persistent transactivation of epidermal growth factor receptor (EGFR) causes subsequent activation of the TGF-β/Smad3 pathway, which is closely associated with fibrosis and cell proliferation in diabetic nephropathy (DN), but the exact mechanism of persistent EGFR transactivation in DN remains unclear. ARAP1, a susceptibility gene for type 2 diabetes, can regulate the endocytosis and ubiquitination of membrane receptors, but the effect of ARAP1 and its natural antisense long non-coding RNA (lncRNA), ARAP1-AS2, on the ubiquitination of EGFR in DN is not clear. In this study, we verified that the expression of ARAP1 and ARAP1-AS2 was significantly up-regulated in high glucose-induced human proximal tubular epithelial cells (HK-2 cells). Moreover, we found that overexpression or knockdown of ARAP1-AS2 could regulate fibrosis and HK-2 cell proliferation through EGFR/TGF-β/Smad3 signalling. RNA pulldown assays revealed that ARAP1-AS2 directly interacts with ARAP1. Coimmunoprecipitation, dual-immunofluorescence and ubiquitination assays showed that ARAP1 may maintain persistent EGFR activation by reducing EGFR ubiquitination through competing with Cbl for CIN85 binding. Taken together, our results suggest that the lncRNA ARAP1-AS2 may promote high glucose-induced proximal tubular cell injury via persistent EGFR/TGF-β/Smad3 pathway activation by interacting with ARAP1.
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Affiliation(s)
- Xin Li
- Department of Nephrology, First Hospital of China Medical University, Shenyang, China
| | - Tian-Kui Ma
- Department of Nephrology, First Hospital of China Medical University, Shenyang, China
| | - Si Wen
- Department of Nephrology, First Hospital of China Medical University, Shenyang, China
| | - Lu-Lu Li
- Department of Nephrology, First Hospital of China Medical University, Shenyang, China
| | - Li Xu
- Department of Clinical Laboratory, First Hospital of China Medical University, Shenyang, China
| | - Xin-Wang Zhu
- Department of Nephrology, First Hospital of China Medical University, Shenyang, China
| | - Cong-Xiao Zhang
- Department of Nephrology, First Hospital of China Medical University, Shenyang, China
| | - Nan Liu
- Department of Nephrology, First Hospital of China Medical University, Shenyang, China
| | - Xu Wang
- Department of Gastroenterology, First Hospital of China Medical University, Shenyang, China
| | - Qiu-Ling Fan
- Department of Nephrology, First Hospital of China Medical University, Shenyang, China
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22
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Eizirik DL, Pasquali L, Cnop M. Pancreatic β-cells in type 1 and type 2 diabetes mellitus: different pathways to failure. Nat Rev Endocrinol 2020; 16:349-362. [PMID: 32398822 DOI: 10.1038/s41574-020-0355-7] [Citation(s) in RCA: 404] [Impact Index Per Article: 101.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/24/2020] [Indexed: 12/12/2022]
Abstract
Loss of functional β-cell mass is the key mechanism leading to the two main forms of diabetes mellitus - type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Understanding the mechanisms behind β-cell failure is critical to prevent or revert disease. Basic pathogenic differences exist in the two forms of diabetes mellitus; T1DM is immune mediated and T2DM is mediated by metabolic mechanisms. These mechanisms differentially affect early β-cell dysfunction and eventual fate. Over the past decade, major advances have been made in the field, mostly delivered by studies on β-cells in human disease. These advances include studies of islet morphology and human β-cell gene expression in T1DM and T2DM, the identification and characterization of the role of T1DM and T2DM candidate genes at the β-cell level and the endoplasmic reticulum stress signalling that contributes to β-cell failure in T1DM (mostly IRE1 driven) and T2DM (mostly PERK-eIF2α dependent). Here, we review these new findings, focusing on studies performed on human β-cells or on samples obtained from patients with diabetes mellitus.
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Affiliation(s)
- Décio L Eizirik
- ULB Center for Diabetes Research, Welbio Investigator, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium.
- Indiana Biosciences Research Institute (IBRI), Indianapolis, IN, USA.
| | - Lorenzo Pasquali
- Endocrine Regulatory Genomics, Department of Experimental & Health Sciences, University Pompeu Fabra, Barcelona, Spain.
- Germans Trias i Pujol University Hospital and Research Institute, Badalona, Spain.
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain.
| | - Miriam Cnop
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, Belgium.
- Division of Endocrinology, Erasmus Hospital, Université Libre de Bruxelles, Brussels, Belgium.
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23
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Domínguez-Cruz MG, Muñoz MDL, Totomoch-Serra A, García-Escalante MG, Burgueño J, Valadez-González N, Pinto-Escalante D, Díaz-Badillo A. Maya gene variants related to the risk of type 2 diabetes in a family-based association study. Gene 2020; 730:144259. [PMID: 31759989 DOI: 10.1016/j.gene.2019.144259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 11/06/2019] [Accepted: 11/07/2019] [Indexed: 12/01/2022]
Abstract
Mexican Maya populations have a notably high prevalence of type 2 diabetes (T2D) as a consequence of the interaction between environmental factors and a genetic component. To assess the impact of 24 single nucleotide variants (SNVs) located in 18 T2D risk genes, we conducted a family-based association evaluation in samples from Maya communities with a high incidence of the disease. A total of four hundred individuals were recruited from three Maya communities with a high T2D incidence. Family pedigrees (100) and 49 nuclear families were included. Genotyping was performed by allelic discrimination with TaqMan probes. This study also included the family-based association test (FBAT) statistic U to assess the genetic associations with T2D, and the multivariate statistical and haplotype analyses. A positive association with TD2 risk was found for WFS1 rs6446482 (p = 0.046, Z = 1.994) under an additive model, and SIRT1 rs7896005 (p = 0.038, Z = 2.073) under the dominant model. Multivariate model analysis, including T2D status, age, and body mass index (BMI), displayed significant covariance in PPARGC-1α rs8192678; SIRT1 rs7896005; TCF7L2 rs7903146 and rs122243326; UCP3 rs3781907; and HHEX rs1111875 with a P < 0.05. This study revealed an association of SIRT1 and WFS1 with T2D risk.
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Affiliation(s)
- Miriam G Domínguez-Cruz
- Department of Genetics and Molecular Biology, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico
| | - María de Lourdes Muñoz
- Department of Genetics and Molecular Biology, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico.
| | - Armando Totomoch-Serra
- Department of Genetics and Molecular Biology, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico; PhD Program in Medical Sciences, Universidad de La Frontera, Chile
| | - María G García-Escalante
- Laboratorios de Genética y Hematología, Centro de Investigaciones Regionales "Dr. Hideyo Noguchi", Universidad Autónoma de Yucatán, Mérida, Yucatán, Mexico
| | - Juan Burgueño
- Centro Internacional de Mejoramiento de Maíz y Trigo, El Batán, Texcoco, State of Mexico, Mexico
| | - Nina Valadez-González
- Laboratorios de Genética y Hematología, Centro de Investigaciones Regionales "Dr. Hideyo Noguchi", Universidad Autónoma de Yucatán, Mérida, Yucatán, Mexico
| | - Doris Pinto-Escalante
- Laboratorios de Genética y Hematología, Centro de Investigaciones Regionales "Dr. Hideyo Noguchi", Universidad Autónoma de Yucatán, Mérida, Yucatán, Mexico
| | - Alvaro Díaz-Badillo
- Maestría en Salud Publica, Universidad México Americana del Norte, Reynosa, Tamaulipas, Mexico; Department of Epidemiology, Human Genetics & Environmental Sciences, The University of Texas Health Science Center at Houston, Brownville, TX, USA
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24
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Mattis KK, Gloyn AL. From Genetic Association to Molecular Mechanisms for Islet-cell Dysfunction in Type 2 Diabetes. J Mol Biol 2020; 432:1551-1578. [PMID: 31945378 DOI: 10.1016/j.jmb.2019.12.045] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/15/2019] [Accepted: 12/17/2019] [Indexed: 12/30/2022]
Abstract
Genome-wide association studies (GWAS) have identified over 400 signals robustly associated with risk for type 2 diabetes (T2D). At the vast majority of these loci, the lead single nucleotide polymorphisms (SNPs) reside in noncoding regions of the genome, which hampers biological inference and translation of genetic discoveries into disease mechanisms. The study of these T2D risk variants in normoglycemic individuals has revealed that a significant proportion are exerting their disease risk through islet-cell dysfunction. The central role of the islet is also demonstrated by numerous studies, which have shown an enrichment of these signals in islet-specific epigenomic annotations. In recent years the emergence of authentic human beta-cell lines, and advances in genome-editing technologies coupled with improved protocols differentiating human pluripotent stem cells into beta-like cells has opened up new opportunities for T2D disease modeling. Here we review the current understanding on the genetic basis of T2D focusing on approaches, which have facilitated the identification of causal variants and their effector transcripts in human islets. We will present examples of functional studies based on animal and conventional cellular systems and highlight the potential of novel stem cell-based T2D disease models.
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Affiliation(s)
- Katia K Mattis
- Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, UK
| | - Anna L Gloyn
- Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, UK; National Institute of Health Research, Biomedical Research Centre, Churchill Hospital, Headington, Oxford, UK.
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25
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Li L, Xu L, Wen S, Yang Y, Li X, Fan Q. The effect of lncRNA-ARAP1-AS2/ARAP1 on high glucose-induced cytoskeleton rearrangement and epithelial-mesenchymal transition in human renal tubular epithelial cells. J Cell Physiol 2020; 235:5787-5795. [PMID: 31975379 DOI: 10.1002/jcp.29512] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 01/06/2020] [Indexed: 12/16/2022]
Abstract
The epithelial-mesenchymal transition (EMT) plays an important role in diabetic renal fibrosis. The ARAP1 gene is located near risk alleles for Type 2 diabetes, and its function has been linked to cytoskeleton rearrangement, Golgi apparatus remodeling, and endocytic trafficking of membrane receptors. The role of ARAP1 and its antisense RNA, ARAP1-AS2, in the pathogenesis of diabetes is unclear. To clarify the roles of ARAP1 and its antisense RNA in diabetes and related complications, we examined if the expression of these transcripts changed under high glucose (HG) conditions. To do this, we examined transcript levels in HK-2 cells, and explored the roles of ARAP1 and ARAP1-AS2 in the EMT process in HK-2 cells. We found increased expression of ARAP1-AS2 and ARAP1 in HK-2 cells under HG condition, and observed that the overexpression of ARAP1-AS2 significantly increased the EMT process. In addition, HG upregulated Cdc42-GTP levels in HK-2 cells, and increased cytoskeleton rearrangement, cell viability, and migration. After knockdown of ARAP1, the level of Cdc42-GTP was decreased; cytoskeleton reorganization, cell viability, and migration processes were decreased; and EMT and expression of fibrosis marker protein. Overall, our results indicated that ARAP1-AS2/ARAP1 may participate in cytoskeleton rearrangement and EMT processes in HK-2 cells through increased Cdc42-GTP levels.
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Affiliation(s)
- Lulu Li
- Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Li Xu
- Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.,Department of Laboratory Medicine, First Hospital of China Medical University, Shenyang, China
| | - Si Wen
- Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ying Yang
- Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xin Li
- Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Qiuling Fan
- Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
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26
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Li Y, Shen K, Li C, Yang Y, Yang M, Tao W, He S, Shi L, Yao Y. Identifying the association between single nucleotide polymorphisms in KCNQ1, ARAP1, and KCNJ11 and type 2 diabetes mellitus in a Chinese population. Int J Med Sci 2020; 17:2379-2386. [PMID: 32922204 PMCID: PMC7484634 DOI: 10.7150/ijms.48072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/21/2020] [Indexed: 12/27/2022] Open
Abstract
Background: Type 2 diabetes mellitus (T2DM) has a high global prevalence, and insufficient insulin secretion is one of the major reasons for its development. Therefore, investigating the association between T2DM and the single nucleotide polymorphisms (SNPs) in genes associated with insulin secretion is necessary. Methods: T2DM (1,194) and nondiabetic (NDM) (1,292) subjects were enrolled and the ten single nucleotide polymorphisms (SNPs) in KCNQ1, ARAP1, and KCNJ11 associated with insulin secretion were genotyped in a Chinese population. Results: Our data revealed that the rs2237897T allele in KCNQ1 is the protective allele for T2DM (P<0.001, OR=0.793; 95%CI: 0.705-0.893). However, the A allele of rs1552224 in ARAP1 may be a risk factor for T2DM (P=0.002, OR=12.070; 95% CI: 1.578-92.337). The haplotype analysis revealed that rs151290-rs2237892CC and rs2237895-rs2237897CC in KCNQ1 constitute the risk haplotype in T2DM development (P=0.010, OR=1.160; 95% CI: 1.037-1.299 and P=0.004, OR=1.192; 95% CI: 1.057-1.344). Moreover, rs2237895-rs2237897AT in KCNQ1 constitutes the protective haplotype in T2DM (P=0.001, OR=0.819; 95% CI: 0.727-0.923). In the inheritance models analysis, the rs2283228 (C/A-C/C) genotype is the protective factor compared to the A/A genotype (P=0.005, OR=0.79; 95% CI: 0.68-0.93). For rs2237897, the C/T-T/T genotype is the protective factor compared to the C/C genotype (P<0.001, OR=0.74; 95% CI: 0.63-0.87). Furthermore, when compared with the rs2237897 (C/T-T/T) genotype, rs2237897C/C genotype showed higher HbA1C levels (8.731±2.697 vs 9.282±2.921, P=0.001). Conclusion: Our results revealed that genetic variations in KCNQ1 and ARAP1 were associated with T2DM susceptibility in a Chinese population.
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Affiliation(s)
- Yiping Li
- Department of Endocrinology and Metabolism, The Second People's Hospital of Yunnan Province & The Fourth Affiliated Hospital of Kunming Medical University, Kunming 650021, Yunnan, China
| | - Keyu Shen
- Department of Medicine, Dentistry and Healthy Science, The University of Melbourne, Melbourne VIC3010, Australia
| | - Chuanyin Li
- Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming 650118, Yunnan, China
| | - Ying Yang
- Department of Endocrinology and Metabolism, The Second People's Hospital of Yunnan Province & The Fourth Affiliated Hospital of Kunming Medical University, Kunming 650021, Yunnan, China
| | - Man Yang
- Department of Endocrinology and Metabolism, The Second People's Hospital of Yunnan Province & The Fourth Affiliated Hospital of Kunming Medical University, Kunming 650021, Yunnan, China
| | - Wenyu Tao
- Department of Endocrinology and Metabolism, The Second People's Hospital of Yunnan Province & The Fourth Affiliated Hospital of Kunming Medical University, Kunming 650021, Yunnan, China
| | - Siqi He
- School of Clinical Medicine, Dali University, Dali 671000, Yunnan, China
| | - Li Shi
- Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming 650118, Yunnan, China
| | - Yufeng Yao
- Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming 650118, Yunnan, China
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27
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Panneerselvam A, Kannan A, Mariajoseph-Antony LF, Prahalathan C. PAX proteins and their role in pancreas. Diabetes Res Clin Pract 2019; 155:107792. [PMID: 31325538 DOI: 10.1016/j.diabres.2019.107792] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 06/27/2019] [Accepted: 07/08/2019] [Indexed: 12/15/2022]
Abstract
Gene regulatory factors that govern the expression of heritable information come in an array of flavors, chiefly with transcription factors, the proteins which bind to regions of specific genes and modulate gene transcription, subsequently altering cellular function. PAX transcription factors are sequence-specific DNA-binding proteins exerting its regulatory activity in many tissues. Notably, three members of the PAX family namely PAX2, PAX4 and PAX6 have emerged as crucial players at multiple steps of pancreatic development and differentiation and also play a pivotal role in the regulation of pancreatic islet hormones synthesis and secretion. Providing a comprehensive outline of these transcription factors and their primordial and divergent roles in the pancreas is far-reaching in contemporary diabetes research. Accordingly, this review furnishes an outline of the role of pancreatic specific PAX regulators in the development of the pancreas and its associated disorders.
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Affiliation(s)
- Antojenifer Panneerselvam
- Molecular Endocrinology Laboratory, Department of Biochemistry, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620 024, India
| | - Arun Kannan
- Molecular Endocrinology Laboratory, Department of Biochemistry, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620 024, India
| | - Lezy Flora Mariajoseph-Antony
- Molecular Endocrinology Laboratory, Department of Biochemistry, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620 024, India
| | - Chidambaram Prahalathan
- Molecular Endocrinology Laboratory, Department of Biochemistry, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620 024, India.
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28
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Open Chromatin Profiling in Adipose Tissue Marks Genomic Regions with Functional Roles in Cardiometabolic Traits. G3-GENES GENOMES GENETICS 2019; 9:2521-2533. [PMID: 31186305 PMCID: PMC6686932 DOI: 10.1534/g3.119.400294] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Identifying the regulatory mechanisms of genome-wide association study (GWAS) loci affecting adipose tissue has been restricted due to limited characterization of adipose transcriptional regulatory elements. We profiled chromatin accessibility in three frozen human subcutaneous adipose tissue needle biopsies and preadipocytes and adipocytes from the Simpson Golabi-Behmel Syndrome (SGBS) cell strain using an assay for transposase-accessible chromatin (ATAC-seq). We identified 68,571 representative accessible chromatin regions (peaks) across adipose tissue samples (FDR < 5%). GWAS loci for eight cardiometabolic traits were enriched in these peaks (P < 0.005), with the strongest enrichment for waist-hip ratio. Of 110 recently described cardiometabolic GWAS loci colocalized with adipose tissue eQTLs, 59 loci had one or more variants overlapping an adipose tissue peak. Annotated variants at the SNX10 waist-hip ratio locus and the ATP2A1-SH2B1 body mass index locus showed allelic differences in regulatory assays. These adipose tissue accessible chromatin regions elucidate genetic variants that may alter adipose tissue function to impact cardiometabolic traits.
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Buckle A, Nozawa RS, Kleinjan DA, Gilbert N. Functional characteristics of novel pancreatic Pax6 regulatory elements. Hum Mol Genet 2019; 27:3434-3448. [PMID: 30007277 PMCID: PMC6140780 DOI: 10.1093/hmg/ddy255] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/02/2018] [Indexed: 12/28/2022] Open
Abstract
Complex diseases, such as diabetes, are influenced by comprehensive transcriptional networks. Genome-wide association studies have revealed that variants located in regulatory elements for pancreatic transcription factors are linked to diabetes, including those functionally linked to the paired box transcription factor Pax6. Pax6 deletions in adult mice cause rapid onset of classic diabetes, but the full spectrum of pancreatic Pax6 regulators is unknown. Using a regulatory element discovery approach, we identified two novel Pax6 pancreatic cis-regulatory elements in a poorly characterized regulatory desert. Both new elements, Pax6 pancreas cis-regulatory element 3 (PE3) and PE4, are located 50 and 100 kb upstream and interact with different parts of the Pax6 promoter and nearby non-coding RNAs. They drive expression in the developing pancreas and brain and code for multiple pancreas-related transcription factor-binding sites. PE3 binds CCCTC-binding factor (CTCF) and is marked by stem cell identity markers in embryonic stem cells, whilst a common variant located in the PE4 element affects binding of Pax4, a known pancreatic regulator, altering Pax6 gene expression. To determine the ability of these elements to regulate gene expression, synthetic transcriptional activators and repressors were targeted to PE3 and PE4, modulating Pax6 gene expression, as well as influencing neighbouring genes and long non-coding RNAs, implicating the Pax6 locus in pancreas function and diabetes.
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Affiliation(s)
- Adam Buckle
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Ryu-Suke Nozawa
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Dirk A Kleinjan
- Centre for Mammalian Synthetic Biology, University of Edinburgh, Edinburgh EH9 3FF, UK
| | - Nick Gilbert
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
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Medina-Rivera A, Santiago-Algarra D, Puthier D, Spicuglia S. Widespread Enhancer Activity from Core Promoters. Trends Biochem Sci 2018; 43:452-468. [PMID: 29673772 DOI: 10.1016/j.tibs.2018.03.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/09/2018] [Accepted: 03/12/2018] [Indexed: 01/04/2023]
Abstract
Gene expression in higher eukaryotes is precisely regulated in time and space through the interplay between promoters and gene-distal regulatory regions, known as enhancers. The original definition of enhancers implies the ability to activate gene expression remotely, while promoters entail the capability to locally induce gene expression. Despite the conventional distinction between them, promoters and enhancers share many genomic and epigenomic features. One intriguing finding in the gene regulation field comes from the observation that many core promoter regions display enhancer activity. Recent high-throughput reporter assays along with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-related approaches have indicated that this phenomenon is common and might have a strong impact on our global understanding of genome organisation and gene expression regulation.
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Affiliation(s)
- Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Mexico
| | - David Santiago-Algarra
- Aix-Marseille University, INSERM, TAGC, UMR 1090, Marseille, France; Equipe Labéllisée, Ligue Contre le Cancer, Paris, France
| | - Denis Puthier
- Aix-Marseille University, INSERM, TAGC, UMR 1090, Marseille, France; Equipe Labéllisée, Ligue Contre le Cancer, Paris, France
| | - Salvatore Spicuglia
- Aix-Marseille University, INSERM, TAGC, UMR 1090, Marseille, France; Equipe Labéllisée, Ligue Contre le Cancer, Paris, France.
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31
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Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR, Chu AY, Gan W, Kitajima H, Taliun D, Rayner NW, Guo X, Lu Y, Li M, Jensen RA, Hu Y, Huo S, Lohman KK, Zhang W, Cook JP, Prins BP, Flannick J, Grarup N, Trubetskoy VV, Kravic J, Kim YJ, Rybin DV, Yaghootkar H, Müller-Nurasyid M, Meidtner K, Li-Gao R, Varga TV, Marten J, Li J, Smith AV, An P, Ligthart S, Gustafsson S, Malerba G, Demirkan A, Tajes JF, Steinthorsdottir V, Wuttke M, Lecoeur C, Preuss M, Bielak LF, Graff M, Highland HM, Justice AE, Liu DJ, Marouli E, Peloso GM, Warren HR, Afaq S, Afzal S, Ahlqvist E, Almgren P, Amin N, Bang LB, Bertoni AG, Bombieri C, Bork-Jensen J, Brandslund I, Brody JA, Burtt NP, Canouil M, Chen YDI, Cho YS, Christensen C, Eastwood SV, Eckardt KU, Fischer K, Gambaro G, Giedraitis V, Grove ML, de Haan HG, Hackinger S, Hai Y, Han S, Tybjærg-Hansen A, Hivert MF, Isomaa B, Jäger S, Jørgensen ME, Jørgensen T, Käräjämäki A, Kim BJ, Kim SS, Koistinen HA, Kovacs P, Kriebel J, Kronenberg F, Läll K, Lange LA, Lee JJ, Lehne B, Li H, Lin KH, Linneberg A, Liu CT, Liu J, Loh M, Mägi R, Mamakou V, McKean-Cowdin R, Nadkarni G, Neville M, Nielsen SF, Ntalla I, Peyser PA, Rathmann W, Rice K, Rich SS, Rode L, Rolandsson O, Schönherr S, Selvin E, Small KS, Stančáková A, Surendran P, Taylor KD, Teslovich TM, Thorand B, Thorleifsson G, Tin A, Tönjes A, Varbo A, Witte DR, Wood AR, Yajnik P, Yao J, Yengo L, Young R, Amouyel P, Boeing H, Boerwinkle E, Bottinger EP, Chowdhury R, Collins FS, Dedoussis G, Dehghan A, Deloukas P, Ferrario MM, Ferrières J, Florez JC, Frossard P, Gudnason V, Harris TB, Heckbert SR, Howson JMM, Ingelsson M, Kathiresan S, Kee F, Kuusisto J, Langenberg C, Launer LJ, Lindgren CM, Männistö S, Meitinger T, Melander O, Mohlke KL, Moitry M, Morris AD, Murray AD, de Mutsert R, Orho-Melander M, Owen KR, Perola M, Peters A, Province MA, Rasheed A, Ridker PM, Rivadineira F, Rosendaal FR, Rosengren AH, Salomaa V, Sheu WHH, Sladek R, Smith BH, Strauch K, Uitterlinden AG, Varma R, Willer CJ, Blüher M, Butterworth AS, Chambers JC, Chasman DI, Danesh J, van Duijn C, Dupuis J, Franco OH, Franks PW, Froguel P, Grallert H, Groop L, Han BG, Hansen T, Hattersley AT, Hayward C, Ingelsson E, Kardia SLR, Karpe F, Kooner JS, Köttgen A, Kuulasmaa K, Laakso M, Lin X, Lind L, Liu Y, Loos RJF, Marchini J, Metspalu A, Mook-Kanamori D, Nordestgaard BG, Palmer CNA, Pankow JS, Pedersen O, Psaty BM, Rauramaa R, Sattar N, Schulze MB, Soranzo N, Spector TD, Stefansson K, Stumvoll M, Thorsteinsdottir U, Tuomi T, Tuomilehto J, Wareham NJ, Wilson JG, Zeggini E, Scott RA, Barroso I, Frayling TM, Goodarzi MO, Meigs JB, Boehnke M, Saleheen D, Morris AP, Rotter JI, McCarthy MI. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet 2018; 50:559-571. [PMID: 29632382 PMCID: PMC5898373 DOI: 10.1038/s41588-018-0084-1] [Citation(s) in RCA: 286] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 01/30/2018] [Indexed: 12/22/2022]
Abstract
We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.
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Affiliation(s)
- Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Jennifer Wessel
- Departments of Epidemiology and Medicine, Diabetes Translational Research Center, Indiana University, Indianapolis, IN, USA
| | - Sara M Willems
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Wei Zhao
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Neil R Robertson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Audrey Y Chu
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wei Gan
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Hidetoshi Kitajima
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - N William Rayner
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yingchang Lu
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Man Li
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Richard A Jensen
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Yao Hu
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Shaofeng Huo
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Kurt K Lohman
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UK
| | - James P Cook
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Jason Flannick
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Jasmina Kravic
- Department of Clinical Sciences, Diabetes, and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Young Jin Kim
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Denis V Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Großhadern, Ludwig-Maximilians-Universität, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Karina Meidtner
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tibor V Varga
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
| | - Jonathan Marten
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Jin Li
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Albert Vernon Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Ping An
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Symen Ligthart
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Stefan Gustafsson
- Department of Medical Sciences, Molecular Epidemiology, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Giovanni Malerba
- Section of Biology and Genetics, Department of Neurosciences, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Juan Fernandez Tajes
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Matthias Wuttke
- Institute of Genetic Epidemiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Cécile Lecoeur
- CNRS, UMR 8199, Lille University, Lille Pasteur Institute, Lille, France
| | - Michael Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Marielisa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Heather M Highland
- Human Genetics Center, University of Texas Graduate School of Biomedical Sciences at Houston, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anne E Justice
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Dajiang J Liu
- Department of Public Health Sciences, Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
| | - Eirini Marouli
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Gina Marie Peloso
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Helen R Warren
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research, Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Saima Afaq
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Shoaib Afzal
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emma Ahlqvist
- Department of Clinical Sciences, Diabetes, and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Peter Almgren
- Department of Clinical Sciences, Hypertension, and Cardiovascular Disease, Lund University, Malmö, Sweden
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Lia B Bang
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Alain G Bertoni
- Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Cristina Bombieri
- Section of Biology and Genetics, Department of Neurosciences, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Noël P Burtt
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Mickaël Canouil
- CNRS, UMR 8199, Lille University, Lille Pasteur Institute, Lille, France
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Republic of Korea
| | | | - Sophie V Eastwood
- Institute of Cardiovascular Science, University College London, London, UK
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité, University Medicine Berlin, Berlin, Germany
| | - Krista Fischer
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Giovanni Gambaro
- Institute of Internal and Geriatric Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | - Megan L Grove
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hugoline G de Haan
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Yang Hai
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Sohee Han
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Anne Tybjærg-Hansen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Marie-France Hivert
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Bo Isomaa
- Malmska Municipal Health Care Center and Hospital, Jakobstad, Finland
- Folkhälsan Research Centre, Helsinki, Finland
| | - Susanne Jäger
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Marit E Jørgensen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- National Institute of Public Health, Southern Denmark University, Copenhagen, Denmark
| | - Torben Jørgensen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Annemari Käräjämäki
- Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland
- Diabetes Center, Vaasa Health Care Center, Vaasa, Finland
| | - Bong-Jo Kim
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Sung Soo Kim
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Heikki A Koistinen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
- Endocrinology, Abdominal Center, Helsinki University Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Peter Kovacs
- Integrated Research and Treatment (IFB) Center Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Jennifer Kriebel
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Kristi Läll
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Mathematical Statistics, University of Tartu, Tartu, Estonia
| | - Leslie A Lange
- Department of Medicine, Division of Bioinformatics and Personalized Medicine, University of Colorado Denver, Aurora, CO, USA
| | - Jung-Jin Lee
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Huaixing Li
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Keng-Hung Lin
- Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Allan Linneberg
- Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jun Liu
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Roberta McKean-Cowdin
- Department of Preventive Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
| | - Girish Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matt Neville
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Sune F Nielsen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Line Rode
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Olov Rolandsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Sebastian Schönherr
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kerrin S Small
- Department of Twins Research and Genetic Epidemiology, King's College London, London, UK
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Praveen Surendran
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tanya M Teslovich
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Barbara Thorand
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Adrienne Tin
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Anette Varbo
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Pranav Yajnik
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jie Yao
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Loïc Yengo
- CNRS, UMR 8199, Lille University, Lille Pasteur Institute, Lille, France
| | - Robin Young
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Philippe Amouyel
- Institut Pasteur de Lille, INSERM U1167, Université Lille Nord de France, Lille, France
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Erwin P Bottinger
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rajiv Chowdhury
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Francis S Collins
- Genome Technology Branch, National Human Genome Research Institute, US National Institutes of Health, Bethesda, MD, USA
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Marco M Ferrario
- Research Centre on Epidemiology and Preventive Medicine (EPIMED), Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Jean Ferrières
- INSERM, UMR 1027, Toulouse, France
- Department of Cardiology, Toulouse University School of Medicine, Rangueil Hospital, Toulouse, France
| | - Jose C Florez
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, US National Institutes of Health, Bethesda, MD, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Joanna M M Howson
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | - Sekar Kathiresan
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Frank Kee
- UKCRC Centre of Excellence for Public Health (NI), Queens University of Belfast, Belfast, UK
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, US National Institutes of Health, Bethesda, MD, USA
| | - Cecilia M Lindgren
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre For Health Information and Discovery, University of Oxford, Oxford, UK
| | - Satu Männistö
- National Institute for Health and Welfare, Helsinki, Finland
| | - Thomas Meitinger
- Institute of Human Genetics, Technische Universität München, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Olle Melander
- Department of Clinical Sciences, Hypertension, and Cardiovascular Disease, Lund University, Malmö, Sweden
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Marie Moitry
- Department of Epidemiology and Public Health, University of Strasbourg, Strasbourg, France
- Department of Public Health, University Hospital of Strasbourg, Strasbourg, France
| | - Andrew D Morris
- Clinical Research Centre, Centre for Molecular Medicine, Ninewells Hospital and Medical School, Dundee, UK
- Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marju Orho-Melander
- Department of Clinical Sciences, Diabetes, and Cardiovascular Disease, Genetic Epidemiology, Lund University, Malmö, Sweden
| | - Katharine R Owen
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
| | - Annette Peters
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Michael A Province
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Asif Rasheed
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Paul M Ridker
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Fernando Rivadineira
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Anders H Rosengren
- Department of Clinical Sciences, Diabetes, and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Wayne H-H Sheu
- Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- National Yang-Ming University, School of Medicine, Taipei, Taiwan
- National Defense Medical Center, School of Medicine, Taipei, Taiwan
| | - Rob Sladek
- McGill University and Génome Québec Innovation Centre, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McGill University, Montreal, QC, Canada
| | - Blair H Smith
- Division of Population Health Sciences, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry, and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - André G Uitterlinden
- Department of Medical Sciences, Molecular Epidemiology, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Rohit Varma
- USC Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Matthias Blüher
- Integrated Research and Treatment (IFB) Center Adiposity Diseases, University of Leipzig, Leipzig, Germany
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Adam S Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Campbell Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Daniel I Chasman
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - John Danesh
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
- British Heart Foundation, Cambridge Centre of Excellence, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Josée Dupuis
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Paul W Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Philippe Froguel
- CNRS, UMR 8199, Lille University, Lille Pasteur Institute, Lille, France
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Clinical Cooperation Group Type 2 Diabetes, Helmholtz Zentrum München, Ludwig-Maximillians University Munich, Munich, Germany
- Clinical Cooperation Group Nutrigenomics and Type 2 Diabetes, Helmholtz Zentrum München, Technical University of Munich, Munich, Germany
| | - Leif Groop
- Department of Clinical Sciences, Diabetes, and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
| | - Bok-Ghee Han
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | | | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Erik Ingelsson
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Jaspal Singh Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- National Heart and Lung Institute, Cardiovascular Sciences, Hammersmith Campus, Imperial College London, London, UK
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kari Kuulasmaa
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Xu Lin
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonathan Marchini
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | | | - Dennis Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Børge G Nordestgaard
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Colin N A Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Rainer Rauramaa
- Foundation for Research in Health, Exercise, and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Nicole Soranzo
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Hematology, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Timothy D Spector
- Department of Twins Research and Genetic Epidemiology, King's College London, London, UK
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Michael Stumvoll
- Divisions of Endocrinology and Nephrology, University Hospital Leipzig, Leipzig, Germany
| | - Unnur Thorsteinsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Tiinamaija Tuomi
- Folkhälsan Research Centre, Helsinki, Finland
- Endocrinology, Abdominal Center, Helsinki University Hospital, Helsinki, Finland
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
- Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Jaakko Tuomilehto
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
- Dasman Diabetes Institute, Dasman, Kuwait
- Department of Neuroscience and Preventive Medicine, Danube University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Inês Barroso
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
- Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - James B Meigs
- General Medicine Division, Massachusetts General Hospital and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA.
- Department of Medicine, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA.
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK.
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Abstract
Transcription is regulated by transcription factor (TF) binding at promoters and distal regulatory elements and histone modifications that control the accessibility of these elements. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has become the standard assay for identifying genome-wide protein-DNA interactions in vitro and in vivo. As large-scale ChIP-seq data sets have been collected for different TFs and histone modifications, their potential to predict gene expression can be used to test hypotheses about the mechanisms of gene regulation. In addition, complementary functional genomics assays provide a global view of chromatin accessibility and long-range cis-regulatory interactions that are being combined with TF binding and histone remodeling to study the regulation of gene expression. Thus, ChIP-seq analysis is now widely integrated with other functional genomics assays to better understand gene regulatory mechanisms. In this review, we discuss advances and challenges in integrating ChIP-seq data to identify context-specific chromatin states associated with gene activity. We describe the overall computational design of integrating ChIP-seq data with other functional genomics assays. We also discuss the challenges of extending these methods to low-input ChIP-seq assays and related single-cell assays.
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Affiliation(s)
| | - Ali Mortazavi
- Corresponding author: Ali Mortazavi, Department of Developmental and Cell Biology, 2300 Biological Sciences 3, University of California, Irvine, CA 92697, USA. Tel: (949)824-6762; E-mail:
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Abstract
The 1000 Genomes Project created a valuable, worldwide reference for human genetic variation. Common uses of the 1000 Genomes dataset include genotype imputation supporting Genome-wide Association Studies, mapping expression Quantitative Trait Loci, filtering non-pathogenic variants from exome, whole genome and cancer genome sequencing projects, and genetic analysis of population structure and molecular evolution. In this article, we will highlight some of the multiple ways that the 1000 Genomes data can be and has been utilized for genetic studies.
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Impact of Genetic Variants on the Individual Potential for Body Fat Loss. Nutrients 2018; 10:nu10030266. [PMID: 29495392 PMCID: PMC5872684 DOI: 10.3390/nu10030266] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 02/09/2018] [Accepted: 02/23/2018] [Indexed: 12/21/2022] Open
Abstract
The past decade has witnessed the discovery of obesity-related genetic variants and their functions through genome-wide association studies. Combinations of risk alleles can influence obesity phenotypes with different degrees of effectiveness across various individuals by interacting with environmental factors. We examined the interaction between genetic variation and changes in dietary habits or exercise that influences body fat loss from a large Korean cohort (n = 8840). Out of 673 obesity-related SNPs, a total of 100 SNPs (37 for carbohydrate intake; 19 for fat intake; 44 for total calories intake; 25 for exercise onset) identified to have gene-environment interaction effect in generalized linear model were used to calculate genetic risk scores (GRS). Based on the GRS distribution, we divided the population into four levels, namely, “very insensitive”, “insensitive”, “sensitive”, and “very sensitive” for each of the four categories, “carbohydrate intake”, “fat intake”, “total calories intake”, and “exercise”. Overall, the mean body fat loss became larger when the sensitivity level was increased. In conclusion, genetic variants influence the effectiveness of dietary regimes for body fat loss. Based on our findings, we suggest a platform for personalized body fat management by providing the most suitable and effective nutrition or activity plan specific to an individual.
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Meng F, Yuan G, Zhu X, Zhou Y, Wang D, Guo Y. Functional Variants Identified Efficiently through an Integrated Transcriptome and Epigenome Analysis. Sci Rep 2018; 8:2959. [PMID: 29440655 PMCID: PMC5811556 DOI: 10.1038/s41598-018-21024-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 01/29/2018] [Indexed: 12/25/2022] Open
Abstract
Although genome-wide association studies (GWAS) have identified numerous genetic loci associated with complex diseases, the underlying molecular mechanisms of how these loci contribute to disease pathogenesis remain largely unknown, due to the lack of an efficient strategy to identify these risk variants. Here, we proposed a new strategy termed integrated transcriptome and epigenome analysis (iTEA) to identify functional genetic variants in non-coding elements. We considered type 2 diabetes mellitus as a model and identified a well-known diabetic risk variant rs35767 using iTEA. Furthermore, we discovered a new functional SNP, rs815815, involved in glucose metabolism. Our study provides an approach to directly and quickly identify functional genetic variants in type 2 diabetes mellitus, and this approach can be extended to study other complex diseases.
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Affiliation(s)
- Fanlin Meng
- School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University, Beijing, 100084, China
| | - Guohong Yuan
- Human Genetic Resource Center, National Research Institute for Health and Family Planning, Beijing, 100081, China
| | - Xiurui Zhu
- School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University, Beijing, 100084, China
| | - Yiming Zhou
- National Engineering Research Center for Beijing Biochip Technology, Beijing, 102206, China
| | - Dong Wang
- Department of Basic Medicine, School of Medicine, Tsinghua University, Beijing, 100084, China.
| | - Yong Guo
- School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University, Beijing, 100084, China.
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Wheeler E, Marenne G, Barroso I. Genetic aetiology of glycaemic traits: approaches and insights. Hum Mol Genet 2017; 26:R172-R184. [PMID: 28977447 PMCID: PMC5886471 DOI: 10.1093/hmg/ddx293] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 07/18/2017] [Accepted: 07/21/2017] [Indexed: 12/17/2022] Open
Abstract
Glycaemic traits such as fasting and post-challenge glucose and insulin measures, as well as glycated haemoglobin (HbA1c), are used to diagnose and monitor diabetes. These traits are risk factors for cardiovascular disease even below the diabetic threshold, and their study can additionally yield insights into the pathophysiology of type 2 diabetes. To date, a diverse set of genetic approaches have led to the discovery of over 97 loci influencing glycaemic traits. In this review, we will focus on recent advances in the genetic aetiology of glycaemic traits, and the resulting biological insights. We will provide a brief overview of results ranging from common, to low- and rare-frequency variant-trait association studies, studies leveraging the diversity across populations, and studies harnessing the power of genetic and genomic approaches to gain insights into the biological underpinnings of these traits.
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Affiliation(s)
- Eleanor Wheeler
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Gaëlle Marenne
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Inês Barroso
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge CB10 1SA, UK
- Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
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Trans-ancestry Fine Mapping and Molecular Assays Identify Regulatory Variants at the ANGPTL8 HDL-C GWAS Locus. G3-GENES GENOMES GENETICS 2017; 7:3217-3227. [PMID: 28754724 PMCID: PMC5592946 DOI: 10.1534/g3.117.300088] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Recent genome-wide association studies (GWAS) have identified variants associated with high-density lipoprotein cholesterol (HDL-C) located in or near the ANGPTL8 gene. Given the extensive sharing of GWAS loci across populations, we hypothesized that at least one shared variant at this locus affects HDL-C. The HDL-C–associated variants are coincident with expression quantitative trait loci for ANGPTL8 and DOCK6 in subcutaneous adipose tissue; however, only ANGPTL8 expression levels are associated with HDL-C levels. We identified a 400-bp promoter region of ANGPTL8 and enhancer regions within 5 kb that contribute to regulating expression in liver and adipose. To identify variants functionally responsible for the HDL-C association, we performed fine-mapping analyses and selected 13 candidate variants that overlap putative regulatory regions to test for allelic differences in regulatory function. Of these variants, rs12463177-G increased transcriptional activity (1.5-fold, P = 0.004) and showed differential protein binding. Six additional variants (rs17699089, rs200788077, rs56322906, rs3760782, rs737337, and rs3745683) showed evidence of allelic differences in transcriptional activity and/or protein binding. Taken together, these data suggest a regulatory mechanism at the ANGPTL8 HDL-C GWAS locus involving tissue-selective expression and at least one functional variant.
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Roman TS, Cannon ME, Vadlamudi S, Buchkovich ML, Wolford BN, Welch RP, Morken MA, Kwon GJ, Varshney A, Kursawe R, Wu Y, Jackson AU, Erdos MR, Kuusisto J, Laakso M, Scott LJ, Boehnke M, Collins FS, Parker SCJ, Stitzel ML, Mohlke KL. A Type 2 Diabetes-Associated Functional Regulatory Variant in a Pancreatic Islet Enhancer at the ADCY5 Locus. Diabetes 2017; 66:2521-2530. [PMID: 28684635 PMCID: PMC5860374 DOI: 10.2337/db17-0464] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 06/22/2017] [Indexed: 12/13/2022]
Abstract
Molecular mechanisms remain unknown for most type 2 diabetes genome-wide association study identified loci. Variants associated with type 2 diabetes and fasting glucose levels reside in introns of ADCY5, a gene that encodes adenylate cyclase 5. Adenylate cyclase 5 catalyzes the production of cyclic AMP, which is a second messenger molecule involved in cell signaling and pancreatic β-cell insulin secretion. We demonstrated that type 2 diabetes risk alleles are associated with decreased ADCY5 expression in human islets and examined candidate variants for regulatory function. rs11708067 overlaps a predicted enhancer region in pancreatic islets. The type 2 diabetes risk rs11708067-A allele showed fewer H3K27ac ChIP-seq reads in human islets, lower transcriptional activity in reporter assays in rodent β-cells (rat 832/13 and mouse MIN6), and increased nuclear protein binding compared with the rs11708067-G allele. Homozygous deletion of the orthologous enhancer region in 832/13 cells resulted in a 64% reduction in expression level of Adcy5, but not adjacent gene Sec22a, and a 39% reduction in insulin secretion. Together, these data suggest that rs11708067-A risk allele contributes to type 2 diabetes by disrupting an islet enhancer, which results in reduced ADCY5 expression and impaired insulin secretion.
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Affiliation(s)
- Tamara S Roman
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Maren E Cannon
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Martin L Buchkovich
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Brooke N Wolford
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Ryan P Welch
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Mario A Morken
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Grace J Kwon
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
- Department of Genetics and Genome Sciences, University of Connecticut Health, Farmington, CT
| | - Arushi Varshney
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
| | - Romy Kursawe
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
| | - Ying Wu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | | | - Michael R Erdos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Johanna Kuusisto
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Stephen C J Parker
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Michael L Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
- Institute for Systems Genomics, University of Connecticut, Farmington, CT
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
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Abstract
PURPOSE OF REVIEW Genome-wide association studies (GWAS) for type 2 diabetes (T2D) risk have identified a large number of genetic loci associated with disease susceptibility. However, progress moving from association signals through causal genes to functional understanding has so far been slow, hindering clinical translation. This review discusses the benefits and limitations of emerging, unbiased approaches for prioritising causal genes at T2D risk loci. RECENT FINDINGS Candidate causal genes can be identified by a number of different strategies that rely on genetic data, genomic annotations, and functional screening of selected genes. To overcome the limitations of each particular method, integration of multiple data sets is proving essential for establishing confidence in the prioritised genes. Previous studies have also highlighted the need to support these efforts through identification of causal variants and disease-relevant tissues. Prioritisation of causal genes at T2D risk loci by integrating complementary lines of evidence promises to accelerate our understanding of disease pathology and promote translation into new therapeutics.
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Affiliation(s)
- Antje K Grotz
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, UK
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- National Institute of Health Research Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Soren K Thomsen
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, UK.
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40
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Abstract
PURPOSE OF REVIEW Deciphering the mechanisms of type 2 diabetes (T2DM) risk loci can greatly inform on disease pathology. This review discusses current knowledge of mechanisms through which genetic variants influence T2DM risk and considerations for future studies. RECENT FINDINGS Over 100 T2DM risk loci to date have been identified. Candidate causal variants at risk loci map predominantly to non-coding sequence. Physiological, epigenomic and gene expression data suggest that variants at many known T2DM risk loci affect pancreatic islet regulation, although variants at other loci also affect protein function and regulatory processes in adipose, pre-adipose, liver, skeletal muscle and brain. The effects of T2DM variants on regulatory activity in these tissues appear largely, but not exclusively, due to altered transcription factor binding. Putative target genes of T2DM variants have been defined at an increasing number of loci and some, such as FTO, may entail several genes and multiple tissues. Gene networks in islets and adipocytes have been implicated in T2DM risk, although the molecular pathways of risk genes remain largely undefined. Efforts to fully define the mechanisms of T2DM risk loci are just beginning. Continued identification of risk mechanisms will benefit from combining genetic fine-mapping with detailed phenotypic association data, high-throughput epigenomics data from diabetes-relevant tissue, functional screening of candidate genes and genome editing of cellular and animal models.
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Affiliation(s)
- Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, CA, 92093, USA.
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41
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Lawlor N, Khetan S, Ucar D, Stitzel ML. Genomics of Islet (Dys)function and Type 2 Diabetes. Trends Genet 2017; 33:244-255. [PMID: 28245910 DOI: 10.1016/j.tig.2017.01.010] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 01/30/2017] [Indexed: 12/28/2022]
Abstract
Pancreatic islet dysfunction and beta cell failure are hallmarks of type 2 diabetes mellitus (T2DM) pathogenesis. In this review, we discuss how genome-wide association studies (GWASs) and recent developments in islet (epi)genome and transcriptome profiling (particularly single cell analyses) are providing novel insights into the genetic, environmental, and cellular contributions to islet (dys)function and T2DM pathogenesis. Moving forward, study designs that interrogate and model genetic variation [e.g., allelic profiling and (epi)genome editing] will be critical to dissect the molecular genetics of T2DM pathogenesis, to build next-generation cellular and animal models, and to develop precision medicine approaches to detect, treat, and prevent islet (dys)function and T2DM.
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Affiliation(s)
- Nathan Lawlor
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Shubham Khetan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Genetics & Genome Sciences, University of Connecticut, Farmington, CT 06032, USA
| | - Duygu Ucar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | - Michael L Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Genetics & Genome Sciences, University of Connecticut, Farmington, CT 06032, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA.
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42
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Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 2017; 114:2301-2306. [PMID: 28193859 DOI: 10.1073/pnas.1621192114] [Citation(s) in RCA: 126] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits. However, the pathogenic mechanisms of most of these SNPs remain elusive. Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D. We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps. Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers. High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATAC-seq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.
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43
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Decreased STARD10 Expression Is Associated with Defective Insulin Secretion in Humans and Mice. Am J Hum Genet 2017; 100:238-256. [PMID: 28132686 PMCID: PMC5294761 DOI: 10.1016/j.ajhg.2017.01.011] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 12/20/2016] [Indexed: 12/30/2022] Open
Abstract
Genetic variants near ARAP1 (CENTD2) and STARD10 influence type 2 diabetes (T2D) risk. The risk alleles impair glucose-induced insulin secretion and, paradoxically but characteristically, are associated with decreased proinsulin:insulin ratios, indicating improved proinsulin conversion. Neither the identity of the causal variants nor the gene(s) through which risk is conferred have been firmly established. Whereas ARAP1 encodes a GTPase activating protein, STARD10 is a member of the steroidogenic acute regulatory protein (StAR)-related lipid transfer protein family. By integrating genetic fine-mapping and epigenomic annotation data and performing promoter-reporter and chromatin conformational capture (3C) studies in β cell lines, we localize the causal variant(s) at this locus to a 5 kb region that overlaps a stretch-enhancer active in islets. This region contains several highly correlated T2D-risk variants, including the rs140130268 indel. Expression QTL analysis of islet transcriptomes from three independent subject groups demonstrated that T2D-risk allele carriers displayed reduced levels of STARD10 mRNA, with no concomitant change in ARAP1 mRNA levels. Correspondingly, β-cell-selective deletion of StarD10 in mice led to impaired glucose-stimulated Ca2+ dynamics and insulin secretion and recapitulated the pattern of improved proinsulin processing observed at the human GWAS signal. Conversely, overexpression of StarD10 in the adult β cell improved glucose tolerance in high fat-fed animals. In contrast, manipulation of Arap1 in β cells had no impact on insulin secretion or proinsulin conversion in mice. This convergence of human and murine data provides compelling evidence that the T2D risk associated with variation at this locus is mediated through reduction in STARD10 expression in the β cell.
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44
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Laakso M, Kuusisto J, Stančáková A, Kuulasmaa T, Pajukanta P, Lusis AJ, Collins FS, Mohlke KL, Boehnke M. The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases. J Lipid Res 2017; 58:481-493. [PMID: 28119442 DOI: 10.1194/jlr.o072629] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 01/15/2017] [Indexed: 12/30/2022] Open
Abstract
The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005-2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a "systems genetics" approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits.
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Affiliation(s)
- Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland .,Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland.,Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Teemu Kuulasmaa
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Päivi Pajukanta
- Departments of Human Genetics David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA.,Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA
| | - Aldons J Lusis
- Departments of Human Genetics David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA.,Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI
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45
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Deplancke B, Alpern D, Gardeux V. The Genetics of Transcription Factor DNA Binding Variation. Cell 2016; 166:538-554. [PMID: 27471964 DOI: 10.1016/j.cell.2016.07.012] [Citation(s) in RCA: 244] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 12/23/2022]
Abstract
Most complex trait-associated variants are located in non-coding regulatory regions of the genome, where they have been shown to disrupt transcription factor (TF)-DNA binding motifs. Variable TF-DNA interactions are therefore increasingly considered as key drivers of phenotypic variation. However, recent genome-wide studies revealed that the majority of variable TF-DNA binding events are not driven by sequence alterations in the motif of the studied TF. This observation implies that the molecular mechanisms underlying TF-DNA binding variation and, by extrapolation, inter-individual phenotypic variation are more complex than originally anticipated. Here, we summarize the findings that led to this important paradigm shift and review proposed mechanisms for local, proximal, or distal genetic variation-driven variable TF-DNA binding. In addition, we discuss the biomedical implications of these findings for our ability to dissect the molecular role(s) of non-coding genetic variants in complex traits, including disease susceptibility.
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Affiliation(s)
- Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Daniel Alpern
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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46
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Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res 2016; 27:208-222. [PMID: 27864352 PMCID: PMC5287227 DOI: 10.1101/gr.212720.116] [Citation(s) in RCA: 333] [Impact Index Per Article: 41.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 11/16/2016] [Indexed: 01/09/2023]
Abstract
Blood glucose levels are tightly controlled by the coordinated action of at least four cell types constituting pancreatic islets. Changes in the proportion and/or function of these cells are associated with genetic and molecular pathophysiology of monogenic, type 1, and type 2 (T2D) diabetes. Cellular heterogeneity impedes precise understanding of the molecular components of each islet cell type that govern islet (dys)function, particularly the less abundant delta and gamma/pancreatic polypeptide (PP) cells. Here, we report single-cell transcriptomes for 638 cells from nondiabetic (ND) and T2D human islet samples. Analyses of ND single-cell transcriptomes identified distinct alpha, beta, delta, and PP/gamma cell-type signatures. Genes linked to rare and common forms of islet dysfunction and diabetes were expressed in the delta and PP/gamma cell types. Moreover, this study revealed that delta cells specifically express receptors that receive and coordinate systemic cues from the leptin, ghrelin, and dopamine signaling pathways implicating them as integrators of central and peripheral metabolic signals into the pancreatic islet. Finally, single-cell transcriptome profiling revealed genes differentially regulated between T2D and ND alpha, beta, and delta cells that were undetectable in paired whole islet analyses. This study thus identifies fundamental cell-type–specific features of pancreatic islet (dys)function and provides a critical resource for comprehensive understanding of islet biology and diabetes pathogenesis.
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47
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Martin-Montalvo A, Lorenzo PI, López-Noriega L, Gauthier BR. Targeting pancreatic expressed PAX genes for the treatment of diabetes mellitus and pancreatic neuroendocrine tumors. Expert Opin Ther Targets 2016; 21:77-89. [PMID: 27841034 DOI: 10.1080/14728222.2017.1257000] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Four members of the PAX family, PAX2, PAX4, PAX6 and PAX8 are known to be expressed in the pancreas. Accumulated evidences indicate that several pancreatic expressed PAX genes play a significant role in pancreatic development/functionality and alterations in these genes are involved in the pathogenesis of pancreatic diseases. Areas covered: In this review, we summarize the ongoing research related to pancreatic PAX genes in diabetes mellitus and pancreatic neuroendocrine tumors. We dissect the current knowledge at different levels; from mechanistic studies in cell lines performed to understand the molecular processes controlled by pancreatic PAX genes, to in vivo studies using rodent models that over-express or lack specific PAX genes. Finally, we describe human studies associating variants on pancreatic-expressed PAX genes with pancreatic diseases. Expert opinion: Based on the current literature, we propose that future interventions to treat pancreatic neuroendocrine tumors and diabetes mellitus could be developed via the modulation of PAX4 and/or PAX6 regulated pathways.
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Affiliation(s)
- Alejandro Martin-Montalvo
- a Department of Stem Cells, CABIMER-Andalusian Center for Molecular Biology and Regenerative Medicine, Avenida Américo Vespucio , Pancreatic Islet Development and Regeneration Unit/Laboratory of Aging Biology (PIDRU LAB) , Sevilla , Spain
| | - Petra I Lorenzo
- a Department of Stem Cells, CABIMER-Andalusian Center for Molecular Biology and Regenerative Medicine, Avenida Américo Vespucio , Pancreatic Islet Development and Regeneration Unit/Laboratory of Aging Biology (PIDRU LAB) , Sevilla , Spain
| | - Livia López-Noriega
- a Department of Stem Cells, CABIMER-Andalusian Center for Molecular Biology and Regenerative Medicine, Avenida Américo Vespucio , Pancreatic Islet Development and Regeneration Unit/Laboratory of Aging Biology (PIDRU LAB) , Sevilla , Spain
| | - Benoit R Gauthier
- a Department of Stem Cells, CABIMER-Andalusian Center for Molecular Biology and Regenerative Medicine, Avenida Américo Vespucio , Pancreatic Islet Development and Regeneration Unit/Laboratory of Aging Biology (PIDRU LAB) , Sevilla , Spain
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48
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Marinelli M, Pappa I, Bustamante M, Bonilla C, Suarez A, Tiesler CM, Vilor-Tejedor N, Zafarmand MH, Alvarez-Pedrerol M, Andersson S, Bakermans-Kranenburg MJ, Estivill X, Evans DM, Flexeder C, Forns J, Gonzalez JR, Guxens M, Huss A, van IJzendoorn MH, Jaddoe VW, Julvez J, Lahti J, López-Vicente M, Lopez-Espinosa MJ, Manz J, Mileva-Seitz VR, Perola M, Pesonen AK, Rivadeneira F, Salo PP, Shahand S, Schulz H, Standl M, Thiering E, Timpson NJ, Torrent M, Uitterlinden AG, Smith GD, Estarlich M, Heinrich J, Räikkönen K, Vrijkotte TG, Tiemeier H, Sunyer J. Heritability and Genome-Wide Association Analyses of Sleep Duration in Children: The EAGLE Consortium. Sleep 2016; 39:1859-1869. [PMID: 27568811 PMCID: PMC5020368 DOI: 10.5665/sleep.6170] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 06/09/2016] [Indexed: 01/20/2023] Open
Abstract
STUDY OBJECTIVES Low or excessive sleep duration has been associated with multiple outcomes, but the biology behind these associations remains elusive. Specifically, genetic studies in children are scarce. In this study, we aimed to: (1) estimate the proportion of genetic variance of sleep duration in children attributed to common single nucleotide polymorphisms (SNPs), (2) identify novel SNPs associated with sleep duration in children, and (3) investigate the genetic overlap of sleep duration in children and related metabolic and psychiatric traits. METHODS We performed a population-based molecular genetic study, using data form the EArly Genetics and Life course Epidemiology (EAGLE) Consortium. 10,554 children of European ancestry were included in the discovery, and 1,250 children in the replication phase. RESULTS We found evidence of significant but modest SNP heritability of sleep duration in children (SNP h2 0.14, 95% CI [0.05, 0.23]) using the LD score regression method. A novel region at chromosome 11q13.4 (top SNP: rs74506765, P = 2.27e-08) was associated with sleep duration in children, but this was not replicated in independent studies. Nominally significant genetic overlap was only found (rG = 0.23, P = 0.05) between sleep duration in children and type 2 diabetes in adults, supporting the hypothesis of a common pathogenic mechanism. CONCLUSIONS The significant SNP heritability of sleep duration in children and the suggestive genetic overlap with type 2 diabetes support the search for genetic mechanisms linking sleep duration in children to multiple outcomes in health and disease.
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Affiliation(s)
- Marcella Marinelli
- Agency for Healthcare Quality and Evaluation of Catalonia (AQuAS), Roc Boronat, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Irene Pappa
- School of Pedagogical and Educational Sciences, Erasmus University Rotterdam, The Netherlands
- Generation R Study Group, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Mariona Bustamante
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Carolina Bonilla
- School of Social and Community Medicine, University of Bristol, Bristol, UK
- MRC/University of Bristol Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Anna Suarez
- Institute of behavioural sciences, University of Helsinki, Helsinki, Finland
| | - Carla M. Tiesler
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Ludwig-Maximilians-University of Munich, Dr. von Hauner Children's Hospital, Division of Metabolic Diseases and Nutritional Medicine, Munich, Germany
| | - Natalia Vilor-Tejedor
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Mohammad Hadi Zafarmand
- Department of Public Health, Academic Medical Center (AMC), University of Amsterdam, The Netherlands
- Department of Obstetrics and Gynaecology, Academic Medical Centre, University of Amsterdam, The Netherlands
| | - Mar Alvarez-Pedrerol
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Sture Andersson
- Children's Hospital, University of Helsinki, and Helsinki University Hospital, Helsinki, Finland
| | | | - Xavier Estivill
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institut Hospital del Mar d'Investigacions Mediques (IMIM), 08003 Barcelona, Spain
| | - David M. Evans
- School of Social and Community Medicine, University of Bristol, Bristol, UK
- MRC/University of Bristol Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
| | - Claudia Flexeder
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Joan Forns
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Juan R. Gonzalez
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Monica Guxens
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Anke Huss
- Institute for Risk Assessment Sciences, Utrecht University, the Netherlands
| | - Marinus H. van IJzendoorn
- School of Pedagogical and Educational Sciences, Erasmus University Rotterdam, The Netherlands
- Generation R Study Group, Erasmus Medical Center, Rotterdam, The Netherlands
- Centre for Child and Family Studies, Leiden University, Leiden, The Netherlands
| | - Vincent W.V. Jaddoe
- Generation R Study Group, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus Medical Center- Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Jordi Julvez
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Jari Lahti
- Institute of behavioural sciences, University of Helsinki, Helsinki, Finland
- Helsinki Collegium for Advanced Studies, Helsinki, Finland
- Folkhälsan Research Centre, Finland
| | - Mónica López-Vicente
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Maria-Jose Lopez-Espinosa
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO, Universitat Jaume I, Universitat de València, Spain
| | - Judith Manz
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Markus Perola
- Public Health Genomics Unit and Institute for Molecular Medicine FIMM, University of Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | | | - Fernando Rivadeneira
- Generation R Study Group, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Perttu P. Salo
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Genomics and Biomarkers Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Shayan Shahand
- Department of Clinical Epidemiology Biostatistics and Bioinformatics, Academic Medical Center (AMC), University of Amsterdam, The Netherlands
| | - Holger Schulz
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Marie Standl
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Elisabeth Thiering
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Ludwig-Maximilians-University of Munich, Dr. von Hauner Children's Hospital, Division of Metabolic Diseases and Nutritional Medicine, Munich, Germany
| | - Nicholas J. Timpson
- School of Social and Community Medicine, University of Bristol, Bristol, UK
- MRC/University of Bristol Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | - André G. Uitterlinden
- Department of Epidemiology, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | - George Davey Smith
- School of Social and Community Medicine, University of Bristol, Bristol, UK
- MRC/University of Bristol Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Marisa Estarlich
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO, Universitat Jaume I, Universitat de València, Spain
| | - Joachim Heinrich
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Katri Räikkönen
- Institute of behavioural sciences, University of Helsinki, Helsinki, Finland
| | - Tanja G.M. Vrijkotte
- Department of Public Health, Academic Medical Center (AMC), University of Amsterdam, The Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
- Department of Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jordi Sunyer
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institut Hospital del Mar d'Investigacions Mediques (IMIM), 08003 Barcelona, Spain
- Address correspondence to: Jordi Sunyer, PhD,
ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Doctor Aiguader, 88, E-08003 Barcelona, Spain+34 93 214 73 00+ 34 93 214 73 02
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49
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Abstract
As with other complex diseases, unbiased association studies followed by physiological and experimental characterization have for years formed a paradigm for identifying genes or processes of relevance to type 2 diabetes mellitus (T2D). Recent large-scale common and rare variant genome-wide association studies (GWAS) suggest that substantially larger association studies are needed to identify most T2D loci in the population. To hasten clinical translation of genetic discoveries, new paradigms are also required to aid specialized investigation of nascent hypotheses. We argue for an integrated T2D knowledgebase, designed for a worldwide community to access aggregated large-scale genetic data sets, as one paradigm to catalyse convergence of these efforts.
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50
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Roman TS, Marvelle AF, Fogarty MP, Vadlamudi S, Gonzalez AJ, Buchkovich ML, Huyghe JR, Fuchsberger C, Jackson AU, Wu Y, Civelek M, Lusis AJ, Gaulton KJ, Sethupathy P, Kangas AJ, Soininen P, Ala-Korpela M, Kuusisto J, Collins FS, Laakso M, Boehnke M, Mohlke KL. Multiple Hepatic Regulatory Variants at the GALNT2 GWAS Locus Associated with High-Density Lipoprotein Cholesterol. Am J Hum Genet 2015; 97:801-15. [PMID: 26637976 PMCID: PMC4678431 DOI: 10.1016/j.ajhg.2015.10.016] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 10/28/2015] [Indexed: 11/24/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified more than 150 loci associated with blood lipid and cholesterol levels; however, the functional and molecular mechanisms for many associations are unknown. We examined the functional regulatory effects of candidate variants at the GALNT2 locus associated with high-density lipoprotein cholesterol (HDL-C). Fine-mapping and conditional analyses in the METSIM study identified a single locus harboring 25 noncoding variants (r(2) > 0.7 with the lead GWAS variants) strongly associated with total cholesterol in medium-sized HDL (e.g., rs17315646, p = 3.5 × 10(-12)). We used luciferase reporter assays in HepG2 cells to test all 25 variants for allelic differences in regulatory enhancer activity. rs2281721 showed allelic differences in transcriptional activity (75-fold [T] versus 27-fold [C] more than the empty-vector control), as did a separate 780-bp segment containing rs4846913, rs2144300, and rs6143660 (49-fold [AT(-) haplotype] versus 16-fold [CC(+) haplotype] more). Using electrophoretic mobility shift assays, we observed differential CEBPB binding to rs4846913, and we confirmed this binding in a native chromatin context by performing chromatin-immunoprecipitation (ChIP) assays in HepG2 and Huh-7 cell lines of differing genotypes. Additionally, sequence reads in HepG2 DNase-I-hypersensitivity and CEBPB ChIP-seq signals spanning rs4846913 showed significant allelic imbalance. Allelic-expression-imbalance assays performed with RNA from primary human hepatocyte samples and expression-quantitative-trait-locus (eQTL) data in human subcutaneous adipose tissue samples confirmed that alleles associated with increased HDL-C are associated with a modest increase in GALNT2 expression. Together, these data suggest that at least rs4846913 and rs2281721 play key roles in influencing GALNT2 expression at this HDL-C locus.
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Affiliation(s)
- Tamara S Roman
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Amanda F Marvelle
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Marie P Fogarty
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Swarooparani Vadlamudi
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Arlene J Gonzalez
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Martin L Buchkovich
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jeroen R Huyghe
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ying Wu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mete Civelek
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biomedical Engineering, Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Aldons J Lusis
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Deparment of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kyle J Gaulton
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Praveen Sethupathy
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Antti J Kangas
- Computational Medicine, Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland
| | - Pasi Soininen
- Computational Medicine, Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland; Nuclear Magnetic Resonance Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland
| | - Mika Ala-Korpela
- Computational Medicine, Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland; Nuclear Magnetic Resonance Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland; Oulu University Hospital, 90220 Oulu, Finland; Computational Medicine, School of Social and Community Medicine and Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland
| | - Francis S Collins
- National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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