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Norris AC, Mansueto AJ, Jimenez M, Yazlovitskaya EM, Jain BK, Graham TR. Flipping the script: Advances in understanding how and why P4-ATPases flip lipid across membranes. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2024; 1871:119700. [PMID: 38382846 DOI: 10.1016/j.bbamcr.2024.119700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 11/15/2023] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
Type IV P-type ATPases (P4-ATPases) are a family of transmembrane enzymes that translocate lipid substrates from the outer to the inner leaflet of biological membranes and thus create an asymmetrical distribution of lipids within membranes. On the cellular level, this asymmetry is essential for maintaining the integrity and functionality of biological membranes, creating platforms for signaling events and facilitating vesicular trafficking. On the organismal level, this asymmetry has been shown to be important in maintaining blood homeostasis, liver metabolism, neural development, and the immune response. Indeed, dysregulation of P4-ATPases has been linked to several diseases; including anemia, cholestasis, neurological disease, and several cancers. This review will discuss the evolutionary transition of P4-ATPases from cation pumps to lipid flippases, the new lipid substrates that have been discovered, the significant advances that have been achieved in recent years regarding the structural mechanisms underlying the recognition and flipping of specific lipids across biological membranes, and the consequences of P4-ATPase dysfunction on cellular and physiological functions. Additionally, we emphasize the requirement for additional research to comprehensively understand the involvement of flippases in cellular physiology and disease and to explore their potential as targets for therapeutics in treating a variety of illnesses. The discussion in this review will primarily focus on the budding yeast, C. elegans, and mammalian P4-ATPases.
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Affiliation(s)
- Adriana C Norris
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | | | - Mariana Jimenez
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | | | - Bhawik K Jain
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Todd R Graham
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA.
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Norris AC, Yazlovitskaya EM, Yang TS, Mansueto A, Stafford JM, Graham TR. ATP10A deficiency results in male-specific infertility in mice. Front Cell Dev Biol 2024; 12:1310593. [PMID: 38415274 PMCID: PMC10896839 DOI: 10.3389/fcell.2024.1310593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/29/2024] [Indexed: 02/29/2024] Open
Abstract
Over 8% of couples worldwide are affected by infertility and nearly half of these cases are due to male-specific issues where the underlying cause is often unknown. Therefore, discovery of new genetic factors contributing to male-specific infertility in model organisms can enhance our understanding of the etiology of this disorder. Here we show that murine ATP10A, a phospholipid flippase, is highly expressed in male reproductive organs, specifically the testes and vas deferens. Therefore, we tested the influence of ATP10A on reproduction by examining fertility of Atp10A knockout mice. Our findings reveal that Atp10A deficiency leads to male-specific infertility, but does not perturb fertility in the females. The Atp10A deficient male mice exhibit smaller testes, reduced sperm count (oligozoospermia) and lower sperm motility (asthenozoospermia). Additionally, Atp10A deficient mice display testes and vas deferens histopathological abnormalities, as well as altered total and relative amounts of hormones associated with the hypothalamic-pituitary-gonadal axis. Surprisingly, circulating testosterone is elevated 2-fold in the Atp10A knockout mice while luteinizing hormone, follicle stimulating hormone, and inhibin B levels were not significantly different from WT littermates. The knockout mice also exhibit elevated levels of gonadotropin receptors and alterations to ERK, p38 MAPK, Akt, and cPLA2-dependent signaling in the testes. Atp10A was knocked out in the C57BL/6J background, which also carries an inactivating nonsense mutation in the closely related lipid flippase, Atp10D. We have corrected the Atp10D nonsense mutation using CRISPR/Cas9 and determined that loss of Atp10A alone is sufficient to cause infertility in male mice. Collectively, these findings highlight the critical role of ATP10A in male fertility in mice and provide valuable insights into the underlying molecular mechanisms.
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Affiliation(s)
- Adriana C Norris
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, United States
| | | | - Tzushan Sharon Yang
- Division of Comparative Medicine, Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Alex Mansueto
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, United States
| | - John M Stafford
- Tennessee Valley Healthcare System, Nashville, TN, United States
- Division of Endocrinology, Diabetes and Metabolism, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, United States
| | - Todd R Graham
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, United States
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Norris AC, Yazlovitskaya EM, Zhu L, Rose BS, May JC, Gibson-Corley KN, McLean JA, Stafford JM, Graham TR. Deficiency of the lipid flippase ATP10A causes diet-induced dyslipidemia in female mice. Sci Rep 2024; 14:343. [PMID: 38172157 PMCID: PMC10764864 DOI: 10.1038/s41598-023-50360-5] [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: 08/11/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
Genetic association studies have linked ATP10A and closely related type IV P-type ATPases (P4-ATPases) to insulin resistance and vascular complications, such as atherosclerosis. ATP10A translocates phosphatidylcholine and glucosylceramide across cell membranes, and these lipids or their metabolites play important roles in signal transduction pathways regulating metabolism. However, the influence of ATP10A on lipid metabolism in mice has not been explored. Here, we generated gene-specific Atp10A knockout mice and show that Atp10A-/- mice fed a high-fat diet did not gain excess weight relative to wild-type littermates. However, Atp10A-/- mice displayed female-specific dyslipidemia characterized by elevated plasma triglycerides, free fatty acids and cholesterol, as well as altered VLDL and HDL properties. We also observed increased circulating levels of several sphingolipid species along with reduced levels of eicosanoids and bile acids. The Atp10A-/- mice also displayed hepatic insulin resistance without perturbations to whole-body glucose homeostasis. Thus, ATP10A has a sex-specific role in regulating plasma lipid composition and maintaining hepatic liver insulin sensitivity in mice.
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Affiliation(s)
- Adriana C Norris
- Department of Biological Sciences, Vanderbilt University, 465 21St Ave S, Nashville, TN, 37212, USA
| | - Eugenia M Yazlovitskaya
- Department of Biological Sciences, Vanderbilt University, 465 21St Ave S, Nashville, TN, 37212, USA
| | - Lin Zhu
- Division of Endocrinology, Diabetes and Metabolism, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bailey S Rose
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Jody C May
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Katherine N Gibson-Corley
- Division of Comparative Medicine, Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John A McLean
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - John M Stafford
- Division of Endocrinology, Diabetes and Metabolism, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Todd R Graham
- Department of Biological Sciences, Vanderbilt University, 465 21St Ave S, Nashville, TN, 37212, USA.
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Norris AC, Yazlovitskaya EM, Zhu L, Rose BS, May JC, Gibson-Corley KN, McLean JA, Stafford JM, Graham TR. Deficiency of the lipid flippase ATP10A causes diet-induced dyslipidemia in female mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.16.545392. [PMID: 37398141 PMCID: PMC10312798 DOI: 10.1101/2023.06.16.545392] [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
Genetic association studies have linked ATP10A and closely related type IV P-type ATPases (P4-ATPases) to insulin resistance and vascular complications, such as atherosclerosis. ATP10A translocates phosphatidylcholine and glucosylceramide across cell membranes, and these lipids or their metabolites play important roles in signal transduction pathways regulating metabolism. However, the influence of ATP10A on lipid metabolism in mice has not been explored. Here, we generated gene-specific Atp10A knockout mice and show that Atp10A-/- mice fed a high-fat diet did not gain excess weight relative to wild-type littermates. However, Atp10A-/- mice displayed female-specific dyslipidemia characterized by elevated plasma triglycerides, free fatty acids and cholesterol, as well as altered VLDL and HDL properties. We also observed increased circulating levels of several sphingolipid species along with reduced levels of eicosanoids and bile acids. The Atp10A-/- mice also displayed hepatic insulin resistance without perturbations to whole-body glucose homeostasis. Thus, ATP10A has a sex-specific role in regulating plasma lipid composition and maintaining hepatic liver insulin sensitivity in mice.
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Affiliation(s)
- Adriana C. Norris
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Lin Zhu
- Division of Endocrinology, Diabetes and Metabolism, Vanderbilt University Medical Center, USA
| | - Bailey S. Rose
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee, USA
| | - Jody C. May
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee, USA
| | - Katherine N. Gibson-Corley
- Division of Comparative Medicine, Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John A. McLean
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee, USA
| | - John M. Stafford
- Division of Endocrinology, Diabetes and Metabolism, Vanderbilt University Medical Center, USA
- Tennessee Valley Healthcare System, Veterans Affairs, Nashville, Tennessee, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Tennessee, USA
| | - Todd R. Graham
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
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Jain BK, Wagner AS, Reynolds TB, Graham TR. Lipid Transport by Candida albicans Dnf2 Is Required for Hyphal Growth and Virulence. Infect Immun 2022; 90:e0041622. [PMID: 36214556 PMCID: PMC9670988 DOI: 10.1128/iai.00416-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/20/2022] Open
Abstract
Candida albicans is a common cause of human mucosal yeast infections, and invasive candidiasis can be fatal. Antifungal medications are limited, but those targeting the pathogen cell wall or plasma membrane have been effective. Therefore, virulence factors controlling membrane biogenesis are potential targets for drug development. P4-ATPases contribute to membrane biogenesis by selecting and transporting specific lipids from the extracellular leaflet to the cytoplasmic leaflet of the bilayer to generate lipid asymmetry. A subset of heterodimeric P4-ATPases, including Dnf1-Lem3 and Dnf2-Lem3 from Saccharomyces cerevisiae, transport phosphatidylcholine (PC), phosphatidylethanolamine (PE), and the sphingolipid glucosylceramide (GlcCer). GlcCer is a critical lipid for Candida albicans polarized growth and virulence, but the role of GlcCer transporters in virulence has not been explored. Here, we show that the Candida albicans Dnf2 (CaDnf2) requires association with CaLem3 to form a functional transporter and flip fluorescent derivatives of GlcCer, PC, and PE across the plasma membrane. Mutation of conserved substrate-selective residues in the membrane domain strongly abrogates GlcCer transport and partially disrupts PC transport by CaDnf2. Candida strains harboring dnf2-null alleles (dnf2ΔΔ) or point mutations that disrupt substrate recognition exhibit defects in yeast-to-hypha growth transition, filamentous growth, and virulence in systemically infected mice. The influence of CaDNF1 deletion on the morphological phenotypes is negligible, although the dnf1ΔΔ dnf2ΔΔ strain was less virulent than the dnf2ΔΔ strain. These results indicate that the transport of GlcCer and/or PC by plasma membrane P4-ATPases is important for the pathogenicity of Candida albicans.
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Affiliation(s)
- Bhawik K. Jain
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Andrew S. Wagner
- Department of Microbiology, University of Tennessee, Knoxville, Tennessee, USA
| | - Todd B. Reynolds
- Department of Microbiology, University of Tennessee, Knoxville, Tennessee, USA
| | - Todd R. Graham
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
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Wang S, Meigs JB, Dupuis J. Genetic association tests in family samples for multi-category phenotypes. BMC Genomics 2021; 22:873. [PMID: 34863089 PMCID: PMC8642939 DOI: 10.1186/s12864-021-08107-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 10/19/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Advancements in statistical methods and sequencing technology have led to numerous novel discoveries in human genetics in the past two decades. Among phenotypes of interest, most attention has been given to studying genetic associations with continuous or binary traits. Efficient statistical methods have been proposed and are available for both types of traits under different study designs. However, for multinomial categorical traits in related samples, there is a lack of efficient statistical methods and software. RESULTS We propose an efficient score test to analyze a multinomial trait in family samples, in the context of genome-wide association/sequencing studies. An alternative Wald statistic is also proposed. We also extend the methodology to be applicable to ordinal traits. We performed extensive simulation studies to evaluate the type-I error of the score test, Wald test compared to the multinomial logistic regression for unrelated samples, under different allele frequency and study designs. We also evaluate the power of these methods. Results show that both the score and Wald tests have a well-controlled type-I error rate, but the multinomial logistic regression has an inflated type-I error rate when applied to family samples. We illustrated the application of the score test with an application to the Framingham Heart Study to uncover genetic variants associated with diabesity, a multi-category phenotype. CONCLUSION Both proposed tests have correct type-I error rate and similar power. However, because the Wald statistics rely on computer-intensive estimation, it is less efficient than the score test in terms of applications to large-scale genetic association studies. We provide computer implementation for both multinomial and ordinal traits.
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Affiliation(s)
- Shuai Wang
- Pfizer Inc, Global Product Development, Groton, CT, 06340, USA.
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA.,Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
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Meeks KAC, Bentley AR, Gouveia MH, Chen G, Zhou J, Lei L, Adeyemo AA, Doumatey AP, Rotimi CN. Genome-wide analyses of multiple obesity-related cytokines and hormones informs biology of cardiometabolic traits. Genome Med 2021; 13:156. [PMID: 34620218 PMCID: PMC8499470 DOI: 10.1186/s13073-021-00971-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/16/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND A complex set of perturbations occur in cytokines and hormones in the etiopathogenesis of obesity and related cardiometabolic conditions such as type 2 diabetes (T2D). Evidence for the genetic regulation of these cytokines and hormones is limited, particularly in African-ancestry populations. In order to improve our understanding of the biology of cardiometabolic traits, we investigated the genetic architecture of a large panel of obesity- related cytokines and hormones among Africans with replication analyses in African Americans. METHODS We performed genome-wide association studies (GWAS) in 4432 continental Africans, enrolled from Ghana, Kenya, and Nigeria as part of the Africa America Diabetes Mellitus (AADM) study, for 13 obesity-related cytokines and hormones, including adipsin, glucose-dependent insulinotropic peptide (GIP), glucagon-like peptide-1 (GLP-1), interleukin-1 receptor antagonist (IL1-RA), interleukin-6 (IL-6), interleukin-10 (IL-10), leptin, plasminogen activator inhibitor-1 (PAI-1), resistin, visfatin, insulin, glucagon, and ghrelin. Exact and local replication analyses were conducted in African Americans (n = 7990). The effects of sex, body mass index (BMI), and T2D on results were investigated through stratified analyses. RESULTS GWAS identified 39 significant (P value < 5 × 10-8) loci across all 13 traits. Notably, 14 loci were African-ancestry specific. In this first GWAS for adipsin and ghrelin, we detected 13 and 4 genome-wide significant loci respectively. Stratified analyses by sex, BMI, and T2D showed a strong effect of these variables on detected loci. Eight novel loci were successfully replicated: adipsin (3), GIP (1), GLP-1 (1), and insulin (3). Annotation of these loci revealed promising links between these adipocytokines and cardiometabolic outcomes as illustrated by rs201751833 for adipsin and blood pressure and locus rs759790 for insulin level and T2D in lean individuals. CONCLUSIONS Our study identified genetic variants underlying variation in multiple adipocytokines, including the first loci for adipsin and ghrelin. We identified population differences in variants associated with adipocytokines and highlight the importance of stratification for discovery of loci. The high number of African-specific loci detected emphasizes the need for GWAS in African-ancestry populations, as these loci could not have been detected in other populations. Overall, our work contributes to the understanding of the biology linking adipocytokines to cardiometabolic traits.
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Affiliation(s)
- Karlijn A C Meeks
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Lin Lei
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA.
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA.
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Yang Y, Sun K, Liu W, Li X, Tian W, Shuai P, Zhu X. The phosphatidylserine flippase β-subunit Tmem30a is essential for normal insulin maturation and secretion. Mol Ther 2021; 29:2854-2872. [PMID: 33895325 PMCID: PMC8417432 DOI: 10.1016/j.ymthe.2021.04.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 03/17/2021] [Accepted: 04/19/2021] [Indexed: 12/12/2022] Open
Abstract
The processing, maturation, and secretion of insulin are under precise regulation, and dysregulation causes profound defects in glucose handling, leading to diabetes. Tmem30a is the β subunit of the phosphatidylserine (PS) flippase, which maintains the membrane asymmetric distribution of PS. Tmem30a regulates cell survival and the localization of subcellular structures and is thus critical to the normal function of multiple physiological systems. Here, we show that conditional knockout of Tmem30a specifically in pancreatic islet β cells leads to obesity, hyperglycemia, glucose intolerance, hyperinsulinemia, and insulin resistance in mice, due to insufficient insulin release. Moreover, we reveal that Tmem30a plays an essential role in clathrin-mediated vesicle transport between the trans Golgi network (TGN) and the plasma membrane (PM), which comprises immature secretory granule (ISG) budding at the TGN. We also find that Tmem30a deficiency impairs clathrin-mediated vesicle budding and thus blocks both insulin maturation in ISGs and the transport of glucose-sensing Glut2 to the PM. Collectively, these disruptions compromise both insulin secretion and glucose sensitivity, thus contributing to impairments in glucose-stimulated insulin secretion. Taken together, our data demonstrate an important role of Tmem30a in insulin maturation and glucose metabolic homeostasis and suggest the importance of membrane phospholipid distribution in metabolic disorders.
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Affiliation(s)
- Yeming Yang
- Health Management Center, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China; The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China
| | - Kuanxiang Sun
- The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China
| | - Wenjing Liu
- The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China
| | - Xiao Li
- The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China
| | - Wanli Tian
- The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China
| | - Ping Shuai
- Health Management Center, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China; The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China; Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences (2019RU026), Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan 610072 China.
| | - Xianjun Zhu
- Health Management Center, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China; The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China; Key Laboratory of Tibetan Medicine Research, Chinese Academy of Sciences and Qinghai Provincial Key Laboratory of Tibetan Medicine Research, Northwest Institute of Plateau Biology, Xining, Qinghai 810008, China; Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences (2019RU026), Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan 610072 China; Natural Products Research Center, Institute of Chengdu Biology, Sichuan Translational Medicine Hospital, Chinese Academy of Sciences, Chengdu, Sichuan 610072, China; Department of Ophthalmology, First People's Hospital of Shangqiu, Shangqiu, Hennan 476100, China.
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Raushan K, Benberin V, Vochshenkova T, Babenko D, Sibagatova A. Association of 3 single nucleotide polymorphisms of the eighth chromosome with remodeling of the myocardium and carotid arteries in the Kazakh population. Medicine (Baltimore) 2021; 100:e24608. [PMID: 33578567 PMCID: PMC7886467 DOI: 10.1097/md.0000000000024608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/25/2020] [Accepted: 01/07/2021] [Indexed: 01/05/2023] Open
Abstract
ABSTRACT Cardiovascular diseases are one of the key health issues in Kazakhstan. According to the WHO, the prevalence of arterial hypertension (AH) was 28% in males and 25% in females in 2015, which puts up vastly to premature mortality from non-communicable diseases.The search for genetic features of target organ lesions processes in AH is relevant. The goal of this study was to search for the genetic markers of myocardial remodeling (MR) and carotid artery remodeling (CAR).A total of 866 hypertensive individuals were recruited in Nur-Sultan, Kazakhstan. Their blood was genotyped for 9 single nucleotide polymorphisms (SNPs) of the eighth chromosome to find an association with remodeling. The analysis was carried out in the group pairs (control and CAR, control and MR, and control and CAR and MR). The genotype-phenotype association was assessed using 5 different inheritance models: dominant, codominant, recessive, overdominant, and log-additive.Statistically significant results were found for 3 SNPs (rs2407103, rs11775334, rs2071518) which minor alleles enlarged risks of MR and CAR in AH in the studied population. Three polymorphisms have previously been associated with АН and some other traits like pulse pressure and blood glucose in other ethnic populations: rs2407103 - in Afro-American population, rs11775334 - in the European population, rs2071518 is well studied in various ethnic populations (European, South Asian, Afro-American, Hispanic, East Asian).
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Affiliation(s)
- Karabayeva Raushan
- Medical Centre Hospital of President's Affairs Administration of the Republic of Kazakhstan
| | - Valeriy Benberin
- Medical Centre Hospital of President's Affairs Administration of the Republic of Kazakhstan
| | - Tamara Vochshenkova
- Medical Centre Hospital of President's Affairs Administration of the Republic of Kazakhstan
| | | | - Ainur Sibagatova
- Medical Centre Hospital of President's Affairs Administration of the Republic of Kazakhstan
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10
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Huang Y, Li Y, Wang X, Yu J, Cai Y, Zheng Z, Li R, Zhang S, Chen N, Asadollahpour Nanaei H, Hanif Q, Chen Q, Fu W, Li C, Cao X, Zhou G, Liu S, He S, Li W, Chen Y, Chen H, Lei C, Liu M, Jiang Y. An atlas of CNV maps in cattle, goat and sheep. SCIENCE CHINA-LIFE SCIENCES 2021; 64:1747-1764. [PMID: 33486588 DOI: 10.1007/s11427-020-1850-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 11/16/2020] [Indexed: 11/26/2022]
Abstract
Copy number variation (CNV) is the most prevalent type of genetic structural variation that has been recognized as an important source of phenotypic variation in humans, animals and plants. However, the mechanisms underlying the evolution of CNVs and their function in natural or artificial selection remain unknown. Here, we generated CNV region (CNVR) datasets which were diverged or shared among cattle, goat, and sheep, including 886 individuals from 171 diverse populations. Using 9 environmental factors for genome-wide association study (GWAS), we identified a series of candidate CNVRs, including genes relating to immunity, tick resistance, multi-drug resistance, and muscle development. The number of CNVRs shared between species is significantly higher than expected (P<0.00001), and these CNVRs may be more persist than the single nucleotide polymorphisms (SNPs) shared between species. We also identified genomic regions under long-term balancing selection and uncovered the potential diversity of the selected CNVRs close to the important functional genes. This study provides the evidence that balancing selection might be more common in mammals than previously considered, and might play an important role in the daily activities of these ruminant species.
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Affiliation(s)
- Yongzhen Huang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Yunjia Li
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Xihong Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Jiantao Yu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Yudong Cai
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Zhuqing Zheng
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Ran Li
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Shunjin Zhang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Ningbo Chen
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | | | - Quratulain Hanif
- National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Punjab, 577, Pakistan
- Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, 45650, Islamabad, Pakistan
| | - Qiuming Chen
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Weiwei Fu
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Chao Li
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Xiukai Cao
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Guangxian Zhou
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Shudong Liu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Sangang He
- Key Laboratory of Genetics Breeding and Reproduction of Grass feeding Livestock, Ministry of Agriculture, Biotechnology Research Institute, Xinjiang Academy of Animal Sciences, Urumqi, 830026, China
| | - Wenrong Li
- Key Laboratory of Genetics Breeding and Reproduction of Grass feeding Livestock, Ministry of Agriculture, Biotechnology Research Institute, Xinjiang Academy of Animal Sciences, Urumqi, 830026, China
| | - Yulin Chen
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Hong Chen
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Chuzhao Lei
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Mingjun Liu
- Key Laboratory of Genetics Breeding and Reproduction of Grass feeding Livestock, Ministry of Agriculture, Biotechnology Research Institute, Xinjiang Academy of Animal Sciences, Urumqi, 830026, China
| | - Yu Jiang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
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11
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Jain BK, Roland BP, Graham TR. Exofacial membrane composition and lipid metabolism regulates plasma membrane P4-ATPase substrate specificity. J Biol Chem 2020; 295:17997-18009. [PMID: 33060204 PMCID: PMC7939387 DOI: 10.1074/jbc.ra120.014794] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 09/22/2020] [Indexed: 01/21/2023] Open
Abstract
The plasma membrane of a cell is characterized by an asymmetric distribution of lipid species across the exofacial and cytofacial aspects of the bilayer. Regulation of membrane asymmetry is a fundamental characteristic of membrane biology and is crucial for signal transduction, vesicle transport, and cell division. The type IV family of P-ATPases, or P4-ATPases, establishes membrane asymmetry by selection and transfer of a subset of membrane lipids from the lumenal or exofacial leaflet to the cytofacial aspect of the bilayer. It is unclear how P4-ATPases sort through the spectrum of membrane lipids to identify their desired substrate(s) and how the membrane environment modulates this activity. Therefore, we tested how the yeast plasma membrane P4-ATPase, Dnf2, responds to changes in membrane composition induced by perturbation of endogenous lipid biosynthetic pathways or exogenous application of lipid. The primary substrates of Dnf2 are glucosylceramide (GlcCer) and phosphatidylcholine (PC, or their lyso-lipid derivatives), and we find that these substrates compete with each other for transport. Acutely inhibiting sphingolipid synthesis using myriocin attenuates transport of exogenously applied GlcCer without perturbing PC transport. Deletion of genes controlling later steps of glycosphingolipid production also perturb GlcCer transport to a greater extent than PC transport. In contrast, perturbation of ergosterol biosynthesis reduces PC and GlcCer transport equivalently. Surprisingly, application of lipids that are poor transport substrates differentially affects PC and GlcCer transport by Dnf2, thus altering substrate preference. Our data indicate that Dnf2 exhibits exquisite sensitivity to the membrane composition, thus providing feedback onto the function of the P4-ATPases.
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Affiliation(s)
- Bhawik Kumar Jain
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Bartholomew P Roland
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Todd R Graham
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA.
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12
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Sung YJ, de las Fuentes L, Winkler TW, Chasman DI, Bentley AR, Kraja AT, Ntalla I, Warren HR, Guo X, Schwander K, Manning AK, Brown MR, Aschard H, Feitosa MF, Franceschini N, Lu Y, Cheng CY, Sim X, Vojinovic D, Marten J, Musani SK, Kilpeläinen TO, Richard MA, Aslibekyan S, Bartz TM, Dorajoo R, Li C, Liu Y, Rankinen T, Smith AV, Tajuddin SM, Tayo BO, Zhao W, Zhou Y, Matoba N, Sofer T, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Giulianini F, Goel A, Harris SE, Hartwig FP, He M, Horimoto ARVR, Hsu FC, Jackson AU, Kammerer CM, Kasturiratne A, Komulainen P, Kühnel B, Leander K, Lee WJ, Lin KH, Luan J, Lyytikäinen LP, McKenzie CA, Nelson CP, Noordam R, Scott RA, Sheu WHH, Stančáková A, Takeuchi F, van der Most PJ, Varga TV, Waken RJ, Wang H, Wang Y, Ware EB, Weiss S, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Alfred T, Amin N, Arking DE, Aung T, Barr RG, Bielak LF, Boerwinkle E, Bottinger EP, Braund PS, Brody JA, Broeckel U, Cade B, Campbell A, Canouil M, Chakravarti A, Cocca M, Collins FS, Connell JM, de Mutsert R, de Silva HJ, Dörr M, Duan Q, Eaton CB, Ehret G, Evangelou E, Faul JD, Forouhi NG, Franco OH, Friedlander Y, Gao H, Gigante B, Gu CC, Gupta P, Hagenaars SP, Harris TB, He J, Heikkinen S, Heng CK, Hofman A, Howard BV, Hunt SC, Irvin MR, Jia Y, Katsuya T, Kaufman J, Kerrison ND, Khor CC, Koh WP, Koistinen HA, Kooperberg CB, Krieger JE, Kubo M, Kutalik Z, Kuusisto J, Lakka TA, Langefeld CD, Langenberg C, Launer LJ, Lee JH, Lehne B, Levy D, Lewis CE, Li Y, Lim SH, Liu CT, Liu J, Liu J, Liu Y, Loh M, Lohman KK, Louie T, Mägi R, Matsuda K, Meitinger T, Metspalu A, Milani L, Momozawa Y, Mosley, Jr TH, Nalls MA, Nasri U, O'Connell JR, Ogunniyi A, Palmas WR, Palmer ND, Pankow JS, Pedersen NL, Peters A, Peyser PA, Polasek O, Porteous D, Raitakari OT, Renström F, Rice TK, Ridker PM, Robino A, Robinson JG, Rose LM, Rudan I, Sabanayagam C, Salako BL, Sandow K, Schmidt CO, Schreiner PJ, Scott WR, Sever P, Sims M, Sitlani CM, Smith BH, Smith JA, Snieder H, Starr JM, Strauch K, Tang H, Taylor KD, Teo YY, Tham YC, Uitterlinden AG, Waldenberger M, Wang L, Wang YX, Wei WB, Wilson G, Wojczynski MK, Xiang YB, Yao J, Yuan JM, Zonderman AB, Becker DM, Boehnke M, Bowden DW, Chambers JC, Chen YDI, Weir DR, de Faire U, Deary IJ, Esko T, Farrall M, Forrester T, Freedman BI, Froguel P, Gasparini P, Gieger C, Horta BL, Hung YJ, Jonas JB, Kato N, Kooner JS, Laakso M, Lehtimäki T, Liang KW, Magnusson PKE, Oldehinkel AJ, Pereira AC, Perls T, Rauramaa R, Redline S, Rettig R, Samani NJ, Scott J, Shu XO, van der Harst P, Wagenknecht LE, Wareham NJ, Watkins H, Wickremasinghe AR, Wu T, Kamatani Y, Laurie CC, Bouchard C, Cooper RS, Evans MK, Gudnason V, Hixson J, Kardia SLR, Kritchevsky SB, Psaty BM, van Dam RM, Arnett DK, Mook-Kanamori DO, Fornage M, Fox ER, Hayward C, van Duijn CM, Tai ES, Wong TY, Loos RJF, Reiner AP, Rotimi CN, Bierut LJ, Zhu X, Cupples LA, Province MA, Rotter JI, Franks PW, Rice K, Elliott P, Caulfield MJ, Gauderman WJ, Munroe PB, Rao DC, Morrison AC. A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure. Hum Mol Genet 2019; 28:2615-2633. [PMID: 31127295 PMCID: PMC6644157 DOI: 10.1093/hmg/ddz070] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/24/2022] Open
Abstract
Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P < 5 × 10-8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.
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Affiliation(s)
- Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Lisa de las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- Cardiovascular Division, Department of Medicine, Washington University, St. Louis, MO, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Daniel I Chasman
- Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Aldi T Kraja
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Ioanna Ntalla
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Helen R Warren
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, London, UK
| | - Xiuqing Guo
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Alisa K Manning
- Center for Human Genetics Research, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Nora Franceschini
- Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Yingchang Lu
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Solomon K Musani
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Melissa A Richard
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, WA, USA
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Changwei Li
- Epidemiology and Biostatistics, University of Georgia at Athens College of Public Health, Athens, GA, USA
| | - Yongmei Liu
- Public Health Sciences, Epidemiology and Prevention, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Albert Vernon Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Salman M Tajuddin
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bamidele O Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yanhua Zhou
- Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Nana Matoba
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Maris Alver
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Marzyeh Amini
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen RB, The Netherlands
| | - Mathilde Boissel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
| | - Jin Fang Chai
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Jasmin Divers
- Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ilaria Gandin
- Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Chuan Gao
- Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Anuj Goel
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, UK
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Medical Genetics Section, University of Edinburgh Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Fernando P Hartwig
- Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Meian He
- Lab Genetics and Molecular Cardiology, Cardiology, Heart Institute, University of Sao Paulo, Sao Paulo, CA, USA
| | - Andrea R V R Horimoto
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Fang-Chi Hsu
- Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Anne U Jackson
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Candace M Kammerer
- Department of Public Health, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - Anuradhani Kasturiratne
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Pirjo Komulainen
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Brigitte Kühnel
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Karin Leander
- Medical Research, Taichung Veterans General Hospital, Department of Social Work, Tunghai University, Taichung, Taiwan
| | - Wen-Jane Lee
- Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Keng-Hung Lin
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Jian’an Luan
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
- Tropical Metabolism Research Unit, Tropical Medicine Research Institute, University of the West Indies, Mona, Jamaica
| | - Colin A McKenzie
- School of Public Health, Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongi Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Raymond Noordam
- Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Robert A Scott
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Wayne H H Sheu
- Endocrinology and Metabolism, Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Yang-ming University, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Institute of Medical Technology, National Chung-Hsing University, Taichung, Taiwan
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen RB, The Netherlands
| | - Tibor V Varga
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Robert J Waken
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Heming Wang
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Yajuan Wang
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - Erin B Ware
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Ernst Moritz Arndt University Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Health), Partner Site Greifswald, Greifswald, Germany
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Lisa R Yanek
- General Internal Medicine, GeneSTAR Research Program, Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Weihua Zhang
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, Middlesex, UK
| | - Jing Hua Zhao
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Saima Afaq
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Tamuno Alfred
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Dan E Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Erwin P Bottinger
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Peter S Braund
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, WA, USA
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Department of Pediatrics, Medicine and Physiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Archie Campbell
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Mickaël Canouil
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
| | - Aravinda Chakravarti
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Francis S Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - John M Connell
- Ninewells Hospital & Medical School, University of Dundee, Dundee, Scotland, UK
| | - Renée de Mutsert
- Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - Marcus Dörr
- DZHK (German Centre for Cardiovascular Health), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, USA
| | - Charles B Eaton
- Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, RI, USA
| | - Georg Ehret
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Cardiology, Department of Specialties of Medicine, Geneva University Hospital, Geneva, Switzerland
| | - Evangelos Evangelou
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Nita G Forouhi
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Yechiel Friedlander
- Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - He Gao
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Bruna Gigante
- Medical Research, Taichung Veterans General Hospital, Department of Social Work, Tunghai University, Taichung, Taiwan
| | - C Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Preeti Gupta
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Saskia P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Psychology, The University of Edinburgh, Edinburgh, UK
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Jiang He
- Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
- Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Sami Heikkinen
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat—National University Children’s Medical Institute, National University Health System, Singapore, Singapore
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Barbara V Howard
- MedStar Health Research Institute, Hyattsville, MD, USA
- Center for Clinical and Translational Sciences and Department of Medicine, Georgetown–Howard Universities, Washington, DC, USA
| | - Steven C Hunt
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Genetic Medicine, Weill Cornell Medicine, Doha, Qatar
| | - Marguerite R Irvin
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Yucheng Jia
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Joel Kaufman
- Epidemiology, Occupational and Environmental Medicine Program, University of Washington, Seattle, WA, USA
| | - Nicola D Kerrison
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Biochemistry, National University of Singapore, Singapore, Singapore
| | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Health Services and Systems Research, Duke–NUS Medical School, Singapore, Singapore
| | - Heikki A Koistinen
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine and Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Biomedicum 2U, Helsinki Finland
| | - Charles B Kooperberg
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, WA, USA
| | - Jose E Krieger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Zoltan Kutalik
- Institute of Social Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Timo A Lakka
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Carl D Langefeld
- Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Joseph H Lee
- Sergievsky Center, College of Physicians and Surgeons, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Benjamin Lehne
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Daniel Levy
- NHLBI Framingham Heart Study, Framingham, MA, USA
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cora E Lewis
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Yize Li
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Sing Hui Lim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ching-Ti Liu
- Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Jingmin Liu
- WHI CCC, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yeheng Liu
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marie Loh
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research, Singapore
| | - Kurt K Lohman
- Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Koichi Matsuda
- Laboratory for Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Minato-ku, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Lili Milani
- Cardiovascular Division, Department of Medicine, Washington University, St. Louis, MO, USA
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | | | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Ubaydah Nasri
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jeff R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | - James S Pankow
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg, Germany
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Ozren Polasek
- Department of Public Health, Department of Medicine, University of Split, Split, Croatia
- Psychiatric Hospital ‘Sveti Ivan’, Zagreb, Croatia
- Gen-info Ltd, Zagreb, Croatia
| | - David Porteous
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Frida Renström
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
- Department of Biobank Research, Umeå University, Umeå, Västerbotten, Sweden
| | - Treva K Rice
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Paul M Ridker
- Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Antonietta Robino
- Institute for Maternal and Child Health—IRCCS ‘Burlo Garofolo’, Trieste, Italy
| | - Jennifer G Robinson
- Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA, USA
| | - Lynda M Rose
- Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | | | - Kevin Sandow
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Carsten O Schmidt
- DZHK (German Centre for Cardiovascular Health), Partner Site Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Pamela J Schreiner
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - William R Scott
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Peter Sever
- International Centre for Circulatory Health, Imperial College London, London, UK
| | - Mario Sims
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, WA, USA
| | - Blair H Smith
- Division of Population Health Sciences, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen RB, The Netherlands
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, UK
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Hua Tang
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Kent D Taylor
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Life Sciences Institute, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Lihua Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Gregory Wilson
- Jackson Heart Study, School of Public Health, Jackson State University, Jackson, MS, USA
| | - Mary K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Yong-Bing Xiang
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, P.R. China
| | - Jie Yao
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jian-Min Yuan
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alan B Zonderman
- Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Diane M Becker
- General Internal Medicine, GeneSTAR Research Program, Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Donald W Bowden
- Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - John C Chambers
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, Middlesex, UK
| | - Yii-Der Ida Chen
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Ulf de Faire
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Psychology, The University of Edinburgh, Edinburgh, UK
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, MA, USA
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, UK
| | - Terrence Forrester
- Tropical Metabolism Research Unit, Tropical Medicine Research Institute, University of the West Indies, Mona, Jamaica
| | - Barry I Freedman
- Nephrology, Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Philippe Froguel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
- Department of Genomics of Common Disease, Imperial College London, London, UK
| | - Paolo Gasparini
- Department of Medical Sciences, University of Trieste, Trieste, Italy
- Department of Genetic Medicine, Weill Cornell Medicine, Doha, Qatar
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Bernardo Lessa Horta
- Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil
| | - Yi-Jen Hung
- Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taipei, Taiwan
| | - Jost Bruno Jonas
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, Middlesex, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Kae-Woei Liang
- School of Medicine, National Yang-ming University, Taipei, Taiwan
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Medicine, China Medical University, Taichung, Taiwan
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Albertine J Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen RB, The Netherlands
| | - Alexandre C Pereira
- Lab Genetics and Molecular Cardiology, Cardiology, Heart Institute, University of Sao Paulo, Sao Paulo, CA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Thomas Perls
- Geriatrics Section, Boston University Medical Center, Boston, MA, USA
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Rainer Rettig
- DZHK (German Centre for Cardiovascular Health), Partner Site Greifswald, Greifswald, Germany
- Institute of Physiology, University of Medicine Greifswald, Greifswald, Germany
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen RB, The Netherlands
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, UK
| | | | - Tangchun Wu
- School of Public Health, Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongi Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Richard S Cooper
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
| | - Michele K Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - James Hixson
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Donna K Arnett
- Dean’s Office, University of Kentucky College of Public Health, Lexington, KY, USA
| | - Dennis O Mook-Kanamori
- Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ervin R Fox
- Cardiology, Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Health Services and Systems Research, Duke–NUS Medical School, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ruth J F Loos
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, The Mindich Child Health and Development Institute, New York, NY, USA
| | - Alex P Reiner
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, WA, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - L Adrienne Cupples
- Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Jerome I Rotter
- Division of Genomic Outcomes, Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
- Harvard T. H. Chan School of Public Health, Department of Nutrition, Harvard University, Boston, MA, USA
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, Västerbotten, Sweden
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Paul Elliott
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Mark J Caulfield
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, London, UK
| | - W James Gauderman
- Biostatistics, Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, London, UK
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Suzuki TA, Phifer-Rixey M, Mack KL, Sheehan MJ, Lin D, Bi K, Nachman MW. Host genetic determinants of the gut microbiota of wild mice. Mol Ecol 2019; 28:3197-3207. [PMID: 31141224 DOI: 10.1111/mec.15139] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 04/26/2019] [Accepted: 05/20/2019] [Indexed: 02/06/2023]
Abstract
Identifying a common set of genes that mediate host-microbial interactions across populations and species of mammals has broad relevance for human health and animal biology. However, the genetic basis of the gut microbial composition in natural populations remains largely unknown outside of humans. Here, we used wild house mouse populations as a model system to ask three major questions: (a) Does host genetic relatedness explain interindividual variation in gut microbial composition? (b) Do population differences in the microbiota persist in a common environment? (c) What are the host genes associated with microbial richness and the relative abundance of bacterial genera? We found that host genetic distance is a strong predictor of the gut microbial composition as characterized by 16S amplicon sequencing. Using a common garden approach, we then identified differences in microbial composition between populations that persisted in a shared laboratory environment. Finally, we used exome sequencing to associate host genetic variants with microbial diversity and relative abundance of microbial taxa in wild mice. We identified 20 genes that were associated with microbial diversity or abundance including a macrophage-derived cytokine (IL12a) that contained three nonsynonymous mutations. Surprisingly, we found a significant overrepresentation of candidate genes that were previously associated with microbial measurements in humans. The homologous genes that overlapped between wild mice and humans included genes that have been associated with traits related to host immunity and obesity in humans. Gene-bacteria associations identified in both humans and wild mice suggest some commonality to the host genetic determinants of gut microbial composition across mammals.
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Affiliation(s)
- Taichi A Suzuki
- Department of Integrative Biology and Museum of Vertebrate Zoology, University of California Berkeley, Berkeley, California, USA
| | - Megan Phifer-Rixey
- Department of Biology, Monmouth University, West Long Branch, New Jersey, USA
| | - Katya L Mack
- Department of Integrative Biology and Museum of Vertebrate Zoology, University of California Berkeley, Berkeley, California, USA
| | - Michael J Sheehan
- Department of Neurobiology and Behavior, Cornell University, Ithaca, New York, USA
| | - Dana Lin
- Department of Integrative Biology and Museum of Vertebrate Zoology, University of California Berkeley, Berkeley, California, USA
| | - Ke Bi
- California Institute for Quantitative Biosciences, University of California Berkeley, Berkeley, California, USA
| | - Michael W Nachman
- Department of Integrative Biology and Museum of Vertebrate Zoology, University of California Berkeley, Berkeley, California, USA
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14
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Takar M, Huang Y, Graham TR. The PQ-loop protein Any1 segregates Drs2 and Neo1 functions required for viability and plasma membrane phospholipid asymmetry. J Lipid Res 2019; 60:1032-1042. [PMID: 30824614 PMCID: PMC6495175 DOI: 10.1194/jlr.m093526] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Indexed: 02/06/2023] Open
Abstract
Membrane asymmetry is a key organizational feature of the plasma membrane. Type IV P-type ATPases (P4-ATPases) are phospholipid flippases that establish membrane asymmetry by translocating phospholipids, such as phosphatidylserine (PS) and phospatidylethanolamine, from the exofacial leaflet to the cytosolic leaflet. Saccharomyces cerevisiae expresses five P4-ATPases: Drs2, Neo1, Dnf1, Dnf2, and Dnf3. The inactivation of Neo1 is lethal, suggesting Neo1 mediates an essential function not exerted by the other P4-ATPases. However, the disruption of ANY1, which encodes a PQ-loop membrane protein, allows the growth of neo1Δ and reveals functional redundancy between Golgi-localized Neo1 and Drs2. Here we show Drs2 PS flippase activity is required to support neo1Δ any1Δ viability. Additionally, a Dnf1 variant with enhanced PS flipping ability can replace Drs2 and Neo1 function in any1Δ cells. any1Δ also suppresses drs2Δ growth defects but not the loss of membrane asymmetry. Any1 overexpression perturbs the growth of cells but does not disrupt membrane asymmetry. Any1 coimmunoprecipitates with Neo1, an association prevented by the Any1-inactivating mutation D84G. These results indicate a critical role for PS flippase activity in Golgi membranes to sustain viability and suggests Any1 regulates Golgi membrane remodeling through protein-protein interactions rather than a previously proposed scramblase activity.
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Affiliation(s)
- Mehmet Takar
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235
| | - Yannan Huang
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235
| | - Todd R Graham
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235.
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15
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Roland BP, Naito T, Best JT, Arnaiz-Yépez C, Takatsu H, Yu RJ, Shin HW, Graham TR. Yeast and human P4-ATPases transport glycosphingolipids using conserved structural motifs. J Biol Chem 2018; 294:1794-1806. [PMID: 30530492 DOI: 10.1074/jbc.ra118.005876] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Revised: 11/29/2018] [Indexed: 12/21/2022] Open
Abstract
Lipid transport is an essential process with manifest importance to human health and disease. Phospholipid flippases (P4-ATPases) transport lipids across the membrane bilayer and are involved in signal transduction, cell division, and vesicular transport. Mutations in flippase genes cause or contribute to a host of diseases, such as cholestasis, neurological deficits, immunological dysfunction, and metabolic disorders. Genome-wide association studies have shown that ATP10A and ATP10D variants are associated with an increased risk of diabetes, obesity, myocardial infarction, and atherosclerosis. Moreover, ATP10D SNPs are associated with elevated levels of glucosylceramide (GlcCer) in plasma from diverse European populations. Although sphingolipids strongly contribute to metabolic disease, little is known about how GlcCer is transported across cell membranes. Here, we identify a conserved clade of P4-ATPases from Saccharomyces cerevisiae (Dnf1, Dnf2), Schizosaccharomyces pombe (Dnf2), and Homo sapiens (ATP10A, ATP10D) that transport GlcCer bearing an sn2 acyl-linked fluorescent tag. Further, we establish structural determinants necessary for recognition of this sphingolipid substrate. Using enzyme chimeras and site-directed mutagenesis, we observed that residues in transmembrane (TM) segments 1, 4, and 6 contribute to GlcCer selection, with a conserved glutamine in the center of TM4 playing an essential role. Our molecular observations help refine models for substrate translocation by P4-ATPases, clarify the relationship between these flippases and human disease, and have fundamental implications for membrane organization and sphingolipid homeostasis.
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Affiliation(s)
- Bartholomew P Roland
- From the Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee 37235 and
| | - Tomoki Naito
- the Graduate School of Pharmaceutical Science, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Jordan T Best
- From the Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee 37235 and
| | - Cayetana Arnaiz-Yépez
- From the Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee 37235 and
| | - Hiroyuki Takatsu
- the Graduate School of Pharmaceutical Science, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Roger J Yu
- From the Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee 37235 and
| | - Hye-Won Shin
- the Graduate School of Pharmaceutical Science, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Todd R Graham
- From the Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee 37235 and
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16
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Shin HW, Takatsu H. Substrates of P4‐ATPases: beyond aminophospholipids (phosphatidylserine and phosphatidylethanolamine). FASEB J 2018; 33:3087-3096. [DOI: 10.1096/fj.201801873r] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Hye-Won Shin
- Graduate School of Pharmaceutical SciencesKyoto University Kyoto Japan
| | - Hiroyuki Takatsu
- Graduate School of Pharmaceutical SciencesKyoto University Kyoto Japan
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17
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18
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Balakrishnan P, Vaidya D, Voruganti VS, Haack K, Kent JW, North KE, Laston S, Howard BV, Umans JG, Lee ET, Best LG, MacCluer JW, Cole SA, Navas-Acien A, Franceschini N. Genetic Variants Related to Cardiometabolic Traits Are Associated to B Cell Function, Insulin Resistance, and Diabetes Among AmeriCan Indians: The Strong Heart Family Study. Front Genet 2018; 9:466. [PMID: 30369944 PMCID: PMC6194194 DOI: 10.3389/fgene.2018.00466] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/24/2018] [Indexed: 01/03/2023] Open
Abstract
Background: Genetic research may inform underlying mechanisms for disparities in the burden of type 2 diabetes mellitus among American Indians. Our objective was to assess the association of genetic variants in cardiometabolic candidate genes with B cell dysfunction via HOMA-B, insulin resistance via HOMA-IR, and type 2 diabetes mellitus in the Strong Heart Family Study (SHFS). Methods and Results: We examined the association of variants, previously associated with cardiometabolic traits (∼200,000 from Illumina Cardio MetaboChip), using mixed models of HOMA-B residuals corrected for HOMA-IR (cHOMA-B), log transformed HOMA-IR, and incident diabetes, adjusted for age, sex, population stratification, and familial relatedness. Center-specific estimates were combined using fixed effect meta-analyses. We used Bonferroni correction to account for multiple testing (P < 4.13 × 10−7). We also assessed the association between variants in candidate diabetes genes with these metabolic traits. We explored the top SNPs in an independent, replication sample from Southwestern Arizona. We identified significant associations with cHOMA-B for common variants at 26 loci of which 8 were novel (PRSS7, FCRL5, PEL1, LRP12, IGLL1, ARHGEF10, PARVA, FLJ16686). The most significant variant association with cHOMA-B was observed on chromosome 5 for an intergenic variant near PARP8 (rs2961831, P = 6.39 × 10−9). In the replication study, we found a signal at rs4607517 near GCK/YKT6 (P = 0.01). Variants near candidate diabetes genes (especially GCK and KCNQ1) were also nominally associated with HOMA-IR and cHOMA-B. Conclusion: We identified variants at novel loci and confirmed those at known candidate diabetes loci associations for cHOMA-B. This study also provided evidence for association of variants at KCNQ2, CTNAA2, and KCNQ1with cHOMA-B among American Indians. Further studies are needed to account for the high heritability of diabetes among the American Indian participants of the SHFS cohort.
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Affiliation(s)
- Poojitha Balakrishnan
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, United States
| | - Dhananjay Vaidya
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.,Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - V Saroja Voruganti
- Department of Nutrition, UNC Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | - Karin Haack
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Jack W Kent
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sandra Laston
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - Barbara V Howard
- MedStar Health Research Institute, Hyattsville, MD, United States
| | - Jason G Umans
- MedStar Health Research Institute, Hyattsville, MD, United States.,Georgetown and Howard Universities Center for Clinical and Translational Science, Washington, DC, United States
| | - Elisa T Lee
- Center for American Indian Health Research, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Lyle G Best
- Missouri Breaks Industries Research, Inc., Eagle Butte, SD, United States
| | - Jean W MacCluer
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Shelley A Cole
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Ana Navas-Acien
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, United States
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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19
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Yan YX, Li JJH, Xiao HB, Wang S, He Y, Wu LJ. Association analysis of copy number variations in type 2 diabetes-related susceptible genes in a Chinese population. Acta Diabetol 2018; 55:909-916. [PMID: 29858661 DOI: 10.1007/s00592-018-1168-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 05/25/2018] [Indexed: 01/19/2023]
Abstract
AIMS Copy number variations (CNVs) have been implicated as an important genetic marker of common disease. In this study, we explored genetic effects of common CNVs in Type 2 diabetes (T2D) related susceptible genes in Chinese population. METHODS Seven common CNV loci were selected from genes enclosing the susceptible single nucleotide polymorphisms (SNPs) of T2D confirmed by genome-wide association studies (GWAS) and replication studies conducted in east Asia population. The CNVs and SNPs were genotyped in 504 T2D patients and 494 non-T2D controls. Cumulative effect of the positive CNV loci was measured using genetic risk score (GRS). Multiplicative and additive interaction between candidate CNV loci and SNPs were assessed. RESULTS Compared with the common two copies, the deletion of nsv6360 (adjusted OR = 2.28, 95% CI 1.37-3.78, P = 0.001), nsv8414 (adjusted OR = 1.89, 95% CI 1.16-3.08, P = 0.006) and nsv1898 (adjusted OR = 1.84, 95% CI 1.19-2.84, P = 0.005) were significantly associated with increased risk of T2D (P < 0.007). Significant dose-response relationship was observed between GRS and the risk of T2D (χ2 for trend = 19.51, P < 0.001). In addition, significant additive interactions between nsv8414 and rs17584499 in PTPRD (AP = 0.60, 95% CI 0.12-1.07) and nsv1898 and rs16955379 in CMIP (AP = 0.46, 95% CI 0.01-0.91) were observed. CONCLUSIONS There were three CNV loci (nsv6360, nsv8414 and nsv1898) associated with T2D, and a significant cumulative effect of these loci on the risk of T2D. The comprehensive effects of both CNVs and SNPs may provide a more useful tool for the identification of genetic susceptibility for T2D.
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Affiliation(s)
- Yu-Xiang Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, No. 10 Xitoutiao, You An Men, Beijing, 100069, People's Republic of China.
- Municipal Key Laboratory of Clinical Epidemiology, Beijing, People's Republic of China.
| | - Jia-Jiang-Hui Li
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, No. 10 Xitoutiao, You An Men, Beijing, 100069, People's Republic of China
| | - Huan-Bo Xiao
- Department of Preventive Medicine, Yanjing Medical College, Capital Medical University, Beijing, People's Republic of China
| | - Shuo Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, No. 10 Xitoutiao, You An Men, Beijing, 100069, People's Republic of China
| | - Yan He
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, No. 10 Xitoutiao, You An Men, Beijing, 100069, People's Republic of China
- Municipal Key Laboratory of Clinical Epidemiology, Beijing, People's Republic of China
| | - Li-Juan Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, No. 10 Xitoutiao, You An Men, Beijing, 100069, People's Republic of China.
- Municipal Key Laboratory of Clinical Epidemiology, Beijing, People's Republic of China.
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20
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Xu Y, Shi W, Song R, Long W, Guo H, Yuan S, Zhang T. Divergent patterns of genic copy number variation in KCNIP1 gene reveal risk locus of type 2 diabetes in Chinese population. Endocr J 2018; 65:537-545. [PMID: 29491224 DOI: 10.1507/endocrj.ej17-0496] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Copy number variation (CNV) has emerged as another important genetic marker in addition to SNP for understanding etiology of complex disease. Kv channel interacting protein 1 (KCNIP1) is a Ca2+-dependent transcriptional modulator that contributes to the regulation of insulin secretion. Previous genome-wide CNV assay identified the KCNIP1 gene encompassing a CNV region, however, its further effect and risk rate on type 2 diabetes (T2D) have rarely been addressed, especially in Chinese population. The current study aims to detect and excavate genetic distribution profile of KCNIP1 CNV in Chinese T2D and control populations, and further to investigate the associations with clinical characteristics. Divergent patterns of the KCNIP1 CNV were identified (p < 0.01), in which the copy number gain was predominant in T2D, while the copy number normal accounted for the most in control group. Consistently, the individuals with copy number gain showed significant risk on T2D (OR = 4.550, p < 0.01). The KCNIP1 copy numbers presented significantly positive correlations with fasting plasma glucose and glycated hemoglobin in T2D. For OGTT test, the T2D patients with copy number gain had remarkably elevated glucose contents (60, 120, 180-min, p < 0.05 or p < 0.01) and diminished insulin levels (60, 120-min, p < 0.05) than those with copy number loss and normal, which suggested that the KCNIP1 CNV was correlated with the glucose and insulin action. This is the first CNV association study of the KCNIP1 gene in Chinese population, and these data indicated that KCNIP1 might function as a T2D-susceptibility gene whose dysregulation alters insulin production.
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Affiliation(s)
- Yao Xu
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Weilin Shi
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Ruhui Song
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Wenlin Long
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Hui Guo
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Shiliang Yuan
- Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, Hubei 430064, China
| | - Tongcun Zhang
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
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21
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Tang R, Liu H, Yuan Y, Xie K, Xu P, Liu X, Wen J. Genetic factors associated with risk of metabolic syndrome and hepatocellular carcinoma. Oncotarget 2018; 8:35403-35411. [PMID: 28515345 PMCID: PMC5471064 DOI: 10.18632/oncotarget.15893] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 02/15/2017] [Indexed: 01/01/2023] Open
Abstract
Although the metabolic syndrome is a commonplace topic, its potential threats to public health is a problem that cannot be neglected. As the living conditions improved significantly over the past few years, the morbidity of metabolic syndrome has also steadily risen, and the onset age is becoming younger. The hepatocellular carcinoma (HCC), is one of the most prevalent life-threatening human cancers worldwide, incidence of which is also on the rise, gradually occupied the top of the list associated with metabolic syndrome related complication. Despite the advanced improvement of HCC management, the lifestyle, environmental factors, obesity, hepatitis B virus (HBV) infection have been recognized as risk factors for the development of liver cancer. In recent years, genetic studies, especially the genome-wide association studies (GWASs) were widely performed, a new era of the human genome research was created, which has significantly promoted the study of complex disease genetics. These progresses have contributed to the discovery of abundant number of genomic loci convincingly linked with complex metabolic feature and HCC. In this review, we briefly summarize the association between metabolic syndrome and HCC, focusing on the genetic factors contributed to metabolic syndrome and HCC.
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Affiliation(s)
- Ranran Tang
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China.,State Key Laboratory of Reproductive Medicine, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Heng Liu
- Department of Pediatrics, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Yingdi Yuan
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Kaipeng Xie
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China.,State Key Laboratory of Reproductive Medicine, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Pengfei Xu
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China.,State Key Laboratory of Reproductive Medicine, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Xiaoyun Liu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Juan Wen
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China.,State Key Laboratory of Reproductive Medicine, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, China
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22
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Makhanova N, Morgan AP, Kayashima Y, Makhanov A, Hiller S, Zhilicheva S, Xu L, Pardo-Manuel de Villena F, Maeda N. Genetic architecture of atherosclerosis dissected by QTL analyses in three F2 intercrosses of apolipoprotein E-null mice on C57BL6/J, DBA/2J and 129S6/SvEvTac backgrounds. PLoS One 2017; 12:e0182882. [PMID: 28837567 PMCID: PMC5570285 DOI: 10.1371/journal.pone.0182882] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 07/26/2017] [Indexed: 12/20/2022] Open
Abstract
Quantitative trait locus (QTL) analyses of intercross populations between widely used mouse inbred strains provide a powerful approach for uncovering genetic factors that influence susceptibility to atherosclerosis. Epistatic interactions are common in complex phenotypes and depend on genetic backgrounds. To dissect genetic architecture of atherosclerosis, we analyzed F2 progeny from a cross between apolipoprotein E-null mice on DBA/2J (DBA-apoE) and C57BL/6J (B6-apoE) genetic backgrounds and compared the results with those from two previous F2 crosses of apolipoprotein E-null mice on 129S6/SvEvTac (129-apoE) and DBA-apoE backgrounds, and B6-apoE and 129-apoE backgrounds. In these round-robin crosses, in which each parental strain was crossed with two others, large-effect QTLs are expected to be detectable at least in two crosses. On the other hand, observation of QTLs in one cross only may indicate epistasis and/or absence of statistical power. For atherosclerosis at the aortic arch, Aath4 on chromosome (Chr)2:66 cM follows the first pattern, with significant QTL peaks in (DBAx129)F2 and (B6xDBA)F2 mice but not in (B6x129)F2 mice. We conclude that genetic variants unique to DBA/2J at Aath4 confer susceptibility to atherosclerosis at the aortic arch. A similar pattern was observed for Aath5 on chr10:35 cM, verifying that the variants unique to DBA/2J at this locus protect against arch plaque development. However, multiple loci, including Aath1 (Chr1:49 cM), and Aath2 (Chr1:70 cM) follow the second type of pattern, showing significant peaks in only one of the three crosses (B6-apoE x 129-apoE). As for atherosclerosis at aortic root, the majority of QTLs, including Ath29 (Chr9:33 cM), Ath44 (Chr1:68 cM) and Ath45 (Chr2:83 cM), was also inconsistent, being significant in only one of the three crosses. Only the QTL on Chr7:37 cM was consistently suggestive in two of the three crosses. Thus QTL analysis of round-robin crosses revealed the genetic architecture of atherosclerosis.
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Affiliation(s)
- Natalia Makhanova
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
| | - Andrew P. Morgan
- Department of Genetics and the Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
| | - Yukako Kayashima
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
| | - Andrei Makhanov
- College of Computing, Georgia Institute of Technology, Atlanta, United States of America
| | - Sylvia Hiller
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
| | - Svetlana Zhilicheva
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
| | - Longquan Xu
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics and the Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
| | - Nobuyo Maeda
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
- * E-mail:
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23
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Chung RH, Chiu YF, Hung YJ, Lee WJ, Wu KD, Chen HL, Lin MW, Chen YDI, Quertermous T, Hsiung CA. Genome-wide copy number variation analysis identified deletions in SFMBT1 associated with fasting plasma glucose in a Han Chinese population. BMC Genomics 2017; 18:591. [PMID: 28789618 PMCID: PMC5549306 DOI: 10.1186/s12864-017-3975-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 07/31/2017] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Fasting glucose and fasting insulin are glycemic traits closely related to diabetes, and understanding the role of genetic factors in these traits can help reveal the etiology of type 2 diabetes. Although single nucleotide polymorphisms (SNPs) in several candidate genes have been found to be associated with fasting glucose and fasting insulin, copy number variations (CNVs), which have been reported to be associated with several complex traits, have not been reported for association with these two traits. We aimed to identify CNVs associated with fasting glucose and fasting insulin. RESULTS We conducted a genome-wide CNV association analysis for fasting plasma glucose (FPG) and fasting plasma insulin (FPI) using a family-based genome-wide association study sample from a Han Chinese population in Taiwan. A family-based CNV association test was developed in this study to identify common CNVs (i.e., CNVs with frequencies ≥ 5%), and a generalized estimating equation approach was used to test the associations between the traits and counts of global rare CNVs (i.e., CNVs with frequencies <5%). We found a significant genome-wide association for common deletions with a frequency of 5.2% in the Scm-like with four mbt domains 1 (SFMBT1) gene with FPG (association p-value = 2×10-4 and an adjusted p-value = 0.0478 for multiple testing). No significant association was observed between global rare CNVs and FPG or FPI. The deletions in 20 individuals with DNA samples available were successfully validated using PCR-based amplification. The association of the deletions in SFMBT1 with FPG was further evaluated using an independent population-based replication sample obtained from the Taiwan Biobank. An association p-value of 0.065, which was close to the significance level of 0.05, for FPG was obtained by testing 9 individuals with CNVs in the SFMBT1 gene region and 11,692 individuals with normal copies in the replication cohort. CONCLUSIONS Previous studies have found that SNPs in SFMBT1 are associated with blood pressure and serum urate concentration, suggesting that SFMBT1 may have functional implications in some metabolic-related traits.
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Affiliation(s)
- Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, No 35, Keyan Road, Zhunan, Miaoli, 350, Taiwan
| | - Yen-Feng Chiu
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, No 35, Keyan Road, Zhunan, Miaoli, 350, Taiwan
| | - Yi-Jen Hung
- Division of Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Social Work, Tunghai University, Taichung, Taiwan
| | - Kwan-Dun Wu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hui-Ling Chen
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, No 35, Keyan Road, Zhunan, Miaoli, 350, Taiwan
| | - Ming-Wei Lin
- Institute of Public Health, National Yang-Ming University School of Medicine, Taipei, Taiwan
| | - Yii-Der I Chen
- Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, USA
| | - Thomas Quertermous
- Division of Cardiovascular Medicine and Stanford Cardiovascular Institute, Falk Cardiovascular Research Center, Stanford University, Stanford, California, USA
| | - Chao A Hsiung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, No 35, Keyan Road, Zhunan, Miaoli, 350, Taiwan.
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24
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Copy Number Variations in Candidate Genes and Intergenic Regions Affect Body Mass Index and Abdominal Obesity in Mexican Children. BIOMED RESEARCH INTERNATIONAL 2017; 2017:2432957. [PMID: 28428959 PMCID: PMC5385910 DOI: 10.1155/2017/2432957] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 01/26/2017] [Accepted: 02/06/2017] [Indexed: 01/11/2023]
Abstract
Introduction. Increase in body weight is a gradual process that usually begins in childhood and in adolescence as a result of multiple interactions among environmental and genetic factors. This study aimed to analyze the relationship between copy number variants (CNVs) in five genes and four intergenic regions with obesity in Mexican children. Methods. We studied 1423 children aged 6–12 years. Anthropometric measurements and blood levels of biochemical parameters were obtained. Identification of CNVs was performed by real-time PCR. The effect of CNVs on obesity or body composition was assessed using regression models adjusted for age, gender, and family history of obesity. Results. Gains in copy numbers of LEPR and NEGR1 were associated with decreased body mass index (BMI), waist circumference (WC), and risk of abdominal obesity, whereas gain in ARHGEF4 and CPXCR1 and the intergenic regions 12q15c, 15q21.1a, and 22q11.21d and losses in INS were associated with increased BMI and WC. Conclusion. Our results indicate a possible contribution of CNVs in LEPR, NEGR1, ARHGEF4, and CPXCR1 and the intergenic regions 12q15c, 15q21.1a, and 22q11.21d to the development of obesity, particularly abdominal obesity in Mexican children.
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Coan PM, Hummel O, Garcia Diaz A, Barrier M, Alfazema N, Norsworthy PJ, Pravenec M, Petretto E, Hübner N, Aitman TJ. Genetic, physiological and comparative genomic studies of hypertension and insulin resistance in the spontaneously hypertensive rat. Dis Model Mech 2017; 10:297-306. [PMID: 28130354 PMCID: PMC5374317 DOI: 10.1242/dmm.026716] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 01/23/2017] [Indexed: 12/18/2022] Open
Abstract
We previously mapped hypertension-related insulin resistance quantitative trait loci (QTLs) to rat chromosomes 4, 12 and 16 using adipocytes from F2 crosses between spontaneously hypertensive (SHR) and Wistar Kyoto (WKY) rats, and subsequently identified Cd36 as the gene underlying the chromosome 4 locus. The identity of the chromosome 12 and 16 genes remains unknown. To identify whole-body phenotypes associated with the chromosome 12 and 16 linkage regions, we generated and characterised new congenic strains, with WKY donor segments introgressed onto an SHR genetic background, for the chromosome 12 and 16 linkage regions. We found a >50% increase in insulin sensitivity in both the chromosome 12 and 16 strains. Blood pressure and left ventricular mass were reduced in the two congenic strains consistent with the congenic segments harbouring SHR genes for insulin resistance, hypertension and cardiac hypertrophy. Integrated genomic analysis, using physiological and whole-genome sequence data across 42 rat strains, identified variants within the congenic regions in Upk3bl, RGD1565131 and AABR06087018.1 that were associated with blood pressure, cardiac mass and insulin sensitivity. Quantitative trait transcript analysis across 29 recombinant inbred strains showed correlation between expression of Hspb1, Zkscan5 and Pdgfrl with adipocyte volume, systolic blood pressure and cardiac mass, respectively. Comparative genome analysis showed a marked enrichment of orthologues for human GWAS-associated genes for insulin resistance within the syntenic regions of both the chromosome 12 and 16 congenic intervals. Our study defines whole-body phenotypes associated with the SHR chromosome 12 and 16 insulin-resistance QTLs, identifies candidate genes for these SHR QTLs and finds human orthologues of rat genes in these regions that associate with related human traits. Further study of these genes in the congenic strains will lead to robust identification of the underlying genes and cellular mechanisms. Summary: Comparative genome analyses identify candidate genes for hypertension and insulin resistance on rat chromosomes 12 and 16, and marked enrichment of insulin resistance genes in the syntenic regions of the human genome.
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Affiliation(s)
- Philip M Coan
- Centre for Genomic and Experimental Medicine & Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Oliver Hummel
- Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC), 13125 Berlin, Germany
| | - Ana Garcia Diaz
- Department of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Marjorie Barrier
- Centre for Genomic and Experimental Medicine & Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Neza Alfazema
- Centre for Genomic and Experimental Medicine & Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Penny J Norsworthy
- MRC Clinical Sciences Centre, Imperial College London, London W12 0NN, UK
| | - Michal Pravenec
- Department of Model Diseases, Institute of Physiology, Czech Academy of Sciences, 142 20 Prague, Czech Republic
| | - Enrico Petretto
- MRC Clinical Sciences Centre, Imperial College London, London W12 0NN, UK.,Duke-NUS Medical School, Singapore 169857, Republic of Singapore
| | - Norbert Hübner
- Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC), 13125 Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), partner site, 13316 Berlin, Germany.,Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Timothy J Aitman
- Centre for Genomic and Experimental Medicine & Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH4 2XU, UK.,Department of Medicine, Imperial College London, London SW7 2AZ, UK
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26
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Mechanistic interplay between ceramide and insulin resistance. Sci Rep 2017; 7:41231. [PMID: 28112248 PMCID: PMC5253739 DOI: 10.1038/srep41231] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 12/16/2016] [Indexed: 12/20/2022] Open
Abstract
Recent research adds to a growing body of literature on the essential role of ceramides in glucose homeostasis and insulin signaling, while the mechanistic interplay between various components of ceramide metabolism remains to be quantified. We present an extended model of C16:0 ceramide production through both the de novo synthesis and the salvage pathways. We verify our model with a combination of published models and independent experimental data. In silico experiments of the behavior of ceramide and related bioactive lipids in accordance with the observed transcriptomic changes in obese/diabetic murine macrophages at 5 and 16 weeks support the observation of insulin resistance only at the later phase. Our analysis suggests the pivotal role of ceramide synthase, serine palmitoyltransferase and dihydroceramide desaturase involved in the de novo synthesis and the salvage pathways in influencing insulin resistance versus its regulation.
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27
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Luizon MR, Eckalbar WL, Wang Y, Jones SL, Smith RP, Laurance M, Lin L, Gallins PJ, Etheridge AS, Wright F, Zhou Y, Molony C, Innocenti F, Yee SW, Giacomini KM, Ahituv N. Genomic Characterization of Metformin Hepatic Response. PLoS Genet 2016; 12:e1006449. [PMID: 27902686 PMCID: PMC5130177 DOI: 10.1371/journal.pgen.1006449] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2016] [Accepted: 10/25/2016] [Indexed: 12/26/2022] Open
Abstract
Metformin is used as a first-line therapy for type 2 diabetes (T2D) and prescribed for numerous other diseases. However, its mechanism of action in the liver has yet to be characterized in a systematic manner. To comprehensively identify genes and regulatory elements associated with metformin treatment, we carried out RNA-seq and ChIP-seq (H3K27ac, H3K27me3) on primary human hepatocytes from the same donor treated with vehicle control, metformin or metformin and compound C, an AMP-activated protein kinase (AMPK) inhibitor (allowing to identify AMPK-independent pathways). We identified thousands of metformin responsive AMPK-dependent and AMPK-independent differentially expressed genes and regulatory elements. We functionally validated several elements for metformin-induced promoter and enhancer activity. These include an enhancer in an ataxia telangiectasia mutated (ATM) intron that has SNPs in linkage disequilibrium with a metformin treatment response GWAS lead SNP (rs11212617) that showed increased enhancer activity for the associated haplotype. Expression quantitative trait locus (eQTL) liver analysis and CRISPR activation suggest that this enhancer could be regulating ATM, which has a known role in AMPK activation, and potentially also EXPH5 and DDX10, its neighboring genes. Using ChIP-seq and siRNA knockdown, we further show that activating transcription factor 3 (ATF3), our top metformin upregulated AMPK-dependent gene, could have an important role in gluconeogenesis repression. Our findings provide a genome-wide representation of metformin hepatic response, highlight important sequences that could be associated with interindividual variability in glycemic response to metformin and identify novel T2D treatment candidates. Metformin is among the most widely prescribed drugs. It is used as a first line therapy for type 2 diabetes (T2D), and for additional diseases including cancer. The variability in response to metformin is substantial and can be caused by genetic factors. However, the molecular mechanisms of metformin action are not fully known. Here, we used various genomic assays to analyze human liver cells treated with or without metformin and identified in a genome-wide manner thousands of differentially expressed genes and gene regulatory elements affected by metformin. Follow up functional assays identified several novel genes and regulatory elements to be associated with metformin response. These include ATF3, a gene that showed gluconeogenesis repression upon metformin response and a potential regulatory element of the ATM gene that is associated with metformin treatment differences through genome-wide association studies. Combined, this work identifies several novel genes and gene regulatory elements that can be activated due to metformin treatment and thus provides candidate sequences in the human genome where nucleotide variation can lead to differences in metformin response. It also enables the identification and prioritization of novel candidates for T2D treatment.
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Affiliation(s)
- Marcelo R. Luizon
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Walter L. Eckalbar
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Yao Wang
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Stacy L. Jones
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Robin P. Smith
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Megan Laurance
- Library and Center for Knowledge Management, University of California San Francisco, San Francisco, California, United States of America
| | - Lawrence Lin
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
| | - Paul J. Gallins
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Amy S. Etheridge
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Fred Wright
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Yihui Zhou
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Cliona Molony
- Merck Research Labs, Merck & Co. Inc., Kenilworth, New Jersey, United States of America
| | - Federico Innocenti
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- * E-mail:
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28
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Goswami S, Yee SW, Xu F, Sridhar SB, Mosley JD, Takahashi A, Kubo M, Maeda S, Davis RL, Roden DM, Hedderson MM, Giacomini KM, Savic RM. A Longitudinal HbA1c Model Elucidates Genes Linked to Disease Progression on Metformin. Clin Pharmacol Ther 2016; 100:537-547. [PMID: 27415606 PMCID: PMC5534241 DOI: 10.1002/cpt.428] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 06/20/2016] [Accepted: 06/22/2016] [Indexed: 12/20/2022]
Abstract
One-third of type-2 diabetic patients respond poorly to metformin. Despite extensive research, the impact of genetic and nongenetic factors on long-term outcome is unknown. In this study we combine nonlinear mixed effect modeling with computational genetic methodologies to identify predictors of long-term response. In all, 1,056 patients contributed their genetic, demographic, and long-term HbA1c data. The top nine variants (of 12,000 variants in 267 candidate genes) accounted for approximately one-third of the variability in the disease progression parameter. Average serum creatinine level, age, and weight were determinants of symptomatic response; however, explaining negligible variability. Two single nucleotide polymorphisms (SNPs) in CSMD1 gene (rs2617102, rs2954625) and one SNP in a pharmacologically relevant SLC22A2 gene (rs316009) influenced disease progression, with minor alleles leading to less and more favorable outcomes, respectively. Overall, our study highlights the influence of genetic factors on long-term HbA1c response and provides a computational model, which when validated, may be used to individualize treatment.
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Affiliation(s)
- S Goswami
- University of California, San Francisco, San Francisco, California, USA
| | - S W Yee
- University of California, San Francisco, San Francisco, California, USA
| | - F Xu
- Kaiser Permanente Northern California, Oakland, California, USA
| | - S B Sridhar
- Kaiser Permanente Northern California, Oakland, California, USA
| | - J D Mosley
- Vanderbilt University, Nashville, Tennessee, USA
| | - A Takahashi
- RIKEN Institute, Center for Genomic Medicine, Saitama, Japan
| | - M Kubo
- RIKEN Institute, Center for Genomic Medicine, Saitama, Japan
| | - S Maeda
- RIKEN Institute, Center for Genomic Medicine, Saitama, Japan
| | - R L Davis
- Kaiser Permanente Georgia, Atlanta, Georgia, USA
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA
| | - D M Roden
- Vanderbilt University, Nashville, Tennessee, USA
| | - M M Hedderson
- Kaiser Permanente Northern California, Oakland, California, USA
| | - K M Giacomini
- University of California, San Francisco, San Francisco, California, USA.
| | - R M Savic
- University of California, San Francisco, San Francisco, California, USA.
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29
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Kin K, Chen X, Gonzalez-Garay M, Fakhouri WD. The effect of non-coding DNA variations on P53 and cMYC competitive inhibition at cis-overlapping motifs. Hum Mol Genet 2016; 25:1517-27. [PMID: 26908612 DOI: 10.1093/hmg/ddw030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 02/01/2016] [Indexed: 01/22/2023] Open
Abstract
Non-coding DNA variations play a critical role in increasing the risk for development of common complex diseases, and account for the majority of SNPs highly associated with cancer. However, it remains a challenge to identify etiologic variants and to predict their pathological effects on target gene expression for clinical purposes. Cis-overlapping motifs (COMs) are elements of enhancer regions that impact gene expression by enabling competitive binding and switching between transcription factors. Mutations within COMs are especially important when the involved transcription factors have opposing effects on gene regulation, like P53 tumor suppressor and cMYC proto-oncogene. In this study, genome-wide analysis of ChIP-seq data from human cancer and mouse embryonic cells identified a significant number of putative regulatory elements with signals for both P53 and cMYC. Each co-occupied element contains, on average, two COMs, and one common SNP every two COMs. Gene ontology of predicted target genes for COMs showed that the majority are involved in DNA damage, apoptosis, cell cycle regulation, and RNA processing. EMSA results showed that both cMYC and P53 bind to cis-overlapping motifs within a ChIP-seq co-occupied region in Chr12. In vitro functional analysis of selected co-occupied elements verified enhancer activity, and also showed that the occurrence of SNPs within three COMs significantly altered enhancer activity. We identified a list of COM-associated functional SNPs that are in close proximity to SNPs associated with common diseases in large population studies. These results suggest a potential molecular mechanism to identify etiologic regulatory mutations associated with common diseases.
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Affiliation(s)
- Katherine Kin
- Department of Diagnostic and Biomedical Sciences, Center for Craniofacial Research, University of Texas Health Science Center at Houston School of Dentistry, Houston, TX 77054, USA and
| | - Xi Chen
- Department of Diagnostic and Biomedical Sciences, Center for Craniofacial Research, University of Texas Health Science Center at Houston School of Dentistry, Houston, TX 77054, USA and
| | - Manuel Gonzalez-Garay
- Center for Molecular Imaging, The Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Walid D Fakhouri
- Department of Diagnostic and Biomedical Sciences, Center for Craniofacial Research, University of Texas Health Science Center at Houston School of Dentistry, Houston, TX 77054, USA and
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30
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Below JE, Parra EJ, Gamazon ER, Torres J, Krithika S, Candille S, Lu Y, Manichakul A, Peralta-Romero J, Duan Q, Li Y, Morris AP, Gottesman O, Bottinger E, Wang XQ, Taylor KD, Ida Chen YD, Rotter JI, Rich SS, Loos RJF, Tang H, Cox NJ, Cruz M, Hanis CL, Valladares-Salgado A. Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects, and tissue-specific enrichment of eQTLs. Sci Rep 2016; 6:19429. [PMID: 26780889 PMCID: PMC4726092 DOI: 10.1038/srep19429] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Accepted: 12/14/2015] [Indexed: 11/21/2022] Open
Abstract
We performed genome-wide meta-analysis of lipid traits on three samples of Mexican and Mexican American ancestry comprising 4,383 individuals, and followed up significant and highly suggestive associations in three additional Hispanic samples comprising 7,876 individuals. Genome-wide significant signals were observed in or near CELSR2, ZNF259/APOA5, KANK2/DOCK6 and NCAN/MAU2 for total cholesterol, LPL, ABCA1, ZNF259/APOA5, LIPC and CETP for HDL cholesterol, CELSR2, APOB and NCAN/MAU2 for LDL cholesterol, and GCKR, TRIB1, ZNF259/APOA5 and NCAN/MAU2 for triglycerides. Linkage disequilibrium and conditional analyses indicate that signals observed at ABCA1 and LIPC for HDL cholesterol and NCAN/MAU2 for triglycerides are independent of previously reported lead SNP associations. Analyses of lead SNPs from the European Global Lipids Genetics Consortium (GLGC) dataset in our Hispanic samples show remarkable concordance of direction of effects as well as strong correlation in effect sizes. A meta-analysis of the European GLGC and our Hispanic datasets identified five novel regions reaching genome-wide significance: two for total cholesterol (FN1 and SAMM50), two for HDL cholesterol (LOC100996634 and COPB1) and one for LDL cholesterol (LINC00324/CTC1/PFAS). The top meta-analysis signals were found to be enriched for SNPs associated with gene expression in a tissue-specific fashion, suggesting an enrichment of tissue-specific function in lipid-associated loci.
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Affiliation(s)
- Jennifer E. Below
- Division of epidemiology, Human Genetics & Environmental Sciences, University of Texas School of Public Health, Houston, Texas, USA
| | - Esteban J. Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Eric R. Gamazon
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Illinois, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Jason Torres
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Illinois, USA
| | - S. Krithika
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Sophie Candille
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ani Manichakul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Jesus Peralta-Romero
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Qing Duan
- Department of Genetics and Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yun Li
- Department of Genetics and Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Andrew P. Morris
- Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
| | - Omri Gottesman
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erwin Bottinger
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Xin-Qun Wang
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Kent D. Taylor
- Institute of Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor/UCLA Medical Center, Torrance, California, USA
| | - Y.-D. Ida Chen
- Institute of Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor/UCLA Medical Center, Torrance, California, USA
| | - Jerome I. Rotter
- Institute of Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor/UCLA Medical Center, Torrance, California, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Nancy J. Cox
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Illinois, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Craig L. Hanis
- Division of epidemiology, Human Genetics & Environmental Sciences, University of Texas School of Public Health, Houston, Texas, USA
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, IMSS, Mexico City, Mexico
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Abstract
Although discussion of the obesity epidemic had become a cocktail party cliché, its impact on public health cannot be dismissed. In the past decade, cancer had joined the list of chronic debilitating diseases whose risk is substantially increased by hypernutrition. Here we discuss recent advances in understanding how obesity increases cancer risk and propose a unifying hypothesis according to which the major tumor-promoting mechanism triggered by hypernutrition is the indolent inflammation that takes place at particular organ sites, including liver, pancreas, and gastrointestinal tract. The mechanisms by which excessive fat deposition feeds this tumor-promoting inflammatory flame are diverse and tissue specific.
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Affiliation(s)
- Joan Font-Burgada
- Laboratory of Gene Regulation and Signal Transduction, Departments of Pharmacology and Pathology, Moores Cancer Center, UCSD School of Medicine, La Jolla, CA 92093-0723, USA
| | - Beicheng Sun
- Liver Transplantation Center of the First Affiliated Hospital and Cancer Center, Nanjing Medical University, Nanjing, Jiangsu Province, P.R. China.
| | - Michael Karin
- Laboratory of Gene Regulation and Signal Transduction, Departments of Pharmacology and Pathology, Moores Cancer Center, UCSD School of Medicine, La Jolla, CA 92093-0723, USA.
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32
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Gomez F, Wang L, Abel H, Zhang Q, Province MA, Borecki IB. Admixture mapping of coronary artery calcification in African Americans from the NHLBI family heart study. BMC Genet 2015; 16:42. [PMID: 25902833 PMCID: PMC4417236 DOI: 10.1186/s12863-015-0196-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 04/06/2015] [Indexed: 12/29/2022] Open
Abstract
Background Coronary artery calcification (CAC) is an imaging biomarker of coronary atherosclerosis. In European Americans, genome-wide association studies (GWAS) have identified several regions associated with coronary artery disease. However, few large studies have been conducted in African Americans. The largest meta-analysis of CAC in African Americans failed to identify genome-wide significant variants despite being powered to detect effects comparable to effects identified in European Americans. Because CAC is different in prevalence and severity in African Americans and European Americans, admixture mapping is a useful approach to identify loci missed by GWAS. Results We applied admixture mapping to the African American cohort of the Family Heart Study and identified one genome-wide significant region on chromosome 12 and three potential regions on chromosomes 6, 15, and 19 that are associated with CAC. Follow-up studies using previously reported GWAS meta-analysis data suggest that the regions identified on chromosome 6 and 15 contain variants that are possibly associated with CAC. The associated region on chromosome 6 contains the gene for BMP-6, which is expressed in vascular calcific lesions. Conclusions Our results suggest that admixture mapping can be a useful hypothesis-generating tool to identify genomic regions that contribute to complex diseases in genetically admixed populations. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0196-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Felicia Gomez
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine in St Louis, 4444 Forest Park Blvd, Campus Box 8506, St Louis, MO, 63108, USA.
| | - Lihua Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine in St Louis, 4444 Forest Park Blvd, Campus Box 8506, St Louis, MO, 63108, USA.
| | - Haley Abel
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine in St Louis, 4444 Forest Park Blvd, Campus Box 8506, St Louis, MO, 63108, USA.
| | - Qunyuan Zhang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine in St Louis, 4444 Forest Park Blvd, Campus Box 8506, St Louis, MO, 63108, USA.
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine in St Louis, 4444 Forest Park Blvd, Campus Box 8506, St Louis, MO, 63108, USA.
| | - Ingrid B Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine in St Louis, 4444 Forest Park Blvd, Campus Box 8506, St Louis, MO, 63108, USA.
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33
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Leiria LB, Dora JM, Wajner SM, Estivalet AAF, Crispim D, Maia AL. The rs225017 polymorphism in the 3'UTR of the human DIO2 gene is associated with increased insulin resistance. PLoS One 2014; 9:e103960. [PMID: 25105294 PMCID: PMC4126657 DOI: 10.1371/journal.pone.0103960] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2014] [Accepted: 07/06/2014] [Indexed: 01/19/2023] Open
Abstract
The Thr92Ala (rs225014) polymorphism in the type 2 deiodinase (DIO2) gene has been associated with insulin resistance (IR) and decreased enzyme activity in human tissues but kinetic studies failed to detect changes in the mutant enzyme, suggesting that this variant might be a marker of abnormal DIO2 expression. Thus, we aimed to investigate whether other DIO2 polymorphisms, individually or in combination with the Thr92Ala, may contribute to IR. The entire coding-region of DIO2 gene was sequenced in 12 patients with type 2 diabetes mellitus (T2DM). Potentially informative variants were evaluated in 1077 T2DM patients and 516 nondiabetic subjects. IR was evaluated using the homeostasis model assessment (HOMA-IR) index. DIO2 gene sequencing revealed no new mutation but 5 previously described single nucleotide polymorphisms (SNPs). We observed that all T2DM patients displaying high HOMA-IR index (n = 6) were homozygous for the rs225017 (T/A) polymorphism. Further analysis showed that the median fasting plasma insulin and HOMA-IR of T2DM patients carrying the T/T genotype were higher than in patients carrying the A allele (P = 0.013 and P = 0.002, respectively). These associations were magnified in the presence of the Ala92Ala genotype of the Thr92Ala polymorphism. Moreover, the rs225017 and the Thr92Ala polymorphisms were in partial linkage disequilibrium (|D'| = 0.811; r2 = 0.365). In conclusion, the rs225017 polymorphism is associated with greater IR in T2DM and it seems to interact with the Thr92Ala polymorphism in the modulation of IR.
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Affiliation(s)
- Leonardo B. Leiria
- Thyroid Section, Endocrine Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - José M. Dora
- Thyroid Section, Endocrine Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Simone M. Wajner
- Thyroid Section, Endocrine Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Aline A. F. Estivalet
- Thyroid Section, Endocrine Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Daisy Crispim
- Thyroid Section, Endocrine Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Ana Luiza Maia
- Thyroid Section, Endocrine Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
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Genome-wide copy number variation study reveals KCNIP1 as a modulator of insulin secretion. Genomics 2014; 104:113-20. [DOI: 10.1016/j.ygeno.2014.05.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Revised: 05/19/2014] [Accepted: 05/23/2014] [Indexed: 01/09/2023]
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Cheng M, Yang L, Yang R, Yang X, Deng J, Yu B, Huang D, Zhang S, Wang H, Qiu F, Zhou Y, Lu J. A microRNA-135a/b binding polymorphism in CD133 confers decreased risk and favorable prognosis of lung cancer in Chinese by reducing CD133 expression. Carcinogenesis 2013; 34:2292-2299. [DOI: 10.1093/carcin/bgt181] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023] Open
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Ho Jang G, Christie JD, Feng R. A method for calling copy number polymorphism using haplotypes. Front Genet 2013; 4:165. [PMID: 24069028 PMCID: PMC3780619 DOI: 10.3389/fgene.2013.00165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 08/07/2013] [Indexed: 12/15/2022] Open
Abstract
Single nucleotide polymorphism (SNP) and copy number variation (CNV) are both widespread characteristic of the human genome, but are often called separately on common genotyping platforms. To capture integrated SNP and CNV information, methods have been developed for calling allelic specific copy numbers or so called copy number polymorphism (CNP), using limited inter-marker correlation. In this paper, we proposed a haplotype-based maximum likelihood method to call CNP, which takes advantage of the valuable multi-locus linkage disequilibrium (LD) information in the population. We also developed a computationally efficient algorithm to estimate haplotype frequencies and optimize individual CNP calls iteratively, even at presence of missing data. Through simulations, we demonstrated our model is more sensitive and accurate in detecting various CNV regions, compared with commonly-used CNV calling methods including PennCNV, another hidden Markov model (HMM) using CNP, a scan statistic, segCNV, and cnvHap. Our method often performs better in the regions with higher LD, in longer CNV regions, and in common CNV than the opposite. We implemented our method on the genotypes of 90 HapMap CEU samples and 23 patients with acute lung injury (ALI). For each ALI patient the genotyping was performed twice. The CNPs from our method show good consistency and accuracy comparable to others.
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Affiliation(s)
- Gun Ho Jang
- Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Philadelphia, PA, USA
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Advantage of using allele-specific copy numbers when testing for association in regions with common copy number variants. PLoS One 2013; 8:e75350. [PMID: 24040408 PMCID: PMC3769257 DOI: 10.1371/journal.pone.0075350] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Accepted: 08/14/2013] [Indexed: 11/19/2022] Open
Abstract
Copy number variants (CNV) can be called from SNP-arrays; however, few studies have attempted to combine both CNV and SNP calls to test for association with complex diseases. Even when SNPs are located within CNVs, two separate association analyses are necessary, to compare the distribution of bi-allelic genotypes in cases and controls (referred to as SNP-only strategy) and the number of copies of a region (referred to as CNV-only strategy). However, when disease susceptibility is actually associated with allele specific copy-number states, the two strategies may not yield comparable results, raising a series of questions about the optimal analytical approach. We performed simulations of the performance of association testing under different scenarios that varied genotype frequencies and inheritance models. We show that the SNP-only strategy lacks power under most scenarios when the SNP is located within a CNV; frequently it is excluded from analysis as it does not pass quality control metrics either because of an increased rate of missing calls or a departure from fitness for Hardy-Weinberg proportion. The CNV-only strategy also lacks power because the association testing depends on the allele which copy number varies. The combined strategy performs well in most of the scenarios. Hence, we advocate the use of this combined strategy when testing for association with SNPs located within CNVs.
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van der Mark VA, Elferink RPJO, Paulusma CC. P4 ATPases: flippases in health and disease. Int J Mol Sci 2013; 14:7897-922. [PMID: 23579954 PMCID: PMC3645723 DOI: 10.3390/ijms14047897] [Citation(s) in RCA: 94] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Revised: 03/28/2013] [Accepted: 04/07/2013] [Indexed: 12/26/2022] Open
Abstract
P4 ATPases catalyze the translocation of phospholipids from the exoplasmic to the cytosolic leaflet of biological membranes, a process termed “lipid flipping”. Accumulating evidence obtained in lower eukaryotes points to an important role for P4 ATPases in vesicular protein trafficking. The human genome encodes fourteen P4 ATPases (fifteen in mouse) of which the cellular and physiological functions are slowly emerging. Thus far, deficiencies of at least two P4 ATPases, ATP8B1 and ATP8A2, are the cause of severe human disease. However, various mouse models and in vitro studies are contributing to our understanding of the cellular and physiological functions of P4-ATPases. This review summarizes current knowledge on the basic function of these phospholipid translocating proteins, their proposed action in intracellular vesicle transport and their physiological role.
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Affiliation(s)
- Vincent A van der Mark
- Tytgat Institute for Liver and Intestinal Research, Academic Medical Center, Meibergdreef 69-71, 1105 BK Amsterdam, The Netherlands.
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Abstract
The global epidemic of type 2 diabetes mellitus (T2D) is one of the most challenging problems of the 21(st) century leading cause of and the fifth death worldwide. Substantial evidence suggests that T2D is a multifactorial disease with a strong genetic component. Recent genome-wide association studies (GWAS) have successfully identified and replicated nearly 75 susceptibility loci associated with T2D and related metabolic traits, mostly in Europeans, and some in African, and South Asian populations. The GWAS serve as a starting point for future genetic and functional studies since the mechanisms of action by which these associated loci influence disease is still unclear and it is difficult to predict potential implication of these findings in clinical settings. Despite extensive replication, no study has unequivocally demonstrated their clinical role in the disease management beyond progression to T2D from impaired glucose tolerance. However, these studies are revealing new molecular pathways underlying diabetes etiology, gene-environment interactions, epigenetic modifications, and gene function. This review highlights evolving progress made in the rapidly moving field of T2D genetics that is starting to unravel the pathophysiology of a complex phenotype and has potential to show clinical relevance in the near future.
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