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Dash M, Mahajan B, Dar GM, Sahu P, Saluja SS. An update on the cell-free DNA-derived methylome as a non-invasive biomarker for coronary artery disease. Int J Biochem Cell Biol 2024; 169:106555. [PMID: 38428633 DOI: 10.1016/j.biocel.2024.106555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 12/22/2023] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
Cardiovascular diseases are the foremost contributor to global mortality, presenting a complex etiology and an expanding array of risk factors. Coronary artery disease characterized by atherosclerotic plaque build-up in the coronary arteries, imposes significant mortality and financial burdens, especially in low- and middle-income nations. The pathogenesis of coronary artery disease involves a multifaceted interplay of genetic, environmental, and epigenetic factors. Epigenetic regulation contributes to the dynamic control of gene expression without altering the underlying DNA sequence. The mounting evidence that highlights the pivotal role of epigenetic regulation in coronary artery disease development and progression, offering potential avenues for the development of novel diagnostic biomarkers and therapeutic targets. Abnormal DNA methylation patterns are linked to the modulation of gene expression involved in crucial processes like lipid metabolism, inflammation, and vascular function in the context of coronary artery disease. Cell-free DNA has become invaluable in tumor biology as a liquid biopsy, while its applications in coronary artery disease are limited, but intriguing. Atherosclerotic plaque rupture causes myocardial infarction, by depriving heart muscles of oxygen, releasing cell-free DNA from dead cardiac cells, and providing a minimally invasive source to explore tissue-specific epigenetic alterations. We discussed the methodologies for studying the global methylome and hydroxy-methylome landscape, their advantages, and limitations. It explores methylome alterations in coronary artery disease, considering risk factors and their relevance in coronary artery disease genesis. The review also details the implications of MI-derived cell-free DNA for developing minimally invasive biomarkers and associated challenges.
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Affiliation(s)
- Manoswini Dash
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research (GIPMER), New Delhi, India; School of Medicine, Center for Aging, Tulane University, LA, United States
| | - Bhawna Mahajan
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research (GIPMER), New Delhi, India; Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research (GIPMER), New Delhi, India.
| | - Ghulam Mehdi Dar
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research (GIPMER), New Delhi, India
| | - Parameswar Sahu
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research (GIPMER), New Delhi, India
| | - Sundeep Singh Saluja
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research (GIPMER), New Delhi, India; Department of GI Surgery, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research (GIPMER), New Delhi, India
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Kumar AAW, Huangfu G, Figtree GA, Dwivedi G. Atherosclerosis as the Damocles' sword of human evolution: insights from nonhuman ape-like primates, ancient human remains, and isolated modern human populations. Am J Physiol Heart Circ Physiol 2024; 326:H821-H831. [PMID: 38305751 DOI: 10.1152/ajpheart.00744.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 02/03/2024]
Abstract
Atherosclerosis is the leading cause of death worldwide, and the predominant risk factors are advanced age and high-circulating low-density lipoprotein cholesterol (LDL-C). However, the findings of atherosclerosis in relatively young mummified remains and a lack of atherosclerosis in chimpanzees despite high LDL-C call into question the role of traditional cardiovascular risk factors. The inflammatory theory of atherosclerosis may explain the discrepancies between traditional risk factors and observed phenomena in current literature. Following the divergence from chimpanzees several millennia ago, loss of function mutations in immune regulatory genes and changes in gene expression have resulted in an overactive human immune system. The ubiquity of atherosclerosis in the modern era may reflect a selective pressure that enhanced the innate immune response at the cost of atherogenesis and other chronic disease states. Evidence provided from the fields of genetics, evolutionary biology, and paleoanthropology demonstrates a sort of circular dependency between inflammation, immune system functioning, and evolution at both a species and cellular level. More recently, the role of proinflammatory stimuli, somatic mutations, and the gene-environment effect appear to be underappreciated elements in the development and progression of atherosclerosis. Neurobiological stress, metabolic syndrome, and traditional cardiovascular risk factors may instead function as intermediary links between inflammation and atherosclerosis. Therefore, considering evolution as a mechanistic process and atherosclerosis as part of the inertia of evolution, greater insight into future preventative and therapeutic interventions for atherosclerosis can be gained by examining the past.
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Affiliation(s)
- Annora Ai-Wei Kumar
- Medical School, The University of Western Australia, Crawley, Western Australia, Australia
| | - Gavin Huangfu
- Medical School, The University of Western Australia, Crawley, Western Australia, Australia
- Department of Cardiology, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
- Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
| | - Gemma A Figtree
- Cardiovascular Discovery Group, Kolling Institute of Medical Research, St. Leonards, New South Wales, Australia
- Department of Cardiology, Royal North Shore Hospital, St. Leonards, New South Wales, Australia
| | - Girish Dwivedi
- Medical School, The University of Western Australia, Crawley, Western Australia, Australia
- Department of Cardiology, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
- Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
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Bergonzini M, Loreni F, Lio A, Russo M, Saitto G, Cammardella A, Irace F, Tramontin C, Chello M, Lusini M, Nenna A, Ferrisi C, Ranocchi F, Musumeci F. Panoramic on Epigenetics in Coronary Artery Disease and the Approach of Personalized Medicine. Biomedicines 2023; 11:2864. [PMID: 37893238 PMCID: PMC10604795 DOI: 10.3390/biomedicines11102864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/02/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Epigenetic modifications play a fundamental role in the progression of coronary artery disease (CAD). This panoramic review aims to provide an overview of the current understanding of the epigenetic mechanisms involved in CAD pathogenesis and highlights the potential implications for personalized medicine approaches. Epigenetics is the study of heritable changes that do not influence alterations in the DNA sequence of the genome. It has been shown that epigenetic processes, including DNA/histone methylation, acetylation, and phosphorylation, play an important role. Additionally, miRNAs, lncRNAs, and circRNAs are also involved in epigenetics, regulating gene expression patterns in response to various environmental factors and lifestyle choices. In the context of CAD, epigenetic alterations contribute to the dysregulation of genes involved in inflammation, oxidative stress, lipid metabolism, and vascular function. These epigenetic changes can occur during early developmental stages and persist throughout life, predisposing individuals to an increased risk of CAD. Furthermore, in recent years, the concept of personalized medicine has gained significant attention. Personalized medicine aims to tailor medical interventions based on an individual's unique genetic, epigenetic, environmental, and lifestyle factors. In the context of CAD, understanding the interplay between genetic variants and epigenetic modifications holds promise for the development of more precise diagnostic tools, risk stratification models, and targeted therapies. This review summarizes the current knowledge of epigenetic mechanisms in CAD and discusses the fundamental principles of personalized medicine.
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Affiliation(s)
- Marcello Bergonzini
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Francesco Loreni
- Cardiac Surgery, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Antonio Lio
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Marco Russo
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Guglielmo Saitto
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Antonio Cammardella
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Francesco Irace
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Corrado Tramontin
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Massimo Chello
- Cardiac Surgery, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Mario Lusini
- Cardiac Surgery, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Antonio Nenna
- Cardiac Surgery, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Chiara Ferrisi
- Cardiac Surgery, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Federico Ranocchi
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Francesco Musumeci
- Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, 00152 Rome, Italy
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Li X, Delerue T, Schöttker B, Holleczek B, Grill E, Peters A, Waldenberger M, Thorand B, Brenner H. Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults. Nat Commun 2022; 13:5269. [PMID: 36071044 PMCID: PMC9450828 DOI: 10.1038/s41467-022-32893-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 08/23/2022] [Indexed: 11/09/2022] Open
Abstract
DNA methylation (DNAm) patterns in peripheral blood have been shown to be associated with aging related health outcomes. We perform an epigenome-wide screening to identify CpGs related to frailty, defined by a frailty index (FI), in a large population-based cohort of older adults from Germany, the ESTHER study. Sixty-five CpGs are identified as frailty related methylation loci. Using LASSO regression, 20 CpGs are selected to derive a DNAm based algorithm for predicting frailty, the epigenetic frailty risk score (eFRS). The eFRS exhibits strong associations with frailty at baseline and after up to five-years of follow-up independently of established frailty risk factors. These associations are confirmed in another independent population-based cohort study, the KORA-Age study, conducted in older adults. In conclusion, we identify 65 CpGs as frailty-related loci, of which 20 CpGs are used to calculate the eFRS with predictive performance for frailty over long-term follow-up.
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Affiliation(s)
- Xiangwei Li
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120, Heidelberg, Germany.,Medical Faculty Heidelberg, University of Heidelberg, Im Neuenheimer Feld 672, 69120, Heidelberg, Germany
| | - Thomas Delerue
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Bavaria, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120, Heidelberg, Germany.,Network Aging Research, University of Heidelberg, Bergheimer Straße 20, 69115, Heidelberg, Germany
| | - Bernd Holleczek
- Saarland Cancer Registry, Krebsregister Saarland, Neugeländstraße 9, 66117, Saarbrücken, Germany
| | - Eva Grill
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.,German Center for Vertigo and Balance Disorders, Klinikum der Universität München, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Bavaria, Germany.,Institute for Medical Informatics, Biometrics and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Bavaria, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Bavaria, Germany
| | - Barbara Thorand
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120, Heidelberg, Germany. .,Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany. .,German Cancer Consortium, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
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Unlocking the potential of forensic traces: Analytical approaches to generate investigative leads. Sci Justice 2022; 62:310-326. [PMID: 35598924 DOI: 10.1016/j.scijus.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 03/17/2022] [Accepted: 03/19/2022] [Indexed: 11/21/2022]
Abstract
Forensic investigation involves gathering the information necessary to understand the criminal events as well as linking objects or individuals to an item, location or other individual(s) for investigative purposes. For years techniques such as presumptive chemical tests, DNA profiling or fingermark analysis have been of great value to this process. However, these techniques have their limitations, whether it is a lack of confidence in the results obtained due to cross-reactivity, subjectivity and low sensitivity; or because they are dependent on holding reference samples in a pre-existing database. There is currently a need to devise new ways to gather as much information as possible from a single trace, particularly from biological traces commonly encountered in forensic casework. This review outlines the most recent advancements in the forensic analysis of biological fluids, fingermarks and hair. Special emphasis is placed on analytical methods that can expand the information obtained from the trace beyond what is achieved in the usual practices. Special attention is paid to those methods that accurately determine the nature of the sample, as well as how long it has been at the crime scene, along with individualising information regarding the donor source of the trace.
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Soda K. Overview of Polyamines as Nutrients for Human Healthy Long Life and Effect of Increased Polyamine Intake on DNA Methylation. Cells 2022; 11:cells11010164. [PMID: 35011727 PMCID: PMC8750749 DOI: 10.3390/cells11010164] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/24/2021] [Accepted: 12/28/2021] [Indexed: 02/04/2023] Open
Abstract
Polyamines, spermidine and spermine, are synthesized in every living cell and are therefore contained in foods, especially in those that are thought to contribute to health and longevity. They have many physiological activities similar to those of antioxidant and anti-inflammatory substances such as polyphenols. These include antioxidant and anti-inflammatory properties, cell and gene protection, and autophagy activation. We have first reported that increased polyamine intake (spermidine much more so than spermine) over a long period increased blood spermine levels and inhibited aging-associated pathologies and pro-inflammatory status in humans and mice and extended life span of mice. However, it is unlikely that the life-extending effect of polyamines is exerted by the same bioactivity as polyphenols because most studies using polyphenols and antioxidants have failed to demonstrate their life-extending effects. Recent investigations revealed that aging-associated pathologies and lifespan are closely associated with DNA methylation, a regulatory mechanism of gene expression. There is a close relationship between polyamine metabolism and DNA methylation. We have shown that the changes in polyamine metabolism affect the concentrations of substances and enzyme activities involved in DNA methylation. I consider that the increased capability of regulation of DNA methylation by spermine is a key of healthy long life of humans.
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Affiliation(s)
- Kuniyasu Soda
- Department Cardiovascular Institute for Medical Research, Saitama Medical Center, Jichi Medical University, 1-847, Amanuma, Saitama-City 330-0834, Saitama, Japan
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Chi Y, Wang X, Jia J, Huang T. Smoking Status and Type 2 Diabetes, and Cardiovascular Disease: A Comprehensive Analysis of Shared Genetic Etiology and Causal Relationship. Front Endocrinol (Lausanne) 2022; 13:809445. [PMID: 35250867 PMCID: PMC8894600 DOI: 10.3389/fendo.2022.809445] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/18/2022] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE This study aimed to explore shared genetic etiology and the causality between smoking status and type 2 diabetes (T2D), cardiovascular diseases (CVDs), and related metabolic traits. METHODS Using summary statistics from publicly available genome-wide association studies (GWASs), we estimated genetic correlations between smoking status and T2D, 6 major CVDs, and 8 related metabolic traits with linkage disequilibrium score regression (LDSC) analysis; identified shared genetic loci with large-scale genome-wide cross-trait meta-analysis; explored potential shared biological mechanisms with a series of post-GWAS analyses; and determined causality with Mendelian randomization (MR). RESULTS We found significant positive genetic associations with smoking status for T2D (Rg = 0.170, p = 9.39 × 10-22), coronary artery disease (CAD) (Rg = 0.234, p = 1.96 × 10-27), myocardial infarction (MI) (Rg = 0.226, p = 1.08 × 10-17), and heart failure (HF) (Rg = 0.276, p = 8.43 × 10-20). Cross-trait meta-analysis and transcriptome-wide association analysis of smoking status identified 210 loci (32 novel loci) and 354 gene-tissue pairs jointly associated with T2D, 63 loci (12 novel loci) and 37 gene-tissue pairs with CAD, 38 loci (6 novel loci) and 17 gene-tissue pairs with MI, and 28 loci (3 novel loci) and one gene-tissue pair with HF. The shared loci were enriched in the exo-/endocrine, cardiovascular, nervous, digestive, and genital systems. Furthermore, we observed that smoking status was causally related to a higher risk of T2D (β = 0.385, p = 3.31 × 10-3), CAD (β = 0.670, p = 7.86 × 10-11), MI (β = 0.725, p = 2.32 × 10-9), and HF (β = 0.520, p = 1.53 × 10-6). CONCLUSIONS Our findings provide strong evidence on shared genetic etiology and causal associations between smoking status and T2D, CAD, MI, and HF, underscoring the potential shared biological mechanisms underlying the link between smoking and T2D and CVDs. This work opens up a new way of more effective and timely prevention of smoking-related T2D and CVDs.
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Affiliation(s)
- Yanna Chi
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Xinpei Wang
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jinzhu Jia
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Statistical Science, Peking University, Beijing, China
- *Correspondence: Jinzhu Jia, ; Tao Huang,
| | - Tao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Global Health, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China
- *Correspondence: Jinzhu Jia, ; Tao Huang,
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Almutairi M, Almutairi B, Almutairi M, Parine NR, Alrefaei A, Alanazi M, Semlali A. Human beta-defensin-1 rs2738047 polymorphism is associated with shisha smoking risk among Saudi population. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:42916-42933. [PMID: 33826097 PMCID: PMC8025738 DOI: 10.1007/s11356-021-13660-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/22/2021] [Indexed: 06/12/2023]
Abstract
Human β-defensin (HBD), a member of the antimicrobial peptides, is essential for respiratory epithelial cells' microbial defense, and is affected by cigarette smoking (CS). Its expression is upregulated by stimulation from microbes or inflammation. Genetic polymorphisms in the HBD-1 gene have been implicated in the development of various smoking-related diseases, including chronic obstructive pulmonary disease and asthma. Thus, we sought to analyze possible associations between HBD-1 single-nucleotide polymorphism (SNP) in HBD-1 gene and CS in ethnic Saudi Arabian subjects. Variants rs1047031 (C/T), rs1799946 (C/T), rs2738047 (C/T), and rs11362 (C/T) were investigated by genotyping 575 blood specimens from males and females, smokers/non-smokers: 288/287. The CT and CT+TT genotypes of rs1799946 presented an ~5-fold increased correlation with CS among the female smokers, compared with the female controls (OR = 5.473, P = 0.02003; and OR = 5.211, P = 0.02028, respectively), an observation similar to rs11362 SNP in female smokers, but with protective effects in TT genotype, compared with the CC reference allele (OR = 0.143, P = 0.04368). In shisha smokers, the heterozygous CT and the CT/TT genotype of rs2738047 polymorphism showed the same results with ~3-fold increased correlation with CS (OR = 2.788; P = 0.03448), compared with the cigarette smokers category. No significant association was shown in genotypic distributions and allelic frequencies of rs1047031. Further investigations, including large study samples, are required to investigate the effects of shisha on human beta-defensin expression and protein levels.
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Affiliation(s)
- Mikhlid Almutairi
- Zoology Department, College of Science, King Saud University, P.O. Box: 2455, Riyadh, 11451, Saudi Arabia.
| | - Bader Almutairi
- Zoology Department, College of Science, King Saud University, P.O. Box: 2455, Riyadh, 11451, Saudi Arabia
| | - Mohammad Almutairi
- Zoology Department, College of Science, King Saud University, P.O. Box: 2455, Riyadh, 11451, Saudi Arabia
| | - Narasimha Reddy Parine
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Abdulwahed Alrefaei
- Zoology Department, College of Science, King Saud University, P.O. Box: 2455, Riyadh, 11451, Saudi Arabia
| | - Mohammad Alanazi
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Abdelhabib Semlali
- Groupe de Recherche en Écologie Buccale, Département de stomatologie, Faculté de Médecine Dentaire, Université Laval, Québec, Québec, Canada
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Qin X, Karlsson IK, Wang Y, Li X, Pedersen N, Reynolds CA, Hägg S. The epigenetic etiology of cardiovascular disease in a longitudinal Swedish twin study. Clin Epigenetics 2021; 13:129. [PMID: 34167563 PMCID: PMC8223329 DOI: 10.1186/s13148-021-01113-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/14/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Studies on DNA methylation have the potential to discover mechanisms of cardiovascular disease (CVD) risk. However, the role of DNA methylation in CVD etiology remains unclear. RESULTS We performed an epigenome-wide association study (EWAS) on CVD in a longitudinal sample of Swedish twins (535 individuals). We selected CpGs reaching the Bonferroni-corrected significance level (2 [Formula: see text] 10-7) or the top-ranked 20 CpGs with the lowest P values if they did not reach this significance level in EWAS analysis associated with non-stroke CVD, overall stroke, and ischemic stroke, respectively. We further applied a bivariate autoregressive latent trajectory model with structured residuals (ALT-SR) to evaluate the cross-lagged effect between DNA methylation of these CpGs and cardiometabolic traits (blood lipids, blood pressure, and body mass index). Furthermore, mediation analysis was performed to evaluate whether the cross-lagged effects had causal impacts on CVD. In the EWAS models, none of the CpGs we selected reached the Bonferroni-corrected significance level. The ALT-SR model showed that DNA methylation levels were more likely to predict the subsequent level of cardiometabolic traits rather than the other way around (numbers of significant cross-lagged paths of methylation → trait/trait → methylation were 84/4, 45/6, 66/1 for the identified three CpG sets, respectively). Finally, we demonstrated significant indirect effects from DNA methylation on CVD mediated by cardiometabolic traits. CONCLUSIONS We present evidence for a directional association from DNA methylation on cardiometabolic traits and CVD, rather than the opposite, highlighting the role of epigenetics in CVD development.
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Affiliation(s)
- Xueying Qin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 17177, Stockholm, Sweden.
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38# Xueyuan Road, Beijing, 100191, China.
| | - Ida K Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 17177, Stockholm, Sweden
- Institute of Gerontology and Aging Research Network - Jönköping (ARN-J), School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 17177, Stockholm, Sweden
| | - Xia Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 17177, Stockholm, Sweden
| | - Nancy Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 17177, Stockholm, Sweden
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | | | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 17177, Stockholm, Sweden
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Nikpay M, McPherson R. Convergence of biomarkers and risk factor trait loci of coronary artery disease at 3p21.31 and HLA region. NPJ Genom Med 2021; 6:12. [PMID: 33574266 PMCID: PMC7878768 DOI: 10.1038/s41525-021-00174-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 01/06/2021] [Indexed: 12/13/2022] Open
Abstract
Here we seek to identify molecular biomarkers that mediate the effect of risk factors on coronary artery disease (CAD). We perform a SNP-based multiomics data analysis to find biomarkers (probes) causally associated with the risk of CAD within known genomic loci for its risk factors. We identify 78 biomarkers, the majority (64%) of which are methylation probes. We detect the convergence of several CNS and lifestyle trait loci and their biomarkers at the 3p21.31 and human leukocyte antigen (HLA) regions. The 3p21.31 locus was the most populated region in the convergence of biomarkers and risk factors. In this region, we noted as the BSN gene becomes methylated the level of stomatin (STOM) in blood increases and this contributes to higher risk of CAD. In the HLA locus, we identify several methylation biomarkers associated with various CAD risk factors. SNPs in the CFB gene display a trans-regulatory impact on the GRIA4 protein level. A methylation site upstream of the APOE gene is associated with a higher protein level of S100A13 which in turn leads to higher LDL-C and greater CAD risk. We find UHRF1BP1 and ILRUN mediate the effect of obesity on CAD whereas methylation sites within NOS3 and CKM mediate the effect of their associated-risk factors on CAD. This study provides further insight into the biology of CAD and identifies a list of biomarkers that mediate the impact of risk factors on CAD. A SNP-based initiative can unite data from various fields of omics into a single network of knowledge.
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Affiliation(s)
- Majid Nikpay
- Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, ON, Canada.
| | - Ruth McPherson
- Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, ON, Canada.
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, ON, Canada.
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11
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An F, Liu C, Wang X, Li T, Fu H, Bao B, Cong H, Zhao J. Effect of ABCA1 promoter methylation on premature coronary artery disease and its relationship with inflammation. BMC Cardiovasc Disord 2021; 21:78. [PMID: 33557767 PMCID: PMC7869242 DOI: 10.1186/s12872-021-01894-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/22/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND ATP-binding cassette transporter A1 (ABCA1) plays a major role in high-density lipoprotein (HDL) metabolism and reverse cholesterol transport (RCT) and exerts anti-inflammatory effects. Increased ABCA1 promoter methylation level may result in the progression of coronary artery disease. Thus, the present study investigated the association between promoter methylation status of ABCA1 and inflammation in the development of premature coronary artery disease (pCAD). METHODS PCAD patients and healthy individuals (n = 90 each) were recruited from the Characteristic Medical Center of the Chinese People's Armed Police Force from June to December 2019. Using pyrosequencing, the levels of ABCA1 promoter methylation in their blood samples were evaluated. Serum concentrations of lipids, interleukin 1β (IL-1β), C-reactive protein (CRP), and circulating free DNA/Neutrophil extracellular traps (cfDNA/NETs) were also routinely measured and compared between the two groups. P values < 0.05 were considered statistically significant. RESULTS The mean ABCA1 promoter methylation levels were significantly higher in the pCAD group than in the control group (44.24% ± 3.66 vs. 36.05% ± 2.99, P < 0.001). Based on binary logistic regression analysis, ABCA1 promoter methylation level was identified as an independent risk factor for pCAD development (odds ratio = 2.878, 95% confidence interval: 1.802-4.594, P < 0.001). Furthermore, ABCA1 promoter methylation levels were negatively correlated with HDL levels (r = - 0.488, P < 0.001) and positively correlated with the levels of CRP, cfDNA/NETs, and IL-1β (r = 0.389, 0.404, 0.385, respectively; P < 0.001). Multiple regression analysis showed that the serum levels of CRP, IL-1β, and cfDNA/NETs independently affect ABCA1 promoter methylation. CONCLUSIONS Our findings indicate that high methylation levels at the ABCA1 promoter are associated with low HDL cholesterol levels and an increased risk of pCAD. Inflammatory factors and NETs may be involved in the progression of pCAD by affecting ABCA1 promoter methylation levels.
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Affiliation(s)
- Fang An
- Graduate School, Tianjin Medical University, Tianjin, 300070, China.,Department of Military General Medicine, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, 300162, China
| | - Chao Liu
- Institute of Cardiovascular disease, Tianjin Chest Hospital, Tianjin, 300222, China
| | - Xiujuan Wang
- Institute of Cardiovascular Disease, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, 300162, China
| | - Tan Li
- Department of Pathogen Biology, Logistics University of Chinese People's Armed Police Force, Tianjin, 300309, China
| | - Hao Fu
- Department of Military General Medicine, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, 300162, China
| | - Buhe Bao
- Department of Clinical Laboratory, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, 300162, China
| | - Hongliang Cong
- Institute of Cardiovascular disease, Tianjin Chest Hospital, Tianjin, 300222, China. .,Department of Cardiology, Tianjin Chest Hospital, Tianjin, 300222, China.
| | - Jihong Zhao
- Department of Military General Medicine, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, 300162, China.
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12
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Sumi MP, Mahajan B, Sattar RSA, Nimisha, Apurva, Kumar A, Sharma AK, Ahmad E, Ali A, Saluja SS. Elucidation of Epigenetic Landscape in Coronary Artery Disease: A Review on Basic Concept to Personalized Medicine. Epigenet Insights 2021; 14:2516865720988567. [PMID: 33598635 PMCID: PMC7863167 DOI: 10.1177/2516865720988567] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 12/26/2020] [Indexed: 12/11/2022] Open
Abstract
Despite extensive clinical research and management protocols applied in the field of coronary artery diseases (CAD), it still holds the number 1 position in mortality worldwide. This indicates that we need to work on precision medicine to discover the diagnostic, therapeutic, and prognostic targets to improve the outcome of CAD. In precision medicine, epigenetic changes play a vital role in disease onset and progression. Epigenetics is the study of heritable changes that do not affect the alterations of DNA sequence in the genome. It comprises various covalent modifications that occur in DNA or histone proteins affecting the spatial arrangement of the DNA and histones. These multiple modifications include DNA/histone methylation, acetylation, phosphorylation, and SUMOylation. Besides these covalent modifications, non-coding RNAs-viz. miRNA, lncRNA, and circRNA are also involved in epigenetics. Smoking, alcohol, diet, environmental pollutants, obesity, and lifestyle are some of the prime factors affecting epigenetic alterations. Novel molecular techniques such as next-generation sequencing, chromatin immunoprecipitation, and mass spectrometry have been developed to identify important cross points in the epigenetic web in relation to various diseases. The studies regarding exploration of epigenetics, have led researchers to identify multiple diagnostic markers and therapeutic targets that are being used in different disease diagnosis and management. Here in this review, we will discuss various ground-breaking contributions of past and recent studies in the epigenetic field in concert with coronary artery diseases. Future prospects of epigenetics and its implication in CAD personalized medicine will also be discussed in brief.
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Affiliation(s)
- Mamta P Sumi
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
| | - Bhawna Mahajan
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
- Department of Biochemistry, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
| | - Real Sumayya Abdul Sattar
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
| | - Nimisha
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
| | - Apurva
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
| | - Arun Kumar
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
| | - Abhay Kumar Sharma
- Department of Biochemistry, All India Institute of Medical Science, Patna, Bihar, India
| | - Ejaz Ahmad
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
| | - Asgar Ali
- Department of Biochemistry, All India Institute of Medical Science, Patna, Bihar, India
| | - Sundeep Singh Saluja
- Central Molecular Laboratory, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research (GIPMER), University of Delhi, New Delhi, India
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13
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Smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic traits. Clin Epigenetics 2020; 12:157. [PMID: 33092652 PMCID: PMC7579899 DOI: 10.1186/s13148-020-00951-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Tobacco smoking is a well-known modifiable risk factor for many chronic diseases, including cardiovascular disease (CVD). One of the proposed underlying mechanism linking smoking to disease is via epigenetic modifications, which could affect the expression of disease-associated genes. Here, we conducted a three-way association study to identify the relationship between smoking-related changes in DNA methylation and gene expression and their associations with cardio-metabolic traits. RESULTS We selected 2549 CpG sites and 443 gene expression probes associated with current versus never smokers, from the largest epigenome-wide association study and transcriptome-wide association study to date. We examined three-way associations, including CpG versus gene expression, cardio-metabolic trait versus CpG, and cardio-metabolic trait versus gene expression, in the Rotterdam study. Subsequently, we replicated our findings in The Cooperative Health Research in the Region of Augsburg (KORA) study. After correction for multiple testing, we identified both cis- and trans-expression quantitative trait methylation (eQTM) associations in blood. Specifically, we found 1224 smoking-related CpGs associated with at least one of the 443 gene expression probes, and 200 smoking-related gene expression probes to be associated with at least one of the 2549 CpGs. Out of these, 109 CpGs and 27 genes were associated with at least one cardio-metabolic trait in the Rotterdam Study. We were able to replicate the associations with cardio-metabolic traits of 26 CpGs and 19 genes in the KORA study. Furthermore, we identified a three-way association of triglycerides with two CpGs and two genes (GZMA; CLDND1), and BMI with six CpGs and two genes (PID1; LRRN3). Finally, our results revealed the mediation effect of cg03636183 (F2RL3), cg06096336 (PSMD1), cg13708645 (KDM2B), and cg17287155 (AHRR) within the association between smoking and LRRN3 expression. CONCLUSIONS Our study indicates that smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic risk factors. These findings may provide additional insights into the molecular mechanisms linking smoking to the development of CVD.
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14
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Dong Z, Ma Y, Zhou H, Shi L, Ye G, Yang L, Liu P, Zhou L. Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets. BMC Pulm Med 2020; 20:270. [PMID: 33066754 PMCID: PMC7568423 DOI: 10.1186/s12890-020-01303-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 09/27/2020] [Indexed: 02/06/2023] Open
Abstract
Background Severe asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown. Methods In the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data (N = 1977) and a large-scale GWAS summary data of moderate-to-severe asthma (N = 30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma. Results In the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P = 4.30 × 10− 6), type I diabetes mellitus (Corrected P = 7.09 × 10− 5), and asthma (Corrected P = 1.72 × 10− 3). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (PeQTL = 2.98 × 10− 8 and PGWAS = 3.40 × 10− 8), rs11265180 (PeQTL = 6.0 × 10− 6 and PGWAS = 1.99 × 10− 3), and rs1867087 (PeQTL = 1.0 × 10− 4 and PGWAS = 1.84 × 10− 5) identified. In addition, GNGT2 is significantly expressed in severe asthma compared with mild-moderate asthma (P = 0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (Anova P = 1.55 × 10− 6). Conclusions Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.
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Affiliation(s)
- Zhouzhou Dong
- Critical Care Unit, Ningbo Medical Center Lihuili Hospital, Taipei Medical University Ningbo Medical Center, Ningbo, Zhejiang, 315100, P.R. China
| | - Yunlong Ma
- Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.,School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Hua Zhou
- Department of Respiratory Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P.R. China
| | - Linhui Shi
- Critical Care Unit, Ningbo Medical Center Lihuili Hospital, Taipei Medical University Ningbo Medical Center, Ningbo, Zhejiang, 315100, P.R. China
| | - Gongjie Ye
- Critical Care Unit, Ningbo Medical Center Lihuili Hospital, Taipei Medical University Ningbo Medical Center, Ningbo, Zhejiang, 315100, P.R. China
| | - Lei Yang
- Critical Care Unit, Ningbo Medical Center Lihuili Hospital, Taipei Medical University Ningbo Medical Center, Ningbo, Zhejiang, 315100, P.R. China
| | - Panpan Liu
- Critical Care Unit, Ningbo Medical Center Lihuili Hospital, Taipei Medical University Ningbo Medical Center, Ningbo, Zhejiang, 315100, P.R. China
| | - Li Zhou
- Department of Immunology and Rheumatology, Ningbo Medical Center Lihuili Hospital, Taipei Medical University Ningbo Medical Center, Ningbo, Zhejiang, 315100, P.R. China.
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15
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Erzurumluoglu AM, Liu M, Jackson VE, Barnes DR, Datta G, Melbourne CA, Young R, Batini C, Surendran P, Jiang T, Adnan SD, Afaq S, Agrawal A, Altmaier E, Antoniou AC, Asselbergs FW, Baumbach C, Bierut L, Bertelsen S, Boehnke M, Bots ML, Brazel DM, Chambers JC, Chang-Claude J, Chen C, Corley J, Chou YL, David SP, de Boer RA, de Leeuw CA, Dennis JG, Dominiczak AF, Dunning AM, Easton DF, Eaton C, Elliott P, Evangelou E, Faul JD, Foroud T, Goate A, Gong J, Grabe HJ, Haessler J, Haiman C, Hallmans G, Hammerschlag AR, Harris SE, Hattersley A, Heath A, Hsu C, Iacono WG, Kanoni S, Kapoor M, Kaprio J, Kardia SL, Karpe F, Kontto J, Kooner JS, Kooperberg C, Kuulasmaa K, Laakso M, Lai D, Langenberg C, Le N, Lettre G, Loukola A, Luan J, Madden PAF, Mangino M, Marioni RE, Marouli E, Marten J, Martin NG, McGue M, Michailidou K, Mihailov E, Moayyeri A, Moitry M, Müller-Nurasyid M, Naheed A, Nauck M, Neville MJ, Nielsen SF, North K, Perola M, Pharoah PDP, Pistis G, Polderman TJ, Posthuma D, Poulter N, Qaiser B, Rasheed A, Reiner A, Renström F, Rice J, Rohde R, Rolandsson O, Samani NJ, Samuel M, Schlessinger D, Scholte SH, Scott RA, Sever P, Shao Y, Shrine N, Smith JA, Starr JM, Stirrups K, Stram D, Stringham HM, Tachmazidou I, Tardif JC, Thompson DJ, Tindle HA, Tragante V, Trompet S, Turcot V, Tyrrell J, Vaartjes I, van der Leij AR, van der Meer P, Varga TV, Verweij N, Völzke H, Wareham NJ, Warren HR, Weir DR, Weiss S, Wetherill L, Yaghootkar H, Yavas E, Jiang Y, Chen F, Zhan X, Zhang W, Zhao W, Zhao W, Zhou K, Amouyel P, Blankenberg S, Caulfield MJ, Chowdhury R, Cucca F, Deary IJ, Deloukas P, Di Angelantonio E, Ferrario M, Ferrières J, Franks PW, Frayling TM, Frossard P, Hall IP, Hayward C, Jansson JH, Jukema JW, Kee F, Männistö S, Metspalu A, Munroe PB, Nordestgaard BG, Palmer CNA, Salomaa V, Sattar N, Spector T, Strachan DP, van der Harst P, Zeggini E, Saleheen D, Butterworth AS, Wain LV, Abecasis GR, Danesh J, Tobin MD, Vrieze S, Liu DJ, Howson JMM. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Mol Psychiatry 2020; 25:2392-2409. [PMID: 30617275 PMCID: PMC7515840 DOI: 10.1038/s41380-018-0313-0] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 09/30/2018] [Accepted: 11/14/2018] [Indexed: 02/02/2023]
Abstract
Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.
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Affiliation(s)
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Victoria E Jackson
- Department of Health Sciences, University of Leicester, Leicester, UK
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Pde, 3052, Parkville, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, 3010, Parkville, Australia
| | - Daniel R Barnes
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Gargi Datta
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Carl A Melbourne
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Robin Young
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Chiara Batini
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Praveen Surendran
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Tao Jiang
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Sheikh Daud Adnan
- National Institute of Cardiovascular Diseases, Sher-e-Bangla Nagar, Dhaka, Bangladesh
| | - Saima Afaq
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - Arpana Agrawal
- Department of Psychiatry, Washington University, St. Louis, MO, USA
| | - Elisabeth Altmaier
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Folkert W Asselbergs
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
- Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, UK
| | - Clemens Baumbach
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Laura Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Sarah Bertelsen
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Michiel L Bots
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508GA, Utrecht, The Netherlands
- Center for Circulatory Health, University Medical Center Utrecht, 3508GA, Utrecht, The Netherlands
| | - David M Brazel
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Department of Cardiology, Ealing Hospital, Middlesex, UB1 3HW, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Chu Chen
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Janie Corley
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Yi-Ling Chou
- Department of Psychiatry, Washington University, St. Louis, MO, USA
| | - Sean P David
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Rudolf A de Boer
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Joe G Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Anna F Dominiczak
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge Centre, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge Centre, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Charles Eaton
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
- National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare NHS Trust and Imperial College London, London, UK
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, UK
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, 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
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Alison Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jian Gong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Jeff Haessler
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Nutritional research, Umeå University, Umeå, Sweden
| | - Anke R Hammerschlag
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Andrew Hattersley
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Andrew Heath
- Department of Psychiatry, Washington University, St. Louis, MO, USA
| | - Chris Hsu
- University of Southern California, California, CA, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Centre for Genomic Health, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Manav Kapoor
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Sharon L Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford National Institute for Health Research, Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Jukka Kontto
- Department of Public Health Solutions, National Institute for Health and Welfare, FI-00271, Helsinki, Finland
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, Middlesex, UB1 3HW, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
| | - Charles Kooperberg
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington School of Medicine, Seattle, WA, USA
| | - Kari Kuulasmaa
- Department of Public Health Solutions, National Institute for Health and Welfare, FI-00271, Helsinki, Finland
| | | | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Nhung Le
- Department of Medical Microbiology, Immunology and Cell Biology, Southern Illinois University School of Medicine, Springfield, IL, USA
| | - Guillaume Lettre
- Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada
- Department of Medicine, Faculty of Medicine, Universite de Montreal, Montreal, Quebec, H3T 1J4, Canada
| | - Anu Loukola
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | | | - Massimo Mangino
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, SE1 9RT, UK
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, SE1 7EH, UK
| | - Riccardo E Marioni
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Eirini Marouli
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Centre for Genomic Health, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Jonathan Marten
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, 1683, Nicosia, Cyprus
| | | | - Alireza Moayyeri
- Institute of Health Informatics, University College London, London, UK
| | - Marie Moitry
- Department of Epidemiology and Public health, University Hospital of Strasbourg, Strasbourg, France
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, Ludwig-Maximilians-University Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Aliya Naheed
- Initiative for Noncommunicable Diseases, Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17475, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
| | - Matthew J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford National Institute for Health Research, Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Sune Fallgaard Nielsen
- Department of Clinical Biochemistry Herlev Hospital, Copenhagen University Hospital, Herlev Ringvej 74, DK-2730, Herlev, Denmark
| | - Kari North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Markus Perola
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health Solutions, National Institute for Health and Welfare, FI-00271, Helsinki, Finland
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge Centre, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Giorgio Pistis
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Tinca J Polderman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
- Department of Clinical Genetics, VU University Medical Centre Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Neil Poulter
- International Centre for Circulatory Health, Imperial College London, London, UK
| | - Beenish Qaiser
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Asif Rasheed
- Centre for Non-Communicable Diseases, Karachi, Pakistan
| | - Alex Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Frida Renström
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Skåne University Hospital, Lund University, SE-214 28, Malmö, Sweden
- Department of Biobank Research, Umeå University, SE-901 87, Umeå, Sweden
| | - John Rice
- Departments of Psychiatry and Mathematics, Washington University St. Louis, St. Louis, MO, USA
| | | | - Olov Rolandsson
- Department of Public Health & Clinical Medicine, Section for Family Medicine, Umeå universitet, SE, 90185, Umeå, Sweden
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Cardiovascular Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
| | - Maria Samuel
- Centre for Non-Communicable Diseases, Karachi, Pakistan
| | - David Schlessinger
- National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Steven H Scholte
- Department of Psychology, University of Amsterdam & Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Peter Sever
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- International Centre for Circulatory Health, Imperial College London, London, UK
| | - Yaming Shao
- University of North Carolina, Chapel Hill, NC, USA
| | - Nick Shrine
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Alzheimer Scotland Research Centre, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Kathleen Stirrups
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Department of Haematology, University of Cambridge, Cambridge, CB2 0PT, UK
| | - Danielle Stram
- Department of Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | | | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada
- Department of Medicine, Faculty of Medicine, Universite de Montreal, Montreal, Quebec, H3T 1J4, Canada
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Hilary A Tindle
- Department of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Vinicius Tragante
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, 3508GA, Utrecht, The Netherlands
| | - Stella Trompet
- Department of gerontology and geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Valerie Turcot
- Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Ilonca Vaartjes
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508GA, Utrecht, The Netherlands
- Center for Circulatory Health, University Medical Center Utrecht, 3508GA, Utrecht, The Netherlands
| | - Andries R van der Leij
- Department of Psychology, University of Amsterdam & Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
| | - Peter van der Meer
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Tibor V Varga
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Skåne University Hospital, Lund University, SE-214 28, Malmö, Sweden
| | - Niek Verweij
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 301 Binney Street, Cambridge, MA, 02142, USA
| | - Henry Völzke
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Helen R Warren
- Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Stefan Weiss
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, 17475, Greifswald, Germany
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Ersin Yavas
- Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania, PA, 16802, USA
| | - Yu Jiang
- Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
| | - Fang Chen
- Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
| | - Xiaowei Zhan
- Department of Clinical Science, Center for Genetics of Host Defense, University of Texas Southwestern, Dallas, TX, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK
| | - Wei Zhao
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Pennsylvania, PA, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kaixin Zhou
- School of Medicine, University of Dundee, Dundee, UK
| | - Philippe Amouyel
- Department of Epidemiology and Public Health, Institut Pasteur de Lille, Lille, France
| | - Stefan Blankenberg
- Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
- University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Mark J Caulfield
- Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Rajiv Chowdhury
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Panos Deloukas
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Emanuele Di Angelantonio
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Marco Ferrario
- EPIMED Research Centre, Department of Medicine and Surgery, University of Insubria at Varese, Varese, Italy
| | - Jean Ferrières
- Department of Epidemiology, UMR 1027- INSERM, Toulouse University-CHU Toulouse, Toulouse, France
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Skåne University Hospital, Lund University, SE-214 28, Malmö, Sweden
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Tim M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | | | - Ian P Hall
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Jan-Håkan Jansson
- Department of Public Health and Clinical Medicine, Skellefteå Research Unit, Umeå University, Umeå, Sweden
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- The Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands
| | - Frank Kee
- UKCRC Centre of Excellence for Public Health, Queens, University, Belfast, Belfast, UK
| | - Satu Männistö
- Department of Public Health Solutions, National Institute for Health and Welfare, FI-00271, Helsinki, Finland
| | | | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Børge Grønne Nordestgaard
- Department of Clinical Biochemistry Herlev Hospital, Copenhagen University Hospital, Herlev Ringvej 74, DK-2730, Herlev, Denmark
| | - Colin N A Palmer
- Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Veikko Salomaa
- Department of Public Health Solutions, National Institute for Health and Welfare, FI-00271, Helsinki, Finland
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Timothy Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, SE1 7EH, UK
| | - David Peter Strachan
- Population Health Research Institute, St George!s, University of London, London, SW17 0RE, UK
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Danish Saleheen
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Adam S Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Louise V Wain
- Department of Health Sciences, University of Leicester, Leicester, UK
- National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Goncalo R Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - John Danesh
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Martin D Tobin
- Department of Health Sciences, University of Leicester, Leicester, UK
- National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Dajiang J Liu
- Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA.
| | - Joanna M M Howson
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
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Nikpay M, Soubeyrand S, Tahmasbi R, McPherson R. Multiomics Screening Identifies Molecular Biomarkers Causally Associated With the Risk of Coronary Artery Disease. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2020; 13:e002876. [PMID: 32969717 DOI: 10.1161/circgen.119.002876] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND In this study, we aimed to investigate functional mechanisms underlying coronary artery disease (CAD) loci and find molecular biomarkers for CAD. METHODS We devised a multiomics data analysis approach based on Mendelian randomization and utilized it to search for molecular biomarkers causally associated with the risk of CAD within genomic regions known to be associated with CAD. RESULTS Through our CAD-centered multiomics data analysis approach, we identified 33 molecular biomarkers (probes) that were causally associated with the risk of CAD. The majority of these (N=19) were methylation probes; moreover, methylation was often behind the causal effect of expression/protein probes. We identified a number of novel loci that have a causal impact on CAD including C5orf38, SF3A3, DHX36, and MRPL33. Furthermore, by integrating the risk factors of CAD in our analysis, we were able to investigate the clinical pathways whereby several of our probes exert their effect. We found that the SELE protein level in the blood is under the trans-regulatory impact of methylation sites within the ABO gene and that SELE exerts its effect on CAD through immune, glycemic, and lipid metabolism, making it a candidate of interest for therapeutic interventions. We found the methylation site, cg05126514 within the BSN gene exert its effect on CAD through central nervous system-lifestyle risk factors. Finally, genes with a transcriptional regulatory role (SF3A3, ILF3, and N4BP2L2) exert their effect on CAD through height. CONCLUSIONS We demonstrate that multiomics data analysis is a powerful approach to unravel the functional mechanisms underlying CAD loci and to identify novel molecular biomarkers. Our results indicate epigenetic modifications are important in the pathogenesis of CAD and identifying and targeting these sites is of potential therapeutic interest to address the detrimental effects of both environmental and genetic factors.
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Affiliation(s)
- Majid Nikpay
- Ruddy Canadian Cardiovascular Genetics Centre (M.N., R.M.), University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Sebastien Soubeyrand
- Atherogenomics Laboratory (S.S., R.M.), University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Rasool Tahmasbi
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado (R.T.)
| | - Ruth McPherson
- Ruddy Canadian Cardiovascular Genetics Centre (M.N., R.M.), University of Ottawa Heart Institute, Ottawa, Ontario, Canada.,Atherogenomics Laboratory (S.S., R.M.), University of Ottawa Heart Institute, Ottawa, Ontario, Canada
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17
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Sameri S, Samadi P, Dehghan R, Salem E, Fayazi N, Amini R. Stem Cell Aging in Lifespan and Disease: A State-of-the-Art Review. Curr Stem Cell Res Ther 2020; 15:362-378. [DOI: 10.2174/1574888x15666200213105155] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 11/09/2019] [Accepted: 12/31/2019] [Indexed: 12/11/2022]
Abstract
Aging is considered as inevitable changes at different levels of genome, cell, and organism.
From the accumulation of DNA damages to imperfect protein homeostasis, altered cellular communication
and exhaustion of stem cells, aging is a major risk factor for many prevalent diseases, such as
cancer, cardiovascular disease, pulmonary disease, diabetes, and neurological disorders. The cells are
dynamic systems, which, through a cycle of processes such as replication, growth, and death, could
replenish the bodies’ organs and tissues, keeping an entire organism in optimal working order. In many
different tissues, adult stem cells are behind these processes, replenishing dying cells to maintain normal
tissue function and regenerating injured tissues. Therefore, adult stem cells play a vital role in preventing
the aging of organs and tissues, and can delay aging. However, during aging, these cells also
undergo some detrimental changes such as alterations in the microenvironment, a decline in the regenerative
capacity, and loss of function. This review aimed to discuss age-related changes of stem cells in
different tissues and cells, including skin, muscles, brain, heart, hair follicles, liver, and lung.
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Affiliation(s)
- Saba Sameri
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Pouria Samadi
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Razieh Dehghan
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Elham Salem
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Nashmin Fayazi
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Razieh Amini
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
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18
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Domingo-Relloso A, Riffo-Campos AL, Haack K, Rentero-Garrido P, Ladd-Acosta C, Fallin DM, Tang WY, Herreros-Martinez M, Gonzalez JR, Bozack AK, Cole SA, Navas-Acien A, Tellez-Plaza M. Cadmium, Smoking, and Human Blood DNA Methylation Profiles in Adults from the Strong Heart Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:67005. [PMID: 32484362 PMCID: PMC7265996 DOI: 10.1289/ehp6345] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 03/18/2020] [Accepted: 04/22/2020] [Indexed: 05/03/2023]
Abstract
BACKGROUND The epigenetic effects of individual environmental toxicants in tobacco remain largely unexplored. Cadmium (Cd) has been associated with smoking-related health effects, and its concentration in tobacco smoke is higher in comparison with other metals. OBJECTIVES We studied the association of Cd and smoking exposures with human blood DNA methylation (DNAm) profiles. We also evaluated the implication of findings to relevant methylation pathways and the potential contribution of Cd exposure from smoking to explain the association between smoking and site-specific DNAm. METHODS We conducted an epigenome-wide association study of urine Cd and self-reported smoking (current and former vs. never, and cumulative smoking dose) with blood DNAm in 790,026 CpGs (methylation sites) measured with the Illumina Infinium Human MethylationEPIC (Illumina Inc.) platform in 2,325 adults 45-74 years of age who participated in the Strong Heart Study in 1989-1991. In a mediation analysis, we estimated the amount of change in DNAm associated with smoking that can be independently attributed to increases in urine Cd concentrations from smoking. We also conducted enrichment analyses and in silico protein-protein interaction networks to explore the biological relevance of the findings. RESULTS At a false discovery rate (FDR)-corrected level of 0.05, we found 6 differentially methylated positions (DMPs) for Cd; 288 and 17, respectively, for current and former smoking status; and 77 for cigarette pack-years. Enrichment analyses of these DMPs displayed enrichment of 58 and 6 Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene sets, respectively, including biological pathways for cancer and cardiovascular disease. In in silico protein-to-protein networks, we observed key proteins in DNAm pathways directly and indirectly connected to Cd- and smoking-DMPs. Among DMPs that were significant for both Cd and current smoking (annotated to PRSS23, AHRR, F2RL3, RARA, and 2q37.1), we found statistically significant contributions of Cd to smoking-related DNAm. CONCLUSIONS Beyond replicating well-known smoking epigenetic signatures, we found novel DMPs related to smoking. Moreover, increases in smoking-related Cd exposure were associated with differential DNAm. Our integrative analysis supports a biological link for Cd and smoking-associated health effects, including the possibility that Cd is partly responsible for smoking toxicity through epigenetic changes. https://doi.org/10.1289/EHP6345.
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Affiliation(s)
- Arce Domingo-Relloso
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA
- Department of Chronic Diseases Epidemiology, National Center of Epidemiology, Carlos III Health Institute, Madrid, Spain
- Department of Statistics and Operations Research, University of Valencia, Spain
| | | | - Karin Haack
- Population Health Program, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Pilar Rentero-Garrido
- Precision Medicine Unit, Institute for Biomedical Research INCLIVA, Valencia, Spain
- Department of Mental Health, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Daniele M Fallin
- Department of Mental Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Wan Yee Tang
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Juan R Gonzalez
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER en Epidemiología y Salud Pública (CIBERESP), National Center of Epidemiology, Carlos III Health Institute, Madrid, Spain
| | - Anne K Bozack
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Shelley A Cole
- Population Health Program, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Ana Navas-Acien
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Maria Tellez-Plaza
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA
- Area of Cardiometabolic and Renal Risk, Institute for Biomedical Research INCLIVA, Valencia, Spain
- Department of Chronic Diseases Epidemiology, National Center of Epidemiology, Carlos III Health Institute, Madrid, Spain
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19
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Lu A, Wang W, Wang-Renault SF, Ring BZ, Tanaka Y, Weng J, Su L. 5-Aza-2'-deoxycytidine advances the epithelial-mesenchymal transition of breast cancer cells by demethylating Sipa1 promoter-proximal elements. J Cell Sci 2020; 133:jcs.236125. [PMID: 32193333 PMCID: PMC7240297 DOI: 10.1242/jcs.236125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 03/02/2020] [Indexed: 12/17/2022] Open
Abstract
Human breast cancer cells exhibit considerable diversity in the methylation status of genomic DNA CpGs that regulate metastatic transcriptome networks. In this study, we identified human Sipa1 promoter-proximal elements that contained a CpG island and demonstrated that the methylation status of the CpG island was inversely correlated with SIPA1 protein expression in cancer cells. 5-Aza-2′-deoxycytidine (5-Aza-CdR), a DNA methyltransferase inhibitor, promoted the expression of Sipa1 in the MCF7 breast cancer cells with a low level of SIPA1 expression. On the contrary, in MDA-MB-231 breast cancer cells with high SIPA1 expression levels, hypermethylation of the CpG island negatively regulated the transcription of Sipa1. In addition, the epithelial–mesenchymal transition (EMT) was reversed after knocking down Sipa1 in MDA-MB-231 cells. However, the EMT was promoted in MCF7 cells with over-expression of SIPA1 or treated with 5-Aza-CdR. Taken together, hypomethylation of the CpG island in Sipa1 promoter-proximal elements could enhance SIPA1 expression in breast cancer cells, which could facilitate EMT of cancer cells, possibly increasing a risk of cancer cell metastasis in individuals treated with 5-Aza-CdR. Summary: Hypomethylation by 5-Aza-CdR upregulates the SIPA1 expression and promotes epithelial–mesenchymal transition in breast cancer cells, possibly increasing the risk of cancer cell metastasis.
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Affiliation(s)
- Ang Lu
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wei Wang
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Shu-Fang Wang-Renault
- INSERM UMR-S1147, CNRS SNC5014; Paris Descartes University, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris 75006, France
| | - Brian Z Ring
- Institute of Genomic and Personalized Medicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yoshimasa Tanaka
- Center for Medical Innovation, Nagasaki University, 1-7-1, Sakamoto, Nagasaki, 852-8588, Japan
| | - Jun Weng
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Li Su
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China .,Research Institute of Huazhong University of Science and Technology in Shenzhen, Shenzhen, 518063, China
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20
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Ochoa-Rosales C, Portilla-Fernandez E, Nano J, Wilson R, Lehne B, Mishra PP, Gao X, Ghanbari M, Rueda-Ochoa OL, Juvinao-Quintero D, Loh M, Zhang W, Kooner JS, Grabe HJ, Felix SB, Schöttker B, Zhang Y, Gieger C, Müller-Nurasyid M, Heier M, Peters A, Lehtimäki T, Teumer A, Brenner H, Waldenberger M, Ikram MA, van Meurs JBJ, Franco OH, Voortman T, Chambers J, Stricker BH, Muka T. Epigenetic Link Between Statin Therapy and Type 2 Diabetes. Diabetes Care 2020; 43:875-884. [PMID: 32033992 DOI: 10.2337/dc19-1828] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 01/14/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To investigate the role of epigenetics in statins' diabetogenic effect comparing DNA methylation (DNAm) between statin users and nonusers in an epigenome-wide association study in blood. RESEARCH DESIGN AND METHODS Five cohort studies' participants (n = 8,270) were classified as statin users when they were on statin therapy at the time of DNAm assessment with Illumina 450K or EPIC array or noncurrent users otherwise. Associations of DNAm with various outcomes like incident type 2 diabetes, plasma glucose, insulin, and insulin resistance (HOMA of insulin resistance [HOMA-IR]) as well as with gene expression were investigated. RESULTS Discovery (n = 6,820) and replication (n = 1,450) phases associated five DNAm sites with statin use: cg17901584 (1.12 × 10-25 [DHCR24]), cg10177197 (3.94 × 10-08 [DHCR24]), cg06500161 (2.67 × 10-23 [ABCG1]), cg27243685 (6.01 × 10-09 [ABCG1]), and cg05119988 (7.26 × 10-12 [SC4MOL]). Two sites were associated with at least one glycemic trait or type 2 diabetes. Higher cg06500161 methylation was associated with higher fasting glucose, insulin, HOMA-IR, and type 2 diabetes (odds ratio 1.34 [95% CI 1.22, 1.47]). Mediation analyses suggested that ABCG1 methylation partially mediates the effect of statins on high insulin and HOMA-IR. Gene expression analyses showed that statin exposure and ABCG1 methylation were associated with ABCG1 downregulation, suggesting epigenetic regulation of ABCG1 expression. Further, outcomes insulin and HOMA-IR were significantly associated with ABCG1 expression. CONCLUSIONS This study sheds light on potential mechanisms linking statins with type 2 diabetes risk, providing evidence on DNAm partially mediating statins' effects on insulin traits. Further efforts shall disentangle the molecular mechanisms through which statins may induce DNAm changes, potentially leading to ABCG1 epigenetic regulation.
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Affiliation(s)
- Carolina Ochoa-Rosales
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Centro de Vida Saludable de la Universidad de Concepción, Concepción, Chile
| | | | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Rory Wilson
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Xu Gao
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Oscar L Rueda-Ochoa
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Electrocardiography Research group, School of Medicine, Universidad Industrial de Santander, Bucaramanga, Colombia
| | | | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, U.K
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, U.K
- National Heart and Lung Institute, Imperial College London, London, U.K
- Imperial College Healthcare NHS Trust, London, U.K
- MRC-PHE Centre for Environment and Health, Imperial College London, London, U.K
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Stephan B Felix
- Partner Site Greifswald, German Center for Cardiovascular Research (DZHK), Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Yan Zhang
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
- Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Margit Heier
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- KORA Study Centre, University Hospital of Augsburg, Augsburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Alexander Teumer
- Partner Site Greifswald, German Center for Cardiovascular Research (DZHK), Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Oscar H Franco
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - John Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, U.K
- Imperial College Healthcare NHS Trust, London, U.K
- MRC-PHE Centre for Environment and Health, Imperial College London, London, U.K
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Taulant Muka
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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21
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Sarlak S, Lalou C, Amoedo ND, Rossignol R. Metabolic reprogramming by tobacco-specific nitrosamines (TSNAs) in cancer. Semin Cell Dev Biol 2020; 98:154-166. [PMID: 31699542 DOI: 10.1016/j.semcdb.2019.09.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 08/25/2019] [Accepted: 09/02/2019] [Indexed: 12/21/2022]
Abstract
Metabolic reprogramming is a hallmark of cancer and the link between oncogenes activation, tumor supressors inactivation and bioenergetics modulation is well established. However, numerous carcinogenic environmental factors are responsible for early cancer initiation and their impact on metabolic reprogramming just starts to be deciphered. For instance, it was recently shown that UVB irradiation triggers metabolic reprogramming at the pre-cancer stage with implication for skin cancer detection and therapy. These observations foster the need to study the early changes in tissue metabolism following exposure to other carcinogenic events. According to the International Agency for Research on Cancer (IARC), tobacco smoke is a major class I-carcinogenic environmental factor that contains different carcinogens, but little is known on the impact of tobacco smoke on tissue metabolism and its participation to cancer initiation. In particular, tobacco-specific nitrosamines (TSNAs) play a central role in tobacco-smoke mediated cancer initiation. Here we describe the recent advances that have led to a new hypothesis regarding the link between nitrosamines signaling and metabolic reprogramming in cancer.
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Affiliation(s)
- Saharnaz Sarlak
- INSERM U1211, 33000 Bordeaux, France; Bordeaux University, 146 rue Léo Saignat, 33000 Bordeaux, France
| | - Claude Lalou
- INSERM U1211, 33000 Bordeaux, France; Bordeaux University, 146 rue Léo Saignat, 33000 Bordeaux, France
| | - Nivea Dias Amoedo
- CELLOMET, Functional Genomics Center (CGFB), 146 rue Léo Saignat, 33000 Bordeaux, France
| | - Rodrigue Rossignol
- INSERM U1211, 33000 Bordeaux, France; Bordeaux University, 146 rue Léo Saignat, 33000 Bordeaux, France; CELLOMET, Functional Genomics Center (CGFB), 146 rue Léo Saignat, 33000 Bordeaux, France.
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22
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Ghose S, Ghosh S, Tanwar VS, Tolani P, Kutum R, Sharma A, Bhardwaj N, Shamsudheen K, Verma A, Jayarajan R, Dash D, Sivasubbu S, Scaria V, Seth S, Sengupta S. Investigating Coronary Artery Disease methylome through targeted bisulfite sequencing. Gene 2019; 721:144107. [DOI: 10.1016/j.gene.2019.144107] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 09/05/2019] [Accepted: 09/05/2019] [Indexed: 01/02/2023]
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23
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Corley J, Cox SR, Harris SE, Hernandez MV, Maniega SM, Bastin ME, Wardlaw JM, Starr JM, Marioni RE, Deary IJ. Epigenetic signatures of smoking associate with cognitive function, brain structure, and mental and physical health outcomes in the Lothian Birth Cohort 1936. Transl Psychiatry 2019; 9:248. [PMID: 31591380 PMCID: PMC6779733 DOI: 10.1038/s41398-019-0576-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 06/29/2019] [Indexed: 12/18/2022] Open
Abstract
Recent advances in genome-wide DNA methylation (DNAm) profiling for smoking behaviour have given rise to a new, molecular biomarker of smoking exposure. It is unclear whether a smoking-associated DNAm (epigenetic) score has predictive value for ageing-related health outcomes which is independent of contributions from self-reported (phenotypic) smoking measures. Blood DNA methylation levels were measured in 895 adults aged 70 years in the Lothian Birth Cohort 1936 (LBC1936) study using the Illumina 450K assay. A DNA methylation score based on 230 CpGs was used as a proxy for smoking exposure. Associations between smoking variables and health outcomes at age 70 were modelled using general linear modelling (ANCOVA) and logistic regression. Additional analyses of smoking with brain MRI measures at age 73 (n = 532) were performed. Smoking-DNAm scores were positively associated with self-reported smoking status (P < 0.001, eta-squared ɳ2 = 0.63) and smoking pack years (r = 0.69, P < 0.001). Higher smoking DNAm scores were associated with variables related to poorer cognitive function, structural brain integrity, physical health, and psychosocial health. Compared with phenotypic smoking, the methylation marker provided stronger associations with all of the cognitive function scores, especially visuospatial ability (P < 0.001, partial eta-squared ɳp2 = 0.022) and processing speed (P < 0.001, ɳp2 = 0.030); inflammatory markers (all P < 0.001, ranges from ɳp2 = 0.021 to 0.030); dietary patterns (healthy diet (P < 0.001, ɳp2 = 0.052) and traditional diet (P < 0.001, ɳp2 = 0.032); stroke (P = 0.006, OR 1.48, 95% CI 1.12, 1.96); mortality (P < 0.001, OR 1.59, 95% CI 1.42, 1.79), and at age 73; with MRI volumetric measures (all P < 0.001, ranges from ɳp2 = 0.030 to 0.052). Additionally, education was the most important life-course predictor of lifetime smoking tested. Our results suggest that a smoking-associated methylation biomarker typically explains a greater proportion of the variance in some smoking-related morbidities in older adults, than phenotypic measures of smoking exposure, with some of the accounted-for variance being independent of phenotypic smoking status.
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Affiliation(s)
- Janie Corley
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Maria Valdéz Hernandez
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Brain Research Imaging Centre, Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Brain Research Imaging Centre, Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Brain Research Imaging Centre, Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Brain Research Imaging Centre, Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Royal Victoria Building, Western General Hospital, Porterfield Road, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
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24
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Maas SCE, Vidaki A, Wilson R, Teumer A, Liu F, van Meurs JBJ, Uitterlinden AG, Boomsma DI, de Geus EJC, Willemsen G, van Dongen J, van der Kallen CJH, Slagboom PE, Beekman M, van Heemst D, van den Berg LH, Duijts L, Jaddoe VWV, Ladwig KH, Kunze S, Peters A, Ikram MA, Grabe HJ, Felix JF, Waldenberger M, Franco OH, Ghanbari M, Kayser M. Validated inference of smoking habits from blood with a finite DNA methylation marker set. Eur J Epidemiol 2019; 34:1055-1074. [PMID: 31494793 PMCID: PMC6861351 DOI: 10.1007/s10654-019-00555-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/22/2019] [Indexed: 12/18/2022]
Abstract
Inferring a person’s smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 ± 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUCcrossvalidation 0.925 ± 0.021, AUCexternalvalidation0.914), former (0.766 ± 0.023, 0.699) and never smoking (0.830 ± 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 ± 0.068, 0.796; 15 pack-years 0.767 ± 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 ± 0.024, 0.760; 10 years 0.766 ± 0.033, 0.764; 15 years 0.767 ± 0.020, 0.754). Model application to children revealed highly accurate inference of the true non-smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications.
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Affiliation(s)
- Silvana C E Maas
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Athina Vidaki
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Rory Wilson
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Str. 48, 17475, Greifswald, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, 17475 Greifswald, Germany
| | - Fan Liu
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, NO.1 Beichen West Road, Chaoyang District, 100101 Beijing, People's Republic of China.,University of Chinese Academy of Sciences, No.19A Yuquan Road, Shijingshan District, 100049 Beijing, People's Republic of China
| | - Joyce B J van Meurs
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7-9, 1081 BT, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7-9, 1081 BT, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7-9, 1081 BT, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7-9, 1081 BT, Amsterdam, The Netherlands
| | - Carla J H van der Kallen
- Department of Internal Medicine, Maastricht University Medical Center, Randwycksingel 35, 6229 EG, Maastricht, The Netherlands.,Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, P.O. box 9600, 2300 RC, Leiden, The Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, P.O. box 9600, 2300 RC, Leiden, The Netherlands
| | - Diana van Heemst
- Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, P.O. box 9600, 2300 RC, Leiden, The Netherlands
| | - Leonard H van den Berg
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Postbus 85500, 3508 GA, Utrecht, The Netherlands
| | | | - Liesbeth Duijts
- Division of Respiratory Medicine and Allergology and Division of Neonatology, Department of Pediatrics, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Vincent W V Jaddoe
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,The Generation R Study Group, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Karl-Heinz Ladwig
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Sonja Kunze
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, 80802 Munich, Germany.,Institute for Medical Informatics, Biometrics and Epidemiology, Ludwig-Maximilians-Universität (LMU) Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Ellernholzstraße 1-2, 17475, Greifswald, Germany
| | - Janine F Felix
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,The Generation R Study Group, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, 80802 Munich, Germany
| | - Oscar H Franco
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands. .,Department of Genetics, School of Medicine, Mashhad University of Medical Science, PO Box 91735-951, 9133913716 Mashhad, Iran.
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
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25
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Semlali A, Almutairi M, Azzi A, Reddy Parine N, AlAmri A, Alsulami S, Meshal Alumri T, Saud Alanazi M, Rouabhia M. TSLP and TSLP receptors variants are associated with smoking. Mol Genet Genomic Med 2019; 7:e842. [PMID: 31290290 PMCID: PMC6687645 DOI: 10.1002/mgg3.842] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 05/21/2019] [Accepted: 05/31/2019] [Indexed: 12/18/2022] Open
Abstract
Background To search for new prevention markers for early detection of the diseases caused by tobacco, we aimed to investigate the polymorphisms in TSLP and TSLPRs associated with cigarette smoking in the Saudi population. Materials and methods Samples were collected from 177 smokers and 126 healthy controls. Three TSLP SNPs [rs3806933, rs2289276, and rs10043985], three TSLPR SNPs [rs36133495, rs36177645, and rs36139698], and two IL7R SNPs rs1053496 and rs12516866 were analyzed by genotyping. Results Two TSLP SNPs (rs10043985 and rs3806933) and one TSLPR SNP (rs36139698) showed significant correlations with smoking behavior, but not IL7R rs12516866 and rs1053496. rs10043985 showed a clear association with long‐term smoking regardless of daily cigarette consumption. rs2289276 was associated with short‐term smoking but not with daily cigarette consumption. rs3806933 was highly associated with different smoker subgroups. Rs36139698 was highly associated with long‐term smokers who consumed ≥20 cigarettes/day, and the “T” allele was associated only with individuals who smoked ≤20 cigarettes/day. Rs36139698 corresponds to a P195L substitution and produces a TSLPR mutant with a predicted ΔΔG increase of 2.15 kcal/mol and has a more stable structure than the wild‐type variant. Conclusions Investigating TSLP and TSLPR polymorphisms is crucial for elucidating the mechanisms underlying tobacco‐induced diseases.
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Affiliation(s)
- Abdelhabib Semlali
- Groupe de Recherche en Écologie Buccale, Département de stomatologie, Faculté de Médecine Dentaire, Université Laval, Québec, Québec, Canada.,Department of Biochemistry, College of Science King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Mikhlid Almutairi
- Zoology Department, College of Science King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Arezki Azzi
- Department of Biochemistry, College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Kingdom of Saudi Arabia
| | - Narasimha Reddy Parine
- Department of Biochemistry, College of Science King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Abdullah AlAmri
- Department of Biochemistry, College of Science King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Saleh Alsulami
- Department of Biochemistry, College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Kingdom of Saudi Arabia
| | - Talal Meshal Alumri
- Department of Biochemistry, College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Kingdom of Saudi Arabia
| | - Mohammad Saud Alanazi
- Department of Biochemistry, College of Science King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Mahmoud Rouabhia
- Groupe de Recherche en Écologie Buccale, Département de stomatologie, Faculté de Médecine Dentaire, Université Laval, Québec, Québec, Canada
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Fragou D, Pakkidi E, Aschner M, Samanidou V, Kovatsi L. Smoking and DNA methylation: Correlation of methylation with smoking behavior and association with diseases and fetus development following prenatal exposure. Food Chem Toxicol 2019; 129:312-327. [PMID: 31063835 DOI: 10.1016/j.fct.2019.04.059] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 04/29/2019] [Accepted: 04/30/2019] [Indexed: 12/13/2022]
Abstract
Among epigenetic mechanisms, DNA methylation has been widely studied with respect to many environmental factors. Smoking is a common factor which affects both global and gene-specific DNA methylation. It is supported that smoking directly affects DNA methylation, and these effects contribute to the development and progression of various diseases, such as cancer, lung and cardiovascular diseases and male infertility. In addition, prenatal smoking influences the normal development of the fetus via DNA methylation changes. The DNA methylation profile and its smoking-induced alterations helps to distinguish current from former smokers and non-smokers and can be used to predict the risk for the development of a disease. This review summarizes the DNA methylation changes induced by smoking, their correlation with smoking behavior and their association with various diseases and fetus development.
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Affiliation(s)
- Domniki Fragou
- Laboratory of Forensic Medicine and Toxicology, School of Medicine, Aristotle University of Thessaloniki, Greece
| | - Eleni Pakkidi
- Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, Greece
| | - Michael Aschner
- Departments of Molecular Pharmacology, Neuroscience, and Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Victoria Samanidou
- Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, Greece
| | - Leda Kovatsi
- Laboratory of Forensic Medicine and Toxicology, School of Medicine, Aristotle University of Thessaloniki, Greece.
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Malik S, Zafar Paracha R, Khalid M, Nisar M, Siddiqa A, Hussain Z, Nawaz R, Ali A, Ahmad J. MicroRNAs and their target mRNAs as potential biomarkers among smokers and non-smokers with lung adenocarcinoma. IET Syst Biol 2019; 13:69-76. [PMID: 33444474 PMCID: PMC8687273 DOI: 10.1049/iet-syb.2018.5040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 07/17/2018] [Accepted: 08/06/2018] [Indexed: 01/09/2023] Open
Abstract
Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non-coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non-smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co-expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA-mRNA pairs which were significantly dysregulated in non-smokers with lung adenocarcinoma. Also two pairs (miR-133b/Protein Kinase C Zeta (PRKCZ) and miR-557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors' understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.
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Affiliation(s)
- Sumaria Malik
- Research Center For Modeling & Simulation (RCMS)National University of Sciences and Technology (NUST)Sector H‐12IslamabadPakistan
| | - Rehan Zafar Paracha
- Research Center For Modeling & Simulation (RCMS)National University of Sciences and Technology (NUST)Sector H‐12IslamabadPakistan
| | - Maryam Khalid
- Research Center For Modeling & Simulation (RCMS)National University of Sciences and Technology (NUST)Sector H‐12IslamabadPakistan
| | - Maryum Nisar
- Research Center For Modeling & Simulation (RCMS)National University of Sciences and Technology (NUST)Sector H‐12IslamabadPakistan
| | - Amnah Siddiqa
- Research Center For Modeling & Simulation (RCMS)National University of Sciences and Technology (NUST)Sector H‐12IslamabadPakistan
| | - Zamir Hussain
- Research Center For Modeling & Simulation (RCMS)National University of Sciences and Technology (NUST)Sector H‐12IslamabadPakistan
| | - Raheel Nawaz
- School of ComputingMathematics and Digital Technology, Manchester Metropolitan UniversityGM459 Geoffrey Manton BuildingManchesterEngland
| | - Amjad Ali
- Atta‐ur‐Rahman School of Applied Biosciences – ASABNational University of Sciences and Technology (NUST)Sector H‐ 12IslamabadPakistan
| | - Jamil Ahmad
- Research Center For Modeling & Simulation (RCMS)National University of Sciences and Technology (NUST)Sector H‐12IslamabadPakistan
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28
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Ye Y, Zhao X, Lu Y, Long B, Zhang S. Varinostat Alters Gene Expression Profiles in Aortic Tissues from ApoE -/- Mice. HUM GENE THER CL DEV 2018; 29:214-225. [PMID: 30284929 DOI: 10.1089/humc.2018.141] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Atherosclerosis (AS) is a complex, chronic inflammatory disease that is characterized by plaque buildup within arterial vessel walls. Preclinical trials have suggested that vorinostat, a pan-histone deacetylase inhibitor (HDACi), reduces vascular inflammation and AS, but the underlying protective mechanism has not been fully elucidated. The present study aimed to identify altered gene expression profiles in aortic tissues from ApoE-/- mice after vorinostat treatment. Male ApoE-/- mice fed a high-fat diet were treated with either vorinostat or vehicle, and the aortic plaque area was quantified 8 weeks after treatment. Aortic tissues were collected from both the vorinostat group (n = 3) and vehicle group (n = 3) for deep sequencing of the cDNA to construct sRNA libraries. Oral administration of vorinostat significantly reduced plaque size in the ApoE-/- mice (p < 0.05). In total, 1,550 differentially expressed mRNAs, 56 differentially expressed miRNAs, and 381 differentially expressed lncRNAs were identified in the vorinostat group compared to the vehicle group. Subsequently, a global lncRNA-miRNA-mRNA triple network was constructed based on the competitive endogenous RNA (ceRNA) theory. The hepatitis C signaling pathway was significantly enriched among the differentially expressed mRNAs from the ceRNA network, which suggests that vorinostat has anti-inflammatory properties. Importantly, the identified target pair of mmu-miR-3075-5p/lncRNA-A330023F24Rik/Ldlr may regulate drug response. Upregulation of low-density lipid receptor (Ldlr) and lncRNA-A330023F24Rik and downregulation of mmu-miR-3075-5p were further verified by quantitative real-time polymerase chain reaction. To conclude, vorinostat reduced AS in ApoE-/- mice. Differentially expressed mRNA, lncRNAs, and miRNAs, as well as their interactions and pathways, were identified, which partially explain vorinostat's anti-atherosclerotic effects.
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Affiliation(s)
- Yicong Ye
- 1 Department of Cardiology and Beijing Anzhen Hospital and Capital Medical University, Beijing, P.R. China.,2 Department of Department of Cardiology, Beijing Anzhen Hospital and Capital Medical University, Beijing, P.R. China
| | - Xiliang Zhao
- 1 Department of Cardiology and Beijing Anzhen Hospital and Capital Medical University, Beijing, P.R. China
| | - Yiyun Lu
- 1 Department of Cardiology and Beijing Anzhen Hospital and Capital Medical University, Beijing, P.R. China
| | - Bo Long
- 3 Department of Central Laboratory, Chinese Academy of Medical College and Peking Union Medical College Hospital, Peking Union Medical College Hospital, Beijing, P.R. China
| | - Shuyang Zhang
- 1 Department of Cardiology and Beijing Anzhen Hospital and Capital Medical University, Beijing, P.R. China
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Soda K. Polyamine Metabolism and Gene Methylation in Conjunction with One-Carbon Metabolism. Int J Mol Sci 2018; 19:E3106. [PMID: 30309036 PMCID: PMC6213949 DOI: 10.3390/ijms19103106] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 10/01/2018] [Accepted: 10/05/2018] [Indexed: 02/07/2023] Open
Abstract
Recent investigations have revealed that changes in DNA methylation status play an important role in aging-associated pathologies and lifespan. The methylation of DNA is regulated by DNA methyltransferases (DNMT1, DNMT3a, and DNMT3b) in the presence of S-adenosylmethionine (SAM), which serves as a methyl group donor. Increased availability of SAM enhances DNMT activity, while its metabolites, S-adenosyl-l-homocysteine (SAH) and decarboxylated S-adenosylmethionine (dcSAM), act to inhibit DNMT activity. SAH, which is converted from SAM by adding a methyl group to cytosine residues in DNA, is an intermediate precursor of homocysteine. dcSAM, converted from SAM by the enzymatic activity of adenosylmethionine decarboxylase, provides an aminopropyl group to synthesize the polyamines spermine and spermidine. Increased homocysteine levels are a significant risk factor for the development of a wide range of conditions, including cardiovascular diseases. However, successful homocysteine-lowering treatment by vitamins (B6, B12, and folate) failed to improve these conditions. Long-term increased polyamine intake elevated blood spermine levels and inhibited aging-associated pathologies in mice and humans. Spermine reversed changes (increased dcSAM, decreased DNMT activity, aberrant DNA methylation, and proinflammatory status) induced by the inhibition of ornithine decarboxylase. The relation between polyamine metabolism, one-carbon metabolism, DNA methylation, and the biological mechanism of spermine-induced lifespan extension is discussed.
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Affiliation(s)
- Kuniyasu Soda
- Cardiovascular Research Institute, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma, Omiya, Saitama-city, Saitama Prefecture 330-8503, Japan.
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30
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Liu B, Pjanic M, Wang T, Nguyen T, Gloudemans M, Rao A, Castano VG, Nurnberg S, Rader DJ, Elwyn S, Ingelsson E, Montgomery SB, Miller CL, Quertermous T. Genetic Regulatory Mechanisms of Smooth Muscle Cells Map to Coronary Artery Disease Risk Loci. Am J Hum Genet 2018; 103:377-388. [PMID: 30146127 PMCID: PMC6128252 DOI: 10.1016/j.ajhg.2018.08.001] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2018] [Accepted: 08/01/2018] [Indexed: 12/30/2022] Open
Abstract
Coronary artery disease (CAD) is the leading cause of death globally. Genome-wide association studies (GWASs) have identified more than 95 independent loci that influence CAD risk, most of which reside in non-coding regions of the genome. To interpret these loci, we generated transcriptome and whole-genome datasets using human coronary artery smooth muscle cells (HCASMCs) from 52 unrelated donors, as well as epigenomic datasets using ATAC-seq on a subset of 8 donors. Through systematic comparison with publicly available datasets from GTEx and ENCODE projects, we identified transcriptomic, epigenetic, and genetic regulatory mechanisms specific to HCASMCs. We assessed the relevance of HCASMCs to CAD risk using transcriptomic and epigenomic level analyses. By jointly modeling eQTL and GWAS datasets, we identified five genes (SIPA1, TCF21, SMAD3, FES, and PDGFRA) that may modulate CAD risk through HCASMCs, all of which have relevant functional roles in vascular remodeling. Comparison with GTEx data suggests that SIPA1 and PDGFRA influence CAD risk predominantly through HCASMCs, while other annotated genes may have multiple cell and tissue targets. Together, these results provide tissue-specific and mechanistic insights into the regulation of a critical vascular cell type associated with CAD in human populations.
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Affiliation(s)
- Boxiang Liu
- Department of Biology, School of Humanities and Sciences, Stanford University, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Milos Pjanic
- Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Ting Wang
- Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Trieu Nguyen
- Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Michael Gloudemans
- Biomedical Informatics Training Program, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Abhiram Rao
- Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Victor G Castano
- Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Sylvia Nurnberg
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel J Rader
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Susannah Elwyn
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erik Ingelsson
- Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Stephen B Montgomery
- Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Clint L Miller
- Center for Public Health Genomics, Department of Public Health Sciences, Biochemistry and Genetics, and Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Thomas Quertermous
- Cardiovascular Institute, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA.
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A systematic analysis highlights multiple long non-coding RNAs associated with cardiometabolic disorders. J Hum Genet 2018; 63:431-446. [PMID: 29382920 DOI: 10.1038/s10038-017-0403-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 12/04/2017] [Accepted: 12/05/2017] [Indexed: 12/19/2022]
Abstract
Genome-wide association studies (GWAS) have identified many susceptibility loci for cardiometabolic disorders. Most of the associated variants reside in non-coding regions of the genome including long non-coding RNAs (lncRNAs), which are thought to play critical roles in diverse biological processes. Here, we leveraged data from the available GWAS meta-analyses on lipid and obesity-related traits, blood pressure, type 2 diabetes, and coronary artery disease and identified 179 associated single-nucleotide polymorphisms (SNPs) in 102 lncRNAs (p-value < 2.3 × 10-7). Of these, 55 SNPs, either the lead SNP or in strong linkage disequilibrium with the lead SNP in the related loci, were selected for further investigations. Our in silico predictions and functional annotations of the SNPs as well as expression and DNA methylation analysis of their lncRNAs demonstrated several lncRNAs that fulfilled predefined criteria for being potential functional targets. In particular, we found evidence suggesting that LOC157273 (at 8p23.1) is involved in regulating serum lipid-cholesterol. Our results showed that rs4841132 in the second exon and cg17371580 in the promoter region of LOC157273 are associated with lipids; the lncRNA is expressed in liver and associates with the expression of its nearby coding gene, PPP1R3B. Collectively, we highlight a number of loci associated with cardiometabolic disorders for which the association may act through lncRNAs.
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Tobacco smoking and methylation of genes related to lung cancer development. Oncotarget 2018; 7:59017-59028. [PMID: 27323854 PMCID: PMC5312292 DOI: 10.18632/oncotarget.10007] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 05/23/2016] [Indexed: 11/25/2022] Open
Abstract
Lung cancer is a leading cause of cancer-related mortality worldwide, and cigarette smoking is the major environmental hazard for its development. This study intended to examine whether smoking could alter methylation of genes at lung cancer risk loci identified by genome-wide association studies (GWASs). By systematic literature review, we selected 75 genomic candidate regions based on 120 single-nucleotide polymorphisms (SNPs). DNA methylation levels of 2854 corresponding cytosine-phosphate-guanine (CpG) candidates in whole blood samples were measured by the Illumina Infinium Human Methylation450 Beadchip array in two independent subsamples of the ESTHER study. After correction for multiple testing, we successfully confirmed associations with smoking for one previously identified CpG site within the KLF6 gene and identified 12 novel sites located in 7 genes: STK32A, TERT, MSH5, ACTA2, GATA3, VTI1A and CHRNA5 (FDR <0.05). Current smoking was linked to a 0.74% to 2.4% decrease of DNA methylation compared to never smoking in 11 loci, and all but one showed significant associations (FDR <0.05) with life-time cumulative smoking (pack-years). In conclusion, our study demonstrates the impact of tobacco smoking on DNA methylation of lung cancer related genes, which may indicate that lung cancer susceptibility genes might be regulated by methylation changes in response to smoking. Nevertheless, this mechanism warrants further exploration in future epigenetic and biomarker studies.
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Hindy G, Wiberg F, Almgren P, Melander O, Orho-Melander M. Polygenic Risk Score for Coronary Heart Disease Modifies the Elevated Risk by Cigarette Smoking for Disease Incidence. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2018; 11:e001856. [PMID: 29874179 PMCID: PMC6319562 DOI: 10.1161/circgen.117.001856] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 11/20/2017] [Indexed: 02/01/2023]
Abstract
BACKGROUND Coronary heart disease (CHD) is a multifactorial disease with both genetic and environmental components. Smoking is the most important modifiable risk factor for CHD. Our aim was to test whether the increased CHD incidence by smoking is modified by genetic predisposition to CHD. METHODS AND RESULTS Our study included 24 443 individuals from the MDCS (Malmö Diet and Cancer Study). A weighted polygenic risk score (PRS) was created by summing the number of risk alleles for 50 single-nucleotide polymorphisms associated with CHD. Individuals were classified as current, former, or never smokers. Interactions were primarily tested between smoking status and PRS and secondarily with individual single-nucleotide polymorphisms. Then, the predictive use of PRS for CHD incidence was tested among different smoking categories. During a median follow-up time of 19.4 years, 3217 incident CHD cases were recorded. The association between smoking and CHD was modified by the PRS (Pinteraction=0.005). The magnitude of increased incidence of CHD by smoking was highest among individuals in the lowest tertile of PRS (odds ratio, 1.42; 95% confidence interval, 1.29-1.56 per smoking risk category) compared with the highest tertile (odds ratio, 1.20; 95% confidence interval, 1.11-1.30 per smoking risk category). This interaction was stronger among men (Pinteraction=0.001) compared with women (Pinteraction=0.44). The PRS provided a significantly better net reclassification and discrimination on top of traditional risk factors among never smokers compared with current smokers (P<0.001). CONCLUSIONS Genetic predisposition to CHD modifies the associated increased CHD risk by smoking. The PRS has a better predictive use among never smokers compared with smokers.
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Affiliation(s)
- George Hindy
- From the Department of Clinical Sciences in Malmö, Lund University, Sweden (G.H., F.W., P.A., O.M., M.O.-M.); and Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA (G.H.)
| | - Frans Wiberg
- From the Department of Clinical Sciences in Malmö, Lund University, Sweden (G.H., F.W., P.A., O.M., M.O.-M.); and Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA (G.H.)
| | - Peter Almgren
- From the Department of Clinical Sciences in Malmö, Lund University, Sweden (G.H., F.W., P.A., O.M., M.O.-M.); and Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA (G.H.)
| | - Olle Melander
- From the Department of Clinical Sciences in Malmö, Lund University, Sweden (G.H., F.W., P.A., O.M., M.O.-M.); and Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA (G.H.)
| | - Marju Orho-Melander
- From the Department of Clinical Sciences in Malmö, Lund University, Sweden (G.H., F.W., P.A., O.M., M.O.-M.); and Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA (G.H.).
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Abstract
Human genetic variation is a major resource in forensics, but does not allow all forensically relevant questions to be answered. Some questions may instead be addressable via epigenomics, as the epigenome acts as an interphase between the fixed genome and the dynamic environment. We envision future forensic applications of DNA methylation analysis that will broaden DNA-based forensic intelligence. Together with genetic prediction of appearance and biogeographic ancestry, epigenomic lifestyle prediction is expected to increase the ability of police to find unknown perpetrators of crime who are not identifiable using current forensic DNA profiling.
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Affiliation(s)
- Athina Vidaki
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Room Ee1051, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Room Ee1051, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
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Ruiz-Arenas C, González JR. Redundancy analysis allows improved detection of methylation changes in large genomic regions. BMC Bioinformatics 2017; 18:553. [PMID: 29237399 PMCID: PMC5729265 DOI: 10.1186/s12859-017-1986-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 12/05/2017] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated. RESULTS Using simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data. CONCLUSIONS We propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.
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Affiliation(s)
- Carlos Ruiz-Arenas
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Juan R. González
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
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Ghosh M, Öner D, Poels K, Tabish AM, Vlaanderen J, Pronk A, Kuijpers E, Lan Q, Vermeulen R, Bekaert B, Hoet PH, Godderis L. Changes in DNA methylation induced by multi-walled carbon nanotube exposure in the workplace. Nanotoxicology 2017; 11:1195-1210. [PMID: 29191063 DOI: 10.1080/17435390.2017.1406169] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
This study was designed to assess the epigenetic alterations in blood cells, induced by occupational exposure to multi-wall carbon nanotubes (MWCNT). The study population comprised of MWCNT-exposed workers (n=24) and unexposed controls (n=43) from the same workplace. We measured global DNA methylation/hydroxymethylation levels on the 5th cytosine residues using a validated liquid chromatography tandem-mass spectrometry (LC-MS/MS) method. Sequence-specific methylation of LINE1 retrotransposable element 1 (L1RE1) elements, and promoter regions of functionally important genes associated with epigenetic regulation [DNA methyltransferase-1 (DNMT1) and histone deacetylase 4 (HDAC4)], DNA damage/repair and cell cycle pathways [nuclear protein, coactivator of histone transcription/ATM serine/threonine kinase (NPAT/ATM)], and a potential transforming growth factor beta (TGF-β) repressor [SKI proto-oncogene (SKI)] were studied using bisulfite pyrosequencing. Analysis of global DNA methylation levels and hydroxymethylation did not reveal significant difference between the MWCNT-exposed and control groups. No significant changes in Cytosine-phosphate-Guanine (CpG) site methylation were observed for the LINE1 (L1RE1) elements. Further analysis of gene-specific DNA methylation showed a significant change in methylation for DNMT1, ATM, SKI, and HDAC4 promoter CpGs in MWCNT-exposed workers. Since DNA methylation plays an important role in silencing/regulation of the genes, and many of these genes have been associated with occupational and smoking-induced diseases and cancer (risk), aberrant methylation of these genes might have a potential effect in MWCNT-exposed workers.
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Affiliation(s)
- Manosij Ghosh
- a Department of Public Health and Primary Care, Centre Environment & Health , KU Leuven , Leuven , Belgium
| | - Deniz Öner
- a Department of Public Health and Primary Care, Centre Environment & Health , KU Leuven , Leuven , Belgium
| | - Katrien Poels
- a Department of Public Health and Primary Care, Centre Environment & Health , KU Leuven , Leuven , Belgium
| | - Ali M Tabish
- a Department of Public Health and Primary Care, Centre Environment & Health , KU Leuven , Leuven , Belgium
| | - Jelle Vlaanderen
- b Division of Environmental Epidemiology, Institute for Risk Assessment Sciences , Utrecht University , Utrecht , The Netherlands
| | - Anjoeka Pronk
- c TNO, Netherlands Organisation for Applied Scientific Research , Zeist , The Netherlands
| | - Eelco Kuijpers
- c TNO, Netherlands Organisation for Applied Scientific Research , Zeist , The Netherlands
| | - Qing Lan
- d Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics , National Cancer Institute , Bethesda , MD , USA
| | - Roel Vermeulen
- b Division of Environmental Epidemiology, Institute for Risk Assessment Sciences , Utrecht University , Utrecht , The Netherlands
| | - Bram Bekaert
- e Department of Forensic Medicine, Laboratory of Forensic Genetics and Molecular Archaeology , University Hospitals Leuven , Leuven , Belgium
| | - Peter Hm Hoet
- a Department of Public Health and Primary Care, Centre Environment & Health , KU Leuven , Leuven , Belgium
| | - Lode Godderis
- a Department of Public Health and Primary Care, Centre Environment & Health , KU Leuven , Leuven , Belgium.,f External Service for Prevention and Protection at Work , Idewe , Heverlee , Belgium
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Seiki T, Naito M, Hishida A, Takagi S, Matsunaga T, Sasakabe T, Hattori Y, Kawai S, Okada R, Yin G, Hamajima N, Wakai K. Association of genetic polymorphisms with erythrocyte traits: Verification of SNPs reported in a previous GWAS in a Japanese population. Gene 2017; 642:172-177. [PMID: 29133146 DOI: 10.1016/j.gene.2017.11.031] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 10/18/2017] [Accepted: 11/09/2017] [Indexed: 11/25/2022]
Abstract
Erythrocyte count and volume are the commonly used hematological indices for anemia that change in various diseases. To date, however, only one study ever exists that addressed erythrocyte trait-associated single nucleotide polymorphisms (SNPs) in a Japanese population. Because that study was performed in patients with various diseases, we confirmed the reported associations in a general population. Participants in the current study were from the Shizuoka component of the Japan Multi-Institutional Collaborative Cohort Study, which included 4971 men and women aged 35 to 69years who were recruited between 2006 and 2007. We analyzed the association of seven selected SNPs with the following erythrocyte traits: red blood cell count, hemoglobin (Hb) and hematocrit (Ht) levels, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration. The erythrocyte traits were regressed on a number of minor alleles of selected SNPs. Then we compared our findings with those from a genome-wide association study performed in a Japanese population. We replicated the association of ABO rs495828, PDGFRA-KIT rs218237, USP49-MED20-BSYL-CCND3 rs3218097, C6orf182-CD164 rs11966072, TERT rs2736100, and TMPRSS6 rs5756504 with erythrocyte traits in our independent Japanese population. In addition, we found a significant interaction between TERT rs2736100 and smoking habit that affected Hb and Ht levels.
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Affiliation(s)
- Toshio Seiki
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Mariko Naito
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sahoko Takagi
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takashi Matsunaga
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tae Sasakabe
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuta Hattori
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sayo Kawai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Rieko Okada
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Guang Yin
- Department of Nutritional Sciences, Faculty of Health and Welfare, Seinan Jo Gakuin University, Kitakyushu, Japan
| | - Nobuyuki Hamajima
- Department of Healthcare Administration, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan.
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Solanki HS, Babu N, Jain AP, Bhat MY, Puttamallesh VN, Advani J, Raja R, Mangalaparthi KK, Kumar MM, Prasad TSK, Mathur PP, Sidransky D, Gowda H, Chatterjee A. Cigarette smoke induces mitochondrial metabolic reprogramming in lung cells. Mitochondrion 2017; 40:58-70. [PMID: 29042306 DOI: 10.1016/j.mito.2017.10.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 09/18/2017] [Accepted: 10/06/2017] [Indexed: 01/01/2023]
Abstract
Cellular transformation owing to cigarette smoking is due to chronic exposure and not acute. However, systematic studies to understand the molecular alterations in lung cells due to cigarette smoke are lacking. To understand these molecular alterations induced by chronic cigarette smoke exposure, we carried out tandem mass tag (TMT) based temporal proteomic profiling of lung cells exposed to cigarette smoke for upto 12months. We identified 2620 proteins in total, of which 671 proteins were differentially expressed (1.5-fold) after 12months of exposure. Prolonged exposure of lung cells to smoke for 12months revealed dysregulation of oxidative phosphorylation and overexpression of enzymes involved in TCA cycle. In addition, we also observed overexpression of enzymes involved in glutamine metabolism, fatty acid degradation and lactate synthesis. This could possibly explain the availability of alternative source of carbon to TCA cycle apart from glycolytic pyruvate. Our data indicates that chronic exposure to cigarette smoke induces mitochondrial metabolic reprogramming in cells to support growth and survival.
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Affiliation(s)
- Hitendra S Solanki
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India; School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Niraj Babu
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India; Manipal University, Madhav Nagar, Manipal 576104, India
| | - Ankit P Jain
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India; School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Mohd Younis Bhat
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India; Amrita School of Biotechnology, Amrita University, Kollam 690 525, India
| | - Vinuth N Puttamallesh
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India; Amrita School of Biotechnology, Amrita University, Kollam 690 525, India
| | - Jayshree Advani
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India; Manipal University, Madhav Nagar, Manipal 576104, India
| | - Remya Raja
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India
| | - Kiran K Mangalaparthi
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India; Amrita School of Biotechnology, Amrita University, Kollam 690 525, India
| | - Mahesh M Kumar
- Department of Neuro-Virology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India
| | - T S Keshava Prasad
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India; NIMHANS-IOB Proteomics and Bioinformatics Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India; YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India
| | | | - David Sidransky
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Harsha Gowda
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India.
| | - Aditi Chatterjee
- Institute of Bioinformatics, International Tech Park, Bangalore 560 066, India.
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Beal MA, Yauk CL, Marchetti F. From sperm to offspring: Assessing the heritable genetic consequences of paternal smoking and potential public health impacts. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2017; 773:26-50. [PMID: 28927533 DOI: 10.1016/j.mrrev.2017.04.001] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Revised: 04/05/2017] [Accepted: 04/07/2017] [Indexed: 12/16/2022]
Abstract
Individuals who smoke generally do so with the knowledge of potential consequences to their own health. What is rarely considered are the effects of smoking on their future children. The objective of this work was to review the scientific literature on the effects of paternal smoking on sperm and assess the consequences to offspring. A literature search identified over 200 studies with relevant data in humans and animal models. The available data were reviewed to assess the weight of evidence that tobacco smoke is a human germ cell mutagen and estimate effect sizes. These results were used to model the potential increase in genetic disease burden in offspring caused by paternal smoking, with specific focus on aneuploid syndromes and intellectual disability, and the socioeconomic impacts of such an effect. The review revealed strong evidence that tobacco smoking is associated with impaired male fertility, and increases in DNA damage, aneuploidies, and mutations in sperm. Studies support that these effects are heritable and adversely impact the offspring. Our model estimates that, with even a modest 25% increase in sperm mutation frequency caused by smoke-exposure, for each generation across the global population there will be millions of smoking-induced de novo mutations transmitted from fathers to offspring. Furthermore, paternal smoking is estimated to contribute to 1.3 million extra cases of aneuploid pregnancies per generation. Thus, the available evidence makes a compelling case that tobacco smoke is a human germ cell mutagen with serious public health and socio-economic implications. Increased public education should be encouraged to promote abstinence from smoking, well in advance of reproduction, to minimize the transmission of harmful mutations to the next-generation.
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Affiliation(s)
- Marc A Beal
- Carleton University, Ottawa, Ontario K1S 5B6, Canada; Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K1A 0K9, Canada
| | - Carole L Yauk
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K1A 0K9, Canada
| | - Francesco Marchetti
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario K1A 0K9, Canada.
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Abstract
PURPOSE OF REVIEW African Americans disproportionately suffer from leading causes of morbidity and mortality including cardiovascular disease (CVD), cancer, and preterm birth. Disparities can arise from multiple social and environmental exposures, but how the human body responds to these exposures to result in pathophysiologic states is incompletely understood. RECENT FINDINGS Epigenetic mechanisms, particularly DNA methylation, can be altered in response to exposures such as air pollution, psychosocial stress, and smoking. Each of these exposures has been linked to the above health states (CVD, cancer, and preterm birth) with striking racial disparities in exposure levels. DNA methylation patterns have also been shown to be associated with each of these health outcomes. SUMMARY Whether DNA methylation mediates exposure-disease relationships and can help explain racial disparities in health is not known. However, because many environmental and adverse social exposures disproportionately affect minorities, understanding the role that epigenetics plays in the human response to these exposures that often result in disease, is critical to reducing disparities in morbidity and mortality.
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Affiliation(s)
- Alexis D. Vick
- Department of Neonatology, Beth Israel Deaconess Medical
Center, Boston, MA
- University of Toledo College of Medicine, Toledo, OH
| | - Heather H. Burris
- Department of Neonatology, Beth Israel Deaconess Medical
Center, Boston, MA
- Departments of Pediatrics and Obstetrics, Gynecology, and
Reproductive Biology, Harvard Medical School, Boston, MA
- Department of Environmental Health, Harvard TH Chan School
of Public Health, Boston, MA
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41
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Kohailan M, Alanazi M, Rouabhia M, Al Amri A, Parine NR, Semlali A. Two SNPs in the promoter region of Toll-like receptor 4 gene are not associated with smoking in Saudi Arabia. Onco Targets Ther 2017; 10:745-752. [PMID: 28223830 PMCID: PMC5308598 DOI: 10.2147/ott.s111971] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Defects in the innate immune system, particularly in Toll-like receptors (TLRs), have been reported in several cigarette smoke-promoted diseases. The aim of this study was to examine the impact of tobacco smoke on allelic frequencies of TLR4 single-nucleotide polymorphisms (SNPs) and to compare the genotypic distribution of these SNPs in a Saudi Arabian population with that in previously studied populations. DNA was extracted from 303 saliva samples collected from smokers and nonsmokers. Two transitional SNPs in the promoter region of TLR4 were selected, rs2770150 (T/C) and rs10759931 (G/A). Genotype frequencies were determined using quantitative polymerase chain reaction. Our results showed a slight effect of smoking on the distribution of rs2770150 and rs10759931. However, the differences were not significant. Thus, we conclude that the SNPs selected for this study were independent of smoking and may not be related to smoking-induced diseases.
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Affiliation(s)
- Muhammad Kohailan
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Mohammad Alanazi
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Mahmoud Rouabhia
- Groupe de Recherche en Écologie Buccale, Département de Stomatologie, Faculté de Médecine Dentaire, Université Laval, Québec, QC, Canada
| | - Abdullah Al Amri
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Narasimha Reddy Parine
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Abdelhabib Semlali
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
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42
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Braun KVE, Dhana K, de Vries PS, Voortman T, van Meurs JBJ, Uitterlinden AG, Hofman A, Hu FB, Franco OH, Dehghan A. Epigenome-wide association study (EWAS) on lipids: the Rotterdam Study. Clin Epigenetics 2017; 9:15. [PMID: 28194238 PMCID: PMC5297218 DOI: 10.1186/s13148-016-0304-4] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 12/19/2016] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND DNA methylation is a key epigenetic mechanism that is suggested to be associated with blood lipid levels. We aimed to identify CpG sites at which DNA methylation levels are associated with blood levels of triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol in 725 participants of the Rotterdam Study, a population-based cohort study. Subsequently, we sought replication in a non-overlapping set of 760 participants. RESULTS Genome-wide methylation levels were measured in whole blood using the Illumina Methylation 450 array. Associations between lipid levels and DNA methylation beta values were examined using linear mixed-effect models. All models were adjusted for sex, age, smoking, white blood cell proportions, array number, and position on array. A Bonferroni-corrected p value lower than 1.08 × 10-7 was considered statistically significant. Five CpG sites annotated to genes including DHCR24, CPT1A, ABCG1, and SREBF1 were identified and replicated. Four CpG sites were associated with triglycerides, including CpG sites annotated to CPT1A (cg00574958 and cg17058475), ABCG1 (cg06500161), and SREBF1 (cg11024682). Two CpG sites were associated with HDL-C, including ABCG1 (cg06500161) and DHCR24 (cg17901584). No significant associations were observed with LDL-C or total cholesterol. CONCLUSIONS We report an association of HDL-C levels with methylation of a CpG site near DHCR24, a protein-coding gene involved in cholesterol biosynthesis, which has previously been reported to be associated with other metabolic traits. Furthermore, we confirmed previously reported associations of methylation of CpG sites within CPT1A, ABCG1, and SREBF1 and lipids. These results provide insight in the mechanisms that are involved in lipid metabolism.
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Affiliation(s)
- Kim V E Braun
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, Office NA2906, PO Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Klodian Dhana
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, Office NA2906, PO Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Paul S de Vries
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, Office NA2906, PO Box 2040, 3000 CA Rotterdam, The Netherlands.,Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Trudy Voortman
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, Office NA2906, PO Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands
| | - Andre G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, Office NA2906, PO Box 2040, 3000 CA Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.,The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam, The Netherlands
| | | | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, Office NA2906, PO Box 2040, 3000 CA Rotterdam, The Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, Office NA2906, PO Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Dr. Molewaterplein 50, Office NA2906, PO Box 2040, 3000 CA Rotterdam, The Netherlands
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Rondon R, Grunau C, Fallet M, Charlemagne N, Sussarellu R, Chaparro C, Montagnani C, Mitta G, Bachère E, Akcha F, Cosseau C. Effects of a parental exposure to diuron on Pacific oyster spat methylome. ENVIRONMENTAL EPIGENETICS 2017; 3:dvx004. [PMID: 29492306 PMCID: PMC5804544 DOI: 10.1093/eep/dvx004] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/14/2017] [Accepted: 03/07/2017] [Indexed: 05/18/2023]
Abstract
Environmental epigenetic is an emerging field that studies the cause-effect relationship between environmental factors and heritable trait via an alteration in epigenetic marks. This field has received much attentions since the impact of environmental factors on different epigenetic marks have been shown to be associated with a broad range of phenotypic disorders in natural ecosystems. Chemical pollutants have been shown to affect immediate epigenetic information carriers of several aquatic species but the heritability of the chromatin marks and the consequences for long term adaptation remain open questions. In this work, we investigated the impact of the diuron herbicide on the DNA methylation pattern of spat from exposed Crassotrea gigas genitors. This oyster is one of the most important mollusk species produced worldwide and a key coastal economic resource in France. The whole genome bisulfite sequencing (WGBS, BS-Seq) was applied to obtain a methylome at single nucleotide resolution on DNA extracted from spat issued from diuron exposed genitors comparatively to control spat. We showed that the parental diuron exposure has an impact on the DNA methylation pattern of its progeny. Most of the differentially methylated regions occurred within coding sequences and we showed that this change in methylation level correlates with RNA level only in a very small group of genes. Although the DNA methylation profile is variable between individuals, we showed conserved DNA methylation patterns in response to parental diuron exposure. This relevant result opens perspectives for the setting of new markers based on epimutations as early indicators of marine pollutions.
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Affiliation(s)
- Rodolfo Rondon
- Ifremer, IHPE UMR 5244, Univ. Perpignan Via Domitia, CNRS, Univ. Montpellier, F-34095 Montpellier, France
- Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, F-66860 Perpignan, France
| | - Christoph Grunau
- Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, F-66860 Perpignan, France
| | - Manon Fallet
- Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, F-66860 Perpignan, France
| | - Nicolas Charlemagne
- Ifremer, Department of Biogeochemistry and Ecotoxicology, Laboratory of Ecotoxicology, Rue de l’ile d’Yeu, BP 21105, 44311 Nantes Cedex 03, France
| | - Rossana Sussarellu
- Ifremer, Department of Biogeochemistry and Ecotoxicology, Laboratory of Ecotoxicology, Rue de l’ile d’Yeu, BP 21105, 44311 Nantes Cedex 03, France
| | - Cristian Chaparro
- Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, F-66860 Perpignan, France
| | - Caroline Montagnani
- Ifremer, IHPE UMR 5244, Univ. Perpignan Via Domitia, CNRS, Univ. Montpellier, F-34095 Montpellier, France
| | - Guillaume Mitta
- Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, F-66860 Perpignan, France
| | - Evelyne Bachère
- Ifremer, IHPE UMR 5244, Univ. Perpignan Via Domitia, CNRS, Univ. Montpellier, F-34095 Montpellier, France
| | - Farida Akcha
- Ifremer, Department of Biogeochemistry and Ecotoxicology, Laboratory of Ecotoxicology, Rue de l’ile d’Yeu, BP 21105, 44311 Nantes Cedex 03, France
| | - Céline Cosseau
- Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, F-66860 Perpignan, France
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Kohailan M, Alanazi M, Rouabhia M, Alamri A, Parine NR, Alhadheq A, Basavarajappa S, Abdullah Al-Kheraif AA, Semlali A. Effect of smoking on the genetic makeup of toll-like receptors 2 and 6. Onco Targets Ther 2016; 9:7187-7198. [PMID: 27920557 PMCID: PMC5123654 DOI: 10.2147/ott.s109650] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Background Cigarette smoking is a major risk factor for lung cancer, asthma, and oral cancer, and is central to the altered innate immune responsiveness to infection. Many hypotheses have provided evidence that cigarette smoking induces more genetic changes in genes involved in the development of many cigarette-related diseases. This alteration may be from single-nucleotide polymorphisms (SNPs) in innate immunity genes, especially the toll-like receptors (TLRs). Objective In this study, the genotype frequencies of TLR2 and TLR6 in smoking and nonsmoking population were examined. Methods Saliva samples were collected from 177 smokers and 126 nonsmokers. The SNPs used were rs3804100 (1350 T/C, Ser450Ser) and rs3804099 (597 T/C, Asn199Asn) for TLR2 and rs3796508 (979 G/A, Val327Met) and rs5743810 (745 T/C, Ser249Pro) for TLR6. Results Results showed that TLR2 rs3804100 has a significant effect in short-term smokers (OR =2.63; P=0.04), and this effect is not observed in long-term smokers (>5 years of smoking). Therefore, this early mutation may be repaired by the DNA repair system. For TLR2 rs3804099, the variation in genotype frequencies between the smokers and control patients was due to a late mutation, and its protective role appears only in long-term smokers (OR =0.40, P=0.018). In TLR6 rs5743810, the TT genotype is significantly higher in smokers than in nonsmokers (OR =6.90). The effect of this SNP is observed in long-term smokers, regardless of the smoking regime per day. Conclusion TLR2 (rs3804100 and rs3804099) and TLR6 (rs5743810) can be used as a potential index in the diagnosis and prevention of more diseases caused by smoking.
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Affiliation(s)
- Muhammad Kohailan
- Genome Research Chair, Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Mohammad Alanazi
- Genome Research Chair, Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Mahmoud Rouabhia
- Département de Stomatologie, Faculté de Médecine Dentaire, Groupe de Recherche en Écologie Buccale, Université Laval, Québec City, QC, Canada
| | - Abdullah Alamri
- Genome Research Chair, Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Narasimha Reddy Parine
- Genome Research Chair, Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Abdullah Alhadheq
- Genome Research Chair, Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Santhosh Basavarajappa
- Dental Biomaterial Research Chair, Department of Dental Health, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Abdul Aziz Abdullah Al-Kheraif
- Dental Biomaterial Research Chair, Department of Dental Health, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Abdelhabib Semlali
- Genome Research Chair, Department of Biochemistry, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
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Ligthart S, Steenaard RV, Peters MJ, van Meurs JBJ, Sijbrands EJG, Uitterlinden AG, Bonder MJ, Hofman A, Franco OH, Dehghan A. Tobacco smoking is associated with DNA methylation of diabetes susceptibility genes. Diabetologia 2016; 59:998-1006. [PMID: 26825526 PMCID: PMC4826423 DOI: 10.1007/s00125-016-3872-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Accepted: 12/22/2015] [Indexed: 01/19/2023]
Abstract
AIMS/HYPOTHESIS Tobacco smoking, a risk factor for diabetes, is an established modifier of DNA methylation. We hypothesised that tobacco smoking modifies DNA methylation of genes previously identified for diabetes. METHODS We annotated CpG sites available on the Illumina Human Methylation 450K array to diabetes genes previously identified by genome-wide association studies (GWAS), and investigated them for an association with smoking by comparing current to never smokers. The discovery study consisted of 630 individuals (Bonferroni-corrected p = 1.4 × 10(-5)), and we sought replication in an independent sample of 674 individuals. The replicated sites were tested for association with nearby genetic variants and gene expression and fasting glucose and insulin levels. RESULTS We annotated 3,620 CpG sites to the genes identified in the GWAS on type 2 diabetes. Comparing current smokers to never smokers, we found 12 differentially methylated CpG sites, of which five replicated: cg23161492 within ANPEP (p = 1.3 × 10(-12)); cg26963277 (p = 1.2 × 10(-9)), cg01744331 (p = 8.0 × 10(-6)) and cg16556677 (p = 1.2 × 10(-5)) within KCNQ1 and cg03450842 (p = 3.1 × 10(-8)) within ZMIZ1. The effect of smoking on DNA methylation at the replicated CpG sites attenuated after smoking cessation. Increased DNA methylation at cg23161492 was associated with decreased gene expression levels of ANPEP (p = 8.9 × 10(-5)). rs231356-T, which was associated with hypomethylation of cg26963277 (KCNQ1), was associated with a higher odds of diabetes (OR 1.06, p = 1.3 × 10(-5)). Additionally, hypomethylation of cg26963277 was associated with lower fasting insulin levels (p = 0.04). CONCLUSIONS/INTERPRETATION Tobacco smoking is associated with differential DNA methylation of the diabetes risk genes ANPEP, KCNQ1 and ZMIZ1. Our study highlights potential biological mechanisms connecting tobacco smoking to excess risk of type 2 diabetes.
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Affiliation(s)
- Symen Ligthart
- Department of Epidemiology, Erasmus University Medical Center, P. O. Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Rebecca V Steenaard
- Department of Epidemiology, Erasmus University Medical Center, P. O. Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Marjolein J Peters
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/ Rotterdam, the Netherlands
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/ Rotterdam, the Netherlands
| | - Eric J G Sijbrands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, P. O. Box 2040, 3000 CA, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/ Rotterdam, the Netherlands
| | - Marc J Bonder
- Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
| | | | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, P. O. Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, P. O. Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, P. O. Box 2040, 3000 CA, Rotterdam, the Netherlands.
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/ Rotterdam, the Netherlands.
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Sierra MI, Fernández AF, Fraga MF. Epigenetics of Aging. Curr Genomics 2016; 16:435-40. [PMID: 27019618 PMCID: PMC4765531 DOI: 10.2174/1389202916666150817203459] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Revised: 06/20/2015] [Accepted: 06/26/2015] [Indexed: 01/10/2023] Open
Abstract
The best-known phenomenon exemplifying epigenetic drift (the alteration of epigenetic patterns during aging) is the gradual decrease of global DNA methylation. Aging cells, different tissue types, as well as a variety of human diseases possess their own distinct DNA methylation profiles, although the functional impact of these is not always clear. DNA methylation appears to be a dynamic tool of transcriptional regulation, with an extra layer of complexity due to the recent discovery of the conversion of 5-methylcytosine into 5-hydroxymethylcytosine. This age-related DNA demethylation is associated with changes in histone modification patterns and, furthermore, we now know that ncRNAs have evolved in eukaryotes as epigenetic regulators of gene expression. In this review, we will discuss current knowledge on how all these epigenetic phenomena are implicated in human aging, and their links with external, internal and stochastic factors which can affect human age-related diseases onset.
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Affiliation(s)
- Marta I Sierra
- Cancer Epigenetics Laboratory, Institute of Oncology of Asturias (IUOPA), HUCA, Universidad de Oviedo and Nanomaterials and Nanotechnology Research Center (CINN-CSIC)-Universidad de Oviedo (UO) -Principado de Asturias, Spain
| | - Agustín F Fernández
- Cancer Epigenetics Laboratory, Institute of Oncology of Asturias (IUOPA), HUCA, Universidad de Oviedo and Nanomaterials and Nanotechnology Research Center (CINN-CSIC)-Universidad de Oviedo (UO) -Principado de Asturias, Spain
| | - Mario F Fraga
- Cancer Epigenetics Laboratory, Institute of Oncology of Asturias (IUOPA), HUCA, Universidad de Oviedo and Nanomaterials and Nanotechnology Research Center (CINN-CSIC)-Universidad de Oviedo (UO) -Principado de Asturias, Spain
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Pennington KL, DeAngelis MM. Epigenetic Mechanisms of the Aging Human Retina. J Exp Neurosci 2016; 9:51-79. [PMID: 26966390 PMCID: PMC4777243 DOI: 10.4137/jen.s25513] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Revised: 01/07/2016] [Accepted: 01/13/2016] [Indexed: 12/20/2022] Open
Abstract
Degenerative retinal diseases, such as glaucoma, age-related macular degeneration, and diabetic retinopathy, have complex etiologies with environmental, genetic, and epigenetic contributions to disease pathology. Much effort has gone into elucidating both the genetic and the environmental risk factors for these retinal diseases. However, little is known about how these genetic and environmental risk factors bring about molecular changes that lead to pathology. Epigenetic mechanisms have received extensive attention of late for their promise of bridging the gap between environmental exposures and disease development via their influence on gene expression. Recent studies have identified epigenetic changes that associate with the incidence and/or progression of each of these retinal diseases. Therefore, these epigenetic modifications may be involved in the underlying pathological mechanisms leading to blindness. Further genome-wide epigenetic studies that incorporate well-characterized tissue samples, consider challenges similar to those relevant to gene expression studies, and combine the genome-wide epigenetic data with genome-wide genetic and expression data to identify additional potentially causative agents of disease are needed. Such studies will allow researchers to create much-needed therapeutics to prevent and/or intervene in disease progression. Improved therapeutics will greatly enhance the quality of life and reduce the burden of disease management for millions of patients living with these potentially blinding conditions.
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Affiliation(s)
- Katie L Pennington
- Postdoctoral Fellow, Department of Ophthalmology & Visual Sciences, John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA
| | - Margaret M DeAngelis
- Associate Professor, Department of Ophthalmology & Visual Sciences, John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA
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Keating DP. Transformative Role of Epigenetics in Child Development Research: Commentary on the Special Section. Child Dev 2016; 87:135-42. [DOI: 10.1111/cdev.12488] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Gao X, Jia M, Zhang Y, Breitling LP, Brenner H. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin Epigenetics 2015; 7:113. [PMID: 26478754 PMCID: PMC4609112 DOI: 10.1186/s13148-015-0148-3] [Citation(s) in RCA: 285] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 10/06/2015] [Indexed: 01/10/2023] Open
Abstract
Active smoking is a major preventable public health problem and an established critical factor for epigenetic modification. In this systematic review, we identified 17 studies addressing the association of active smoking exposure with methylation modifications in blood DNA, including 14 recent epigenome-wide association studies (EWASs) and 3 gene-specific methylation studies (GSMSs) on the gene regions identified by EWASs. Overall, 1460 smoking-associated CpG sites were identified in the EWASs, of which 62 sites were detected in multiple (≥3) studies. The three most frequently reported CpG sites (genes) in whole blood samples were cg05575921 (AHRR), cg03636183 (F2RL3), and cg19859270 (GPR15), followed by other loci within intergenic regions 2q37.1 and 6p21.33. These significant smoking-related genes were further assessed by specific methylation assays in three GSMSs and reflected not only current but also lifetime or long-term exposure to active smoking. In conclusion, this review summarizes the evidences for the use of blood DNA methylation patterns as biomarkers of smoking exposure for research and clinical practice. In particular, it provides a reservoir for constructing a smoking exposure index score which could be used to more precisely quantify long-term smoking exposure and evaluate the risks of smoking-induced diseases.
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Affiliation(s)
- Xu Gao
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany
| | - Min Jia
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany
| | - Yan Zhang
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany
| | - Lutz Philipp Breitling
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany ; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, D-69120 Heidelberg, Germany ; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany
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