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Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

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      Abstract

      Background

      Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or metabolic health, remains unclear.

      Methods

      We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies from 542 healthy female twins. Replication, tissue specificity, and longitudinal stability of the smoking-associated effects were explored in additional adipose, blood, skin, and lung samples. We characterized the impact of adipose tissue smoking methylation and expression signals on metabolic disease risk phenotypes, including visceral fat.

      Results

      We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes ( AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. CYP1A1 gene expression achieved 95% prediction performance of current smoking status. We validated and replicated a proportion of the signals in additional primary tissue samples, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. Metabolic disease risk traits such as visceral fat and android-to-gynoid ratio showed association with methylation at smoking markers with functional impacts on expression, such as CYP1A1, and at tissue-shared smoking signals, such as NOTCH1. At smoking-signals, BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation.

      Conclusions

      Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue. The findings relate to human metabolic health and give insights into understanding the widespread health consequence of smoking outside of the lung.

      Electronic supplementary material

      The online version of this article (10.1186/s13148-018-0558-0) contains supplementary material, which is available to authorized users.

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      Most cited references 89

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      Fast and accurate short read alignment with Burrows–Wheeler transform

      Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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        GENCODE: the reference human genome annotation for The ENCODE Project.

        The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.
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          Integrative analysis of 111 reference human epigenomes

          The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but a similar reference has lacked for epigenomic studies. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection to-date of human epigenomes for primary cells and tissues. Here, we describe the integrative analysis of 111 reference human epigenomes generated as part of the program, profiled for histone modification patterns, DNA accessibility, DNA methylation, and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically-relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation, and human disease.
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            Author and article information

            Affiliations
            [1 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Department of Twin Research and Genetic Epidemiology, , King’s College London, ; London, SE1 7EH UK
            [2 ]GRID grid.145695.a, Department of Biomedical Sciences, , Chang Gung University, ; Taoyuan, Taiwan
            [3 ]Division of Allergy, Asthma, and Rheumatology, Department of Pediatrics, Chang Gung Memorial Hospital, Linkou, Taiwan
            [4 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, , University of Oxford, ; Oxford, OX3 7LF UK
            [5 ]ISNI 0000 0004 1936 9094, GRID grid.40263.33, Department of Epidemiology, , Brown University School of Public Health, ; Providence, RI 02912 USA
            [6 ]ISNI 0000 0004 0410 2071, GRID grid.7737.4, Institute for Molecular Medicine Finland (FIMM) and Department of Public Health, , University of Helsinki, ; Helsinki, Finland
            [7 ]ISNI 0000 0001 2342 7339, GRID grid.14442.37, Department of Bioinformatics, Institute of Health Sciences, , Hacettepe University, ; 06100 Ankara, Turkey
            [8 ]ISNI 0000000121678994, GRID grid.4489.1, Pfizer - University of Granada - Andalusian Government Center for Genomics and Oncological Research (GENYO), ; Granada, Spain
            [9 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Division of Cancer Studies, , King’s College London, ; London, SE1 9RT UK
            [10 ]ISNI 0000 0001 0670 2351, GRID grid.59734.3c, Department of Oncological Sciences, , Icahn School of Medicine at Mount Sinai, ; New York City, NY 10029 USA
            [11 ]ISNI 0000 0001 0670 2351, GRID grid.59734.3c, The Tisch Cancer Institute, , Icahn School of Medicine at Mount Sinai, ; New York City, NY 10029 USA
            [12 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Centre for Stem Cells and Regenerative Medicine, , King’s College London, ; Floor 28, Tower Wing, Guy’s Hospital, Great Maze Pond, London, SE1 9RT UK
            [13 ]GRID grid.420545.2, NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, ; London, SE1 9RT UK
            [14 ]ISNI 0000 0004 0410 2071, GRID grid.7737.4, Research Programs Unit, Diabetes and Obesity, Obesity Research Unit, , University of Helsinki, ; Helsinki, Finland
            [15 ]ISNI 0000 0000 9950 5666, GRID grid.15485.3d, Endocrinology, Abdominal Center, , Helsinki University Hospital, ; Helsinki, Finland
            [16 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, , Queen Mary University of London, ; London, EC1M 6BQ UK
            [17 ]ISNI 0000 0001 0619 1117, GRID grid.412125.1, Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), , King Abdulaziz University, ; Jeddah, Saudi Arabia
            [18 ]ISNI 0000 0001 2322 4988, GRID grid.8591.5, Department of Genetic Medicine and Development, , University of Geneva Medical School, ; 1211 Geneva, Switzerland
            [19 ]ISNI 0000 0001 2322 4988, GRID grid.8591.5, Institute for Genetics and Genomics in Geneva (iGE3), , University of Geneva, ; 1211 Geneva, Switzerland
            [20 ]ISNI 0000 0001 2223 3006, GRID grid.419765.8, Swiss Institute of Bioinformatics, ; 1211 Geneva, Switzerland
            [21 ]ISNI 0000 0004 1936 9094, GRID grid.40263.33, Department of Laboratory Medicine & Pathology, , Brown University, ; Providence, RI 02912 USA
            Contributors
            pei-chien.tsai@kcl.ac.uk
            ORCID: http://orcid.org/0000-0002-3858-5986, jordana.bell@kcl.ac.uk
            Journal
            Clin Epigenetics
            Clin Epigenetics
            Clinical Epigenetics
            BioMed Central (London )
            1868-7075
            1868-7083
            20 October 2018
            20 October 2018
            2018
            : 10
            30342560
            6196025
            558
            10.1186/s13148-018-0558-0
            © The Author(s). 2018

            Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

            Funding
            Funded by: FundRef http://dx.doi.org/10.13039/501100000269, Economic and Social Research Council;
            Award ID: ES/N000404/1
            Award Recipient :
            Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
            Award ID: MR/L01999X/1
            Award ID: MR/N013700/1
            Award Recipient :
            Funded by: FundRef http://dx.doi.org/10.13039/501100002341, Academy of Finland;
            Award ID: 297908
            Award ID: 266286, 272376, 314383
            Award Recipient :
            Categories
            Research
            Custom metadata
            © The Author(s) 2018

            Genetics

            adipose tissue, gene expression, dna methylation, smoking, rna-sequencing

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