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Herzog C, Jones A, Evans I, Raut JR, Zikan M, Cibula D, Wong A, Brenner H, Richmond RC, Widschwendter M. Cigarette Smoking and E-cigarette Use Induce Shared DNA Methylation Changes Linked to Carcinogenesis. Cancer Res 2024; 84:1898-1914. [PMID: 38503267 PMCID: PMC11148547 DOI: 10.1158/0008-5472.can-23-2957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/30/2023] [Accepted: 03/11/2024] [Indexed: 03/21/2024]
Abstract
Tobacco use is a major modifiable risk factor for adverse health outcomes, including cancer, and elicits profound epigenetic changes thought to be associated with long-term cancer risk. While electronic cigarettes (e-cigarettes) have been advocated as harm reduction alternatives to tobacco products, recent studies have revealed potential detrimental effects, highlighting the urgent need for further research into the molecular and health impacts of e-cigarettes. Here, we applied computational deconvolution methods to dissect the cell- and tissue-specific epigenetic effects of tobacco or e-cigarette use on DNA methylation (DNAme) in over 3,500 buccal/saliva, cervical, or blood samples, spanning epithelial and immune cells at directly and indirectly exposed sites. The 535 identified smoking-related DNAme loci [cytosine-phosphate-guanine sites (CpG)] clustered into four functional groups, including detoxification or growth signaling, based on cell type and anatomic site. Loci hypermethylated in buccal epithelial cells of smokers associated with NOTCH1/RUNX3/growth factor receptor signaling also exhibited elevated methylation in cancer tissue and progressing lung carcinoma in situ lesions, and hypermethylation of these sites predicted lung cancer development in buccal samples collected from smokers up to 22 years prior to diagnosis, suggesting a potential role in driving carcinogenesis. Alarmingly, these CpGs were also hypermethylated in e-cigarette users with a limited smoking history. This study sheds light on the cell type-specific changes to the epigenetic landscape induced by smoking-related products. SIGNIFICANCE The use of both cigarettes and e-cigarettes elicits cell- and exposure-specific epigenetic effects that are predictive of carcinogenesis, suggesting caution when broadly recommending e-cigarettes as aids for smoking cessation.
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Affiliation(s)
- Chiara Herzog
- European Translational Oncology Prevention and Screening (EUTOPS) Institute, Universität Innsbruck, Innsbruck, Austria
- Research Institute for Biomedical Aging, Universität Innsbruck, Innsbruck, Austria
| | - Allison Jones
- Department of Women's Cancer, UCL EGA Institute for Women's Health, University College London, London, United Kingdom
| | - Iona Evans
- Department of Women's Cancer, UCL EGA Institute for Women's Health, University College London, London, United Kingdom
| | - Janhavi R Raut
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michal Zikan
- Department of Gynecology and Obstetrics, First Faculty of Medicine and Hospital Na Bulovce, Charles University in Prague, Prague, Czech Republic
| | - David Cibula
- Gynecologic Oncology Center, Department of Obstetrics and Gynecology, First Faculty of Medicine, Charles University in Prague, General University Hospital in Prague, Prague, Czech Republic
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Martin Widschwendter
- European Translational Oncology Prevention and Screening (EUTOPS) Institute, Universität Innsbruck, Innsbruck, Austria
- Research Institute for Biomedical Aging, Universität Innsbruck, Innsbruck, Austria
- Department of Women's Cancer, UCL EGA Institute for Women's Health, University College London, London, United Kingdom
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
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2
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Tong H, Dwaraka VB, Chen Q, Luo Q, Lasky-Su JA, Smith R, Teschendorff AE. Quantifying the stochastic component of epigenetic aging. NATURE AGING 2024; 4:886-901. [PMID: 38724732 PMCID: PMC11186785 DOI: 10.1038/s43587-024-00600-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/21/2024] [Indexed: 05/15/2024]
Abstract
DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock's predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66-75% of the accuracy underpinning Horvath's clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang's clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath's age acceleration in males and PhenoAge's age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.
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Affiliation(s)
- Huige Tong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | | | - Qingwen Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
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3
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Zhu T, Tong H, Du Z, Beck S, Teschendorff AE. An improved epigenetic counter to track mitotic age in normal and precancerous tissues. Nat Commun 2024; 15:4211. [PMID: 38760334 PMCID: PMC11101651 DOI: 10.1038/s41467-024-48649-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 05/09/2024] [Indexed: 05/19/2024] Open
Abstract
The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC's mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.
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Affiliation(s)
- Tianyu Zhu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Huige Tong
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Zhaozhen Du
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Stephan Beck
- Medical Genomics Group, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, UK
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
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4
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Peng Q, Liu X, Li W, Jing H, Li J, Gao X, Luo Q, Breeze CE, Pan S, Zheng Q, Li G, Qian J, Yuan L, Yuan N, You C, Du S, Zheng Y, Yuan Z, Tan J, Jia P, Wang J, Zhang G, Lu X, Shi L, Guo S, Liu Y, Ni T, Wen B, Zeng C, Jin L, Teschendorff AE, Liu F, Wang S. Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits. Nat Genet 2024; 56:846-860. [PMID: 38641644 DOI: 10.1038/s41588-023-01494-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 08/02/2023] [Indexed: 04/21/2024]
Abstract
Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height. Trans-mQTL hotspots revealed biological pathways contributing to EA-specific genetic associations, including an ERG-mediated 233 trans-mCpG network, implicated in hematopoietic cell differentiation, which likely reflects binding efficiency modulation of the ERG protein complex. More than 90% of mQTLs were shared between different blood cell lineages, with a smaller fraction of lineage-specific mQTLs displaying preferential hypomethylation in the respective lineages. Our study provides new insights into the mQTL landscape across genetic ancestries and their downstream effects on cellular processes and diseases/traits.
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Affiliation(s)
- Qianqian Peng
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Wenran Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Han Jing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jiarui Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xingjian Gao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | | | - Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Qiwen Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Guochao Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Jiaqiang Qian
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Liyun Yuan
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Na Yuan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Chenglong You
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Ziyu Yuan
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Guoqing Zhang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Xianping Lu
- Shenzhen Chipscreen Biosciences Co. Ltd., Shenzhen, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Shicheng Guo
- Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, China
| | - Bo Wen
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- The Fifth People's Hospital of Shanghai and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Changqing Zeng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
- Department of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, Kingdom of Saudi Arabia.
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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5
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Lee MK, Azizgolshani N, Zhang Z, Perreard L, Kolling FW, Nguyen LN, Zanazzi GJ, Salas LA, Christensen BC. Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors. Nat Commun 2024; 15:3635. [PMID: 38688903 PMCID: PMC11061294 DOI: 10.1038/s41467-024-47943-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.
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Affiliation(s)
- Min Kyung Lee
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Surgery, Columbia University Medical Center, New York, NY, USA
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Laurent Perreard
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Fred W Kolling
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lananh N Nguyen
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - George J Zanazzi
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Pathology and Laboratory Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
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6
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Bell CG. Epigenomic insights into common human disease pathology. Cell Mol Life Sci 2024; 81:178. [PMID: 38602535 PMCID: PMC11008083 DOI: 10.1007/s00018-024-05206-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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7
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Zhang X, Hu Y, Vandenhoudt RE, Yan C, Marconi VC, Cohen MH, Wang Z, Justice AC, Aouizerat BE, Xu K. Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts. PLoS Pathog 2024; 20:e1012063. [PMID: 38466776 PMCID: PMC10957090 DOI: 10.1371/journal.ppat.1012063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 03/21/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. METHODS Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. RESULTS The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. CONCLUSION Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.
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Affiliation(s)
- Xinyu Zhang
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Ying Hu
- Center for Biomedical Information and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America
| | - Ral E. Vandenhoudt
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Chunhua Yan
- Center for Biomedical Information and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America
| | - Vincent C. Marconi
- Division of Infectious Diseases, Emory University School of Medicine and Department of Global Health, Rollins School of Public Health, Emory University, Georgia, United States of America
- Atlanta Veterans Affairs Healthcare System, Decatur, Georgia, United States of America
| | - Mardge H. Cohen
- Department of Medicine, Stroger Hospital of Cook County, Chicago, Illinois, United States of America
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Amy C. Justice
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Bradley E. Aouizerat
- Translational Research Center, College of Dentistry, New York University, New York, New York, United States of America
- Department of Oral and Maxillofacial Surgery, College of Dentistry, New York University, New York, New York, United States of America
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, United States of America
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8
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Maciejewski E, Horvath S, Ernst J. Cross-species and tissue imputation of species-level DNA methylation samples across mammalian species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.26.568769. [PMID: 38076978 PMCID: PMC10705269 DOI: 10.1101/2023.11.26.568769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
DNA methylation data offers valuable insights into various aspects of mammalian biology. The recent introduction and large-scale application of the mammalian methylation array has significantly expanded the availability of such data across conserved sites in many mammalian species. In our study, we consider 13,245 samples profiled on this array encompassing 348 species and 59 tissues from 746 species-tissue combinations. While having some coverage of many different species and tissue types, this data captures only 3.6% of potential species-tissue combinations. To address this gap, we developed CMImpute (Cross-species Methylation Imputation), a method based on a Conditional Variational Autoencoder, to impute DNA methylation for non-profiled species-tissue combinations. In cross-validation, we demonstrate that CMImpute achieves a strong correlation with actual observed values, surpassing several baseline methods. Using CMImpute we imputed methylation data for 19,786 new species-tissue combinations. We believe that both CMImpute and our imputed data resource will be useful for DNA methylation analyses across a wide range of mammalian species.
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Affiliation(s)
- Emily Maciejewski
- Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Steve Horvath
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095 USA
- Dept. of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, US
- Altos Labs, Cambridge, UK, WA14 2DT
| | - Jason Ernst
- Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
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9
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Harvanek ZM, Boks MP, Vinkers CH, Higgins-Chen AT. The Cutting Edge of Epigenetic Clocks: In Search of Mechanisms Linking Aging and Mental Health. Biol Psychiatry 2023; 94:694-705. [PMID: 36764569 PMCID: PMC10409884 DOI: 10.1016/j.biopsych.2023.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/11/2023]
Abstract
Individuals with psychiatric disorders are at increased risk of age-related diseases and early mortality. Recent studies demonstrate that this link between mental health and aging is reflected in epigenetic clocks, aging biomarkers based on DNA methylation. The reported relationships between epigenetic clocks and mental health are mostly correlational, and the mechanisms are poorly understood. Here, we review recent progress concerning the molecular and cellular processes underlying epigenetic clocks as well as novel technologies enabling further studies of the causes and consequences of epigenetic aging. We then review the current literature on how epigenetic clocks relate to specific aspects of mental health, such as stress, medications, substance use, health behaviors, and symptom clusters. We propose an integrated framework where mental health and epigenetic aging are each broken down into multiple distinct processes, which are then linked to each other, using stress and schizophrenia as examples. This framework incorporates the heterogeneity and complexity of both mental health conditions and aging, may help reconcile conflicting results, and provides a basis for further hypothesis-driven research in humans and model systems to investigate potentially causal mechanisms linking aging and mental health.
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Affiliation(s)
- Zachary M Harvanek
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Marco P Boks
- Department of Psychiatry, University Medical Center Utrecht Brain Center, University of Utrecht, Utrecht, the Netherlands
| | - Christiaan H Vinkers
- Department of Psychiatry, Amsterdam University Medical Center, location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Department of Pathology, Yale University School of Medicine, New Haven, Connecticut.
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10
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Luo Q, Dwaraka VB, Chen Q, Tong H, Zhu T, Seale K, Raffaele JM, Zheng SC, Mendez TL, Chen Y, Carreras N, Begum S, Mendez K, Voisin S, Eynon N, Lasky-Su JA, Smith R, Teschendorff AE. A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes. Genome Med 2023; 15:59. [PMID: 37525279 PMCID: PMC10388560 DOI: 10.1186/s13073-023-01211-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/10/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition. METHODS Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes. RESULTS Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67-0.72), which increased to 0.83 (0.80-0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity. CONCLUSIONS This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.
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Affiliation(s)
- Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Varun B Dwaraka
- TruDiagnostics, 881 Corporate Dr., Lexington, KY, 40503, USA
| | - Qingwen Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Huige Tong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Tianyu Zhu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Kirsten Seale
- Institute for Health and Sport (iHeS), Victoria University, Footscray, VIC, 3011, Australia
| | - Joseph M Raffaele
- PhysioAge LLC, 30 Central Park South / Suite 8A, New York, NY, 10019, USA
| | - Shijie C Zheng
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Tavis L Mendez
- TruDiagnostics, 881 Corporate Dr., Lexington, KY, 40503, USA
| | - Yulu Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | | | - Sofina Begum
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Kevin Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Sarah Voisin
- Institute for Health and Sport (iHeS), Victoria University, Footscray, VIC, 3011, Australia
| | - Nir Eynon
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
| | - Ryan Smith
- TruDiagnostics, 881 Corporate Dr., Lexington, KY, 40503, USA.
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
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11
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Vidaki A, Planterose Jiménez B, Poggiali B, Kalamara V, van der Gaag KJ, Maas SCE, Ghanbari M, Sijen T, Kayser M. Targeted DNA methylation analysis and prediction of smoking habits in blood based on massively parallel sequencing. Forensic Sci Int Genet 2023; 65:102878. [PMID: 37116245 DOI: 10.1016/j.fsigen.2023.102878] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/28/2023] [Accepted: 04/18/2023] [Indexed: 04/30/2023]
Abstract
Tobacco smoking is a frequent habit sustained by > 1.3 billion people in 2020 and the leading preventable factor for health risk and premature mortality worldwide. In the forensic context, predicting smoking habits from biological samples may allow broadening DNA phenotyping. In this study, we aimed to implement previously published smoking habit classification models based on blood DNA methylation at 13 CpGs. First, we developed a matching lab tool based on bisulfite conversion and multiplex PCR followed by amplification-free library preparation and targeted paired-end massively parallel sequencing (MPS). Analysis of six technical duplicates revealed high reproducibility of methylation measurements (Pearson correlation of 0.983). Artificially methylated standards uncovered marker-specific amplification bias, which we corrected via bi-exponential models. We then applied our MPS tool to 232 blood samples from Europeans of a wide age range, of which 90 were current, 71 former and 71 never smokers. On average, we obtained 189,000 reads/sample and 15,000 reads/CpG, without marker drop-out. Methylation distributions per smoking category roughly corresponded to previous microarray analysis, showcasing large inter-individual variation but with technology-driven bias. Methylation at 11 out of 13 smoking-CpGs correlated with daily cigarettes in current smokers, while solely one was weakly correlated with time since cessation in former smokers. Interestingly, eight smoking-CpGs correlated with age, and one displayed weak but significant sex-associated methylation differences. Using bias-uncorrected MPS data, smoking habits were relatively accurately predicted using both two- (current/non-current) and three- (never/former/current) category model, but bias correction resulted in worse prediction performance for both models. Finally, to account for technology-driven variation, we built new, joint models with inter-technology corrections, which resulted in improved prediction results for both models, with or without PCR bias correction (e.g. MPS cross-validation F1-score > 0.8; 2-categories). Overall, our novel assay takes us one step closer towards the forensic application of viable smoking habit prediction from blood traces. However, future research is needed towards forensically validating the assay, especially in terms of sensitivity. We also need to further shed light on the employed biomarkers, particularly on the mechanistics, tissue specificity and putative confounders of smoking epigenetic signatures.
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Affiliation(s)
- Athina Vidaki
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Benjamin Planterose Jiménez
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Brando Poggiali
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Vivian Kalamara
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | | - Silvana C E Maas
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Titia Sijen
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, the Netherlands; Swammerdam Institute of Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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12
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Zhang X, Hu Y, Vandenhoudt RE, Yan C, Marconi VC, Cohen MH, Justice AC, Aouizerat BE, Xu K. Cell-type specific EWAS identifies genes involved in HIV pathogenesis and oncogenesis among people with HIV infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533691. [PMID: 36993343 PMCID: PMC10055405 DOI: 10.1101/2023.03.21.533691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Epigenome-wide association studies (EWAS) of heterogenous blood cells have identified CpG sites associated with chronic HIV infection, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. Applying a computational deconvolution method validated by capture bisulfite DNA methylation sequencing, we conducted a cell type-based EWAS and identified differentially methylated CpG sites specific for chronic HIV infection among five immune cell types in blood: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes in two independent cohorts (N total =1,134). Differentially methylated CpG sites for HIV-infection were highly concordant between the two cohorts. Cell-type level meta-EWAS revealed distinct patterns of HIV-associated differential CpG methylation, where 67% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of HIV-associated CpG sites (N=1,472) compared to any other cell type. Genes harboring statistically significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CX3CR1 in CD4+ T-cells, CCR7 in B cells, IL12R in NK cells, LCK in monocytes). More importantly, HIV-associated CpG sites were overrepresented for hallmark genes involved in cancer pathology ( FDR <0.05) (e.g. BCL family, PRDM16, PDCD1LGD, ESR1, DNMT3A, NOTCH2 ). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis such as Kras-signaling, interferon-α and -γ, TNF-α, inflammatory, and apoptotic pathways. Our findings are novel, uncovering cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding pathogen-induced epigenetic oncogenicity, specifically on HIV and its comorbidity with cancers.
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13
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Lee MK, Azizgolshani N, Zhang Z, Perreard L, Kolling FW, Nguyen LN, Zanazzi GJ, Salas LA, Christensen BC. Hydroxymethylation alterations in progenitor-like cell types of pediatric central nervous system tumors are associated with cell type-specific transcriptional changes. RESEARCH SQUARE 2023:rs.3.rs-2517758. [PMID: 36909536 PMCID: PMC10002842 DOI: 10.21203/rs.3.rs-2517758/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors we utilized a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identified a preponderance differential CpG hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like HDAC4 and IGF1R, were associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric CNS tumors.
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Affiliation(s)
- Min Kyung Lee
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Cardiothoracic Surgery, Columbia University Medical Center, New York, NY, USA
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Laurent Perreard
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Fred W Kolling
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lananh N Nguyen
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - George J Zanazzi
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Pathology and Laboratory Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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14
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Haftorn KL, Denault WRP, Lee Y, Page CM, Romanowska J, Lyle R, Næss ØE, Kristjansson D, Magnus PM, Håberg SE, Bohlin J, Jugessur A. Nucleated red blood cells explain most of the association between DNA methylation and gestational age. Commun Biol 2023; 6:224. [PMID: 36849614 PMCID: PMC9971030 DOI: 10.1038/s42003-023-04584-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Determining if specific cell type(s) are responsible for an association between DNA methylation (DNAm) and a given phenotype is important for understanding the biological mechanisms underlying the association. Our EWAS of gestational age (GA) in 953 newborns from the Norwegian MoBa study identified 13,660 CpGs significantly associated with GA (pBonferroni<0.05) after adjustment for cell type composition. When the CellDMC algorithm was applied to explore cell-type specific effects, 2,330 CpGs were significantly associated with GA, mostly in nucleated red blood cells [nRBCs; n = 2,030 (87%)]. Similar patterns were found in another dataset based on a different array and when applying an alternative algorithm to CellDMC called Tensor Composition Analysis (TCA). Our findings point to nRBCs as the main cell type driving the DNAm-GA association, implicating an epigenetic signature of erythropoiesis as a likely mechanism. They also explain the poor correlation observed between epigenetic age clocks for newborns and those for adults.
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Affiliation(s)
- Kristine L Haftorn
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
- Institute of Health and Society, University of Oslo, Oslo, Norway.
| | - William R P Denault
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Yunsung Lee
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Christian M Page
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Physical Health and Ageing, Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Julia Romanowska
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, , University of Bergen, Bergen, Norway
| | - Robert Lyle
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Øyvind E Næss
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Dana Kristjansson
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway
| | - Per M Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Siri E Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Jon Bohlin
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Division for Infection Control and Environmental Health, Department of Infectious Disease Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway
| | - Astanand Jugessur
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, , University of Bergen, Bergen, Norway
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15
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Abstract
DNA methylation data generated from bulk tissue represents a mixture of many different cell types. Variation in the cell-type composition of tissues is thus a major confounder when inferring differential DNA methylation. Due to the high cost of single-cell methylome sequencing, computational methods that can dissect the cell-type heterogeneity of bulk DNA methylomes offer an efficient and cost-effective solution, especially in the context of large-scale EWAS. In this chapter, we present a step-by-step tutorial of Epigenetic cell-type deconvolution using Single-Cell Omic References (EpiSCORE), a reference-based method that leverages the high-resolution nature of single-cell RNA-Seq datasets to facilitate microdissection of bulk-tissue DNA methylomes.
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Affiliation(s)
- Tianyu Zhu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- UCL Cancer Institute, Paul O'Gorman Building, University College London, London, UK.
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16
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The immune factors driving DNA methylation variation in human blood. Nat Commun 2022; 13:5895. [PMID: 36202838 PMCID: PMC9537159 DOI: 10.1038/s41467-022-33511-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/21/2022] [Indexed: 11/08/2022] Open
Abstract
Epigenetic changes are required for normal development, yet the nature and respective contribution of factors that drive epigenetic variation in humans remain to be fully characterized. Here, we assessed how the blood DNA methylome of 884 adults is affected by DNA sequence variation, age, sex and 139 factors relating to life habits and immunity. Furthermore, we investigated whether these effects are mediated or not by changes in cellular composition, measured by deep immunophenotyping. We show that DNA methylation differs substantially between naïve and memory T cells, supporting the need for adjustment on these cell-types. By doing so, we find that latent cytomegalovirus infection drives DNA methylation variation and provide further support that the increased dispersion of DNA methylation with aging is due to epigenetic drift. Finally, our results indicate that cellular composition and DNA sequence variation are the strongest predictors of DNA methylation, highlighting critical factors for medical epigenomics studies.
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17
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Keshawarz A, Joehanes R, Guan W, Huan T, DeMeo DL, Grove ML, Fornage M, Levy D, O’Connor G. Longitudinal change in blood DNA epigenetic signature after smoking cessation. Epigenetics 2022; 17:1098-1109. [PMID: 34570667 PMCID: PMC9542417 DOI: 10.1080/15592294.2021.1985301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/20/2021] [Accepted: 09/21/2021] [Indexed: 12/14/2022] Open
Abstract
Cigarette smoking is associated with epigenetic changes that may be reversible following smoking cessation. Whole blood DNA methylation was evaluated in Framingham Heart Study Offspring (n = 169) and Third Generation (n = 30) cohort participants at two study visits 6 years apart and in Atherosclerosis Risk in Communities (ARIC) study (n = 222) participants at two study visits 20 years apart. Changes in DNA methylation (delta β values) at 483,565 cytosine-phosphate-guanine (CpG) sites and differentially methylated regions (DMRs) were compared between participants who were current, former, or never smokers at both visits (current-current, former-former, never-never, respectively), versus those who quit in the interim (current-former). Interim quitters had more hypermethylation at four CpGs annotated to AHRR, one CpG annotated to F2RL3, and one intergenic CpG (cg21566642) compared with current-current smokers (FDR < 0.02 for all), and two significant DMRs were identified. While there were no significant differentially methylated CpGs in the comparison of interim quitters and former-former smokers, 106 DMRs overlapping with small nucleolar RNA were identified. As compared with all non-smokers, current-current smokers additionally had more hypermethylation at two CpG sites annotated to HIVEP3 and TMEM126A, respectively, and another intergenic CpG (cg14339116). Gene transcripts associated with smoking cessation were implicated in immune responses, cell homoeostasis, and apoptosis. Smoking cessation is associated with early reversion of blood DNA methylation changes at CpG sites annotated to AHRR and F2RL3 towards those of never smokers. Associated gene expression suggests a role of longitudinal smoking-related DNA methylation changes in immune response processes.
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Affiliation(s)
- Amena Keshawarz
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Roby Joehanes
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Tianxiao Huan
- Framingham Heart Study, Framingham, MA, USA
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worcester, MA, USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Megan L. Grove
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Myriam Fornage
- McGovern Medical School and Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Brown Foundation Institute of Molecular Medicine, Houston, TX, USA
| | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - George O’Connor
- Pulmonary Center, Boston University School of Medicine, Boston, MA, USA
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18
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Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Background: Understanding the proportion of cell types in heterogeneous tissue samples is important in bioinformatics. It is a challenge to infer the proportion of tissues using bulk RNA sequencing data in bioinformatics because most traditional algorithms for predicting tissue cell ratios heavily rely on standardized specific cell-type gene expression profiles, and do not consider tissue heterogeneity. The prediction accuracy of algorithms is limited, and robustness is lacking. This means that new approaches are needed urgently. Methods: In this study, we introduced an algorithm that automatically predicts tissue cell ratios named Autoptcr. The algorithm uses the data simulated by single-cell RNA sequencing (ScRNA-Seq) for model training, using convolutional neural networks (CNNs) to extract intrinsic relationships between genes and predict the cell proportions of tissues. Results: We trained the algorithm using simulated bulk samples and made predictions using real bulk PBMC data. Comparing Autoptcr with existing advanced algorithms, the Pearson correlation coefficient between the actual value of Autoptcr and the predicted value was the highest, reaching 0.903. Tested on a bulk sample, the correlation coefficient of Lin was 41% higher than that of CSx. The algorithm can infer tissue cell proportions directly from tissue gene expression data. Conclusions: The Autoptcr algorithm uses simulated ScRNA-Seq data for training to solve the problem of specific cell-type gene expression profiles. It also has high prediction accuracy and strong noise resistance for the tissue cell ratio. This work is expected to provide new research ideas for the prediction of tissue cell proportions.
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19
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Cosin-Tomas M, Cilleros-Portet A, Aguilar-Lacasaña S, Fernandez-Jimenez N, Bustamante M. Prenatal Maternal Smoke, DNA Methylation, and Multi-omics of Tissues and Child Health. Curr Environ Health Rep 2022; 9:502-512. [PMID: 35670920 PMCID: PMC9363403 DOI: 10.1007/s40572-022-00361-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/05/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW Maternal tobacco smoking during pregnancy is of public health concern, and understanding the biological mechanisms can help to promote smoking cessation campaigns. This non-systematic review focuses on the effects of maternal smoking during pregnancy on offspring's epigenome, consistent in chemical modifications of the genome that regulate gene expression. RECENT FINDINGS Recent meta-analyses of epigenome-wide association studies have shown that maternal smoking during pregnancy is consistently associated with offspring's DNA methylation changes, both in the placenta and blood. These studies indicate that effects on blood DNA methylation can persist for years, and that the longer the duration of the exposure and the higher the dose, the larger the effects. Hence, DNA methylation scores have been developed to estimate past exposure to maternal smoking during pregnancy as biomarkers. There is robust evidence for DNA methylation alterations associated with maternal smoking during pregnancy; however, the role of sex, ethnicity, and genetic background needs further exploration. Moreover, there are no conclusive studies about exposure to low doses or during the preconception period. Similarly, studies on tissues other than the placenta and blood are scarce, and cell-type specificity within tissues needs further investigation. In addition, biological interpretation of DNA methylation findings requires multi-omics data, poorly available in epidemiological settings. Finally, although several mediation analyses link DNA methylation changes with health outcomes, they do not allow causal inference. For this, a combination of data from multiple study designs will be essential in the future to better address this topic.
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Affiliation(s)
- Marta Cosin-Tomas
- ISGlobal, Institute for Global Health, Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain. .,CIBER Epidemiología Y Salud Pública, Madrid, Spain.
| | - Ariadna Cilleros-Portet
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU) and Biocruces-Bizkaia Health Research Institute, Basque Country, Spain
| | - Sofía Aguilar-Lacasaña
- ISGlobal, Institute for Global Health, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología Y Salud Pública, Madrid, Spain
| | - Nora Fernandez-Jimenez
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU) and Biocruces-Bizkaia Health Research Institute, Basque Country, Spain
| | - Mariona Bustamante
- ISGlobal, Institute for Global Health, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología Y Salud Pública, Madrid, Spain
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20
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Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet 2022; 23:369-383. [PMID: 35304597 DOI: 10.1038/s41576-022-00465-w] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2022] [Indexed: 12/12/2022]
Abstract
DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.
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Affiliation(s)
- Paul D Yousefi
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Matthew Suderman
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Ryan Langdon
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Oliver Whitehurst
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Caroline L Relton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.
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21
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Julià A, Gómez A, López-Lasanta M, Blanco F, Erra A, Fernández-Nebro A, Mas AJ, Pérez-García C, Vivar MLG, Sánchez-Fernández S, Alperi-López M, Sanmartí R, Ortiz AM, Fernandez-Cid CM, Díaz-Torné C, Moreno E, Li T, Martínez-Mateu SH, Absher DM, Myers RM, Molina JT, Marsal S. Longitudinal analysis of blood DNA methylation identifies mechanisms of response to tumor necrosis factor inhibitor therapy in rheumatoid arthritis. EBioMedicine 2022; 80:104053. [PMID: 35576644 PMCID: PMC9118662 DOI: 10.1016/j.ebiom.2022.104053] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 04/25/2022] [Accepted: 04/25/2022] [Indexed: 11/07/2022] Open
Abstract
Background Rheumatoid arthritis (RA) is a chronic, immune-mediated inflammatory disease of the joints that has been associated with variation in the peripheral blood methylome. In this study, we aim to identify epigenetic variation that is associated with the response to tumor necrosis factor inhibitor (TNFi) therapy. Methods Peripheral blood genome-wide DNA methylation profiles were analyzed in a discovery cohort of 62 RA patients at baseline and at week 12 of TNFi therapy. DNA methylation of individual CpG sites and enrichment of biological pathways were evaluated for their association with drug response. Using a novel cell deconvolution approach, altered DNA methylation associated with TNFi response was also tested in the six main immune cell types in blood. Validation of the results was performed in an independent longitudinal cohort of 60 RA patients. Findings Treatment with TNFi was associated with significant longitudinal peripheral blood methylation changes in biological pathways related to RA (FDR<0.05). 139 biological functions were modified by therapy, with methylation levels changing systematically towards a signature similar to that of healthy controls. Differences in the methylation profile of T cell activation and differentiation, GTPase-mediated signaling, and actin filament organization pathways were associated with the clinical response to therapy. Cell type deconvolution analysis identified CpG sites in CD4+T, NK, neutrophils and monocytes that were significantly associated with the response to TNFi. Interpretation Our results show that treatment with TNFi restores homeostatic blood methylation in RA. The clinical response to TNFi is associated to methylation variation in specific biological pathways, and it involves cells from both the innate and adaptive immune systems. Funding The Instituto de Salud Carlos III.
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Affiliation(s)
- Antonio Julià
- Rheumatology Research Group, Vall d'Hebron University Hospital Research Institute, Barcelona 08035, Spain.
| | - Antonio Gómez
- Rheumatology Research Group, Vall d'Hebron University Hospital Research Institute, Barcelona 08035, Spain
| | - María López-Lasanta
- Rheumatology Research Group, Vall d'Hebron University Hospital Research Institute, Barcelona 08035, Spain
| | - Francisco Blanco
- Rheumatology Department, INIBIC-Hospital Universitario A Coruña, A Coruña, Spain
| | - Alba Erra
- Rheumatology Research Group, Vall d'Hebron University Hospital Research Institute, Barcelona 08035, Spain; Rheumatology Department, Hospital de San Rafael, Barcelona, Spain
| | | | - Antonio Juan Mas
- Rheumatology Department, Hospital Universitario Son Llàtzer, Mallorca, Spain
| | | | | | | | | | - Raimon Sanmartí
- Rheumatology Department, Fundació Clínic Recerca Biomèdica, Barcelona, Spain
| | - Ana María Ortiz
- Rheumatology Department, Hospital Universitario La Princesa, Madrid, Spain
| | | | - César Díaz-Torné
- Rheumatology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Estefania Moreno
- Rheumatology Research Group, Vall d'Hebron University Hospital Research Institute, Barcelona 08035, Spain; Rheumatology Unit, Consorci Sanitari de l'Alt Penedès, Spain
| | - Tianlu Li
- Rheumatology Research Group, Vall d'Hebron University Hospital Research Institute, Barcelona 08035, Spain
| | - Sergio H Martínez-Mateu
- Rheumatology Research Group, Vall d'Hebron University Hospital Research Institute, Barcelona 08035, Spain
| | - Devin M Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
| | | | - Sara Marsal
- Rheumatology Research Group, Vall d'Hebron University Hospital Research Institute, Barcelona 08035, Spain.
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22
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Qi L, Teschendorff AE. Cell-type heterogeneity: Why we should adjust for it in epigenome and biomarker studies. Clin Epigenetics 2022; 14:31. [PMID: 35227298 PMCID: PMC8887190 DOI: 10.1186/s13148-022-01253-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/21/2022] [Indexed: 12/18/2022] Open
Abstract
Most studies aiming to identify epigenetic biomarkers do so from complex tissues that are composed of many different cell-types. By definition, these cell-types vary substantially in terms of their epigenetic profiles. This cell-type specific variation among healthy cells is completely independent of the variation associated with disease, yet it dominates the epigenetic variability landscape. While cell-type composition of tissues can change in disease and this may provide accurate and reproducible biomarkers, not adjusting for the underlying cell-type heterogeneity may seriously limit the sensitivity and precision to detect disease-relevant biomarkers or hamper our understanding of such biomarkers. Given that computational and experimental tools for tackling cell-type heterogeneity are available, we here stress that future epigenetic biomarker studies should aim to provide estimates of underlying cell-type fractions for all samples in the study, and to identify biomarkers before and after adjustment for cell-type heterogeneity, in order to obtain a more complete and unbiased picture of the biomarker-landscape. This is critical, not only to improve reproducibility and for the eventual clinical application of such biomarkers, but importantly, to also improve our molecular understanding of disease itself.
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Affiliation(s)
- Luo Qi
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. .,UCL Cancer Institute, University College London, London, WC1E 8BT, UK.
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23
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Sikdar S. Robust meta-analysis for large-scale genomic experiments based on an empirical approach. BMC Med Res Methodol 2022; 22:43. [PMID: 35144554 PMCID: PMC8832678 DOI: 10.1186/s12874-022-01530-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 01/18/2022] [Indexed: 11/19/2022] Open
Abstract
Background Recent high-throughput technologies have opened avenues for simultaneous analyses of thousands of genes. With the availability of a multitude of public databases, one can easily access multiple genomic study results where each study comprises of significance testing results of thousands of genes. Researchers currently tend to combine this genomic information from these multiple studies in the form of a meta-analysis. As the number of genes involved is very large, the classical meta-analysis approaches need to be updated to acknowledge this large-scale aspect of the data. Methods In this article, we discuss how application of standard theoretical null distributional assumptions of the classical meta-analysis methods, such as Fisher’s p-value combination and Stouffer’s Z, can lead to incorrect significant testing results, and we propose a robust meta-analysis method that empirically modifies the individual test statistics and p-values before combining them. Results Our proposed meta-analysis method performs best in significance testing among several meta-analysis approaches, especially in presence of hidden confounders, as shown through a wide variety of simulation studies and real genomic data analysis. Conclusion The proposed meta-analysis method produces superior meta-analysis results compared to the standard p-value combination approaches for large-scale simultaneous testing in genomic experiments. This is particularly useful in studies with large number of genes where the standard meta-analysis approaches can result in gross false discoveries due to the presence of unobserved confounding variables. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01530-y.
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Affiliation(s)
- Sinjini Sikdar
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, USA.
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24
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Salas LA, Zhang Z, Koestler DC, Butler RA, Hansen HM, Molinaro AM, Wiencke JK, Kelsey KT, Christensen BC. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nat Commun 2022; 13:761. [PMID: 35140201 PMCID: PMC8828780 DOI: 10.1038/s41467-021-27864-7] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 12/20/2021] [Indexed: 12/12/2022] Open
Abstract
DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.
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Affiliation(s)
- Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Devin C Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA
| | - Rondi A Butler
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, USA
| | - Helen M Hansen
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Karl T Kelsey
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, USA.
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
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25
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Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort. Cent Eur J Immunol 2022; 47:246-259. [PMID: 36817262 PMCID: PMC9896985 DOI: 10.5114/ceji.2022.120618] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction Smoking is known to affect whole-blood expression and methylation profiles. Although whole-genome methylation studies indicated that effects observed in blood may be driven by changes within leukocyte subtypes, these phenomena have not been explored using expression profiling. Material and methods This study reanalyzed data from the Correlated Expression and Disease Association Research (CEDAR) patient cohort recruited by Momozawa et al. (E-MTAB-6667). Data from gene expression profiling of immunomagnetically sorted CD4+, CD8+, CD14+, CD15+, and CD19+ cells were processed. Differential expression analyses were conducted in each immune cell type, followed by gene ontology analysis and supplementary investigations. Results Ninety-four differentially expressed genes were found (CD8+ n = 58, CD14+ n = 20, CD4+ n = 14, CD19+ n = 2). Two key smoking-related genes were overexpressed in specific cell types: LRRN3 (CD4+, CD8+) and MMP25 (CD8+, CD14+). In CD4+ cells smoking was associated with reduced expression of the NK cell receptor KLRB1, suggesting CD4+ subpopulation shifts and differences in interferon signaling (reduced IRF1 and IL18RAP in smokers). Key results and their integration with an immune protein-protein interaction network revealed that smoking influences integrins in CD8+ cells (ITGB7, ITGAL, ITGAM, ITGB2). C-type lectin CLEC4A was reduced in CD8+ cells and CLEC10A was increased in CD14+ cells from smokers; moreover, CLEC5A (CD8+), CLEC7A (CD8+) and CLEC9A (CD19+) were related to smoking in supplementary analyses. CD14+ cells from smokers exhibited overexpression of LDLR and the formyl peptide receptor FPR3. Conclusions Smoking specifically alters vital immune regulation genes in lymphocyte subtypes, especially CD4+, CD8+ and CD14+ cells.
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26
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Bhat B, Jones GT. Data Analysis of DNA Methylation Epigenome-Wide Association Studies (EWAS): A Guide to the Principles of Best Practice. Methods Mol Biol 2022; 2458:23-45. [PMID: 35103960 DOI: 10.1007/978-1-0716-2140-0_2] [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] [Indexed: 06/14/2023]
Abstract
Array-based EWAS have become an increasingly popular technique to identify population epigenetic effects, particularly in humans. With the arrival of nonhuman species arrays, such as the mouse, this is likely to become an even more widely used technology. This chapter provides the less experienced researcher a guide to the analysis of data from the most widely used platform, the Illumina Infinium Methylation assay. This includes an overview of quality filtering, data normalization, analysis options, and techniques to improve the interpretation of results.
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Affiliation(s)
- Basharat Bhat
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Gregory T Jones
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
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27
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Zeng Y, Amador C, Gao C, Walker RM, Morris SW, Campbell A, Frkatović A, Madden RA, Adams MJ, He S, Bretherick AD, Hayward C, Porteous DJ, Wilson JF, Evans KL, McIntosh AM, Navarro P, Haley CS. Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome. EBioMedicine 2021; 74:103730. [PMID: 34883445 PMCID: PMC8654798 DOI: 10.1016/j.ebiom.2021.103730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 11/19/2021] [Accepted: 11/19/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND parent-of-origin effects (POE) play important roles in complex disease and thus understanding their regulation and associated molecular and phenotypic variation are warranted. Previous studies mainly focused on the detection of genomic regions or phenotypes regulated by POE. Understanding whether POE may be modified by environmental or genetic exposures is important for understanding of the source of POE-associated variation, but only a few case studies addressing modifiable POE exist. METHODS in order to understand this high order of POE regulation, we screened 101 genetic and environmental factors such as 'predicted mRNA expression levels' of DNA methylation/imprinting machinery genes and environmental exposures. POE-mQTL-modifier interaction models were proposed to test the potential of these factors to modify POE at DNA methylation using data from Generation Scotland: The Scottish Family Health Study(N=2315). FINDINGS a set of vulnerable/modifiable POE-CpGs were identified (modifiable-POE-regulated CpGs, N=3). Four factors, 'lifetime smoking status' and 'predicted mRNA expression levels' of TET2, SIRT1 and KDM1A, were found to significantly modify the POE on the three CpGs in both discovery and replication datasets. We further identified plasma protein and health-related phenotypes associated with the methylation level of one of the identified CpGs. INTERPRETATION the modifiable POE identified here revealed an important yet indirect path through which genetic background and environmental exposures introduce their effect on DNA methylation, motivating future comprehensive evaluation of the role of these modifiers in complex diseases. FUNDING NSFC (81971270),H2020-MSCA-ITN(721815), Wellcome (204979/Z/16/Z,104036/Z/14/Z), MRC (MC_UU_00007/10, MC_PC_U127592696), CSO (CZD/16/6,CZB/4/276, CZB/4/710), SFC (HR03006), EUROSPAN (LSHG-CT-2006-018947), BBSRC (BBS/E/D/30002276), SYSU, Arthritis Research UK, NHLBI, NIH.
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Affiliation(s)
- Yanni Zeng
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China; Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China.
| | - Carmen Amador
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Chenhao Gao
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, Chancellor's Building, The University of Edinburgh, Edinburgh, UK
| | - Stewart W Morris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Azra Frkatović
- Genos Glycoscience Research Laboratory, Borongajska cesta 83h, 10000 Zagreb, Croatia
| | - Rebecca A Madden
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Shuai He
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Andrew D Bretherick
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK; Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Pau Navarro
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Chris S Haley
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK; Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK.
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28
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Suhre K, Zaghlool S. Connecting the epigenome, metabolome and proteome for a deeper understanding of disease. J Intern Med 2021; 290:527-548. [PMID: 33904619 DOI: 10.1111/joim.13306] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/18/2021] [Accepted: 03/22/2021] [Indexed: 12/26/2022]
Abstract
Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.
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Affiliation(s)
- K Suhre
- From the, Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, USA
| | - S Zaghlool
- From the, Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, USA
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29
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Zhou J, Li M, Wang X, He Y, Xia Y, Sweeney JA, Kopp RF, Liu C, Chen C. Drug Response-Related DNA Methylation Changes in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder. Front Neurosci 2021; 15:674273. [PMID: 34054421 PMCID: PMC8155631 DOI: 10.3389/fnins.2021.674273] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/14/2021] [Indexed: 12/12/2022] Open
Abstract
Pharmacotherapy is the most common treatment for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). Pharmacogenetic studies have achieved results with limited clinical utility. DNA methylation (DNAm), an epigenetic modification, has been proposed to be involved in both the pathology and drug treatment of these disorders. Emerging data indicates that DNAm could be used as a predictor of drug response for psychiatric disorders. In this study, we performed a systematic review to evaluate the reproducibility of published changes of drug response-related DNAm in SCZ, BD and MDD. A total of 37 publications were included. Since the studies involved patients of different treatment stages, we partitioned them into three groups based on their primary focuses: (1) medication-induced DNAm changes (n = 8); (2) the relationship between DNAm and clinical improvement (n = 24); and (3) comparison of DNAm status across different medications (n = 14). We found that only BDNF was consistent with the DNAm changes detected in four independent studies for MDD. It was positively correlated with clinical improvement in MDD. To develop better predictive DNAm factors for drug response, we also discussed future research strategies, including experimental, analytical procedures and statistical criteria. Our review shows promising possibilities for using BDNF DNAm as a predictor of antidepressant treatment response for MDD, while more pharmacoepigenetic studies are needed for treatments of various diseases. Future research should take advantage of a system-wide analysis with a strict and standard analytical procedure.
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Affiliation(s)
- Jiaqi Zhou
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Miao Li
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xueying Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yuwen He
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yan Xia
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Psychiatry, State University of New York Upstate Medical University, Syracuse, NY, United States
| | - John A. Sweeney
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, United States
| | - Richard F. Kopp
- Department of Psychiatry, State University of New York Upstate Medical University, Syracuse, NY, United States
| | - Chunyu Liu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Psychiatry, State University of New York Upstate Medical University, Syracuse, NY, United States
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Hunan Key Laboratory of Animal Models for Human Diseases, Central South University, Changsha, Hunan, China
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Cosín-Tomás M, Bustamante M, Sunyer J. Epigenetic association studies at birth and the origin of lung function development. Eur Respir J 2021; 57:57/4/2100109. [PMID: 33858853 DOI: 10.1183/13993003.00109-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 02/04/2021] [Indexed: 11/05/2022]
Affiliation(s)
- Marta Cosín-Tomás
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Centro de investigación biomédica en red en epidemiología y salud pública (ciberesp), Madrid, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Centro de investigación biomédica en red en epidemiología y salud pública (ciberesp), Madrid, Spain
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain .,Universitat Pompeu Fabra, Barcelona, Spain.,Centro de investigación biomédica en red en epidemiología y salud pública (ciberesp), Madrid, Spain.,IMIM Parc Salut Mar, Barcelona, Spain
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