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Sahoo K, Sundararajan V. Methods in DNA methylation array dataset analysis: A review. Comput Struct Biotechnol J 2024; 23:2304-2325. [PMID: 38845821 PMCID: PMC11153885 DOI: 10.1016/j.csbj.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.
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Affiliation(s)
| | - Vino Sundararajan
- Correspondence to: Department of Bio Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India.
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2
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Brasil S, Neves CJ, Rijoff T, Falcão M, Valadão G, Videira PA, Dos Reis Ferreira V. Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases. Front Mol Biosci 2021; 8:648012. [PMID: 34026829 PMCID: PMC8131862 DOI: 10.3389/fmolb.2021.648012] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 04/09/2021] [Indexed: 12/29/2022] Open
Abstract
More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.
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Affiliation(s)
- Sandra Brasil
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Cátia José Neves
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Tatiana Rijoff
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Marta Falcão
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Gonçalo Valadão
- Instituto de Telecomunicações, Lisbon, Portugal.,Departamento de Ciências e Tecnologias, Autónoma Techlab - Universidade Autónoma de Lisboa, Lisbon, Portugal.,Electronics, Telecommunications and Computers Engineering Department, Instituto Superior de Engenharia de Lisboa, Lisbon, Portugal
| | - Paula A Videira
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal.,UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Vanessa Dos Reis Ferreira
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
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3
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Kong Y, Rastogi D, Seoighe C, Greally JM, Suzuki M. Insights from deconvolution of cell subtype proportions enhance the interpretation of functional genomic data. PLoS One 2019; 14:e0215987. [PMID: 31022271 PMCID: PMC6483354 DOI: 10.1371/journal.pone.0215987] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 04/11/2019] [Indexed: 02/07/2023] Open
Abstract
Cell subtype proportion variability between samples contributes significantly to the variation of functional genomic properties such as gene expression or DNA methylation. Although the impact of the variation of cell subtype composition on measured genomic quantities is recognized, and some innovative tools have been developed for the analysis of heterogeneous samples, most functional genomics studies using samples with mixed cell types still ignore the influence of cell subtype proportion variation, or just deal with it as a nuisance variable to be eliminated. Here we demonstrate how harvesting information about cell subtype proportions from functional genomics data can provide insights into cellular changes associated with phenotypes. We focused on two types of mixed cell populations, human blood and mouse kidney. Cell type prediction is well developed in the former, but not currently in the latter. Estimating the cellular repertoire is easier when a reference dataset from purified samples of all cell types in the tissue is available, as is the case for blood. However, reference datasets are not available for most other tissues, such as the kidney. In this study, we showed that the proportion of alterations attributable to changes in the cellular composition varies strikingly in the two disorders (asthma and systemic lupus erythematosus), suggesting that the contribution of cell subtype proportion changes to functional genomic properties can be disease-specific. We also showed that a reference dataset from a single-cell RNA-seq study successfully estimated the cell subtype proportions in mouse kidney and allowed us to distinguish altered cell subtype differences between two different knock-out mouse models, both of which had reported a reduced number of glomeruli compared to their wild-type counterparts. These findings demonstrate that testing for changes in cell subtype proportions between conditions can yield important insights in functional genomics studies.
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Affiliation(s)
- Yu Kong
- Department of Genetics and Center for Epigenomics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Deepa Rastogi
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Cathal Seoighe
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, University Road, Galway, Ireland
| | - John M. Greally
- Department of Genetics and Center for Epigenomics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Masako Suzuki
- Department of Genetics and Center for Epigenomics, Albert Einstein College of Medicine, Bronx, New York, United States of America
- * E-mail:
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4
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Salas LA, Johnson KC, Koestler DC, O'Sullivan DE, Christensen BC. Integrative epigenetic and genetic pan-cancer somatic alteration portraits. Epigenetics 2017; 12:561-574. [PMID: 28426276 PMCID: PMC5687331 DOI: 10.1080/15592294.2017.1319043] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 04/02/2017] [Accepted: 04/07/2017] [Indexed: 12/11/2022] Open
Abstract
Genetic and epigenetic alterations are required for carcinogenesis and the mutation burden across tumor types has been investigated. Here, we investigate epigenetic alterations with a novel measure of global DNA methylation dysregulation, the methylation dysregulation index (MDI), across 14 cancer types in The Cancer Genome Atlas (TCGA) database. DNA methylation data-obtained using Illumina HumanMethylation450 BeadChip-was accessed from TCGA. We calculated the MDI in 14 tumor types (n = 5,592 tumors), using adjacent normal tissues (n = 701) from each tumor site. Copy number alteration, and mutation burden were retrieved from cBioportal (n = 5,152). We tested the relation of subject MDI across tumors and with age, gender, tumor stage, estimated tumor purity, and copy number alterations for both overall MDI and genomic-context-specific MDI. We also investigated the top most dysregulated loci shared across tumor types. There was a broad range of extent in methylation dysregulation across tumor types (P < 2.2E-16). However, a consistent pattern of methylation dysregulation stratified by genomic context was observed across tumor types where the highest dysregulation occurred at non-CpG island regions. Considering other summary measures of somatic alteration, MDI was correlated with copy number alterations but not with mutation burden. Using the top dysregulated CpG sites in common across tumors, 4 classes of cancer types were observed, and the functional consequences of these alterations to gene expression were confirmed. This work identified the global DNA methylation dysregulation patterns across 14 cancer types showing a higher impact for the non-CpG island areas. The most dysregulated loci across cancer types identified common clusters across cancer types that may have implications for future treatment and prevention measures.
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Affiliation(s)
- Lucas A. Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Kevin C. Johnson
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Devin C. Koestler
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Dylan E. O'Sullivan
- Department of Public Health Sciences, Queen's University, Kingston, ON, Canada
| | - Brock C. Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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5
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Jusakul A, Cutcutache I, Yong CH, Lim JQ, Huang MN, Padmanabhan N, Nellore V, Kongpetch S, Ng AWT, Ng LM, Choo SP, Myint SS, Thanan R, Nagarajan S, Lim WK, Ng CCY, Boot A, Liu M, Ong CK, Rajasegaran V, Lie S, Lim AST, Lim TH, Tan J, Loh JL, McPherson JR, Khuntikeo N, Bhudhisawasdi V, Yongvanit P, Wongkham S, Totoki Y, Nakamura H, Arai Y, Yamasaki S, Chow PKH, Chung AYF, Ooi LLPJ, Lim KH, Dima S, Duda DG, Popescu I, Broet P, Hsieh SY, Yu MC, Scarpa A, Lai J, Luo DX, Carvalho AL, Vettore AL, Rhee H, Park YN, Alexandrov LB, Gordân R, Rozen SG, Shibata T, Pairojkul C, Teh BT, Tan P. Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma. Cancer Discov 2017; 7:1116-1135. [PMID: 28667006 DOI: 10.1158/2159-8290.cd-17-0368] [Citation(s) in RCA: 572] [Impact Index Per Article: 81.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 06/07/2017] [Accepted: 06/28/2017] [Indexed: 02/07/2023]
Abstract
Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analyzed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined 4 CCA clusters-fluke-positive CCAs (clusters 1/2) are enriched in ERBB2 amplifications and TP53 mutations; conversely, fluke-negative CCAs (clusters 3/4) exhibit high copy-number alterations and PD-1/PD-L2 expression, or epigenetic mutations (IDH1/2, BAP1) and FGFR/PRKA-related gene rearrangements. Whole-genome analysis highlighted FGFR2 3' untranslated region deletion as a mechanism of FGFR2 upregulation. Integration of noncoding promoter mutations with protein-DNA binding profiles demonstrates pervasive modulation of H3K27me3-associated sites in CCA. Clusters 1 and 4 exhibit distinct DNA hypermethylation patterns targeting either CpG islands or shores-mutation signature and subclonality analysis suggests that these reflect different mutational pathways. Our results exemplify how genetics, epigenetics, and environmental carcinogens can interplay across different geographies to generate distinct molecular subtypes of cancer.Significance: Integrated whole-genome and epigenomic analysis of CCA on an international scale identifies new CCA driver genes, noncoding promoter mutations, and structural variants. CCA molecular landscapes differ radically by etiology, underscoring how distinct cancer subtypes in the same organ may arise through different extrinsic and intrinsic carcinogenic processes. Cancer Discov; 7(10); 1116-35. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 1047.
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Affiliation(s)
- Apinya Jusakul
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.,Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore.,The Centre for Research and Development of Medical Diagnostic Laboratories and Department of Clinical Immunology and Transfusion Sciences, Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand
| | - Ioana Cutcutache
- Centre for Computational Biology, Duke-NUS Medical School, Singapore
| | - Chern Han Yong
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.,Centre for Computational Biology, Duke-NUS Medical School, Singapore
| | - Jing Quan Lim
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore.,Lymphoma Genomic Translational Research Laboratory, National Cancer Centre Singapore, Division of Medical Oncology, Singapore
| | - Mi Ni Huang
- Centre for Computational Biology, Duke-NUS Medical School, Singapore
| | - Nisha Padmanabhan
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore
| | - Vishwa Nellore
- Department of Biostatistics and Bioinformatics, Center for Genomic and Computational Biology, Duke University, Durham, North Carolina
| | - Sarinya Kongpetch
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore.,Cholangiocarcinoma Screening and Care Program and Liver Fluke and Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.,Department of Pharmacology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Alvin Wei Tian Ng
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
| | - Ley Moy Ng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Su Pin Choo
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Swe Swe Myint
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore
| | - Raynoo Thanan
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Sanjanaa Nagarajan
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore
| | - Weng Khong Lim
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.,Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore
| | - Cedric Chuan Young Ng
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore
| | - Arnoud Boot
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.,Centre for Computational Biology, Duke-NUS Medical School, Singapore
| | - Mo Liu
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.,Centre for Computational Biology, Duke-NUS Medical School, Singapore
| | - Choon Kiat Ong
- Lymphoma Genomic Translational Research Laboratory, National Cancer Centre Singapore, Division of Medical Oncology, Singapore
| | - Vikneswari Rajasegaran
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore
| | - Stefanus Lie
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore.,Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Alvin Soon Tiong Lim
- Cytogenetics Laboratory, Department of Molecular Pathology, Singapore General Hospital, Singapore
| | - Tse Hui Lim
- Cytogenetics Laboratory, Department of Molecular Pathology, Singapore General Hospital, Singapore
| | - Jing Tan
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore
| | - Jia Liang Loh
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore
| | - John R McPherson
- Centre for Computational Biology, Duke-NUS Medical School, Singapore
| | - Narong Khuntikeo
- Cholangiocarcinoma Screening and Care Program and Liver Fluke and Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.,Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | | | - Puangrat Yongvanit
- Cholangiocarcinoma Screening and Care Program and Liver Fluke and Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Sopit Wongkham
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Yasushi Totoki
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan
| | - Hiromi Nakamura
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan
| | - Yasuhito Arai
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan
| | - Satoshi Yamasaki
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Japan
| | - Pierce Kah-Hoe Chow
- Division of Surgical Oncology, National Cancer Center Singapore and Office of Clinical Sciences, Duke-NUS Medical School, Singapore
| | - Alexander Yaw Fui Chung
- Department of Hepatopancreatobiliary/Transplant Surgery, Singapore General Hospital, Singapore
| | | | - Kiat Hon Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore
| | - Simona Dima
- Center of Digestive Diseases and Liver Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Dan G Duda
- Edwin L. Steele Laboratories for Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Irinel Popescu
- Center of Digestive Diseases and Liver Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Philippe Broet
- DHU Hepatinov, Hôpital Paul Brousse, AP-HP, Villejuif, France
| | - Sen-Yung Hsieh
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Ming-Chin Yu
- Department of General Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Aldo Scarpa
- Applied Research on Cancer Centre (ARC-Net), University and Hospital Trust of Verona, Verona, Italy
| | - Jiaming Lai
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, P. R. China
| | - Di-Xian Luo
- National and Local Joint Engineering Laboratory of High-through Molecular Diagnostic Technology, the First People's Hospital of Chenzhou, Southern Medical University, Chenzhou, P. R. China
| | | | - André Luiz Vettore
- Laboratory of Cancer Molecular Biology, Department of Biological Sciences, Federal University of São Paulo, Rua Pedro de Toledo, São Paulo, Brazil
| | - Hyungjin Rhee
- Department of Pathology, Brain Korea 21 PLUS Project for Medical Science, Integrated Genomic Research Center for Metabolic Regulation, Yonsei University College of Medicine, Seoul, Korea
| | - Young Nyun Park
- Department of Pathology, Brain Korea 21 PLUS Project for Medical Science, Integrated Genomic Research Center for Metabolic Regulation, Yonsei University College of Medicine, Seoul, Korea
| | - Ludmil B Alexandrov
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Raluca Gordân
- Department of Biostatistics and Bioinformatics, Center for Genomic and Computational Biology, Duke University, Durham, North Carolina. .,Department of Computer Science, Duke University, Durham, North Carolina
| | - Steven G Rozen
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore. .,Centre for Computational Biology, Duke-NUS Medical School, Singapore.,SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre, Singapore
| | - Tatsuhiro Shibata
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan. .,Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Japan
| | - Chawalit Pairojkul
- Department of Pathology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
| | - Bin Tean Teh
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore. .,Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore.,Cancer Science Institute of Singapore, National University of Singapore, Singapore.,SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre, Singapore.,Institute of Molecular and Cell Biology, Singapore
| | - Patrick Tan
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore. .,Cancer Science Institute of Singapore, National University of Singapore, Singapore.,SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre, Singapore.,Genome Institute of Singapore, Singapore
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6
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Singhal SK, Usmani N, Michiels S, Metzger-Filho O, Saini KS, Kovalchuk O, Parliament M. Towards understanding the breast cancer epigenome: a comparison of genome-wide DNA methylation and gene expression data. Oncotarget 2016; 7:3002-17. [PMID: 26657508 PMCID: PMC4823086 DOI: 10.18632/oncotarget.6503] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 11/16/2015] [Indexed: 02/06/2023] Open
Abstract
Until recently, an elevated disease risk has been ascribed to a genetic predisposition, however, exciting progress over the past years has discovered alternate elements of inheritance that involve epigenetic regulation. Epigenetic changes are heritably stable alterations that include DNA methylation, histone modifications and RNA-mediated silencing. Aberrant DNA methylation is a common molecular basis for a number of important human diseases, including breast cancer. Changes in DNA methylation profoundly affect global gene expression patterns. What is emerging is a more dynamic and complex association between DNA methylation and gene expression than previously believed. Although many tools have already been developed for analyzing genome-wide gene expression data, tools for analyzing genome-wide DNA methylation have not yet reached the same level of refinement. Here we provide an in-depth analysis of DNA methylation in parallel with gene expression data characteristics and describe the particularities of low-level and high-level analyses of DNA methylation data. Low-level analysis refers to pre-processing of methylation data (i.e. normalization, transformation and filtering), whereas high-level analysis is focused on illustrating the application of the widely used class comparison, class prediction and class discovery methods to DNA methylation data. Furthermore, we investigate the influence of DNA methylation on gene expression by measuring the correlation between the degree of CpG methylation and the level of expression and to explore the pattern of methylation as a function of the promoter region.
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Affiliation(s)
- Sandeep K Singhal
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
| | - Stefan Michiels
- Service de Biostatistique et d'Epidémiologie, Gustave Roussy, Villejuif, France.,INSERM U1018, CESP, Université Paris-Sud, Villejuif, France
| | - Otto Metzger-Filho
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | - Olga Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, Canada.,Canada Cancer and Aging Research Laboratories Ltd., Lethbridge, Canada
| | - Matthew Parliament
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
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7
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Cordero F, Ferrero G, Polidoro S, Fiorito G, Campanella G, Sacerdote C, Mattiello A, Masala G, Agnoli C, Frasca G, Panico S, Palli D, Krogh V, Tumino R, Vineis P, Naccarati A. Differentially methylated microRNAs in prediagnostic samples of subjects who developed breast cancer in the European Prospective Investigation into Nutrition and Cancer (EPIC-Italy) cohort. Carcinogenesis 2015; 36:1144-53. [DOI: 10.1093/carcin/bgv102] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Accepted: 07/08/2015] [Indexed: 12/20/2022] Open
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8
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The meta-epigenomic structure of purified human stem cell populations is defined at cis-regulatory sequences. Nat Commun 2014; 5:5195. [PMID: 25327398 PMCID: PMC4300104 DOI: 10.1038/ncomms6195] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Accepted: 09/08/2014] [Indexed: 12/14/2022] Open
Abstract
The mechanism and significance of epigenetic variability in the same cell type between healthy individuals are not clear. Here, we purify human CD34+ hematopoietic stem and progenitor cells (HSPCs) from different individuals and find that there is increased variability of DNA methylation at loci with properties of promoters and enhancers. The variability is especially enriched at candidate enhancers near genes transitioning between silent and expressed states, and encoding proteins with leukocyte differentiation properties. Our findings of increased variability at loci with intermediate DNA methylation values, at candidate “poised” enhancers, and at genes involved in HSPC lineage commitment suggest that CD34+ cell subtype heterogeneity between individuals is a major mechanism for the variability observed. Epigenomic studies performed on cell populations, even when purified, are testing collections of epigenomes, or meta-epigenomes. Our findings show that meta-epigenomic approaches to data analysis can provide insights into cell subpopulation structure.
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9
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Cruickshank MN, Oshlack A, Theda C, Davis PG, Martino D, Sheehan P, Dai Y, Saffery R, Doyle LW, Craig JM. Analysis of epigenetic changes in survivors of preterm birth reveals the effect of gestational age and evidence for a long term legacy. Genome Med 2013; 5:96. [PMID: 24134860 PMCID: PMC3978871 DOI: 10.1186/gm500] [Citation(s) in RCA: 94] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Accepted: 09/26/2013] [Indexed: 02/08/2023] Open
Abstract
Background Preterm birth confers a high risk of adverse long term health outcomes for survivors, yet the underlying molecular mechanisms are unclear. We hypothesized that effects of preterm birth can be mediated through measurable epigenomic changes throughout development. We therefore used a longitudinal birth cohort to measure the epigenetic mark of DNA methylation at birth and 18 years comparing survivors of extremely preterm birth with infants born at term. Methods Using 12 extreme preterm birth cases and 12 matched, term controls, we extracted DNA from archived neonatal blood spots and blood collected in a similar way at 18 years of age. DNA methylation was measured at 347,789 autosomal locations throughout the genome using Infinium HM450 arrays. Representative methylation differences were confirmed by Sequenom MassArray EpiTYPER. Results At birth we found 1,555 sites with significant differences in methylation between term and preterm babies. At 18 years of age, these differences had largely resolved, suggesting that DNA methylation differences at birth are mainly driven by factors relating to gestational age, such as cell composition and/or maturity. Using matched longitudinal samples, we found evidence for an epigenetic legacy associated with preterm birth, identifying persistent methylation differences at ten genomic loci. Longitudinal comparisons of DNA methylation at birth and 18 years uncovered a significant overlap between sites that were differentially-methylated at birth and those that changed with age. However, we note that overlapping sites may either differ in the same (300/1,555) or opposite (431/1,555) direction during gestation and aging respectively. Conclusions We present evidence for widespread methylation differences between extreme preterm and term infants at birth that are largely resolved by 18 years of age. These results are consistent with methylation changes associated with blood cell development, cellular composition, immune induction and age at these time points. Finally, we identified ten probes significantly associated with preterm individuals and with greater than 5% methylation discordance at birth and 18 years that may reflect a long term epigenetic legacy of preterm birth.
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Affiliation(s)
- Mark N Cruickshank
- Early Life Epigenetics Group, Murdoch Childrens Research Institute (MCRI), Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Present address: Telethon Institute for Child Health Research, University of Western Australia, 100 Roberts Road, Subiaco, WA 6008, Australia
| | - Alicia Oshlack
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Bioinformatics Group, MCRI, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
| | - Christiane Theda
- Early Life Epigenetics Group, Murdoch Childrens Research Institute (MCRI), Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Neonatal Services, Royal Women's Hospital, Parkville, Victoria 3052, Australia ; Department of Obstetrics and Gynaecology, University of Melbourne, Royal Women's Hospital, Parkville, Victoria 3052, Australia
| | - Peter G Davis
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Neonatal Services, Royal Women's Hospital, Parkville, Victoria 3052, Australia ; Department of Obstetrics and Gynaecology, University of Melbourne, Royal Women's Hospital, Parkville, Victoria 3052, Australia
| | - David Martino
- Cancer and Developmental Epigenetics Group, MCRI, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
| | - Penelope Sheehan
- Department of Obstetrics and Gynaecology, University of Melbourne, Royal Women's Hospital, Parkville, Victoria 3052, Australia
| | - Yun Dai
- Early Life Epigenetics Group, Murdoch Childrens Research Institute (MCRI), Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
| | - Richard Saffery
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Cancer and Developmental Epigenetics Group, MCRI, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
| | - Lex W Doyle
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Neonatal Services, Royal Women's Hospital, Parkville, Victoria 3052, Australia ; Department of Obstetrics and Gynaecology, University of Melbourne, Royal Women's Hospital, Parkville, Victoria 3052, Australia
| | - Jeffrey M Craig
- Early Life Epigenetics Group, Murdoch Childrens Research Institute (MCRI), Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
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10
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Petersen AK, Zeilinger S, Kastenmüller G, Römisch-Margl W, Brugger M, Peters A, Meisinger C, Strauch K, Hengstenberg C, Pagel P, Huber F, Mohney RP, Grallert H, Illig T, Adamski J, Waldenberger M, Gieger C, Suhre K. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet 2013; 23:534-45. [PMID: 24014485 PMCID: PMC3869358 DOI: 10.1093/hmg/ddt430] [Citation(s) in RCA: 130] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Previously, we reported strong influences of genetic variants on metabolic phenotypes, some of them with clinical relevance. Here, we hypothesize that DNA methylation may have an important and potentially independent effect on human metabolism. To test this hypothesis, we conducted what is to the best of our knowledge the first epigenome-wide association study (EWAS) between DNA methylation and metabolic traits (metabotypes) in human blood. We assess 649 blood metabolic traits from 1814 participants of the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) population study for association with methylation of 457 004 CpG sites, determined on the Infinium HumanMethylation450 BeadChip platform. Using the EWAS approach, we identified two types of methylome–metabotype associations. One type is driven by an underlying genetic effect; the other type is independent of genetic variation and potentially driven by common environmental and life-style-dependent factors. We report eight CpG loci at genome-wide significance that have a genetic variant as confounder (P = 3.9 × 10−20 to 2.0 × 10−108, r2 = 0.036 to 0.221). Seven loci display CpG site-specific associations to metabotypes, but do not exhibit any underlying genetic signals (P = 9.2 × 10−14 to 2.7 × 10−27, r2 = 0.008 to 0.107). We further identify several groups of CpG loci that associate with a same metabotype, such as 4-vinylphenol sulfate and 4-androsten-3-beta,17-beta-diol disulfate. In these cases, the association between CpG-methylation and metabotype is likely the result of a common external environmental factor, including smoking. Our study shows that analysis of EWAS with large numbers of metabolic traits in large population cohorts are, in principle, feasible. Taken together, our data suggest that DNA methylation plays an important role in regulating human metabolism.
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