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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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Rönn T, Perfilyev A, Oskolkov N, Ling C. Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets. Sci Rep 2024; 14:14637. [PMID: 38918439 PMCID: PMC11199577 DOI: 10.1038/s41598-024-64846-3] [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: 12/14/2023] [Accepted: 06/13/2024] [Indexed: 06/27/2024] Open
Abstract
Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.
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Affiliation(s)
- Tina Rönn
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden
| | - Alexander Perfilyev
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden
| | - Nikolay Oskolkov
- Science for Life Laboratory, Department of Biology, National Bioinformatics Infrastructure Sweden, Lund University, Sölvegatan 35, 223 62, Lund, Sweden
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden.
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Lester BM, Camerota M, Everson TM, Shuster CL, Marsit CJ. Toward a more holistic approach to the study of exposures and child outcomes. Epigenomics 2024; 16:635-651. [PMID: 38482639 PMCID: PMC11157992 DOI: 10.2217/epi-2023-0424] [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: 11/30/2023] [Accepted: 02/27/2024] [Indexed: 06/09/2024] Open
Abstract
Aim: The current work was designed to demonstrate the application of the exposome framework in examining associations between exposures and children's long-term neurodevelopmental and behavioral outcomes. Methods: Longitudinal data were collected from birth through age 6 from 402 preterm infants. Three statistical methods were utilized to demonstrate the exposome framework: exposome-wide association study, cumulative exposure and machine learning models, with and without epigenetic data. Results: Each statistical approach answered a distinct research question regarding the impact of exposures on longitudinal child outcomes. Findings highlight associations between exposures, epigenetics and executive function. Conclusion: Findings demonstrate how an exposome-based approach can be utilized to understand relationships between internal (e.g., DNA methylation) and external (e.g., prenatal risk) exposures and long-term developmental outcomes in preterm children.
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Affiliation(s)
- Barry M Lester
- Department of Pediatrics, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
- Brown Center for the Study of Children at Risk, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
- Department of Psychiatry & Human Behavior, Brown Alpert Medical School, Providence, RI 02905, USA
| | - Marie Camerota
- Brown Center for the Study of Children at Risk, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
- Department of Psychiatry & Human Behavior, Brown Alpert Medical School, Providence, RI 02905, USA
| | - Todd M Everson
- Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Coral L Shuster
- Department of Pediatrics, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
- Brown Center for the Study of Children at Risk, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
| | - Carmen J Marsit
- Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
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Camerota M, Lester BM, Castellanos FX, Carter BS, Check J, Helderman J, Hofheimer JA, McGowan EC, Neal CR, Pastyrnak SL, Smith LM, O'Shea TM, Marsit CJ, Everson TM. Epigenome-wide association study identifies neonatal DNA methylation associated with two-year attention problems in children born very preterm. Transl Psychiatry 2024; 14:126. [PMID: 38418845 PMCID: PMC10902402 DOI: 10.1038/s41398-024-02841-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 02/07/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Prior research has identified epigenetic predictors of attention problems in school-aged children but has not yet investigated these in young children, or children at elevated risk of attention problems due to preterm birth. The current study evaluated epigenome-wide associations between neonatal DNA methylation and attention problems at age 2 years in children born very preterm. Participants included 441 children from the Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) Study, a multi-site study of infants born < 30 weeks gestational age. DNA methylation was measured from buccal swabs collected at NICU discharge using the Illumina MethylationEPIC Bead Array. Attention problems were assessed at 2 years of adjusted age using the attention problems subscale of the Child Behavior Checklist (CBCL). After adjustment for multiple testing, DNA methylation at 33 CpG sites was associated with child attention problems. Differentially methylated CpG sites were located in genes previously linked to physical and mental health, including several genes associated with ADHD in prior epigenome-wide and genome-wide association studies. Several CpG sites were located in genes previously linked to exposure to prenatal risk factors in the NOVI sample. Neonatal epigenetics measured at NICU discharge could be useful in identifying preterm children at risk for long-term attention problems and related psychiatric disorders, who could benefit from early prevention and intervention efforts.
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Affiliation(s)
- Marie Camerota
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.
- Brown Center for the Study of Children at Risk, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA.
| | - Barry M Lester
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- Brown Center for the Study of Children at Risk, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
- Department of Pediatrics, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Brian S Carter
- Department of Pediatrics-Neonatology, Children's Mercy Hospital, Kansas City, MO, USA
| | - Jennifer Check
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer Helderman
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Julie A Hofheimer
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Elisabeth C McGowan
- Department of Pediatrics, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
| | - Charles R Neal
- Department of Pediatrics, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
| | - Steven L Pastyrnak
- Department of Pediatrics, Spectrum Health-Helen DeVos Hospital, Grand Rapids, MI, USA
| | - Lynne M Smith
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Thomas Michael O'Shea
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Carmen J Marsit
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Todd M Everson
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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Roy R, Kuo PL, Candia J, Sarantopoulou D, Ubaida-Mohien C, Hernandez D, Kaileh M, Arepalli S, Singh A, Bektas A, Kim J, Moore AZ, Tanaka T, McKelvey J, Zukley L, Nguyen C, Wallace T, Dunn C, Wood W, Piao Y, Coletta C, De S, Sen J, Weng NP, Sen R, Ferrucci L. Epigenetic signature of human immune aging in the GESTALT study. eLife 2023; 12:e86136. [PMID: 37589453 PMCID: PMC10506794 DOI: 10.7554/elife.86136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 08/16/2023] [Indexed: 08/18/2023] Open
Abstract
Age-associated DNA methylation in blood cells convey information on health status. However, the mechanisms that drive these changes in circulating cells and their relationships to gene regulation are unknown. We identified age-associated DNA methylation sites in six purified blood-borne immune cell types (naive B, naive CD4+ and CD8+ T cells, granulocytes, monocytes, and NK cells) collected from healthy individuals interspersed over a wide age range. Of the thousands of age-associated sites, only 350 sites were differentially methylated in the same direction in all cell types and validated in an independent longitudinal cohort. Genes close to age-associated hypomethylated sites were enriched for collagen biosynthesis and complement cascade pathways, while genes close to hypermethylated sites mapped to neuronal pathways. In silico analyses showed that in most cell types, the age-associated hypo- and hypermethylated sites were enriched for ARNT (HIF1β) and REST transcription factor (TF) motifs, respectively, which are both master regulators of hypoxia response. To conclude, despite spatial heterogeneity, there is a commonality in the putative regulatory role with respect to TF motifs and histone modifications at and around these sites. These features suggest that DNA methylation changes in healthy aging may be adaptive responses to fluctuations of oxygen availability.
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Affiliation(s)
- Roshni Roy
- Laboratory of Molecular Biology and Immunology, National Institute on AgingBaltimoreUnited States
| | - Pei-Lun Kuo
- Translational Gerontology Branch, National Institute on AgingBaltimoreUnited States
| | - Julián Candia
- Translational Gerontology Branch, National Institute on AgingBaltimoreUnited States
| | - Dimitra Sarantopoulou
- Laboratory of Molecular Biology and Immunology, National Institute on AgingBaltimoreUnited States
| | | | - Dena Hernandez
- Laboratory of Neurogenetics, National Institute on AgingBethesdaUnited States
| | - Mary Kaileh
- Laboratory of Molecular Biology and Immunology, National Institute on AgingBaltimoreUnited States
| | - Sampath Arepalli
- Laboratory of Neurogenetics, National Institute on AgingBethesdaUnited States
| | - Amit Singh
- Laboratory of Molecular Biology and Immunology, National Institute on AgingBaltimoreUnited States
| | - Arsun Bektas
- Translational Gerontology Branch, National Institute on AgingBaltimoreUnited States
| | - Jaekwan Kim
- Laboratory of Molecular Biology and Immunology, National Institute on AgingBaltimoreUnited States
| | - Ann Z Moore
- Translational Gerontology Branch, National Institute on AgingBaltimoreUnited States
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on AgingBaltimoreUnited States
| | - Julia McKelvey
- Clinical Research Core, National Institute on AgingBaltimoreUnited States
| | - Linda Zukley
- Clinical Research Core, National Institute on AgingBaltimoreUnited States
| | - Cuong Nguyen
- Flow Cytometry Unit, National Institute on AgingBaltimoreUnited States
| | - Tonya Wallace
- Flow Cytometry Unit, National Institute on AgingBaltimoreUnited States
| | - Christopher Dunn
- Flow Cytometry Unit, National Institute on AgingBaltimoreUnited States
| | - William Wood
- Laboratory of Genetics and Genomics, National Institute on AgingBaltimoreUnited States
| | - Yulan Piao
- Laboratory of Genetics and Genomics, National Institute on AgingBaltimoreUnited States
| | - Christopher Coletta
- Laboratory of Genetics and Genomics, National Institute on AgingBaltimoreUnited States
| | - Supriyo De
- Laboratory of Genetics and Genomics, National Institute on AgingBaltimoreUnited States
| | - Jyoti Sen
- Laboratory of Clinical Investigation, National Institute on AgingBaltimoreUnited States
| | - Nan-ping Weng
- Laboratory of Molecular Biology and Immunology, National Institute on AgingBaltimoreUnited States
| | - Ranjan Sen
- Laboratory of Molecular Biology and Immunology, National Institute on AgingBaltimoreUnited States
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on AgingBaltimoreUnited States
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Dragomir MP, Calina TG, Perez E, Schallenberg S, Chen M, Albrecht T, Koch I, Wolkenstein P, Goeppert B, Roessler S, Calin GA, Sers C, Horst D, Roßner F, Capper D. DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours. EBioMedicine 2023; 93:104657. [PMID: 37348162 DOI: 10.1016/j.ebiom.2023.104657] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 05/21/2023] [Accepted: 06/02/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task. METHODS We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier. FINDINGS On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%. INTERPRETATION We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver. FUNDING This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 - SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).
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Affiliation(s)
- Mihnea P Dragomir
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Berlin Institute of Health, Berlin, Germany.
| | | | - Eilís Perez
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; Berlin School of Integrative Oncology (BSIO), Charite - Universitätsmedizin Berlin (CVK), Berlin, Germany
| | - Simon Schallenberg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Meng Chen
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Thomas Albrecht
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Ines Koch
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Peggy Wolkenstein
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Benjamin Goeppert
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany; Institute of Pathology and Neuropathology, Hospital RKH Kliniken Ludwigsburg, 71640 Ludwigsburg, Germany
| | - Stephanie Roessler
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - George A Calin
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Center for RNA Interference and Non-coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Christine Sers
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Florian Roßner
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Capper
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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Inkster AM, Wong MT, Matthews AM, Brown CJ, Robinson WP. Who's afraid of the X? Incorporating the X and Y chromosomes into the analysis of DNA methylation array data. Epigenetics Chromatin 2023; 16:1. [PMID: 36609459 PMCID: PMC9825011 DOI: 10.1186/s13072-022-00477-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/27/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Many human disease phenotypes manifest differently by sex, making the development of methods for incorporating X and Y-chromosome data into analyses vital. Unfortunately, X and Y chromosome data are frequently excluded from large-scale analyses of the human genome and epigenome due to analytical complexity associated with sex chromosome dosage differences between XX and XY individuals, and the impact of X-chromosome inactivation (XCI) on the epigenome. As such, little attention has been given to considering the methods by which sex chromosome data may be included in analyses of DNA methylation (DNAme) array data. RESULTS With Illumina Infinium HumanMethylation450 DNAme array data from 634 placental samples, we investigated the effects of probe filtering, normalization, and batch correction on DNAme data from the X and Y chromosomes. Processing steps were evaluated in both mixed-sex and sex-stratified subsets of the analysis cohort to identify whether including both sexes impacted processing results. We found that identification of probes that have a high detection p-value, or that are non-variable, should be performed in sex-stratified data subsets to avoid over- and under-estimation of the quantity of probes eligible for removal, respectively. All normalization techniques investigated returned X and Y DNAme data that were highly correlated with the raw data from the same samples. We found no difference in batch correction results after application to mixed-sex or sex-stratified cohorts. Additionally, we identify two analytical methods suitable for XY chromosome data, the choice between which should be guided by the research question of interest, and we performed a proof-of-concept analysis studying differential DNAme on the X and Y chromosome in the context of placental acute chorioamnionitis. Finally, we provide an annotation of probe types that may be desirable to filter in X and Y chromosome analyses, including probes in repetitive elements, the X-transposed region, and cancer-testis gene promoters. CONCLUSION While there may be no single "best" approach for analyzing DNAme array data from the X and Y chromosome, analysts must consider key factors during processing and analysis of sex chromosome data to accommodate the underlying biology of these chromosomes, and the technical limitations of DNA methylation arrays.
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Affiliation(s)
- Amy M Inkster
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada.
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada.
| | - Martin T Wong
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
| | - Allison M Matthews
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, V6T 1Z7, Canada
| | - Carolyn J Brown
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
| | - Wendy P Robinson
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
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8
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Zhang Z, Butler R, Koestler DC, Bell-Glenn S, Warrier G, Molinaro AM, Christensen BC, Wiencke JK, Kelsey KT, Salas LA. Comparative analysis of the DNA methylation landscape in CD4, CD8, and B memory lineages. Clin Epigenetics 2022; 14:173. [PMID: 36522672 PMCID: PMC9753273 DOI: 10.1186/s13148-022-01399-0] [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: 05/23/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND There is considerable evidence that epigenetic mechanisms and DNA methylation are critical drivers of immune cell lineage differentiation and activation. However, there has been limited coordinated investigation of common epigenetic pathways among cell lineages. Further, it remains unclear if long-lived memory cell subtypes differentiate distinctly by cell lineages. RESULTS We used the Illumina EPIC array to investigate the consistency of DNA methylation in B cell, CD4 T, and CD8 T naïve and memory cells states. In the process of naïve to memory activation across the three lineages, we identify considerable shared epigenetic regulation at the DNA level for immune memory generation. Further, in central to effector memory differentiation, our analyses revealed specific CpG dinucleotides and genes in CD4 T and CD8 T cells with DNA methylation changes. Finally, we identified unique DNA methylation patterns in terminally differentiated effector memory (TEMRA) CD8 T cells compared to other CD8 T memory cell subtypes. CONCLUSIONS Our data suggest that epigenetic alterations are widespread and essential in generating human lymphocyte memory. Unique profiles are involved in methylation changes that accompany memory genesis in the three subtypes of lymphocytes.
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Affiliation(s)
- Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Rondi Butler
- Department of Epidemiology, Pathology and Laboratory Medicine, Brown University, Providence, RI, USA
| | - Devin C Koestler
- Department of Biostatistics and Data Science, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Shelby Bell-Glenn
- Department of Biostatistics and Data Science, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Gayathri Warrier
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, USA
| | - Annette M Molinaro
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, 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
| | - John K Wiencke
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, USA
| | - Karl T Kelsey
- Department of Epidemiology, Pathology and Laboratory Medicine, Brown University, Providence, RI, USA.
| | - Lucas A Salas
- 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.
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Wu J, Zhang L, Kuchi A, Otohinoyi D, Hicks C. CpG Site-Based Signature Predicts Survival of Colorectal Cancer. Biomedicines 2022; 10:biomedicines10123163. [PMID: 36551919 PMCID: PMC9776399 DOI: 10.3390/biomedicines10123163] [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: 10/29/2022] [Revised: 11/26/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND A critical unmet medical need in clinical management of colorectal cancer (CRC) pivots around lack of noninvasive and or minimally invasive techniques for early diagnosis and prognostic prediction of clinical outcomes. Because DNA methylation can capture the regulatory landscape of tumors and can be measured in body fluids, it provides unparalleled opportunities for the discovery of early diagnostic and prognostics markers predictive of clinical outcomes. Here we investigated use of DNA methylation for the discovery of potential clinically actionable diagnostic and prognostic markers for predicting survival in CRC. METHODS We analyzed DNA methylation patterns between tumor and control samples to discover signatures of CpG sites and genes associated with CRC and predictive of survival. We conducted functional analysis to identify molecular networks and signaling pathways driving clinical outcomes. RESULTS We discovered a signature of aberrantly methylated genes associated with CRC and a signature of thirteen (13) CpG sites predictive of survival. We discovered molecular networks and signaling pathways enriched for CpG sites likely to drive clinical outcomes. CONCLUSIONS The investigation revealed that CpG sites can predict survival in CRC and that DNA methylation can capture the regulatory state of tumors through aberrantly methylated molecular networks and signaling pathways.
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Affiliation(s)
- Jiande Wu
- Department of Genetics and the Bioinformatics and Genomics Program, School of Medicine, Louisiana State University Health Sciences Center, Bolivar 533, New Orleans, LA 70112, USA
| | - Lu Zhang
- Department of Public Health Sciences, Clemson University, Clemson, SC 29634, USA
| | - Aditi Kuchi
- Department of Genetics and the Bioinformatics and Genomics Program, School of Medicine, Louisiana State University Health Sciences Center, Bolivar 533, New Orleans, LA 70112, USA
| | - David Otohinoyi
- Department of Genetics and the Bioinformatics and Genomics Program, School of Medicine, Louisiana State University Health Sciences Center, Bolivar 533, New Orleans, LA 70112, USA
| | - Chindo Hicks
- Department of Genetics and the Bioinformatics and Genomics Program, School of Medicine, Louisiana State University Health Sciences Center, Bolivar 533, New Orleans, LA 70112, USA
- Correspondence:
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10
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Du X, Jiang Y, Li H, Zhang Q, Zhu X, Zhou L, Wang W, Zhang Y, Liu C, Niu Y, Chu C, Cai J, Chen R, Kan H. Traffic-related air pollution and genome-wide DNA methylation: A randomized, crossover trial. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:157968. [PMID: 35963411 DOI: 10.1016/j.scitotenv.2022.157968] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 08/03/2022] [Accepted: 08/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Traffic-related air pollution (TRAP) has been associated with changes in gene-specific DNA methylation. However, few studies have investigated impact of TRAP exposure on genome-wide DNA methylation in circulating blood of human. OBJECTIVE To explore the association between TRAP exposure and genome-wide DNA methylation. METHODS We conducted a randomized, crossover exposure trial among 35 healthy adults in Shanghai, China. All subjects were randomly allocated to a traffic-free park or a main road for consecutive 4 h, respectively. Blood genome-wide DNA methylation after each exposure session was measured by the Infinium Methylation EPIC BeadChip (850K). The differentially methylated CpGs loci associated with TRAP exposure were identified using linear mixed-effect model. RESULTS The average concentrations of traffic-related air pollutants including black carbon, ultrafine particles, carbon dioxide, and nitrogen dioxide were 2-3 times higher in the road compared to those in the park. Methylation levels of 68 CpG loci were significantly changed (false discovery rate < 0.05) following TRAP exposure, among which 49 were hypermethylated and 19 were hypomethylated. The annotated genes based on the differential CpGs loci were related to pathways in cardiovascular signaling, cytokine signaling, immune response, nervous system signaling, and metabolism. CONCLUSIONS We found that TRAP exposure was associated with DNA methylation in dozens of genes concerning cardiometabolic health. This trial for the first-time profiled genome-wide methylation changes induced by TRAP exposure using the 850K assay, providing epigenetic insights in understanding the cardiometabolic effects of TRAP exposure.
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Affiliation(s)
- Xihao Du
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Yixuan Jiang
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Huichu Li
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Qingli Zhang
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Xinlei Zhu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Lu Zhou
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Weidong Wang
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Yang Zhang
- Department of Systems Biology for Medicine and Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cong Liu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Yue Niu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Chen Chu
- Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, China
| | - Jing Cai
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China.
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China; Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, China
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11
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Lussier AA, Zhu Y, Smith BJ, Simpkin AJ, Smith AD, Suderman MJ, Walton E, Ressler KJ, Dunn EC. Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies. Epigenetics 2022; 17:1373-1388. [PMID: 35156895 PMCID: PMC9601563 DOI: 10.1080/15592294.2022.2028072] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 12/02/2021] [Accepted: 01/04/2022] [Indexed: 11/03/2022] Open
Abstract
Biomedical research has grown increasingly cooperative through the sharing of consortia-level epigenetic data. Since consortia preprocess data prior to distribution, new processing pipelines can lead to different versions of the same dataset. Similarly, analytic frameworks evolve to incorporate cutting-edge methods and best practices. However, it remains unknown how different data and analytic versions alter the results of epigenome-wide analyses, which could influence the replicability of epigenetic associations. Thus, we assessed the impact of these changes using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. We analysed DNA methylation from two data versions, processed using separate preprocessing and analytic pipelines, examining associations between seven childhood adversities or prenatal smoking exposure and DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modelling Approach (SLCMA), a two-stage method that models time-dependent effects. SLCMA results were also compared across two analytic versions. Data version changes impacted both EWAS and SLCMA analyses, yielding different associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of p-values. Differences were especially apparent in analyses of childhood adversity, while smoking associations were more consistent using significance thresholds. SLCMA analytic versions similarly altered top associations, but time-dependent effects remained concordant. Alterations to data and analytic versions influenced the results of epigenome-wide analyses. Our findings highlight that magnitude and direction are better measures for replication and stability than p-value thresholds.
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Affiliation(s)
- Alexandre A. Lussier
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yiwen Zhu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brooke J. Smith
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew J. Simpkin
- School of Mathematics,Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland
| | - Andrew D.A.C. Smith
- Mathematics and Statistics Research Group, University of the West of England, Bristol, UK
| | - Matthew J. Suderman
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, UK
| | - Kerry J. Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Erin C. Dunn
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center on the Developing Child, Harvard University, Cambridge, MA, USA
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12
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Schaffner SL, Kobor MS. DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome. Front Genet 2022; 13:971298. [PMID: 36061205 PMCID: PMC9437223 DOI: 10.3389/fgene.2022.971298] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/25/2022] [Indexed: 12/15/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.
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Affiliation(s)
- Samantha L. Schaffner
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Michael S. Kobor
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
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13
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Di Lena P, Sala C, Nardini C. Evaluation of different computational methods for DNA methylation-based biological age. Brief Bioinform 2022; 23:6632619. [PMID: 35794713 DOI: 10.1093/bib/bbac274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/27/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.
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Affiliation(s)
- Pietro Di Lena
- Department of Computer Science and Engineering, University of Bologna, Mura Anteo Zamboni 7, 40126 Bologna, Italy
| | - Claudia Sala
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Via Massarenti 9, 40138, Bologna, Italy
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14
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Xu K, Li S, Muskens IS, Elliott N, Myint SS, Pandey P, Hansen HM, Morimoto LM, Kang AY, Ma X, Metayer C, Mueller BA, Roberts I, Walsh K, Horvath S, Wiemels JL, de Smith AJ. Accelerated epigenetic aging in newborns with Down syndrome. Aging Cell 2022; 21:e13652. [PMID: 35661546 PMCID: PMC9282838 DOI: 10.1111/acel.13652] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/11/2022] [Accepted: 05/18/2022] [Indexed: 01/07/2023] Open
Abstract
Accelerated aging is a hallmark of Down syndrome (DS), with adults experiencing early-onset Alzheimer's disease and premature aging of the skin, hair, and immune and endocrine systems. Accelerated epigenetic aging has been found in the blood and brain tissue of adults with DS but when premature aging in DS begins remains unknown. We investigated whether accelerated aging in DS is already detectable in blood at birth. We assessed the association between age acceleration and DS using five epigenetic clocks in 346 newborns with DS and 567 newborns without DS using Illumina MethylationEPIC DNA methylation array data. We compared two epigenetic aging clocks (DNAmSkinBloodClock and pan-tissue DNAmAge) and three epigenetic gestational age clocks (Haftorn, Knight, and Bohlin) between DS and non-DS newborns using linear regression adjusting for observed age, sex, batch, deconvoluted blood cell proportions, and genetic ancestry. Targeted sequencing of GATA1 was performed in a subset of 184 newborns with DS to identify somatic mutations associated with transient abnormal myelopoiesis. DS was significantly associated with increased DNAmSkinBloodClock (effect estimate = 0.2442, p < 0.0001), with an epigenetic age acceleration of 244 days in newborns with DS after adjusting for potential confounding factors (95% confidence interval: 196-292 days). We also found evidence of epigenetic age acceleration associated with somatic GATA1 mutations among newborns with DS (p = 0.015). DS was not associated with epigenetic gestational age acceleration. We demonstrate that accelerated epigenetic aging in the blood of DS patients begins prenatally, with implications for the pathophysiology of immunosenescence and other aging-related traits in DS.
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Affiliation(s)
- Keren Xu
- Center for Genetic Epidemiology, Department of Population and Public Health SciencesKeck School of Medicine of the University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Shaobo Li
- Center for Genetic Epidemiology, Department of Population and Public Health SciencesKeck School of Medicine of the University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Ivo S. Muskens
- Center for Genetic Epidemiology, Department of Population and Public Health SciencesKeck School of Medicine of the University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Natalina Elliott
- Department of Paediatrics and MRC Molecular Haematology Unit, Weatherall Institute of Molecular MedicineOxford University and BRC Blood Theme, NIHR Oxford Biomedical CentreOxfordUK
| | - Swe Swe Myint
- Center for Genetic Epidemiology, Department of Population and Public Health SciencesKeck School of Medicine of the University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Priyatama Pandey
- Center for Genetic Epidemiology, Department of Population and Public Health SciencesKeck School of Medicine of the University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Helen M. Hansen
- Department of Neurological SurgeryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Libby M. Morimoto
- School of Public HealthUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Alice Y. Kang
- School of Public HealthUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Xiaomei Ma
- Department of Chronic Disease EpidemiologyYale School of Public HealthNew HavenConnecticutUSA
| | - Catherine Metayer
- School of Public HealthUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Beth A. Mueller
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, and Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Irene Roberts
- Department of Paediatrics and MRC Molecular Haematology Unit, Weatherall Institute of Molecular MedicineOxford University and BRC Blood Theme, NIHR Oxford Biomedical CentreOxfordUK
| | - Kyle M. Walsh
- Department of NeurosurgeryDuke UniversityDurhamNorth CarolinaUSA
| | - Steve Horvath
- Department of Human GeneticsDavid Geffen School of Medicine, University of CaliforniaLos AngelesCaliforniaUSA
| | - Joseph L. Wiemels
- Center for Genetic Epidemiology, Department of Population and Public Health SciencesKeck School of Medicine of the University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Adam J. de Smith
- Center for Genetic Epidemiology, Department of Population and Public Health SciencesKeck School of Medicine of the University of Southern CaliforniaLos AngelesCaliforniaUSA
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15
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Barbosa P, Landes RD, Graw S, Byrum SD, Bennuri S, Delhey L, Randolph C, MacLeod S, Reis A, Børsheim E, Rose S, Carvalho E. Effect of excess weight and insulin resistance on DNA methylation in prepubertal children. Sci Rep 2022; 12:8430. [PMID: 35589784 PMCID: PMC9120504 DOI: 10.1038/s41598-022-12325-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play a role in the development of insulin resistance. This study evaluates how the BMI z-score (BMIz) and the homeostatic model assessment of insulin resistance (HOMA-IR), alone or in combination, relate to clinical outcomes and DNA methylation patterns in prepubertal children. DNA methylation in peripheral blood mononuclear cells (PBMCs) and clinical outcomes were measured in a cohort of 41 prepubertal children. Children with higher HOMA-IR had higher blood pressure and plasma lactate levels while children with higher BMIz had higher triglycerides levels. Moreover, the DNA methylation analysis demonstrated that a 1 unit increase in the BMIz was associated with a 0.41 (95% CI: 0.29, 0.53) increase in methylation of a CpG near the PPP6R2 gene. This gene is important in the regulation of NF-kB expression. However, there was no strong evidence that the BMIz and the HOMA-IR were synergistically related to any clinical or DNA methylation outcomes. In summary, the results suggest that obesity and insulin resistance may impact metabolic health both independently in prepubertal children. In addition, obesity also has an impact on the DNA methylation of the PPP6R2 gene. This may be a novel underlying starting point for the systemic inflammation associated with obesity and insulin resistance, in this population.
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Affiliation(s)
- Pedro Barbosa
- PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Coimbra, Portugal.,Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal.,Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Coimbra, Portugal
| | - Reid D Landes
- Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Stefan Graw
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.,Arkansas Children's Research Institute, Little Rock, AR, USA.,Everest Clinical Research Corporation, Markham, ON, Canada
| | - Stephanie D Byrum
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.,Arkansas Children's Research Institute, Little Rock, AR, USA
| | - Sirish Bennuri
- Arkansas Children's Research Institute, Little Rock, AR, USA
| | - Leanna Delhey
- Arkansas Children's Research Institute, Little Rock, AR, USA.,Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Chris Randolph
- Arkansas Children's Research Institute, Little Rock, AR, USA
| | - Stewart MacLeod
- Arkansas Children's Research Institute, Little Rock, AR, USA
| | - Andreia Reis
- Department of Medical Sciences (DCM), Institute for Research in Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Elisabet Børsheim
- Arkansas Children's Research Institute, Little Rock, AR, USA.,Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.,Arkansas Children's Nutrition Center, Little Rock, AR, USA.,Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Shannon Rose
- Arkansas Children's Research Institute, Little Rock, AR, USA.,Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Eugenia Carvalho
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal. .,Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Coimbra, Portugal. .,Arkansas Children's Research Institute, Little Rock, AR, USA. .,Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
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16
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Ross JP, van Dijk S, Phang M, Skilton MR, Molloy PL, Oytam Y. Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data. Clin Epigenetics 2022; 14:58. [PMID: 35488315 PMCID: PMC9055778 DOI: 10.1186/s13148-022-01277-9] [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: 02/28/2022] [Accepted: 04/10/2022] [Indexed: 11/20/2022] Open
Abstract
Background Genomic technologies can be subject to significant batch-effects which are known to reduce experimental power and to potentially create false positive results. The Illumina Infinium Methylation BeadChip is a popular technology choice for epigenome-wide association studies (EWAS), but presently, little is known about the nature of batch-effects on these designs. Given the subtlety of biological phenotypes in many EWAS, control for batch-effects should be a consideration.
Results Using the batch-effect removal approaches in the ComBat and Harman software, we examined two in-house datasets and compared results with three large publicly available datasets, (1214 HumanMethylation450 and 1094 MethylationEPIC BeadChips in total), and find that despite various forms of preprocessing, some batch-effects persist. This residual batch-effect is associated with the day of processing, the individual glass slide and the position of the array on the slide. Consistently across all datasets, 4649 probes required high amounts of correction. To understand the impact of this set to EWAS studies, we explored the literature and found three instances where persistently batch-effect prone probes have been reported in abstracts as key sites of differential methylation. As well as batch-effect susceptible probes, we also discover a set of probes which are erroneously corrected. We provide batch-effect workflows for Infinium Methylation data and provide reference matrices of batch-effect prone and erroneously corrected features across the five datasets spanning regionally diverse populations and three commonly collected biosamples (blood, buccal and saliva). Conclusions Batch-effects are ever present, even in high-quality data, and a strategy to deal with them should be part of experimental design, particularly for EWAS. Batch-effect removal tools are useful to reduce technical variance in Infinium Methylation data, but they need to be applied with care and make use of post hoc diagnostic measures. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-022-01277-9.
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Affiliation(s)
- Jason P Ross
- Human Health Program, Health and Biosecurity, CSIRO, Sydney, Australia.
| | - Susan van Dijk
- Human Health Program, Health and Biosecurity, CSIRO, Sydney, Australia
| | - Melinda Phang
- Charles Perkins Centre, The University of Sydney, Sydney, Australia
| | - Michael R Skilton
- Charles Perkins Centre, The University of Sydney, Sydney, Australia.,Sydney Medical School, The University of Sydney, Sydney, Australia.,Sydney Institute for Women, Children and Their Families, Sydney Local Health District, Sydney, Australia
| | - Peter L Molloy
- Human Health Program, Health and Biosecurity, CSIRO, Sydney, Australia
| | - Yalchin Oytam
- Clinical Insights and Analytics Unit, South Eastern Sydney Local Health District, Sydney, Australia
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17
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Zhang Z, Zeng C, Zhang W. Characterization of the Illumina EPIC Array for Optimal Applications in Epigenetic Research Targeting Diverse Human Populations. EPIGENETICS COMMUNICATIONS 2022; 2:7. [PMCID: PMC9718568 DOI: 10.1186/s43682-022-00015-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The Illumina EPIC array is widely used for high-throughput profiling of DNA cytosine modifications in human samples, covering more than 850,000 modification sites across various genomic features. The application of this platform is expected to provide novel insights into the epigenetic contribution to human complex traits and diseases. Considering the diverse inter-population genetic and epigenetic variation, it will benefit the research community with a comprehensive characterization of this platform for its applicability to major global populations. Specifically, we mapped 866,836 CpG probes from the EPIC array to the human genome reference. We detected 91,034 CpG probes that did not align reliably to the human genome reference. In addition, 21,256 CpG probes were found to ambiguously map to multiple loci in the human genome, and 448 probes showing inaccurate genomic information from the original Illumina annotations. We further characterized those uniquely mapped CpG probes in terms of whether they contained common genetic variants, i.e., single nucleotide polymorphisms (SNPs), in major global populations, by utilizing the 1000 Genomes Project data. A list of optimal CpG probes on the EPIC array was generated for major global populations, with the aim of providing a resource to facilitate future studies of diverse human populations. In conclusion, our analysis indicated that studies of diverse human populations using the EPIC array would be benefited by taking into account of the technical features of this platform.
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Affiliation(s)
- Zhou Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Chang Zeng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Wei Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA.,The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
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18
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Roy R, Ramamoorthy S, Shapiro BD, Kaileh M, Hernandez D, Sarantopoulou D, Arepalli S, Boller S, Singh A, Bektas A, Kim J, Moore AZ, Tanaka T, McKelvey J, Zukley L, Nguyen C, Wallace T, Dunn C, Wersto R, Wood W, Piao Y, Becker KG, Coletta C, De S, Sen JM, Battle A, Weng NP, Grosschedl R, Ferrucci L, Sen R. DNA methylation signatures reveal that distinct combinations of transcription factors specify human immune cell epigenetic identity. Immunity 2021; 54:2465-2480.e5. [PMID: 34706222 DOI: 10.1016/j.immuni.2021.10.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 06/06/2021] [Accepted: 09/30/2021] [Indexed: 10/20/2022]
Abstract
Epigenetic reprogramming underlies specification of immune cell lineages, but patterns that uniquely define immune cell types and the mechanisms by which they are established remain unclear. Here, we identified lineage-specific DNA methylation signatures of six immune cell types from human peripheral blood and determined their relationship to other epigenetic and transcriptomic patterns. Sites of lineage-specific hypomethylation were associated with distinct combinations of transcription factors in each cell type. By contrast, sites of lineage-specific hypermethylation were restricted mostly to adaptive immune cells. PU.1 binding sites were associated with lineage-specific hypo- and hypermethylation in different cell types, suggesting that it regulates DNA methylation in a context-dependent manner. These observations indicate that innate and adaptive immune lineages are specified by distinct epigenetic mechanisms via combinatorial and context-dependent use of key transcription factors. The cell-specific epigenomics and transcriptional patterns identified serve as a foundation for future studies on immune dysregulation in diseases and aging.
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Affiliation(s)
- Roshni Roy
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, MD, USA
| | | | - Benjamin D Shapiro
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Mary Kaileh
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, MD, USA
| | - Dena Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, Baltimore, MD, USA
| | - Dimitra Sarantopoulou
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, MD, USA
| | - Sampath Arepalli
- Laboratory of Neurogenetics, National Institute on Aging, Baltimore, MD, USA
| | - Sören Boller
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Amit Singh
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, MD, USA
| | - Arsun Bektas
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Jaekwan Kim
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, MD, USA
| | - Ann Zenobia Moore
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Julia McKelvey
- Clinical Research Core, National Institute on Aging, Baltimore, MD, USA
| | - Linda Zukley
- Clinical Research Core, National Institute on Aging, Baltimore, MD, USA
| | - Cuong Nguyen
- Flow Cytometry Unit, National Institute on Aging, Baltimore, MD, USA
| | - Tonya Wallace
- Flow Cytometry Unit, National Institute on Aging, Baltimore, MD, USA
| | - Christopher Dunn
- Flow Cytometry Unit, National Institute on Aging, Baltimore, MD, USA
| | - Robert Wersto
- Flow Cytometry Unit, National Institute on Aging, Baltimore, MD, USA
| | - William Wood
- Laboratory of Genetics & Genomics, National Institute on Aging, Baltimore, MD, USA
| | - Yulan Piao
- Laboratory of Genetics & Genomics, National Institute on Aging, Baltimore, MD, USA
| | - Kevin G Becker
- Laboratory of Genetics & Genomics, National Institute on Aging, Baltimore, MD, USA
| | - Christopher Coletta
- Laboratory of Genetics & Genomics, National Institute on Aging, Baltimore, MD, USA
| | - Supriyo De
- Laboratory of Genetics & Genomics, National Institute on Aging, Baltimore, MD, USA
| | - Jyoti Misra Sen
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Nan-Ping Weng
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, MD, USA
| | - Rudolf Grosschedl
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Ranjan Sen
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, MD, USA.
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19
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NEOage clocks - epigenetic clocks to estimate post-menstrual and postnatal age in preterm infants. Aging (Albany NY) 2021; 13:23527-23544. [PMID: 34655469 PMCID: PMC8580352 DOI: 10.18632/aging.203637] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/28/2021] [Indexed: 02/02/2023]
Abstract
Epigenetic clocks based on DNA methylation (DNAm) can accurately predict chronological age and are thought to capture biological aging. A variety of epigenetic clocks have been developed for different tissue types and age ranges, but none have focused on postnatal age prediction for preterm infants. Epigenetic estimators of biological age might be especially informative in epidemiologic studies of neonates since DNAm is highly dynamic during the neonatal period and this is a key developmental window. Additionally, markers of biological aging could be particularly important for those born preterm since they are at heightened risk of developmental impairments. We aimed to fill this gap by developing epigenetic clocks for neonatal aging in preterm infants. As part of the Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) study, buccal cells were collected at NICU discharge to profile DNAm levels in 542 very preterm infants. We applied elastic net regression to identify four epigenetic clocks (NEOage Clocks) predictive of post-menstrual and postnatal age, compatible with the Illumina EPIC and 450K arrays. We observed high correlations between predicted and reported ages (0.93 - 0.94) with root mean squared errors (1.28 - 1.63 weeks). Epigenetic estimators of neonatal aging in preterm infants can be useful tools to evaluate biological maturity and associations with neonatal and long-term morbidities.
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20
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Camerota M, Graw S, Everson TM, McGowan EC, Hofheimer JA, O'Shea TM, Carter BS, Helderman JB, Check J, Neal CR, Pastyrnak SL, Smith LM, Dansereau LM, DellaGrotta SA, Marsit CJ, Lester BM. Prenatal risk factors and neonatal DNA methylation in very preterm infants. Clin Epigenetics 2021; 13:171. [PMID: 34507616 PMCID: PMC8434712 DOI: 10.1186/s13148-021-01164-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/02/2021] [Indexed: 11/28/2022] Open
Abstract
Background Prenatal risk factors are related to poor health and developmental outcomes for infants, potentially via epigenetic mechanisms. We tested associations between person-centered prenatal risk profiles, cumulative prenatal risk models, and epigenome-wide DNA methylation (DNAm) in very preterm neonates. Methods We studied 542 infants from a multi-center study of infants born < 30 weeks postmenstrual age. We assessed 24 prenatal risk factors via maternal report and medical record review. Latent class analysis was used to define prenatal risk profiles. DNAm was quantified from neonatal buccal cells using the Illumina MethylationEPIC Beadarray. Results We identified three latent profiles of women: a group with few risk factors (61%) and groups with elevated physical (26%) and psychological (13%) risk factors. Neonates born to women in higher risk subgroups had differential DNAm at 2 CpG sites. Higher cumulative prenatal risk was associated with methylation at 15 CpG sites, 12 of which were located in genes previously linked to physical and mental health and neurodevelopment. Conclusion We observed associations between prenatal risk factors and DNAm in very preterm infants using both person-centered and cumulative risk approaches. Epigenetics offers a potential biological indicator of prenatal risk exposure. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01164-9.
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Affiliation(s)
- Marie Camerota
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA. .,Department of Pediatrics, Women and Infants Hospital of Rhode Island, 101 Dudley Street, Providence, RI, 02905, USA.
| | - Stefan Graw
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Todd M Everson
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA.,Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Elisabeth C McGowan
- Department of Pediatrics, Alpert Medical School of Brown University, Providence, RI, USA
| | - Julie A Hofheimer
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - T Michael O'Shea
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Brian S Carter
- Department of Pediatrics-Neonatology, Children's Mercy Hospital, Kansas City, MO, USA
| | - Jennifer B Helderman
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer Check
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Charles R Neal
- Department of Pediatrics, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
| | - Steven L Pastyrnak
- Department of Pediatrics, Spectrum Health-Helen DeVos Hospital, Grand Rapids, MI, USA
| | - Lynne M Smith
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Lynne M Dansereau
- Department of Pediatrics, Women and Infants Hospital of Rhode Island, 101 Dudley Street, Providence, RI, 02905, USA
| | - Sheri A DellaGrotta
- Department of Pediatrics, Women and Infants Hospital of Rhode Island, 101 Dudley Street, Providence, RI, 02905, USA
| | - Carmen J Marsit
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Barry M Lester
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.,Department of Pediatrics, Women and Infants Hospital of Rhode Island, 101 Dudley Street, Providence, RI, 02905, USA.,Department of Pediatrics, Alpert Medical School of Brown University, Providence, RI, USA
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21
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Redox Imbalance and Methylation Disturbances in Early Childhood Obesity. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2021; 2021:2207125. [PMID: 34457110 PMCID: PMC8387800 DOI: 10.1155/2021/2207125] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 07/13/2021] [Accepted: 08/02/2021] [Indexed: 11/29/2022]
Abstract
Obesity is increasing worldwide in prepubertal children, reducing the age of onset of associated comorbidities, including type 2 diabetes. Sulfur-containing amino acids, methionine, cysteine, and their derivatives play important roles in the transmethylation and transsulfuration pathways. Dysregulation of these pathways leads to alterations in the cellular methylation patterns and an imbalanced redox state. Therefore, we tested the hypothesis that one-carbon metabolism is already dysregulated in prepubertal children with obesity. Peripheral blood was collected from 64 children, and the plasma metabolites from transmethylation and transsulfuration pathways were quantified by HPLC. The cohort was stratified by BMI z-scores and HOMA-IR indices into healthy lean (HL), healthy obese (HO), and unhealthy obese (UHO). Fasting insulin levels were higher in the HO group compared to the HL, while the UHO had the highest. All groups presented normal fasting glycemia. Furthermore, high-density lipoprotein (HDL) was lower while triglycerides and lactate levels were higher in the UHO compared to HO subjects. S-adenosylhomocysteine (SAH) and total homocysteine levels were increased in the HO group compared to HL. Additionally, glutathione metabolism was also altered. Free cystine and oxidized glutathione (GSSG) were increased in the HO as compared to HL subjects. Importantly, the adipocyte secretory function was already compromised at this young age. Elevated circulating leptin and decreased adiponectin levels were observed in the UHO as compared to the HO subjects. Some of these alterations were concomitant with alterations in the DNA methylation patterns in the obese group, independent of the impaired insulin levels. In conclusion, our study informs on novel and important metabolic alterations in the transmethylation and the transsulfuration pathways in the early stages of obesity. Moreover, the altered secretory function of the adipocyte very early in life may be relevant in identifying early metabolic markers of disease that may inform on the increased risk for specific future comorbidities in this population.
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22
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Lucia RM, Huang WL, Alvarez A, Masunaka I, Ziogas A, Goodman D, Odegaard AO, Norden-Krichmar TM, Park HL. Association of mammographic density with blood DNA methylation. Epigenetics 2021; 17:531-546. [PMID: 34116608 PMCID: PMC9067527 DOI: 10.1080/15592294.2021.1928994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background: Altered DNA methylation may be an intermediate phenotype between breast cancer risk factors and disease. Mammographic density is a strong risk factor for breast cancer. However, no studies to date have identified an epigenetic signature of mammographic density. We performed an epigenome-wide association study of mammographic density. Methods: White blood cell DNA methylation was measured for 385 postmenopausal women using the Illumina Infinium MethylationEPIC BeadChip array. Differential methylation was assessed using genome-wide, probe-level, and regional analyses. We implemented a resampling-based approach to improve the stability of our findings. Results: On average, women with elevated mammographic density exhibited DNA hypermethylation within CpG islands and gene promoters compared to women with lower mammographic density. We identified 250 CpG sites for which DNA methylation was significantly associated with mammographic density. The top sites were located within genes associated with cancer, including HDLBP, TGFB2, CCT4, and PAX8, and were more likely to be located in regulatory regions of the genome. We also identified differential DNA methylation in 37 regions, including within the promoters of PAX8 and PF4, a gene involved in the regulation of angiogenesis. Overall, our results paint a picture of epigenetic dysregulation associated with mammographic density. Conclusion: Mammographic density is associated with differential DNA methylation throughout the genome, including within genes associated with cancer. Our results suggest the potential involvement of several genes in the biological mechanisms behind differences in breast density between women. Further studies are warranted to explore these potential mechanisms and potential links to breast cancer risk.
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Affiliation(s)
- Rachel M Lucia
- Department of Epidemiology, University of California, Irvine, USA
| | - Wei-Lin Huang
- Department of Epidemiology, University of California, Irvine, USA
| | - Andrea Alvarez
- Department of Medicine, University of California, Irvine, USA
| | - Irene Masunaka
- Department of Medicine, University of California, Irvine, USA
| | - Argyrios Ziogas
- Department of Medicine, University of California, Irvine, USA
| | - Deborah Goodman
- Department of Epidemiology, University of California, Irvine, USA
| | | | | | - Hannah Lui Park
- Department of Epidemiology, University of California, Irvine, USA.,Department of Pathology and Laboratory Medicine, University of California, Irvine, USA
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23
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Villicaña S, Bell JT. Genetic impacts on DNA methylation: research findings and future perspectives. Genome Biol 2021; 22:127. [PMID: 33931130 PMCID: PMC8086086 DOI: 10.1186/s13059-021-02347-6] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/09/2021] [Indexed: 12/17/2022] Open
Abstract
Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.
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Affiliation(s)
- Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
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24
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Elucidation of the Genomic-Epigenomic Interaction Landscape of Aggressive Prostate Cancer. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6641429. [PMID: 33511206 PMCID: PMC7825361 DOI: 10.1155/2021/6641429] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/31/2020] [Indexed: 12/16/2022]
Abstract
Background Majority of prostate cancer (PCa) deaths are attributed to localized high-grade aggressive tumours which progress rapidly to metastatic disease. A critical unmet need in clinical management of PCa is discovery and characterization of the molecular drivers of aggressive tumours. The development and progression of aggressive PCa involve genetic and epigenetic alterations occurring in the germline, somatic (tumour), and epigenomes. To date, interactions between genes containing germline, somatic, and epigenetic mutations in aggressive PCa have not been characterized. The objective of this investigation was to elucidate the genomic-epigenomic interaction landscape in aggressive PCa to identify potential drivers aggressive PCa and the pathways they control. We hypothesized that aggressive PCa originates from a complex interplay between genomic (both germline and somatic mutations) and epigenomic alterations. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect gene expression, molecular networks, and signaling pathways which in turn drive aggressive PCa. Methods We addressed these hypotheses by performing integrative data analysis combining information on germline mutations from genome-wide association studies with somatic and epigenetic mutations from The Cancer Genome Atlas using gene expression as the intermediate phenotype. Results The investigation revealed signatures of genes containing germline, somatic, and epigenetic mutations associated with aggressive PCa. Aberrant DNA methylation had effect on gene expression. In addition, the investigation revealed molecular networks and signalling pathways enriched for germline, somatic, and epigenetic mutations including the STAT3, PTEN, PCa, ATM, AR, and P53 signalling pathways implicated in aggressive PCa. Conclusions The study demonstrated that integrative analysis combining diverse omics data is a powerful approach for the discovery of potential clinically actionable biomarkers, therapeutic targets, and elucidation of oncogenic interactions between genomic and epigenomic alterations in aggressive PCa.
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25
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Abstract
Simultaneous measurement of 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level can be obtained by combining data from DNA processing methods including traditional bisulfite (BS), oxidative bisulfite (oxBS), or Tet-assisted (TAB) bisulfite conversion. Array-based technologies have been widely used in this task, due to their time and cost efficiency. For methylation studies using BS data, many protocols and related packages have been suggested in the literature to deal with limitations and confounders that arise from array data. In this chapter, we illustrate how the reader can make small adjustments to these protocols to obtain estimates of methylation and hydroxymethylation proportions.
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26
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Gao GF, Parker JS, Reynolds SM, Silva TC, Wang LB, Zhou W, Akbani R, Bailey M, Balu S, Berman BP, Brooks D, Chen H, Cherniack AD, Demchok JA, Ding L, Felau I, Gaheen S, Gerhard DS, Heiman DI, Hernandez KM, Hoadley KA, Jayasinghe R, Kemal A, Knijnenburg TA, Laird PW, Mensah MKA, Mungall AJ, Robertson AG, Shen H, Tarnuzzer R, Wang Z, Wyczalkowski M, Yang L, Zenklusen JC, Zhang Z, Liang H, Noble MS. Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons' Data. Cell Syst 2020; 9:24-34.e10. [PMID: 31344359 DOI: 10.1016/j.cels.2019.06.006] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 03/18/2019] [Accepted: 06/13/2019] [Indexed: 01/09/2023]
Abstract
We present a systematic analysis of the effects of synchronizing a large-scale, deeply characterized, multi-omic dataset to the current human reference genome, using updated software, pipelines, and annotations. For each of 5 molecular data platforms in The Cancer Genome Atlas (TCGA)-mRNA and miRNA expression, single nucleotide variants, DNA methylation and copy number alterations-comprehensive sample, gene, and probe-level studies were performed, towards quantifying the degree of similarity between the 'legacy' GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as 'harmonized' by the Genomic Data Commons. We offer gene lists to elucidate differences that remained after controlling for confounders, and strategies to mitigate their impact on biological interpretation. Our results demonstrate that the hg19 and hg38 TCGA datasets are very highly concordant, promote informed use of either legacy or harmonized omics data, and provide a rubric that encourages similar comparisons as new data emerge and reference data evolve.
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Affiliation(s)
- Galen F Gao
- Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; The University of Texas Southwestern Medical School, Dallas, TX 75390, USA
| | - Joel S Parker
- Department of Genetics, Lineberger Comprehensive Cancer Center, the University of North Carolin at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - Tiago C Silva
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP 14.040-905, Brazil
| | - Liang-Bo Wang
- Department of Medicine, Washington University in St Louis, Saint Louis, MO 63108, USA; McDonnell Genome Institute, Washington University in St Louis, Saint Louis, MO 63108, USA; Siteman Cancer Center, Washington University in St Louis, Saint Louis, MO 63108, USA
| | - Wanding Zhou
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Rehan Akbani
- Department of Bioinformatics and Computational Biology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Matthew Bailey
- Department of Medicine, Washington University in St Louis, Saint Louis, MO 63108, USA; McDonnell Genome Institute, Washington University in St Louis, Saint Louis, MO 63108, USA; Siteman Cancer Center, Washington University in St Louis, Saint Louis, MO 63108, USA
| | - Saianand Balu
- Lineberger Comprehensive Cancer Center, Bioinformatics Core, the University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Benjamin P Berman
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Faculty of Medicine, Department of Developmental Biology and Cancer Research, the Hebrew University of Jerusalem, Jerusalem 91120, Israel
| | - Denise Brooks
- Canada's Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada
| | - Hu Chen
- Department of Bioinformatics and Computational Biology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andrew D Cherniack
- Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | | | - Li Ding
- Department of Medicine, Washington University in St Louis, Saint Louis, MO 63108, USA; McDonnell Genome Institute, Washington University in St Louis, Saint Louis, MO 63108, USA; Siteman Cancer Center, Washington University in St Louis, Saint Louis, MO 63108, USA
| | - Ina Felau
- National Cancer Institute, Bethesda, MD 20892, USA
| | - Sharon Gaheen
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA
| | | | - David I Heiman
- Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Kyle M Hernandez
- Department of Pediatrics, the University of Chicago, Chicago, IL 60637, USA; Center for Research Informatics, the University of Chicago, Chicago, IL 60637, USA
| | - Katherine A Hoadley
- Department of Genetics, Lineberger Comprehensive Cancer Center, the University of North Carolin at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Reyka Jayasinghe
- Department of Medicine, Washington University in St Louis, Saint Louis, MO 63108, USA
| | - Anab Kemal
- National Cancer Institute, Bethesda, MD 20892, USA
| | | | - Peter W Laird
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | | | - Andrew J Mungall
- Canada's Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada
| | - A Gordon Robertson
- Canada's Michael Smith Genome Sciences Centre, Vancouver, BC V5Z 4S6, Canada
| | - Hui Shen
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | | | - Zhining Wang
- National Cancer Institute, Bethesda, MD 20892, USA
| | - Matthew Wyczalkowski
- Department of Medicine, Washington University in St Louis, Saint Louis, MO 63108, USA; McDonnell Genome Institute, Washington University in St Louis, Saint Louis, MO 63108, USA; Siteman Cancer Center, Washington University in St Louis, Saint Louis, MO 63108, USA
| | - Liming Yang
- National Cancer Institute, Bethesda, MD 20892, USA
| | | | - Zhenyu Zhang
- Center for Translational Data Science, the University of Chicago, Chicago, IL 60615, USA
| | | | - Han Liang
- Department of Bioinformatics and Computational Biology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA; Department of Systems Biology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Michael S Noble
- Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA.
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Wu J, Mamidi TKK, Zhang L, Hicks C. Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer. Cancers (Basel) 2020; 12:cancers12061559. [PMID: 32545594 PMCID: PMC7352267 DOI: 10.3390/cancers12061559] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 01/01/2023] Open
Abstract
Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.
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Affiliation(s)
- Jiande Wu
- Health Sciences Center, Department of Genetic, Louisiana State University School of Medicine, 533 Bolivar Street, New Orleans, LA 70112, USA;
| | - Tarun Karthik Kumar Mamidi
- Center for Computational Genomics and Data Science, Departments of Pediatrics and Pathology, University of Alabama–Birmingham School of Medicine, Birmingham, AL 35233, USA;
| | - Lu Zhang
- Department of Public Health Sciences, Clemson University, 513 Edwards Hall, Clemson, SC 29634, USA;
| | - Chindo Hicks
- Health Sciences Center, Department of Genetic, Louisiana State University School of Medicine, 533 Bolivar Street, New Orleans, LA 70112, USA;
- Correspondence: ; Tel.: +1-504-568-2657
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28
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Sala C, Di Lena P, Fernandes Durso D, Prodi A, Castellani G, Nardini C. Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform. PLoS One 2020; 15:e0229763. [PMID: 32155174 PMCID: PMC7064179 DOI: 10.1371/journal.pone.0229763] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 02/13/2020] [Indexed: 01/06/2023] Open
Abstract
Introduction Meta-analysis is a powerful means for leveraging the hundreds of experiments being run worldwide into more statistically powerful analyses. This is also true for the analysis of omic data, including genome-wide DNA methylation. In particular, thousands of DNA methylation profiles generated using the Illumina 450k are stored in the publicly accessible Gene Expression Omnibus (GEO) repository. Often, however, the intensity values produced by the BeadChip (raw data) are not deposited, therefore only pre-processed values -obtained after computational manipulation- are available. Pre-processing is possibly different among studies and may then affect meta-analysis by introducing non-biological sources of variability. Material and methods To systematically investigate the effect of pre-processing on meta-analysis, we analysed four different collections of DNA methylation samples (datasets), each composed of two subsets, for which raw data from controls (i.e. healthy subjects) and cases (i.e. patients) are available. We pre-processed the data from each dataset with nine among the most common pipelines found in literature. Moreover, we evaluated the performance of regRCPqn, a modification of the RCP algorithm that aims to improve data consistency. For each combination of pre-processing (9 × 9), we first evaluated the between-sample variability among control subjects and, then, we identified genomic positions that are differentially methylated between cases and controls (differential analysis). Results and conclusion The pre-processing of DNA methylation data affects both the between-sample variability and the loci identified as differentially methylated, and the effects of pre-processing are strongly dataset-dependent. By contrast, application of our renormalization algorithm regRCPqn: (i) reduces variability and (ii) increases agreement between meta-analysed datasets, both critical components of data harmonization.
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Affiliation(s)
- Claudia Sala
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
- * E-mail: (CS); (CN)
| | - Pietro Di Lena
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Danielle Fernandes Durso
- Division of Infectious Diseases and Immunology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Andrea Prodi
- Smart Cities Living Lab, Institute of Organic Synthesis and Photoreactivity, CNR, Bologna, Italy
| | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
- Interdepartmental Center “L. Galvani”, University of Bologna, Bologna, Italy
| | - Christine Nardini
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- CNR IAC “Mauro Picone”, Roma, Italy
- Sol Group, Monza, Italy
- * E-mail: (CS); (CN)
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29
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Silva R, Moran B, Russell NM, Fahey C, Vlajnic T, Manecksha RP, Finn SP, Brennan DJ, Gallagher WM, Perry AS. Evaluating liquid biopsies for methylomic profiling of prostate cancer. Epigenetics 2020; 15:715-727. [PMID: 32000564 PMCID: PMC7574384 DOI: 10.1080/15592294.2020.1712876] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background: Liquid biopsies offer significant potential for informing on cancer progression and therapeutic resistance via minimally invasive serial monitoring of genetic alterations. Although the cancer epigenome is a central driving force in most neoplasia, the accuracy of monitoring the tumor methylome using liquid biopsies remains relatively unknown. Objectives: to investigate how well two types of liquid biopsy (urine and blood) capture the prostate cancer methylome, and may thus serve as a non-invasive surrogate for studying the tumor epigenome. Methods: A cohort of four metastatic treatment naïve prostate cancer (PCa) patients was selected. Matched biopsy cores (tumor and histologically matched-normal), post-DRE, pre-biopsy urine, and peripheral blood plasma were available for each subject. DNA methylation was profiled utilizing the Infinium® MethylationEPIC BeadChip (Illumina) and analysed using the RnBeads software. Significantly (FDR adjusted P < 0.05) differentially methylated probes (DMPs) between tumor and MN were identified and examined in the liquids (done at a grouped and individual subject level). Results: DNA methylation analysis of urine and blood in men with metastatic PCa showed highly correlated patterns between the different liquid types (ρ = 0.93, P < 0.0001), with large contributions from non-tumor sources. DNA methylation profiles of liquids were more similar between subjects, than intra-individual liquid-tumor correlations. Overall, both urine and plasma are viable surrogates for tumor tissue biopsies, capturing up to 39.40% and 64.14% of tumor-specific methylation alterations, respectively. Conclusion: We conclude that both urine and blood plasma are easily accessible and sensitive biofluids for the study of PCa epigenomic alterations.
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Affiliation(s)
- Romina Silva
- Cancer Biology and Therapeutics Laboratory, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin , Dublin, Ireland.,School of Medicine, University College Dublin , Dublin, Ireland
| | - Bruce Moran
- Cancer Biology and Therapeutics Laboratory, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin , Dublin, Ireland.,Ireland East Hospital Group (IEHG), St. Vincent's University Hospital , Dublin, Ireland
| | - Niamh M Russell
- Cancer Biology and Therapeutics Laboratory, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin , Dublin, Ireland.,School of Biomolecular and Biomedical Science, University College Dublin , Dublin, Ireland
| | - Ciara Fahey
- Prostate Molecular Oncology, Trinity Translational Medicine Institute, Trinity College Dublin , Dublin, Ireland
| | - Tatjana Vlajnic
- Department of Histopathology, St James's Hospital , Dublin, Ireland.,Institute of Pathology, University Hospital Basel , Basel, Switzerland
| | - Rustom P Manecksha
- Department of Urology, St. James's Hospital and Trinity College Dublin , Dublin, Ireland
| | - Stephen P Finn
- Department of Histopathology, St James's Hospital , Dublin, Ireland
| | - Donal J Brennan
- Cancer Biology and Therapeutics Laboratory, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin , Dublin, Ireland.,School of Medicine, University College Dublin , Dublin, Ireland
| | - William M Gallagher
- Cancer Biology and Therapeutics Laboratory, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin , Dublin, Ireland.,School of Biomolecular and Biomedical Science, University College Dublin , Dublin, Ireland
| | - Antoinette S Perry
- Cancer Biology and Therapeutics Laboratory, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin , Dublin, Ireland.,Prostate Molecular Oncology, Trinity Translational Medicine Institute, Trinity College Dublin , Dublin, Ireland.,School of Biology and Environmental Science, University College Dublin , Dublin, Ireland
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30
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Ciuculete DM, Voisin S, Kular L, Welihinda N, Jonsson J, Jagodic M, Mwinyi J, Schiöth HB. Longitudinal DNA methylation changes at MET may alter HGF/c-MET signalling in adolescents at risk for depression. Epigenetics 2019; 15:646-663. [PMID: 31852353 PMCID: PMC7574381 DOI: 10.1080/15592294.2019.1700628] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Unrecognized depression during adolescence can result in adult suicidal behaviour. The aim of this study was to identify, replicate and characterize DNA methylation (DNAm) shifts in depression aetiology, using a longitudinal, multi-tissue (blood and brain) and multi-layered (genetics, epigenetics, transcriptomics) approach. We measured genome-wide blood DNAm data at baseline and one-year follow-up, and imputed genetic variants, in 59 healthy adolescents comprising the discovery cohort. Depression and suicidal symptoms were determined using the Development and Well-Being Assessment (DAWBA) depression band, Montgomery-Åsberg Depression Rating Scale-Self (MADRS-S) and SUicide Assessment Scale (SUAS). DNAm levels at follow-up were regressed against depression scores, adjusting for sex, age and the DNAm residuals at baseline. Higher methylation levels of 5% and 13% at cg24627299 within the MET gene were associated with higher depression scores (praw<1e-4) and susceptibility for suicidal symptoms (padj.<0.005). The nearby rs39748 was discovered to be a methylation and expression quantitative trait locus in blood cells. mRNA levels of hepatocyte growth factor (HGF) expression, known to strongly interact with MET, were inversely associated with methylation levels at cg24627299, in an independent cohort of 1180 CD14+ samples. In an open-access dataset of brain tissue, lower methylation at cg24627299 was found in 45 adults diagnosed with major depressive disorder compared with matched controls (padj.<0.05). Furthermore, lower MET expression was identified in the hippocampus of depressed individuals compared with controls in a fourth, independent cohort. Our findings reveal methylation changes at MET in the pathology of depression, possibly involved in downregulation of HGF/c-MET signalling the hippocampal region.
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Affiliation(s)
- Diana M Ciuculete
- Department of Neuroscience, Functional Pharmacology, Uppsala University , Uppsala, Sweden
| | - Sarah Voisin
- Institute for Health and Sport (iHeS), Victoria University , Footscray, Australian
| | - Lara Kular
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet , Stockholm, Sweden
| | - Nipuni Welihinda
- Department of Neuroscience, Functional Pharmacology, Uppsala University , Uppsala, Sweden
| | - Jörgen Jonsson
- Department of Neuroscience, Functional Pharmacology, Uppsala University , Uppsala, Sweden
| | - Maja Jagodic
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet , Stockholm, Sweden
| | - Jessica Mwinyi
- Department of Neuroscience, Functional Pharmacology, Uppsala University , Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Neuroscience, Functional Pharmacology, Uppsala University , Uppsala, Sweden.,Institute for Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University , Moscow, Russia
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31
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Wu J, Mamidi TKK, Zhang L, Hicks C. Deconvolution of the Genomic and Epigenomic Interaction Landscape of Triple-Negative Breast Cancer. Cancers (Basel) 2019; 11:cancers11111692. [PMID: 31683572 PMCID: PMC6896043 DOI: 10.3390/cancers11111692] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 10/07/2019] [Accepted: 10/19/2019] [Indexed: 12/26/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. Emerging evidenced suggests that both genetics and epigenetic factors play a role in the pathogenesis of TNBC. However, oncogenic interactions and cooperation between genomic and epigenomic variation have not been characterized. The objective of this study was to deconvolute the genomic and epigenomic interaction landscape in TNBC using an integrative genomics approach, which integrates information on germline, somatic, epigenomic and gene expression variation. We hypothesized that TNBC originates from a complex interplay between genomic (both germline and somatic variation) and epigenomic variation. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect entire molecular networks and signaling pathways which, in turn, drive TNBC. We addressed these hypotheses using germline variation from genome-wide association studies and somatic, epigenomic and gene expression variation from The Cancer Genome Atlas (TCGA). The investigation revealed signatures of functionally related genes containing germline, somatic and epigenetic variations. DNA methylation had an effect on gene expression. Network and pathway analysis revealed molecule networks and signaling pathways enriched for germline, somatic and epigenomic variation, among them: Role of BRCA1 in DNA Damage Response, Hereditary Breast Cancer Signaling, Molecular Mechanisms of Cancer, Estrogen-Dependent Breast Cancer, p53, MYC Mediated Apoptosis, and PTEN Signaling pathways. The investigation revealed that integrative genomics is a powerful approach for deconvoluting the genomic-epigenomic interaction landscape in TNBC. Further studies are needed to understand the biological mechanisms underlying oncogenic interactions between genomic and epigenomic factors in TNBC.
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Affiliation(s)
- Jiande Wu
- Department of Genetics, Louisiana State University Health Sciences Center, School of Medicine, 533 Bolivar Street, New Orleans, LA 70112, USA.
| | - Tarun Karthik Kumar Mamidi
- Graduate Biomedical Sciences, The University of Alabama at Birmingham, 1825 University Blvd, Birmingham, AL 35233, USA.
| | - Lu Zhang
- Department of Public Health Sciences, Clemson University, 513 Edwards Hall, Clemson, SC 29634, USA.
| | - Chindo Hicks
- Department of Genetics, Louisiana State University Health Sciences Center, School of Medicine, 533 Bolivar Street, New Orleans, LA 70112, USA.
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Kim GS, Smith AK, Xue F, Michopoulos V, Lori A, Armstrong DL, Aiello AE, Koenen KC, Galea S, Wildman DE, Uddin M. Methylomic profiles reveal sex-specific differences in leukocyte composition associated with post-traumatic stress disorder. Brain Behav Immun 2019; 81:280-291. [PMID: 31228611 PMCID: PMC6754791 DOI: 10.1016/j.bbi.2019.06.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 02/07/2023] Open
Abstract
Post-traumatic stress disorder (PTSD) is a debilitating mental disorder precipitated by trauma exposure. However, only some persons exposed to trauma develop PTSD. There are sex differences in risk; twice as many women as men develop a lifetime diagnosis of PTSD. Methylomic profiles derived from peripheral blood are well-suited for investigating PTSD because DNA methylation (DNAm) encodes individual response to trauma and may play a key role in the immune dysregulation characteristic of PTSD pathophysiology. In the current study, we leveraged recent methodological advances to investigate sex-specific differences in DNAm-based leukocyte composition that are associated with lifetime PTSD. We estimated leukocyte composition on a combined methylation array dataset (483 participants, ∼450 k CpG sites) consisting of two civilian cohorts, the Detroit Neighborhood Health Study and Grady Trauma Project. Sex-stratified Mann-Whitney U test and two-way ANCOVA revealed that lifetime PTSD was associated with significantly higher monocyte proportions in males, but not in females (Holm-adjusted p-val < 0.05). No difference in monocyte proportions was observed between current and remitted PTSD cases in males, suggesting that this sex-specific difference may reflect a long-standing trait of lifetime history of PTSD, rather than current state of PTSD. Associations with lifetime PTSD or PTSD status were not observed in any other leukocyte subtype and our finding in monocytes was confirmed using cell estimates based on a different deconvolution algorithm, suggesting that our sex-specific findings are robust across cell estimation approaches. Overall, our main finding of elevated monocyte proportions in males, but not in females with lifetime history of PTSD provides evidence for a sex-specific difference in peripheral blood leukocyte composition that is detectable in methylomic profiles and that may reflect long-standing changes associated with PTSD diagnosis.
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Affiliation(s)
- Grace S Kim
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Medical Scholars Program, University of Illinois College of Medicine, Urbana, IL, USA
| | - Alicia K Smith
- Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA, USA; Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
| | - Fei Xue
- Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Vasiliki Michopoulos
- Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Adriana Lori
- Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Don L Armstrong
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Allison E Aiello
- Gillings School of Global Public Health, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sandro Galea
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Derek E Wildman
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Monica Uddin
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA.
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33
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Zhou W, Triche TJ, Laird PW, Shen H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res 2019; 46:e123. [PMID: 30085201 PMCID: PMC6237738 DOI: 10.1093/nar/gky691] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 07/20/2018] [Indexed: 12/18/2022] Open
Abstract
We report a new class of artifacts in DNA methylation measurements from Illumina HumanMethylation450 and MethylationEPIC arrays. These artifacts reflect failed hybridization to target DNA, often due to germline or somatic deletions and manifest as incorrectly reported intermediate methylation. The artifacts often survive existing preprocessing pipelines, masquerade as epigenetic alterations and can confound discoveries in epigenome-wide association studies and studies of methylation-quantitative trait loci. We implement a solution, P-value with out-of-band (OOB) array hybridization (pOOBAH), in the R package SeSAMe. Our method effectively masks deleted and hyperpolymorphic regions, reducing or eliminating spurious reports of epigenetic silencing at oft-deleted tumor suppressor genes such as CDKN2A and RB1 in cases with somatic deletions. Furthermore, our method substantially decreases technical variation whilst retaining biological variation, both within and across HM450 and EPIC platform measurements. SeSAMe provides a light-weight, modular DNA methylation data analysis suite, with a performant implementation suitable for efficient analysis of thousands of samples.
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Affiliation(s)
- Wanding Zhou
- Center for Epigenetics, Van Andel Research Institute, 333 Bostwick Ave., N.E., Grand Rapids, MI 49503 USA
| | - Timothy J Triche
- Center for Epigenetics, Van Andel Research Institute, 333 Bostwick Ave., N.E., Grand Rapids, MI 49503 USA
| | - Peter W Laird
- Center for Epigenetics, Van Andel Research Institute, 333 Bostwick Ave., N.E., Grand Rapids, MI 49503 USA
| | - Hui Shen
- Center for Epigenetics, Van Andel Research Institute, 333 Bostwick Ave., N.E., Grand Rapids, MI 49503 USA
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34
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Different Methylation of CpG-SNPs in Behcet's Disease. BIOMED RESEARCH INTERNATIONAL 2019; 2019:3489305. [PMID: 31223615 PMCID: PMC6541967 DOI: 10.1155/2019/3489305] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 05/04/2019] [Accepted: 05/07/2019] [Indexed: 01/08/2023]
Abstract
Purpose We recently performed an Epigenome-Wide Association Studies (EWAS) study in Behcet's disease (BD) and identified various cytosine–phosphate–guanine (CpG) loci that were aberrantly methylated. In the current study, we wanted to investigate whether these sites contained genetic polymorphisms and whether the frequency of these polymorphisms was altered in BD. Methods A two-stage study was performed. The first stage involved 358 BD patients and 704 healthy controls to investigate genetic variants of 10 CpG-SNPs (rs10454134, rs176249, rs3808620, rs10176517, rs11247118, rs78016579, rs9461624, rs10492166, rs34929465, and rs6507921) using an iPLEX Gold genotyping assay and a Sequenom MassARRAY. In the second stage, an additional 172 independent BD patients and 330 healthy individuals are to confirm trends found in the first stage. Results A higher frequency of both the rs10454134 AG genotypes (p = 0.008, OR = 1.413, 95% CI = 1.094-1.826) and a lower GG genotype frequency (p = 0.003, OR = 0.630, 95% CI = 0.465-0.854) were found in BD patients compared to the controls in the first stage. However, after correcting for multiple comparisons, all associations identified in the first stage lost statistical significance. The frequencies of the other CpG-SNPs investigated were not different between BD patients and controls. The second stage was designed using an additional cohort to confirm the association with CpG-SNP, rs10454134. The data failed to confirm the association between this CpG-SNP and BD. Conclusions This study did not show an association between BD and CpG-SNPs in gene sites that were earlier shown to be aberrantly methylated.
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Gu S, Lin S, Ye D, Qian S, Jiang D, Zhang X, Li Q, Yang J, Ying X, Li Z, Tang M, Wang J, Jin M, Chen K. Genome-wide methylation profiling identified novel differentially hypermethylated biomarker MPPED2 in colorectal cancer. Clin Epigenetics 2019; 11:41. [PMID: 30846004 PMCID: PMC6407227 DOI: 10.1186/s13148-019-0628-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 02/04/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Epigenetic alternation is a common contributing factor to neoplastic transformation. Although previous studies have reported a cluster of aberrant promoter methylation changes associated with silencing of tumor suppressor genes, little is known concerning their sequential DNA methylation changes during the carcinogenetic process. The aim of the present study was to address a genome-wide search for identifying potentially important methylated changes and investigate the onset and pattern of methylation changes during the progression of colorectal neoplasia. METHODS A three-phase design was employed in this study. In the screening phase, DNA methylation profile of 12 pairs of colorectal cancer (CRC) and adjacent normal tissues was analyzed by using the Illumina MethylationEPIC BeadChip. Significant CpG sites were selected based on a cross-validation analysis from The Cancer Genome Atlas (TCGA) database. Methylation levels of candidate CpGs were assessed using pyrosequencing in the training dataset (tumor lesions and adjacent normal tissues from 46 CRCs) and the validation dataset (tumor lesions and paired normal tissues from 13 hyperplastic polyps, 129 adenomas, and 256 CRCs). A linear mixed-effects model was used to examine the incremental changes of DNA methylation during the progression of colorectal neoplasia. RESULTS The comparisons between normal and tumor samples in the screening phase revealed an extensive CRC-specific methylomic pattern with 174,006 (21%) methylated CpG sites, of which 22,232 (13%) were hyermethylated and 151,774 (87%) were hypomethylated. Hypermethylation mostly occurred in CpG islands with an overlap of gene promoters, while hypomethylation tended to be mapped far away from functional regions. Further cross validation analysis from TCGA dataset confirmed 265 hypermethylated promoters coupling with downregulated gene expression. Among which, hypermethylated changes in MEEPD2 promoter was successfully replicated in both training and validation phase. Significant hypermethylation appeared since precursor lesions with an extensive modification in CRCs. The linear mixed-effects modeling analysis found that a cumulative pattern of MPPED2 methylation changes from normal mucosa to hyperplastic polyp to adenoma, and to carcinoma (P < 0.001). CONCLUSIONS Our findings indicate that epigenetic alterations of MPPED2 promoter region appear sequentially during the colorectal neoplastic progression. It might be able to serve as a promising biomarker for early diagnosis and stage surveillance of colorectal tumorigenesis.
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Affiliation(s)
- Simeng Gu
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Shujuan Lin
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Ding Ye
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China.,Department of Epidemiology and Biostatistics, Zhejiang Chinese Medical University School of Public Health, 548 Binwen Road, Hangzhou, 310053, China
| | - Sangni Qian
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Danjie Jiang
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Xiaocong Zhang
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Qilong Li
- Jiashan Institute of Cancer Prevention and Treatment, 345 Jiefangdong Road, Jiashan, 314100, China
| | - Jinhua Yang
- Jiashan Institute of Cancer Prevention and Treatment, 345 Jiefangdong Road, Jiashan, 314100, China
| | - Xiaojiang Ying
- Department of Anorectal Surgery, Shaoxing People's Hospital, 568 Zhongxingbei Road, Shaoxing, 312000, China
| | - Zhenjun Li
- Department of Anorectal Surgery, Shaoxing People's Hospital, 568 Zhongxingbei Road, Shaoxing, 312000, China
| | - Mengling Tang
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Jianbing Wang
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Mingjuan Jin
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China.
| | - Kun Chen
- Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, 866 Yuhangtang Road, Hangzhou, 310058, China. .,Cancer Institute, the Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, China.
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Min JL, Hemani G, Davey Smith G, Relton C, Suderman M. Meffil: efficient normalization and analysis of very large DNA methylation datasets. Bioinformatics 2018; 34:3983-3989. [PMID: 29931280 PMCID: PMC6247925 DOI: 10.1093/bioinformatics/bty476] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 06/18/2018] [Indexed: 12/11/2022] Open
Abstract
Motivation DNA methylation datasets are growing ever larger both in sample size and genome coverage. Novel computational solutions are required to efficiently handle these data. Results We have developed meffil, an R package designed for efficient quality control, normalization and epigenome-wide association studies of large samples of Illumina Methylation BeadChip microarrays. A complete re-implementation of functional normalization minimizes computational memory without increasing running time. Incorporating fixed and random effects within functional normalization, and automated estimation of functional normalization parameters reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. Support for normalization of datasets distributed across physically different locations without needing to share biologically-based individual-level data means that meffil can be used to reduce heterogeneity in meta-analyses of epigenome-wide association studies. Availability and implementation https://github.com/perishky/meffil/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- J L Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol Medical School, University of Bristol, Bristol, UK
| | - G Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol Medical School, University of Bristol, Bristol, UK
| | - G Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol Medical School, University of Bristol, Bristol, UK
| | - C Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol Medical School, University of Bristol, Bristol, UK
| | - M Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol Medical School, University of Bristol, Bristol, UK
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LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer. PLoS One 2018; 13:e0204371. [PMID: 30388122 PMCID: PMC6214495 DOI: 10.1371/journal.pone.0204371] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 09/06/2018] [Indexed: 12/23/2022] Open
Abstract
Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analysis. Therefore, it is crucial to be able to identify a relatively small number of methylation sites that provide high precision and sensitivity for the diagnosis of pathological states. We propose an algorithm for constructing limited subsamples from high-dimensional data to form diagnostic panels. We have developed a tool that utilizes different methods of selection to find an optimal, minimum necessary combination of factors using cross-entropy loss metrics (LogLoss) to identify a subset of methylation sites. We show that the algorithm can work effectively with different genome methylation patterns using ensemble-based machine learning methods. Algorithm efficiency, precision and robustness were evaluated using five genome-wide DNA methylation datasets (totaling 626 samples), and each dataset was classified into tumor and non-tumor samples. The algorithm produced an AUC of 0.97 (95% CI: 0.94-0.99, 9 sites) for prostate adenocarcinoma and an AUC of 1.0 (from 2 to 6 sites) for urothelial bladder carcinoma, two types of kidney carcinoma and colorectal carcinoma. For prostate adenocarcinoma we showed that identified differential variability methylation patterns distinguish cluster of samples with higher recurrence rate (hazard ratio for recurrence = 0.48, 95% CI: 0.05-0.92; log-rank test, p-value < 0.03). We also identified several clusters of correlated interchangeable methylation sites that can be used for the elaboration of biological interpretation of the resulting models and for further selection of the sites most suitable for designing diagnostic panels. LogLoss-BERAF is implemented as a standalone python code and open-source code is freely available from https://github.com/bioinformatics-IBCH/logloss-beraf along with the models described in this article.
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38
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Grieshober L, Graw S, Barnett MJ, Thornquist MD, Goodman GE, Chen C, Koestler DC, Marsit CJ, Doherty JA. Methylation-derived Neutrophil-to-Lymphocyte Ratio and Lung Cancer Risk in Heavy Smokers. Cancer Prev Res (Phila) 2018; 11:727-734. [PMID: 30254071 PMCID: PMC6214718 DOI: 10.1158/1940-6207.capr-18-0111] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 08/13/2018] [Accepted: 09/21/2018] [Indexed: 12/28/2022]
Abstract
The neutrophil-to-lymphocyte ratio (NLR) is a biomarker that indicates systemic inflammation and can be estimated using array-based DNA methylation data as methylation-derived NLR (mdNLR). We assessed the relationship between prediagnosis mdNLR and lung cancer risk in a nested case-control study in the β-Carotene and Retinol Efficacy Trial (CARET) of individuals at high risk for lung cancer due to heavy smoking or substantial occupational asbestos exposure. We matched 319 incident lung cancer cases to controls based on age at blood draw, smoking, sex, race, asbestos, enrollment year, and time at risk. We computed mdNLR using the ratio of predicted granulocyte and lymphocyte proportions derived from DNA methylation signatures in whole blood collected prior to diagnosis (median 4.4 years in cases). Mean mdNLR was higher in cases than controls (2.06 vs. 1.86, P = 0.03). Conditional logistic regression models adjusted for potential confounders revealed a 21% increased risk of lung cancer per unit increase in mdNLR [OR 1.21; 95% confidence interval (CI) 1.01-1.45]. A 30% increased risk of non-small cell lung cancer (NSCLC) was observed for each unit increase in mdNLR (n = 240 pairs; OR 1.30, 95% CI, 1.03-1.63), and there was no statistically significant association between mdNLR and small-cell lung cancer risk. The mdNLR-NSCLC association was most pronounced in those with asbestos exposure (n = 42 male pairs; OR 3.39; 95% CI, 1.32-8.67). A better understanding of the role of mdNLR in lung cancer etiology may improve prevention and detection of lung cancer. Cancer Prev Res; 11(11); 727-34. ©2018 AACR.
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Affiliation(s)
- Laurie Grieshober
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Stefan Graw
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas
- University of Kansas Cancer Center, Kansas City, Kansas
| | - Matt J Barnett
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mark D Thornquist
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Gary E Goodman
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Chu Chen
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Department of Otolaryngology, Head and Neck Surgery, School of Medicine, University of Washington, Seattle, Washington
| | - Devin C Koestler
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas
- University of Kansas Cancer Center, Kansas City, Kansas
| | - Carmen J Marsit
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Jennifer A Doherty
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
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39
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Shiah YJ, Fraser M, Bristow RG, Boutros PC. Comparison of pre-processing methods for Infinium HumanMethylation450 BeadChip array. Bioinformatics 2018; 33:3151-3157. [PMID: 28605401 DOI: 10.1093/bioinformatics/btx372] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 06/06/2017] [Indexed: 12/15/2022] Open
Abstract
Motivation Microarrays are widely used to quantify DNA methylation because they are economical, require only small quantities of input DNA and focus on well-characterized regions of the genome. However, pre-processing of methylation microarray data is challenging because of confounding factors that include background fluorescence, dye bias and the impact of germline polymorphisms. Therefore, we present valuable insights and a framework for those seeking the most optimal pre-processing method through a data-driven approach. Results Here, we show that Dasen is the optimal pre-processing methodology for the Infinium HumanMethylation450 BeadChip array in prostate cancer, a frequently employed platform for tumour methylome profiling in both the TCGA and ICGC consortia. We evaluated the impact of 11 pre-processing methods on batch effects, replicate variabilities, sensitivities and sample-to-sample correlations across 809 independent prostate cancer samples, including 150 reported for the first time in this study. Overall, Dasen is the most effective for removing artefacts and detecting biological differences associated with tumour aggressivity. Relative to the raw dataset, it shows a reduction in replicate variances of 67% and 76% for β- and M-values, respectively. Our study provides a unique pre-processing benchmark for the community with an emphasis on biological implications. Availability and implementation All software used in this study are publicly available as detailed in the article. Contact paul.boutros@oicr.on.ca. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yu-Jia Shiah
- Informatics, Ontario Institute for Cancer Research
| | - Michael Fraser
- Princess Margaret Cancer Centre, University Health Network
| | - Robert G Bristow
- Princess Margaret Cancer Centre, University Health Network.,Department of Radiation Oncology.,Department of Medical Biophysics
| | - Paul C Boutros
- Informatics, Ontario Institute for Cancer Research.,Department of Medical Biophysics.,Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
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Vidaki A, Kalamara V, Carnero-Montoro E, Spector TD, Bell JT, Kayser M. Investigating the Epigenetic Discrimination of Identical Twins Using Buccal Swabs, Saliva, and Cigarette Butts in the Forensic Setting. Genes (Basel) 2018; 9:E252. [PMID: 29758014 PMCID: PMC5977192 DOI: 10.3390/genes9050252] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 05/07/2018] [Accepted: 05/07/2018] [Indexed: 12/28/2022] Open
Abstract
Monozygotic (MZ) twins are typically indistinguishable via forensic DNA profiling. Recently, we demonstrated that epigenetic differentiation of MZ twins is feasible; however, proportions of twin differentially methylated CpG sites (tDMSs) identified in reference-type blood DNA were not replicated in trace-type blood DNA. Here we investigated buccal swabs as typical forensic reference material, and saliva and cigarette butts as commonly encountered forensic trace materials. As an analog to a forensic case, we analyzed one MZ twin pair. Epigenome-wide microarray analysis in reference-type buccal DNA revealed 25 candidate tDMSs with >0.5 twin-to-twin differences. MethyLight quantitative PCR (qPCR) of 22 selected tDMSs in trace-type DNA revealed in saliva DNA that six tDMSs (27.3%) had >0.1 twin-to-twin differences, seven (31.8%) had smaller (<0.1) but robustly detected differences, whereas for nine (40.9%) the differences were in the opposite direction relative to the microarray data; for cigarette butt DNA, results were 50%, 22.7%, and 27.3%, respectively. The discrepancies between reference-type and trace-type DNA outcomes can be explained by cell composition differences, method-to-method variation, and other technical reasons including bisulfite conversion inefficiency. Our study highlights the importance of the DNA source and that careful characterization of biological and technical effects is needed before epigenetic MZ twin differentiation is applicable in forensic casework.
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Affiliation(s)
- Athina Vidaki
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, 3015 CN Rotterdam, The Netherlands.
| | - Vivian Kalamara
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, 3015 CN Rotterdam, The Netherlands.
| | - Elena Carnero-Montoro
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK.
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK.
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK.
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, 3015 CN Rotterdam, The Netherlands.
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41
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Chen CH, Jiang SS, Chang IS, Wen HJ, Sun CW, Wang SL. Association between fetal exposure to phthalate endocrine disruptor and genome-wide DNA methylation at birth. ENVIRONMENTAL RESEARCH 2018; 162:261-270. [PMID: 29367177 DOI: 10.1016/j.envres.2018.01.009] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 11/20/2017] [Accepted: 01/11/2018] [Indexed: 05/18/2023]
Abstract
BACKGROUND Phthalic acid esters are ubiquitous and antiandrogenic, and may cause systemic effects in humans, particularly with in utero exposure. Epigenetic modification, such as DNA methylation, has been hypothesized to be an important mechanism that mediates certain biological processes and pathogenic effects of in utero phthalate exposure. OBJECTIVE The aim of this study was to examine the association between genome-wide DNA methylation at birth and prenatal exposure to phthalate. METHODS We studied 64 infant-mother pairs included in TMICS (Taiwan Maternal and Infant Cohort Study), a long-term follow-up birth cohort from the general population. DNA methylation levels at more than 450,000 CpG sites were measured in cord blood samples using Illumina Infinium HumanMethylation450 BeadChips. The concentrations of three metabolites of di-(2-ethylhexyl) phthalate (DEHP) were measured using liquid chromatography tandem-mass spectrometry (LC-MS/MS) in urine samples collected from the pregnant women during 28-36 weeks gestation. RESULTS We identified 25 CpG sites whose methylation levels in cord blood were significantly correlated with prenatal DEHP exposure using a false discovery rate (FDR) of 5% (q-value < 0.05). Via gene-set enrichment analysis (GSEA), we also found that there was significant enrichment of genes involved in the androgen response, estrogen response, and spermatogenesis within those genes showing DNA methylation changes in response to exposure. Specifically, PA2G4, HMGCR, and XRCC6 genes were involved in genes in response to androgen. CONCLUSIONS Phthalate exposure in utero may cause significant alterations in the DNA methylation in cord blood. These changes in DNA methylation might serve as biomarkers of maternal exposure to phthalate in infancy and potential candidates for studying mechanisms via which phthalate may impact on health in later life. Future investigations are warranted.
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Affiliation(s)
- Chung-Hsing Chen
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan; Taiwan Bioinformatics Core, National Health Research Institutes, Zhunan, Taiwan
| | - Shih Sheng Jiang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan.
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan; Taiwan Bioinformatics Core, National Health Research Institutes, Zhunan, Taiwan; Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Hui-Ju Wen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Chien-Wen Sun
- National Institute of Environmental Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Shu-Li Wang
- National Institute of Environmental Health Sciences, National Health Research Institutes, Zhunan, Taiwan; School of Public Health, National Defense Medical Center, Taipei.
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42
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Forest M, O'Donnell KJ, Voisin G, Gaudreau H, MacIsaac JL, McEwen LM, Silveira PP, Steiner M, Kobor MS, Meaney MJ, Greenwood CMT. Agreement in DNA methylation levels from the Illumina 450K array across batches, tissues, and time. Epigenetics 2018; 13:19-32. [PMID: 29381404 PMCID: PMC5837078 DOI: 10.1080/15592294.2017.1411443] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Epigenome-wide association studies (EWAS) have focused primarily on DNA methylation as a chemically stable and functional epigenetic modification. However, the stability and accuracy of the measurement of methylation in different tissues and extraction types is still being actively studied, and the longitudinal stability of DNA methylation in commonly studied peripheral tissues is of great interest. Here, we used data from two studies, three tissue types, and multiple time points to assess the stability of DNA methylation measured with the Illumina Infinium HumanMethylation450 BeadChip array. Redundancy analysis enabled visual assessment of agreement of replicate samples overall and showed good agreement after removing effects of tissue type, age, and sex. At the probe level, analysis of variance contrasts separating technical and biological replicates clearly showed better agreement between technical replicates versus longitudinal samples, and suggested increased stability for buccal cells versus blood or blood spots. Intraclass correlations (ICCs) demonstrated that inter-individual variability is of similar magnitude to within-sample variability at many probes; however, as inter-individual variability increased, so did ICC. Furthermore, we were able to demonstrate decreasing agreement in methylation levels with time, despite a maximal sampling interval of only 576 days. Finally, at 6 popular candidate genes, there was a large range of stability across probes. Our findings highlight important sources of technical and biological variation in DNA methylation across different tissues over time. These data will help to inform longitudinal sampling strategies of future EWAS.
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Affiliation(s)
- Marie Forest
- a Lady Davis Institute , Jewish General Hospital , Montreal , QC , Canada.,b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada
| | - Kieran J O'Donnell
- b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,c Douglas Hospital Research Centre , McGill University , Montreal , QC , Canada.,d Sackler Program for Epigenetics & Psychobiology , McGill University , Montreal , QC , Canada.,e Canadian Institute for Advanced Research , Child and Brain Development Program , Toronto , ON , Canada
| | - Greg Voisin
- a Lady Davis Institute , Jewish General Hospital , Montreal , QC , Canada
| | - Helene Gaudreau
- b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,c Douglas Hospital Research Centre , McGill University , Montreal , QC , Canada
| | - Julia L MacIsaac
- f Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics , and BC Children's Hospital Research Institute, University of British Columbia , Vancouver , BC , Canada
| | - Lisa M McEwen
- f Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics , and BC Children's Hospital Research Institute, University of British Columbia , Vancouver , BC , Canada
| | - Patricia P Silveira
- b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,c Douglas Hospital Research Centre , McGill University , Montreal , QC , Canada.,d Sackler Program for Epigenetics & Psychobiology , McGill University , Montreal , QC , Canada
| | | | - Michael S Kobor
- f Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics , and BC Children's Hospital Research Institute, University of British Columbia , Vancouver , BC , Canada
| | - Michael J Meaney
- b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,c Douglas Hospital Research Centre , McGill University , Montreal , QC , Canada.,d Sackler Program for Epigenetics & Psychobiology , McGill University , Montreal , QC , Canada.,e Canadian Institute for Advanced Research , Child and Brain Development Program , Toronto , ON , Canada.,h Singapore Institute of Clinical Sciences , Singapore
| | - Celia M T Greenwood
- a Lady Davis Institute , Jewish General Hospital , Montreal , QC , Canada.,b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,i Departments of Oncology and Human Genetics , McGill University , Montreal , QC , Canada.,j Department of Epidemiology, Biostatistics and Occupational Health , McGill University , Montreal , QC , Canada
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Zheleznyakova GY, Piket E, Marabita F, Pahlevan Kakhki M, Ewing E, Ruhrmann S, Needhamsen M, Jagodic M, Kular L. Epigenetic research in multiple sclerosis: progress, challenges, and opportunities. Physiol Genomics 2017; 49:447-461. [PMID: 28754822 DOI: 10.1152/physiolgenomics.00060.2017] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 07/24/2017] [Indexed: 01/02/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory and demyelinating disease of the central nervous system. MS likely results from a complex interplay between predisposing causal gene variants (the strongest influence coming from HLA class II locus) and environmental risk factors such as smoking, infectious mononucleosis, and lack of sun exposure/vitamin D. However, little is known about the mechanisms underlying MS development and progression. Moreover, the clinical heterogeneity and variable response to treatment represent additional challenges to a comprehensive understanding and efficient treatment of disease. Epigenetic processes, such as DNA methylation and histone posttranslational modifications, integrate influences from the genes and the environment to regulate gene expression accordingly. Studying epigenetic modifications, which are stable and reversible, may provide an alternative approach to better understand and manage disease. We here aim to review findings from epigenetic studies in MS and further discuss the challenges and clinical opportunities arising from epigenetic research, many of which apply to other diseases with similar complex etiology. A growing body of evidence supports a role of epigenetic processes in the mechanisms underlying immune pathogenesis and nervous system dysfunction in MS. However, disparities between studies shed light on the need to consider possible confounders and methodological limitations for a better interpretation of the data. Nevertheless, translational use of epigenetics might offer new opportunities in epigenetic-based diagnostics and therapeutic tools for a personalized care of MS patients.
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Affiliation(s)
- Galina Y Zheleznyakova
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Eliane Piket
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Francesco Marabita
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Majid Pahlevan Kakhki
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Ewoud Ewing
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Sabrina Ruhrmann
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Maria Needhamsen
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Maja Jagodic
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Lara Kular
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
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