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Geng Z, Wafula E, Corbett RJ, Zhang Y, Jin R, Gaonkar KS, Shukla S, Rathi KS, Hill D, Lahiri A, Miller DP, Sickler A, Keith K, Blackden C, Chroni A, Brown MA, Kraya AA, Koschmann CJ, Aldape K, Huang X, Rood BR, Mason JL, Trooskin GR, Abdullaev Z, Wang P, Zhu Y, Farrow BK, Farrel A, Dybas JM, Zhong C, Kuren NV, Zhang B, Santi M, Phul S, Chinwalla AT, Resnick AC, Diskin SJ, Tasian S, Stefankiewicz S, Maris JM, Ennis BM, Lueder MR, Naqvi AS, Coleman N, Ma W, Taylor D, Rokita JL. The Open Pediatric Cancer Project. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.599086. [PMID: 39026781 PMCID: PMC11257555 DOI: 10.1101/2024.07.09.599086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Background In 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we extend the OpenPBTA to create the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). Findings We utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCI's pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research- grade integrated diagnoses for these tumors. Conclusions OpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.
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Affiliation(s)
- Zhuangzhuang Geng
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Eric Wafula
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ryan J Corbett
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Yuanchao Zhang
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Run Jin
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Krutika S Gaonkar
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Sangeeta Shukla
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Komal S Rathi
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Dave Hill
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Aditya Lahiri
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Daniel P Miller
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Alex Sickler
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Kelsey Keith
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Christopher Blackden
- Center for Data- Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Antonia Chroni
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Miguel A Brown
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Adam A Kraya
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Carl J Koschmann
- Department of Pediatrics, University of Michigan Health, Ann Arbor, MI, 48105, USA; Pediatric Hematology Oncology, Mott Children's Hospital, Ann Arbor, MI, 48109, USA
| | - Kenneth Aldape
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Xiaoyan Huang
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Brian R Rood
- Children's National Research Institute, Washington, D.C.; George Washington University School of Medicine and Health Sciences, Washington, D.C., 20052, USA
| | - Jennifer L Mason
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Gerri R Trooskin
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Zied Abdullaev
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yuankun Zhu
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Bailey K Farrow
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Alvin Farrel
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA · Funded by NCI/NIH Contract No. 75N91019D00024, Task Order No. 75N91020F00003
| | - Joseph M Dybas
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Chuwei Zhong
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Nicholas Van Kuren
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Bo Zhang
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Mariarita Santi
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Saksham Phul
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Asif T Chinwalla
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Adam C Resnick
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA · Funded by Children's Brain Tumor Network; NIH 3P30 CA016520- 44S5, U2C HL138346-03, U24 CA220457-03; NCI/NIH Contract No. 75N91019D00024, Task Order No. 75N91020F00003; Children's Hospital of Philadelphia Division of Neurosurgery
| | - Sharon J Diskin
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah Tasian
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stephanie Stefankiewicz
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - John M Maris
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Brian M Ennis
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew R Lueder
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ammar S Naqvi
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Noel Coleman
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Deanne Taylor
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman Medical School, Philadelphia, PA, 19104, USA · Funded by NCI/NIH Contract No. 75N91019D00024, Task Order No. 75N91020F00003
| | - Jo Lynne Rokita
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA · Funded by NCI/NIH Contract No. 75N91019D00024, Task Order No. 75N91020F00003
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2
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Coussement L, Van Criekinge W, De Meyer T. Quantitative transcriptomic and epigenomic data analysis: a primer. BIOINFORMATICS ADVANCES 2024; 4:vbae019. [PMID: 38586118 PMCID: PMC10997052 DOI: 10.1093/bioadv/vbae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/01/2024] [Accepted: 02/09/2024] [Indexed: 04/09/2024]
Abstract
The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.
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Affiliation(s)
- Louis Coussement
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, 9000, Belgium
| | - Wim Van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, 9000, Belgium
| | - Tim De Meyer
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, 9000, Belgium
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3
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Hageman I, Mol F, Atiqi S, Joustra V, Sengul H, Henneman P, Visman I, Hakvoort T, Nurmohamed M, Wolbink G, Levin E, Li Yim AY, D’Haens G, de Jonge WJ. Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients. Front Immunol 2023; 14:1303231. [PMID: 38187379 PMCID: PMC10771853 DOI: 10.3389/fimmu.2023.1303231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/04/2023] [Indexed: 01/09/2024] Open
Abstract
Background and aims Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL). Methods DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers. Results Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis. Conclusion Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.
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Affiliation(s)
- Ishtu Hageman
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
- Tytgat Institute for Liver and Intestinal Research, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
| | - Femke Mol
- Tytgat Institute for Liver and Intestinal Research, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
| | - Sadaf Atiqi
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center, Vrije Universiteit (VU) University Medical Center, Amsterdam, Netherlands
| | - Vincent Joustra
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
| | - Hilal Sengul
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
| | - Peter Henneman
- Genome Diagnostics Laboratory, Department of Human Genetics, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
| | - Ingrid Visman
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center, Vrije Universiteit (VU) University Medical Center, Amsterdam, Netherlands
| | - Theodorus Hakvoort
- Tytgat Institute for Liver and Intestinal Research, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
| | - Mike Nurmohamed
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center, Vrije Universiteit (VU) University Medical Center, Amsterdam, Netherlands
| | - Gertjan Wolbink
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center, Vrije Universiteit (VU) University Medical Center, Amsterdam, Netherlands
| | - Evgeni Levin
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
- Horaizon BV, Delft, Netherlands
| | - Andrew Y.F. Li Yim
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
- Tytgat Institute for Liver and Intestinal Research, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
- Genome Diagnostics Laboratory, Department of Human Genetics, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
| | - Geert D’Haens
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
| | - Wouter J. de Jonge
- Tytgat Institute for Liver and Intestinal Research, Amsterdam University Medical Centers (UMC), University of Amsterdam, Amsterdam, Netherlands
- Department of Surgery, University of Bonn, Bonn, Germany
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4
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Porter HL, Ansere VA, Undi RB, Hoolehan W, Giles CB, Brown CA, Stanford D, Huycke MM, Freeman WM, Wren JD. Methylation Array Signals are Predictive of Chronological Age Without Bisulfite Conversion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572465. [PMID: 38187520 PMCID: PMC10769286 DOI: 10.1101/2023.12.20.572465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
DNA methylation data has been used to make "epigenetic clocks" which attempt to measure chronological and biological aging. These models rely on data derived from bisulfite-based measurements, which exploit a semi-selective deamination and a genomic reference to determine methylation states. Here, we demonstrate how another hallmark of aging, genomic instability, influences methylation measurements in both bisulfite sequencing and methylation arrays. We found that non-methylation factors lead to "pseudomethylation" signals that are both confounding of epigenetic clocks and uniquely age predictive. Quantifying these covariates in aging studies will be critical to building better clocks and designing appropriate studies of epigenetic aging.
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Affiliation(s)
- Hunter L Porter
- Oklahoma Medical Research Foundation
- University of Oklahoma Health Sciences Center
- Oklahoma Nathan Shock Center
| | - Victor A Ansere
- Oklahoma Medical Research Foundation
- University of Oklahoma Health Sciences Center
| | | | - Walker Hoolehan
- Oklahoma Medical Research Foundation
- University of Oklahoma Health Sciences Center
| | | | - Chase A Brown
- Oklahoma Medical Research Foundation
- University of Oklahoma Health Sciences Center
| | | | | | - Willard M Freeman
- Oklahoma Medical Research Foundation
- University of Oklahoma Health Sciences Center
- Oklahoma Nathan Shock Center
| | - Jonathan D Wren
- Oklahoma Medical Research Foundation
- University of Oklahoma Health Sciences Center
- Oklahoma Nathan Shock Center
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5
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Radhakrishna U, Nath SK, Uppala LV, Veerappa A, Forray A, Muvvala SB, Metpally RP, Crist RC, Berrettini WH, Mausi LM, Vishweswaraiah S, Bahado-Singh RO. Placental microRNA methylome signatures may serve as biomarkers and therapeutic targets for prenatally opioid-exposed infants with neonatal opioid withdrawal syndrome. Front Genet 2023; 14:1215472. [PMID: 37434949 PMCID: PMC10332887 DOI: 10.3389/fgene.2023.1215472] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/01/2023] [Indexed: 07/13/2023] Open
Abstract
Introduction: The neonate exposed to opioids in utero faces a constellation of withdrawal symptoms postpartum commonly called neonatal opioid withdrawal syndrome (NOWS). The incidence of NOWS has increased in recent years due to the opioid epidemic. MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in gene regulation. Epigenetic variations in microRNAs (miRNAs) and their impact on addiction-related processes is a rapidly evolving area of research. Methods: The Illumina Infinium Methylation EPIC BeadChip was used to analyze DNA methylation levels of miRNA-encoding genes in 96 human placental tissues to identify miRNA gene methylation profiles as-sociated with NOWS: 32 from mothers whose prenatally opioid-exposed infants required pharmacologic management for NOWS, 32 from mothers whose prenatally opioid-exposed infants did not require treat-ment for NOWS, and 32 unexposed controls. Results: The study identified 46 significantly differentially methylated (FDR p-value ≤ 0.05) CpGs associated with 47 unique miRNAs, with a receiver operating characteristic (ROC) area under the curve (AUC) ≥0.75 including 28 hypomethylated and 18 hypermethylated CpGs as potentially associated with NOWS. These dysregulated microRNA methylation patterns may be a contributing factor to NOWS pathogenesis. Conclusion: This is the first study to analyze miRNA methylation profiles in NOWS infants and illustrates the unique role miRNAs might have in diagnosing and treating the disease. Furthermore, these data may provide a step toward feasible precision medicine for NOWS babies as well.
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Affiliation(s)
- Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
| | - Swapan K. Nath
- Arthritis and Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
| | - Lavanya V. Uppala
- College of Information Science and Technology, Peter Kiewit Institute, The University of Nebraska at Omaha, Omaha, NE, United States
| | - Avinash Veerappa
- Department of Genetics, Cell Biology and Anatomy College of Medicine, University of Nebraska Medical Center, Omaha, NE, United States
| | - Ariadna Forray
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
| | - Srinivas B. Muvvala
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
| | - Raghu P. Metpally
- Department of Molecular and Functional Genomics, Danville, PA, United States
| | - Richard C. Crist
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Wade H. Berrettini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Geisinger Clinic, Danville, PA, United States
| | - Lori M. Mausi
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
| | - Ray O. Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
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Joustra V, Li Yim AYF, Hageman I, Levin E, Adams A, Satsangi J, de Jonge WJ, Henneman P, D'Haens G. Long-term Temporal Stability of Peripheral Blood DNA Methylation Profiles in Patients With Inflammatory Bowel Disease. Cell Mol Gastroenterol Hepatol 2023; 15:869-885. [PMID: 36581079 PMCID: PMC9972576 DOI: 10.1016/j.jcmgh.2022.12.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/20/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND & AIMS There is great current interest in the potential application of DNA methylation alterations in peripheral blood leukocytes (PBLs) as biomarkers of susceptibility, progression, and treatment response in inflammatory bowel disease (IBD). However, the intra-individual stability of PBL methylation in IBD has not been characterized. Here, we studied the long-term stability of all probes located on the Illumina HumanMethylation EPIC BeadChip array. METHODS We followed a cohort of 46 adult patients with IBD (36 Crohn's disease [CD], 10 ulcerative colitis [UC]; median age, 44 years; interquartile range [IQR] 27-56 years; 50% female) that received standard care follow-up at the Amsterdam University Medical Centers. Paired PBL samples were collected at 2 time points with a median of 7 years (range, 2-9 years) in between. Differential methylation and intra-class correlation (ICC) analyses were used to identify time-associated differences and temporally stable CpGs, respectively. RESULTS Around 60% of all EPIC array loci presented poor intra-individual stability (ICC <0.50); 78.114 (≈9%) showed good (ICC, 0.75-0.89), and 41.274 (≈5%) showed excellent (ICC ≥0.90) stability, between both measured time points. Focusing on previously identified consistently differentially methylated positions indicated that 22 CD-, 11 UC-, and 24 IBD-associated loci demonstrated high stability (ICC ≥0.75) over time; of these, we observed a marked stability of CpG loci associated to the HLA genes. CONCLUSIONS Our data provide insight into the long-term stability of the PBL DNA methylome within an IBD context, facilitating the selection of biologically relevant and robust IBD-associated epigenetic biomarkers with increased potential for independent validation. These data also have potential implications in understanding disease pathogenesis.
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Affiliation(s)
- Vincent Joustra
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Andrew Y F Li Yim
- Genome Diagnostics Laboratory, Department of Human Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Tytgat Institute for Liver and Intestinal Research, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Ishtu Hageman
- Tytgat Institute for Liver and Intestinal Research, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Evgeni Levin
- Department of Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Horaizon BV, Delft, the Netherlands
| | - Alex Adams
- Oxford University- Hospitals NHS Foundation Trust- John Radcliffe Hospital, Translational Gastroenterology Unit- NIHR Oxford Biomedical Research Centre, Oxford, United Kingdom
| | - Jack Satsangi
- Oxford University- Hospitals NHS Foundation Trust- John Radcliffe Hospital, Translational Gastroenterology Unit- NIHR Oxford Biomedical Research Centre, Oxford, United Kingdom
| | - Wouter J de Jonge
- Tytgat Institute for Liver and Intestinal Research, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Peter Henneman
- Genome Diagnostics Laboratory, Department of Human Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Geert D'Haens
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
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de Krijger M, Hageman IL, Li Yim AYF, Verhoeff J, Garcia Vallejo JJ, van Hamersveld PHP, Levin E, Hakvoort TBM, Wildenberg ME, Henneman P, Ponsioen CY, de Jonge WJ. Epigenetic Signatures Discriminate Patients With Primary Sclerosing Cholangitis and Ulcerative Colitis From Patients With Ulcerative Colitis. Front Immunol 2022; 13:840935. [PMID: 35371111 PMCID: PMC8965896 DOI: 10.3389/fimmu.2022.840935] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/14/2022] [Indexed: 12/12/2022] Open
Abstract
Background Primary sclerosing cholangitis (PSC) is a chronic inflammatory liver disease affecting the intra- and extrahepatic bile ducts, and is strongly associated with ulcerative colitis (UC). In this study, we explored the peripheral blood DNA methylome and its immune cell composition in patients with PSC-UC, UC, and healthy controls (HC) with the aim to develop a predictive assay in distinguishing patients with PSC-UC from those with UC alone. Methods The peripheral blood DNA methylome of male patients with PSC and concomitant UC, UC and HCs was profiled using the Illumina HumanMethylation Infinium EPIC BeadChip (850K) array. Differentially methylated CpG position (DMP) and region (DMR) analyses were performed alongside gradient boosting classification analyses to discern PSC-UC from UC patients. As observed differences in the DNA methylome could be the result of differences in cellular populations, we additionally employed mass cytometry (CyTOF) to characterize the immune cell compositions. Results Genome wide methylation analysis did not reveal large differences between PSC-UC and UC patients nor HCs. Nonetheless, using gradient boosting we were capable of discerning PSC-UC from UC with an area under the receiver operator curve (AUROC) of 0.80. Four CpG sites annotated to the NINJ2 gene were found to strongly contribute to the predictive performance. While CyTOF analyses corroborated the largely similar blood cell composition among patients with PSC-UC, UC and HC, a higher abundance of myeloid cells was observed in UC compared to PSC-UC patients. Conclusion DNA methylation enables discerning PSC-UC from UC patients, with a potential for biomarker development.
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Affiliation(s)
- Manon de Krijger
- Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Ishtu L Hageman
- Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Andrew Y F Li Yim
- Department of Clinical Genetics, Genome Diagnostics Laboratory, Amsterdam Reproduction and Development, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Jan Verhoeff
- Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Department of Molecular Cell Biology and Immunology, Amsterdam Infection & Immunity and Cancer Center Amsterdam, Amsterdam University Medical Centers, Free University of Amsterdam, Amsterdam, Netherlands
| | - Juan J Garcia Vallejo
- Department of Molecular Cell Biology and Immunology, Amsterdam Infection & Immunity and Cancer Center Amsterdam, Amsterdam University Medical Centers, Free University of Amsterdam, Amsterdam, Netherlands
| | - Patricia H P van Hamersveld
- Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Evgeni Levin
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Horaizon BV, Delft, Netherlands
| | - Theodorus B M Hakvoort
- Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Manon E Wildenberg
- Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Peter Henneman
- Department of Clinical Genetics, Genome Diagnostics Laboratory, Amsterdam Reproduction and Development, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Cyriel Y Ponsioen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Wouter J de Jonge
- Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Department of Surgery, University Clinic of Bonn, Bonn, Germany
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8
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Murgas KA, Ma Y, Shahidi LK, Mukherjee S, Allen AS, Shibata D, Ryser MD. A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors. Bioinformatics 2021; 38:22-29. [PMID: 34487148 DOI: 10.1093/bioinformatics/btab637] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 07/30/2021] [Accepted: 09/01/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. RESULTS We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model's ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. AVAILABILITY AND IMPLEMENTATION The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kevin A Murgas
- Department of Biomedical Informatics, Stony Brook University School of Medicine, Stony Brook, NY 11794, USA
| | - Yanlin Ma
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Lidea K Shahidi
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Sayan Mukherjee
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
- Department of Computer Science, Duke University, Durham, NC 27708, USA
- Department of Mathematics, Duke University, Durham, NC 27708, USA
- Department of Bioinformatics and Biostatistics, Duke University, Durham, NC 27710, USA
| | - Andrew S Allen
- Department of Bioinformatics and Biostatistics, Duke University, Durham, NC 27710, USA
- Duke Center for Statistical Genetics and Genomics, Duke University, Durham, NC 27710, USA
| | - Darryl Shibata
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Marc D Ryser
- Department of Mathematics, Duke University, Durham, NC 27708, USA
- Department of Population Health Sciences, Duke University Medical Center, Durham, NC 27701, USA
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9
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Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data. Biomedicines 2021; 9:biomedicines9111733. [PMID: 34829962 PMCID: PMC8615388 DOI: 10.3390/biomedicines9111733] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/26/2021] [Accepted: 11/17/2021] [Indexed: 12/25/2022] Open
Abstract
Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.
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10
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Riley M. Critical review of the evidence base regarding theories conceptualising the aetiology of psychosis. ACTA ACUST UNITED AC 2021; 29:1030-1037. [PMID: 32972234 DOI: 10.12968/bjon.2020.29.17.1030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
A critical review of literature related to the aetiology of psychosis was conducted with specific emphasis on genetics. It was found that, although many published articles were retrieved via database searches, the format of the information was disparate in presentation leading to unnecessary inconsistences. This suggests the need for insightful collaboration by authors and standardisation of published articles to prevent academic and specialism barriers remaining as a discouragement to non-specialists wishing to access this information.
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Affiliation(s)
- Miv Riley
- Senior Care Co-ordinator, Early Intervention Service (Psychosis), Lancashire Care Foundation Trust and Manchester University
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11
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Oestreich M, Chen D, Schultze JL, Fritz M, Becker M. Privacy considerations for sharing genomics data. EXCLI JOURNAL 2021; 20:1243-1260. [PMID: 34345236 PMCID: PMC8326502 DOI: 10.17179/excli2021-4002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 07/07/2021] [Indexed: 01/23/2023]
Abstract
An increasing amount of attention has been geared towards understanding the privacy risks that arise from sharing genomic data of human origin. Most of these efforts have focused on issues in the context of genomic sequence data, but the popularity of techniques for collecting other types of genome-related data has prompted researchers to investigate privacy concerns in a broader genomic context. In this review, we give an overview of different types of genome-associated data, their individual ways of revealing sensitive information, the motivation to share them as well as established and upcoming methods to minimize information leakage. We further discuss the concise threats that are being posed, who is at risk, and how the risk level compares to potential benefits, all while addressing the topic in the context of modern technology, methodology, and information sharing culture. Additionally, we will discuss the current legal situation regarding the sharing of genomic data in a selection of countries, evaluating the scope of their applicability as well as their limitations. We will finalize this review by evaluating the development that is required in the scientific field in the near future in order to improve and develop privacy-preserving data sharing techniques for the genomic context.
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Affiliation(s)
- Marie Oestreich
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Venusberg-Campus 1/99, 53127 Bonn, Germany
| | - Dingfan Chen
- CISPA Helmholtz Center for Information Security, Saarbrücken, Germany, Stuhlsatzenhaus 5, 66123 Saarbrücken, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Venusberg-Campus 1/99, 53127 Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany, Carl-Troll-Straße 31, 53115 Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics at Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Germany, Venusberg-Campus 1/99, 53127 Bonn, Germany
| | - Mario Fritz
- CISPA Helmholtz Center for Information Security, Saarbrücken, Germany, Stuhlsatzenhaus 5, 66123 Saarbrücken, Germany
| | - Matthias Becker
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Venusberg-Campus 1/99, 53127 Bonn, Germany
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12
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Chilunga FP, Henneman P, Venema A, Meeks KAC, Gonzalez JR, Ruiz-Arenas C, Requena-Méndez A, Beune E, Spranger J, Smeeth L, Bahendeka S, Owusu-Dabo E, Klipstein-Grobusch K, Adeyemo A, Mannens MMAM, Agyemang C. DNA methylation as the link between migration and the major noncommunicable diseases: the RODAM study. Epigenomics 2021; 13:653-666. [PMID: 33890479 PMCID: PMC8173498 DOI: 10.2217/epi-2020-0329] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 03/29/2021] [Indexed: 01/19/2023] Open
Abstract
Aim: We assessed epigenome-wide DNA methylation (DNAm) differences between migrant and non-migrant Ghanaians. Materials & methods: We used the Illumina Infinium® HumanMethylation450 BeadChip to profile DNAm of 712 Ghanaians in whole blood. We used linear models to detect differentially methylated positions (DMPs) associated with migration. We performed multiple post hoc analyses to validate our findings. Results: We identified 13 DMPs associated with migration (delta-beta values: 0.2-4.5%). Seven DMPs in CPLX2, EIF4E3, MEF2D, TLX3, ST8SIA1, ANG and CHRM3 were independent of extrinsic genomic influences in public databases. Two DMPs in NLRC5 were associated with duration of stay in Europe among migrants. All DMPs were biologically linked to migration-related factors. Conclusion: Our findings provide the first insights into DNAm differences between migrants and non-migrants.
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Affiliation(s)
- Felix P Chilunga
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Peter Henneman
- Department of Clinical Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Andrea Venema
- Department of Clinical Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Karlijn AC Meeks
- Center for Research on Genomics & Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA
| | - Juan R Gonzalez
- Barcelona Institute for Global Health (ISGlobal, University of Barcelona), 08003 Barcelona, Spain
| | - Carlos Ruiz-Arenas
- Barcelona Institute for Global Health (ISGlobal, University of Barcelona), 08003 Barcelona, Spain
| | - Ana Requena-Méndez
- Barcelona Institute for Global Health (ISGlobal, University of Barcelona), 08003 Barcelona, Spain
- Department of Global Public Health, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Erik Beune
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Joachim Spranger
- Department of Endocrinology, Diabetes & Metabolism, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Liam Smeeth
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, 1E 7HT, UK
| | - Silver Bahendeka
- Department of Medicine, MKPGMS-Uganda Martyrs University, 8H33+5M Kampala, Uganda
| | - Ellis Owusu-Dabo
- School of Public Health, Kwame Nkrumah University of Science & Technology, MCFH+R9 Kumasi, Ghana
| | - Kerstin Klipstein-Grobusch
- Julius Global Health, Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, The Netherlands
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of The Witwatersrand, 2193 Johannesburg, South Africa
| | - Adebowale Adeyemo
- Center for Research on Genomics & Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA
| | - Marcel MAM Mannens
- Department of Clinical Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Charles Agyemang
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
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13
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Yao C, Joehanes R, Wilson R, Tanaka T, Ferrucci L, Kretschmer A, Prokisch H, Schramm K, Gieger C, Peters A, Waldenberger M, Marzi C, Herder C, Levy D. Epigenome-wide association study of whole blood gene expression in Framingham Heart Study participants provides molecular insight into the potential role of CHRNA5 in cigarette smoking-related lung diseases. Clin Epigenetics 2021; 13:60. [PMID: 33752734 PMCID: PMC7986283 DOI: 10.1186/s13148-021-01041-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 02/28/2021] [Indexed: 11/28/2022] Open
Abstract
Background DNA methylation is a key epigenetic modification that can directly affect gene regulation. DNA methylation is highly influenced by environmental factors such as cigarette smoking, which is causally related to chronic obstructive pulmonary disease (COPD) and lung cancer. To date, there have been few large-scale, combined analyses of DNA methylation and gene expression and their interrelations with lung diseases. Results We performed an epigenome-wide association study of whole blood gene expression in ~ 6000 individuals from four cohorts. We discovered and replicated numerous CpGs associated with the expression of cis genes within 500 kb of each CpG, with 148 to 1,741 cis CpG-transcript pairs identified across cohorts. We found that the closer a CpG resided to a transcription start site, the larger its effect size, and that 36% of cis CpG-transcript pairs share the same causal genetic variant. Mendelian randomization analyses revealed that hypomethylation and lower expression of CHRNA5, which encodes a smoking-related nicotinic receptor, are causally linked to increased risk of COPD and lung cancer. This putatively causal relationship was further validated in lung tissue data. Conclusions Our results provide a large and comprehensive association study of whole blood DNA methylation with gene expression. Expression platform differences rather than population differences are critical to the replication of cis CpG-transcript pairs. The low reproducibility of trans CpG-transcript pairs suggests that DNA methylation regulates nearby rather than remote gene expression. The putatively causal roles of methylation and expression of CHRNA5 in relation to COPD and lung cancer provide evidence for a mechanistic link between patterns of smoking-related epigenetic variation and lung diseases, and highlight potential therapeutic targets for lung diseases and smoking cessation. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01041-5.
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Affiliation(s)
- Chen Yao
- The Framingham Heart Study, 73 Mt. Wayte Avenue, Framingham, MA, 01702, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Roby Joehanes
- The Framingham Heart Study, 73 Mt. Wayte Avenue, Framingham, MA, 01702, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Rory Wilson
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany
| | - Toshiko Tanaka
- Longitudinal Study Section, National Institute On Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Study Section, National Institute On Aging, Baltimore, MD, USA
| | - Anja Kretschmer
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, München, Germany.,Institute for Neurogenomics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Katharina Schramm
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, 81377, Munich, Germany.,Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, 81377, Munich, Germany
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany
| | - Annette Peters
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Carola Marzi
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research At Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD), Partner Düsseldorf, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Daniel Levy
- The Framingham Heart Study, 73 Mt. Wayte Avenue, Framingham, MA, 01702, USA. .,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.
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14
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Radhakrishna U, Vishweswaraiah S, Uppala LV, Szymanska M, Macknis J, Kumar S, Saleem-Rasheed F, Aydas B, Forray A, Muvvala SB, Mishra NK, Guda C, Carey DJ, Metpally RP, Crist RC, Berrettini WH, Bahado-Singh RO. Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome. Genomics 2021; 113:1127-1135. [PMID: 33711455 DOI: 10.1016/j.ygeno.2021.03.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/29/2020] [Accepted: 03/02/2021] [Indexed: 12/11/2022]
Abstract
Opioid abuse during pregnancy can result in Neonatal Opioid Withdrawal Syndrome (NOWS). We investigated genome-wide methylation analyses of 96 placental tissue samples, including 32 prenatally opioid-exposed infants with NOWS who needed therapy (+Opioids/+NOWS), 32 prenatally opioid-exposed infants with NOWS who did not require treatment (+Opioids/-NOWS), and 32 prenatally unexposed controls (-Opioids/-NOWS, control). Statistics, bioinformatics, Artificial Intelligence (AI), including Deep Learning (DL), and Ingenuity Pathway Analyses (IPA) were performed. We identified 17 dysregulated pathways thought to be important in the pathophysiology of NOWS and reported accurate AI prediction of NOWS diagnoses. The DL had an AUC (95% CI) =0.98 (0.95-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS from the +Opioids/-NOWS group and AUCs (95% CI) =1.00 (1.0-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS versus control and + Opioids/-NOWS group versus controls. This study provides strong evidence of methylation dysregulation of placental tissue in NOWS development.
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Affiliation(s)
- Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA.
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Lavanya V Uppala
- College of Information Science & Technology, University of Nebraska at Omaha, Peter Kiewit Institute, Omaha, NE, USA
| | - Marta Szymanska
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | | | - Sandeep Kumar
- Department of Pathology, Beaumont Health System, Royal Oak, MI, USA
| | - Fozia Saleem-Rasheed
- Department of Newborn Medicine, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Buket Aydas
- Department of Healthcare Analytics, Meridian Health Plans, Detroit, MI, USA
| | - Ariadna Forray
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Nitish K Mishra
- Department of Genetics, Cell Biology & Anatomy College of Medicine, University of Nebraska Medical Center Omaha, NE, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology & Anatomy College of Medicine, University of Nebraska Medical Center Omaha, NE, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Raghu P Metpally
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Richard C Crist
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Wade H Berrettini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Geisinger Clinic, Danville, PA, USA
| | - Ray O Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
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15
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Luo H, Wei W, Ye Z, Zheng J, Xu RH. Liquid Biopsy of Methylation Biomarkers in Cell-Free DNA. Trends Mol Med 2021; 27:482-500. [PMID: 33500194 DOI: 10.1016/j.molmed.2020.12.011] [Citation(s) in RCA: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 12/28/2020] [Accepted: 12/30/2020] [Indexed: 02/09/2023]
Abstract
Liquid biopsies, in particular, analysis of cell-free DNA (cfDNA), have emerged as a promising noninvasive diagnostic approach in oncology. Abnormal distribution of DNA methylation is one of the hallmarks of many cancers and methylation changes occur early during carcinogenesis. Systemic analysis of cfDNA methylation profiles is being developed for cancer early detection, monitoring for minimal residual disease (MRD), predicting treatment response and prognosis, and tracing the tissue origin. This review highlights the advantages and disadvantages of ctDNA profiling for noninvasive diagnosis of early-stage cancers and explores recent advances in the clinical application of ctDNA methylation assays. We also summarize the technologies for ctDNA methylation analysis and provide a brief overview of the bioinformatic approaches for analyzing DNA methylation sequencing data.
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Affiliation(s)
- Huiyan Luo
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Wei Wei
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Ziyi Ye
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Jiabo Zheng
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Rui-Hua Xu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
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16
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DNA Methylation Analysis Identifies Patterns in Progressive Glioma Grades to Predict Patient Survival. Int J Mol Sci 2021; 22:ijms22031020. [PMID: 33498463 PMCID: PMC7864199 DOI: 10.3390/ijms22031020] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/15/2021] [Accepted: 01/17/2021] [Indexed: 12/15/2022] Open
Abstract
DNA methylation is an epigenetic change to the genome that impacts gene activities without modification to the DNA sequence. Alteration in the methylation pattern is a naturally occurring event throughout the human life cycle which may result in the development of diseases such as cancer. In this study, we analyzed methylation data from The Cancer Genome Atlas, under the Lower-Grade Glioma (LGG) and Glioblastoma Multiforme (GBM) projects, to identify methylation markers that exhibit unique changes in DNA methylation pattern along with tumor grade progression, to predict patient survival. We found ten glioma grade-associated Cytosine-phosphate-Guanine (CpG) sites that targeted four genes (SMOC1, KCNA4, SLC25A21, and UPP1) and the methylation pattern is strongly associated with glioma specific molecular alterations, primarily isocitrate dehydrogenase (IDH) mutation and chromosome 1p/19q codeletion. The ten CpG sites collectively distinguished a cohort of diffuse glioma patients with remarkably poor survival probability. Our study highlights genes (KCNA4 and SLC25A21) that were not previously associated with gliomas to have contributed to the poorer patient outcome. These CpG sites can aid glioma tumor progression monitoring and serve as prognostic markers to identify patients diagnosed with less aggressive and malignant gliomas that exhibit similar survival probability to GBM patients.
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17
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Daca-Roszak P, Jaksik R, Paczkowska J, Witt M, Ziętkiewicz E. Discrimination between human populations using a small number of differentially methylated CpG sites: a preliminary study using lymphoblastoid cell lines and peripheral blood samples of European and Chinese origin. BMC Genomics 2020; 21:706. [PMID: 33045984 PMCID: PMC7549247 DOI: 10.1186/s12864-020-07092-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 09/22/2020] [Indexed: 02/08/2023] Open
Abstract
Background Epigenetics is one of the factors shaping natural variability observed among human populations. A small proportion of heritable inter-population differences are observed in the context of both the genome-wide methylation level and the methylation status of individual CpG sites. It has been demonstrated that a limited number of carefully selected differentially methylated sites may allow discrimination between main human populations. However, most of the few published results have been performed exclusively on B-lymphocyte cell lines. Results The goal of our study was to identify a set of CpG sites sufficient to discriminate between populations of European and Chinese ancestry based on the difference in the DNA methylation profile not only in cell lines but also in primary cell samples. The preliminary selection of CpG sites differentially methylated in these two populations (pop-CpGs) was based on the analysis of two groups of commercially available ethnically-specific B-lymphocyte cell lines, performed using Illumina Infinium Human Methylation 450 BeadChip Array. A subset of 10 pop-CpGs characterized by the best differentiating criteria (|Mdiff| > 1, q < 0.05; lack of the confounding genomic features), and 10 additional CpGs in their immediate vicinity, were further tested using pyrosequencing technology in both B-lymphocyte cell lines and in the primary samples of the peripheral blood representing two analyzed populations. To assess the population-discriminating potential of the selected set of CpGs (further referred to as “composite pop (CEU-CHB)-CpG marker”), three classification methods were applied. The predictive ability of the composite 8-site pop (CEU-CHB)-CpG marker was assessed using 10-fold cross-validation method on two independent sets of samples. Conclusions Our results showed that less than 10 pop-CpG sites may distinguish populations of European and Chinese ancestry; importantly, this small composite pop-CpG marker performs well in both lymphoblastoid cell lines and in non-homogenous blood samples regardless of a gender.
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Affiliation(s)
- Patrycja Daca-Roszak
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszynska 32, 60-479, Poznan, Poland.
| | - Roman Jaksik
- Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
| | - Julia Paczkowska
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszynska 32, 60-479, Poznan, Poland
| | - Michał Witt
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszynska 32, 60-479, Poznan, Poland
| | - Ewa Ziętkiewicz
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszynska 32, 60-479, Poznan, Poland
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18
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LaBarre BA, Goncearenco A, Petrykowska HM, Jaratlerdsiri W, Bornman MSR, Hayes VM, Elnitski L. MethylToSNP: identifying SNPs in Illumina DNA methylation array data. Epigenetics Chromatin 2019; 12:79. [PMID: 31861999 PMCID: PMC6923858 DOI: 10.1186/s13072-019-0321-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 12/09/2019] [Indexed: 12/16/2022] Open
Abstract
Background Current array-based methods for the measurement of DNA methylation rely on the process of sodium bisulfite conversion to differentiate between methylated and unmethylated cytosine bases in DNA. In the absence of genotype data this process can lead to ambiguity in data interpretation when a sample has polymorphisms at a methylation probe site. A common way to minimize this problem is to exclude such potentially problematic sites, with some methods removing as much as 60% of array probes from consideration before data analysis. Results Here, we present an algorithm implemented in an R Bioconductor package, MethylToSNP, which detects a characteristic data pattern to infer sites likely to be confounded by polymorphisms. Additionally, the tool provides a stringent reliability score to allow thresholding on SNP predictions. We calibrated parameters and thresholds used by the algorithm on simulated and real methylation data sets. We illustrate findings using methylation data from YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent) and KhoeSan (southern African) populations. Our polymorphism predictions made using MethylToSNP have been validated through SNP databases and bisulfite and genomic sequencing. Conclusions The benefits of this method are threefold. First, it prevents extensive data loss by considering only SNPs specific to the individuals in the study. Second, it offers the possibility to identify new polymorphisms in samples for which there is little known about the genetic landscape. Third, it identifies variants as they exist in functional regions of a genome, such as in CTCF (transcriptional repressor) sites and enhancers, that may be common alleles or personal mutations with potential to deleteriously affect genomic regulatory activities. We demonstrate that MethylToSNP is applicable to the Illumina 450K and Illumina 850K EPIC array data and is also backwards compatible to the 27K methylation arrays. Going forward, this kind of nuanced approach can increase the amount of information derived from precious data sets by considering samples of the project individually to enable more informed decisions about data cleaning.
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Affiliation(s)
- Brenna A LaBarre
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA.,Genomic Functional Analysis Section, Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 49 Convent Dr., Bethesda, MD, 20892, USA
| | - Alexander Goncearenco
- Genomic Functional Analysis Section, Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 49 Convent Dr., Bethesda, MD, 20892, USA
| | - Hanna M Petrykowska
- Genomic Functional Analysis Section, Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 49 Convent Dr., Bethesda, MD, 20892, USA
| | - Weerachai Jaratlerdsiri
- Laboratory for Human Comparative & Prostate Cancer Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - M S Riana Bornman
- School of Health Systems and Public Health, University of Pretoria, Hatfield, Pretoria, South Africa
| | - Vanessa M Hayes
- Laboratory for Human Comparative & Prostate Cancer Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,School of Health Systems and Public Health, University of Pretoria, Hatfield, Pretoria, South Africa.,Sydney Medical School, University of Sydney, Camperdown, NSW, Australia
| | - Laura Elnitski
- Genomic Functional Analysis Section, Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 49 Convent Dr., Bethesda, MD, 20892, USA.
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19
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Bahado-Singh RO, Vishweswaraiah S, Aydas B, Mishra NK, Yilmaz A, Guda C, Radhakrishna U. Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism. Brain Res 2019; 1724:146457. [DOI: 10.1016/j.brainres.2019.146457] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 09/10/2019] [Accepted: 09/11/2019] [Indexed: 01/05/2023]
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20
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Kader F, Ghai M, Olaniran AO. Characterization of DNA methylation-based markers for human body fluid identification in forensics: a critical review. Int J Legal Med 2019; 134:1-20. [PMID: 31713682 DOI: 10.1007/s00414-019-02181-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 10/15/2019] [Indexed: 02/07/2023]
Abstract
Body fluid identification in crime scene investigations aids in reconstruction of crime scenes. Several studies have identified and reported differentially methylated sites (DMSs) and regions (DMRs) which differ between forensically relevant tissues (tDMRs) and body fluids. Diverse factors affect methylation patterns such as the environment, diets, lifestyle, disease, ethnicity, genetic variation, amongst others. Thus, it is important to analyse the stability of markers employed for forensic identification. Furthermore, even though epigenetic modifications are described as stable and heritable, epigenetic inheritance of potential markers for body fluid identification needs to be assessed in the long term. Here, we discuss the current status of reported DNA methylation-based markers and their verification studies. Such thorough investigation is crucial to develop a stable panel of DNA methylation-based markers for accurate body fluid identification.
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Affiliation(s)
- Farzeen Kader
- Discipline of Genetics, School of Life Sciences, College of Agriculture, Engineering and Science, University of KwaZulu-Natal (Westville Campus), Private Bag X54001, Durban, Republic of South Africa
| | - Meenu Ghai
- Discipline of Genetics, School of Life Sciences, College of Agriculture, Engineering and Science, University of KwaZulu-Natal (Westville Campus), Private Bag X54001, Durban, Republic of South Africa.
| | - Ademola O Olaniran
- Discipline of Microbiology, School of Life Sciences, College of Agriculture, Engineering and Science, University of KwaZulu-Natal (Westville Campus), Private Bag X54001, Durban, Republic of South Africa
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21
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Huang J, Wang L. Cell-Free DNA Methylation Profiling Analysis-Technologies and Bioinformatics. Cancers (Basel) 2019; 11:cancers11111741. [PMID: 31698791 PMCID: PMC6896050 DOI: 10.3390/cancers11111741] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 12/24/2022] Open
Abstract
Analysis of circulating nucleic acids in bodily fluids, referred to as “liquid biopsies”, is rapidly gaining prominence. Studies have shown that cell-free DNA (cfDNA) has great potential in characterizing tumor status and heterogeneity, as well as the response to therapy and tumor recurrence. DNA methylation is an epigenetic modification that plays an important role in a broad range of biological processes and diseases. It is well known that aberrant DNA methylation is generalizable across various samples and occurs early during the pathogenesis of cancer. Methylation patterns of cfDNA are also consistent with their originated cells or tissues. Systemic analysis of cfDNA methylation profiles has emerged as a promising approach for cancer detection and origin determination. In this review, we will summarize the technologies for DNA methylation analysis and discuss their feasibility for liquid biopsy applications. We will also provide a brief overview of the bioinformatic approaches for analysis of DNA methylation sequencing data. Overall, this review provides informative guidance for the selection of experimental and computational methods in cfDNA methylation-based studies.
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22
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Artificial Intelligence and the detection of pediatric concussion using epigenomic analysis. Brain Res 2019; 1726:146510. [PMID: 31628932 DOI: 10.1016/j.brainres.2019.146510] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 10/14/2019] [Accepted: 10/15/2019] [Indexed: 12/12/2022]
Abstract
Concussion, also referred to as mild traumatic brain injury (mTBI) is the most common type of traumatic brain injury. Currently concussion is an area ofintensescientific interest to better understand the biological mechanisms and for biomarker development. We evaluated whole genome-wide blood DNA cytosine ('CpG') methylation in 17 pediatric concussion isolated cases and 18 unaffected controls using Illumina Infinium MethylationEPIC assay. Pathway analysis was performed using Ingenuity Pathway Analysis to help elucidate the epigenetic and molecular mechanisms of the disorder. Area under the receiver operating characteristics (AUC) curves and FDR p-values were calculated for mTBI detection based on CpG methylation levels. Multiple Artificial Intelligence (AI) platforms including Deep Learning (DL), the newest form of AI, were used to predict concussion based on i) CpG methylation markers alone, and ii) combined epigenetic, clinical and demographic predictors. We found 449 CpG sites (473 genes), those were statistically significantly methylated in mTBI compared to controls. There were four CpGs with excellent individual accuracy (AUC ≥ 0.90-1.00) while 119 displayed good accuracy (AUC ≥ 0.80-0.89) for the prediction of mTBI. The CpG methylation changes ≥10% were observed in many CpG loci after concussion suggesting biological significance. Pathway analysis identified several biologically important neurological pathways that were perturbed including those associated with: impaired brain function, cognition, memory, neurotransmission, intellectual disability and behavioral change and associated disorders. The combination of epigenomic and clinical predictors were highly accurate for the detection of concusion using AI techniques. Using DL/AI, a combination of epigenomic and clinical markers had sensitivity and specificity ≧95% for prediction of mTBI. In this novel study, we identified significant methylation changes in multiple genes in response to mTBI. Gene pathways that were epigenetically dysregulated included several known to be involved in neurological function, thus giving biological plausibility to our findings.
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23
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Lanata CM, Paranjpe I, Nititham J, Taylor KE, Gianfrancesco M, Paranjpe M, Andrews S, Chung SA, Rhead B, Barcellos LF, Trupin L, Katz P, Dall'Era M, Yazdany J, Sirota M, Criswell LA. A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus. Nat Commun 2019; 10:3902. [PMID: 31467281 PMCID: PMC6715644 DOI: 10.1038/s41467-019-11845-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 07/13/2019] [Indexed: 01/05/2023] Open
Abstract
Systemic lupus erythematous (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. Here, we aim to identify SLE subgroups within a multiethnic cohort using an unsupervised clustering approach based on the American College of Rheumatology (ACR) classification criteria. We identify three patient clusters that vary according to disease severity. Methylation association analysis identifies a set of 256 differentially methylated CpGs across clusters, including 101 CpGs in genes in the Type I Interferon pathway, and we validate these associations in an external cohort. A cis-methylation quantitative trait loci analysis identifies 744 significant CpG-SNP pairs. The methylation signature is enriched for ethnic-associated CpGs suggesting that genetic and non-genetic factors may drive outcomes and ethnic-associated methylation differences. Our computational approach highlights molecular differences associated with clusters rather than single outcome measures. This work demonstrates the utility of applying integrative methods to address clinical heterogeneity in multifactorial multi-ethnic disease settings.
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Affiliation(s)
- Cristina M Lanata
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Ishan Paranjpe
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joanne Nititham
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Kimberly E Taylor
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Milena Gianfrancesco
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Manish Paranjpe
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Shan Andrews
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Sharon A Chung
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | | | | | - Laura Trupin
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Patricia Katz
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Maria Dall'Era
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Jinoos Yazdany
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Lindsey A Criswell
- Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
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24
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Yuan V, Price EM, Del Gobbo G, Mostafavi S, Cox B, Binder AM, Michels KB, Marsit C, Robinson WP. Accurate ethnicity prediction from placental DNA methylation data. Epigenetics Chromatin 2019; 12:51. [PMID: 31399127 PMCID: PMC6688210 DOI: 10.1186/s13072-019-0296-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 07/22/2019] [Indexed: 12/19/2022] Open
Abstract
Background The influence of genetics on variation in DNA methylation (DNAme) is well documented. Yet confounding from population stratification is often unaccounted for in DNAme association studies. Existing approaches to address confounding by population stratification using DNAme data may not generalize to populations or tissues outside those in which they were developed. To aid future placental DNAme studies in assessing population stratification, we developed an ethnicity classifier, PlaNET (Placental DNAme Elastic Net Ethnicity Tool), using five cohorts with Infinium Human Methylation 450k BeadChip array (HM450k) data from placental samples that is also compatible with the newer EPIC platform. Results Data from 509 placental samples were used to develop PlaNET and show that it accurately predicts (accuracy = 0.938, kappa = 0.823) major classes of self-reported ethnicity/race (African: n = 58, Asian: n = 53, Caucasian: n = 389), and produces ethnicity probabilities that are highly correlated with genetic ancestry inferred from genome-wide SNP arrays (> 2.5 million SNP) and ancestry informative markers (n = 50 SNPs). PlaNET’s ethnicity classification relies on 1860 HM450K microarray sites, and over half of these were linked to nearby genetic polymorphisms (n = 955). Our placental-optimized method outperforms existing approaches in assessing population stratification in placental samples from individuals of Asian, African, and Caucasian ethnicities. Conclusion PlaNET provides an improved approach to address population stratification in placental DNAme association studies. The method can be applied to predict ethnicity as a discrete or continuous variable and will be especially useful when self-reported ethnicity information is missing and genotyping markers are unavailable. Electronic supplementary material The online version of this article (10.1186/s13072-019-0296-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Victor Yuan
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada.,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada
| | - E Magda Price
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada.,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada
| | - Giulia Del Gobbo
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada.,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada
| | - Sara Mostafavi
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada.,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada.,Department of Statistics, University of British Columbia, 3182 Earth Sciences Building, 2207 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Brian Cox
- Department of Physiology, University of Toronto, Medical Sciences Building, 3rd Floor, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada
| | - Alexandra M Binder
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | - Carmen Marsit
- Department of Environmental Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, USA
| | - Wendy P Robinson
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada. .,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada.
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25
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Mäkinen H, Viitaniemi HM, Visser ME, Verhagen I, van Oers K, Husby A. Temporally replicated DNA methylation patterns in great tit using reduced representation bisulfite sequencing. Sci Data 2019; 6:136. [PMID: 31341168 PMCID: PMC6656709 DOI: 10.1038/s41597-019-0136-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 06/19/2019] [Indexed: 12/28/2022] Open
Abstract
Seasonal timing of reproduction is an important fitness trait in many plants and animals but the underlying molecular mechanism for this trait is poorly known. DNA methylation is known to affect timing of reproduction in various organisms and is therefore a potential mechanism also in birds. Here we describe genome wide data aiming to detect temporal changes in methylation in relation to timing of breeding using artificial selection lines of great tits (Parus major) exposed to contrasting temperature treatments. Methylation levels of DNA extracted from erythrocytes were examined using reduced representation bisulfite sequencing (RRBS). In total, we obtained sequencing data from 63 libraries over four different time points from 16 birds with on average 20 million quality filtered reads per library. These data describe individual level temporal variation in DNA methylation throughout the breeding season under experimental temperature regimes and provides a resource for future studies investigating the role of temporal changes in DNA methylation in timing of reproduction.
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Affiliation(s)
- Hannu Mäkinen
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland.
- Evolutionary Biology, Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden.
- Centre for Biodiversity Dynamics, Department of Biology, NTNU, Trondheim, Norway.
| | - Heidi M Viitaniemi
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
| | - Marcel E Visser
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Irene Verhagen
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Kees van Oers
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Arild Husby
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland.
- Evolutionary Biology, Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden.
- Centre for Biodiversity Dynamics, Department of Biology, NTNU, Trondheim, Norway.
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Comparative DNA methylomic analyses reveal potential origins of novel epigenetic biomarkers of insulin resistance in monocytes from virally suppressed HIV-infected adults. Clin Epigenetics 2019; 11:95. [PMID: 31253200 PMCID: PMC6599380 DOI: 10.1186/s13148-019-0694-1] [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] [Received: 03/15/2019] [Accepted: 06/11/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Compared to healthy individuals, those with stably repressed HIV experience a higher risk of developing insulin resistance, a hallmark of pre-diabetes and a major determinant for cardiometabolic diseases. Although epigenetic processes, including in particular DNA methylation, appear to be dysregulated in individuals with insulin resistance, little is known about where these occur in the genomes of immune cells and the origins of these alterations in HIV-infected individuals. Here, we examined the genome-wide DNA methylation states of monocytes in HIV-infected individuals (n = 37) with varying levels of insulin sensitivity measured by the homeostatic model assessment of insulin resistance (HOMA-IR). RESULTS By profiling DNA methylation at single-nucleotide resolution using the Illumina Infinium HumanMethylation450 BeadChip in monocytes from insulin-resistant (IR; HOMA-IR ≥ 2.0; n = 14) and insulin-sensitive (IS; HOMA-IR < 2.0; n = 23) individuals, we identified 123 CpGs with significantly different DNA methylation levels. These CpGs were enriched at genes involved in pathways relating to glucose metabolism, immune activation, and insulin-relevant signaling, with the majority (86.2%) being hypomethylated in IR relative to IS individuals. Using a stepwise multiple logistic regression analysis, we observed 4 CpGs (cg27655935, cg02000426, cg10184328, and cg23085143) whose methylation levels independently predicted the insulin-resistant state at a higher confidence than that of clinical risk factors typically associated with insulin resistance (i.e., fasting glucose, 120-min oral glucose tolerance test, Framingham Risk Score, and Total to HDL cholesterol ratio). Interestingly, 79 of the 123 CpGs (64%) exhibited remarkably similar levels of methylation as that of hematopoietic stem cells (HSC) in monocytes from IR individuals, implicating epigenetic defects in myeloid differentiation as a possible origin for the methylation landscape underlying the insulin resistance phenotype. In support of this, gene ontology analysis of these 79 CpGs revealed overrepresentation of these CpGs at genes relevant to HSC function, including involvement in stem cell pluripotency, differentiation, and Wnt signaling pathways. CONCLUSION Altogether, our data suggests a possible role for DNA methylation in regulating monocyte activity that may associate with the insulin-resistant phenotype. The methylomic landscape of insulin resistance in monocytes could originate from epigenetic dysregulation during HSC differentiation through the myeloid lineage. Understanding the factors involved with changes in the myeloid trajectory may provide further insight into the development of insulin resistance. Furthermore, regulation of specific genes that were implicated in our analysis reveal possible targets for modulating immune activity to ameliorate insulin resistance.
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27
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Lu YH, Wang BH, Jiang F, Mo XB, Wu LF, He P, Lu X, Deng FY, Lei SF. Multi-omics integrative analysis identified SNP-methylation-mRNA: Interaction in peripheral blood mononuclear cells. J Cell Mol Med 2019; 23:4601-4610. [PMID: 31106970 PMCID: PMC6584519 DOI: 10.1111/jcmm.14315] [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: 10/16/2018] [Revised: 02/18/2019] [Accepted: 03/14/2019] [Indexed: 11/29/2022] Open
Abstract
Genetic variants have potential influence on DNA methylation and thereby regulate mRNA expression. This study aimed to comprehensively reveal the relationships among SNP, methylation and mRNA, and identify methylation-mediated regulation patterns in human peripheral blood mononuclear cells (PBMCs). Based on in-house multi-omics datasets from 43 Chinese Han female subjects, genome-wide association trios were constructed by simultaneously testing the following three association pairs: SNP-methylation, methylation-mRNA and SNP-mRNA. Causal inference test (CIT) was used to identify methylation-mediated genetic effects on mRNA. A total of 64,184 significant cis-methylation quantitative trait loci (meQTLs) were identified (FDR < 0.05). Among the 745 constructed trios, 464 trios formed SNP-methylation-mRNA regulation chains (CIT). Network analysis (Cytoscape 3.3.0) constructed multiple complex regulation networks among SNP, methylation and mRNA (eg a total of 43 SNPs simultaneously connected to cg22517527 and further to PRMT2, DIP2A and YBEY). The regulation chains were supported by the evidence from 4DGenome database, relevant to immune or inflammatory related diseases/traits, and overlapped with previous eQTLs from dbGaP and GTEx. The results provide new insights into the regulation patterns among SNP, DNA methylation and mRNA expression, especially for the methylation-mediated effects, and also increase our understanding of functional mechanisms underlying the established associations.
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Affiliation(s)
- Yi-Hua Lu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Department of Epidemiology and Health Statistics, School of Public Health, Nantong University, Nantong, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
| | - Bing-Hua Wang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
| | - Fei Jiang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
| | - Xing-Bo Mo
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
| | - Long-Fei Wu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
| | - Pei He
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
| | - Xin Lu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
| | - Fei-Yan Deng
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
| | - Shu-Feng Lei
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, P. R. China
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Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy. Int J Mol Sci 2019; 20:ijms20092075. [PMID: 31035542 PMCID: PMC6539236 DOI: 10.3390/ijms20092075] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 03/29/2019] [Accepted: 04/17/2019] [Indexed: 02/07/2023] Open
Abstract
The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this improves long term outcomes. We performed genome-wide DNA methylation analysis to identify epigenomic predictors of CP in newborns and to investigate disease pathogenesis. Methylation analysis of newborn blood DNA using an Illumina HumanMethylation450K array was performed in 23 CP cases and 21 unaffected controls. There were 230 significantly differentially-methylated CpG loci in 258 genes. Each locus had at least 2.0-fold change in methylation in CP versus controls with a FDR p-value ≤ 0.05. Methylation level for each CpG locus had an area under the receiver operating curve (AUC) ≥ 0.75 for CP detection. Using Artificial Intelligence (AI) platforms/Machine Learning (ML) analysis, CpG methylation levels in a combination of 230 significantly differentially-methylated CpG loci in 258 genes had a 95% sensitivity and 94.4% specificity for newborn prediction of CP. Using pathway analysis, multiple canonical pathways plausibly linked to neuronal function were over-represented. Altered biological processes and functions included: neuromotor damage, malformation of major brain structures, brain growth, neuroprotection, neuronal development and de-differentiation, and cranial sensory neuron development. In conclusion, blood leucocyte epigenetic changes analyzed using AI/ML techniques appeared to accurately predict CP and provided plausible mechanistic information on CP pathogenesis.
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29
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Radhakrishna U, Albayrak S, Zafra R, Baraa A, Vishweswaraiah S, Veerappa AM, Mahishi D, Saiyed N, Mishra NK, Guda C, Ali-Fehmi R, Bahado-Singh RO. Placental epigenetics for evaluation of fetal congenital heart defects: Ventricular Septal Defect (VSD). PLoS One 2019; 14:e0200229. [PMID: 30897084 PMCID: PMC6428297 DOI: 10.1371/journal.pone.0200229] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 03/11/2019] [Indexed: 12/19/2022] Open
Abstract
Ventricular Septal Defect (VSD), the most common congenital heart defect, is characterized by a hole in the septum between the right and left ventricles. The pathogenesis of VSD is unknown in most clinical cases. There is a paucity of data relevant to epigenetic changes in VSD. The placenta is a fetal tissue crucial in cardiac development and a potentially useful surrogate for evaluating the development of heart tissue. To understand epigenetic mechanisms that may play a role in the development of VSD, genome-wide DNA methylation assay on placentas of 8 term subjects with isolated VSD and no known or suspected genetic syndromes and 10 unaffected controls was performed using the Illumina HumanMethylation450 BeadChip assay. We identified a total of 80 highly accurate potential CpGs in 80 genes for detection of VSD; area under the receiver operating characteristic curve (AUC ROC) 1.0 with significant 95% CI (FDR) p-values < 0.05 for each individual locus. The biological processes and functions for many of these differentially methylated genes are previously known to be associated with heart development or disease, including cardiac ventricle development (HEY2, ISL1), heart looping (SRF), cardiac muscle cell differentiation (ACTC1, HEY2), cardiac septum development (ISL1), heart morphogenesis (SRF, HEY2, ISL1, HEYL), Notch signaling pathway (HEY2, HEYL), cardiac chamber development (ISL1), and cardiac muscle tissue development (ACTC1, ISL1). In addition, we identified 8 microRNAs that have the potential to be biomarkers for the detection of VSD including: miR-191, miR-548F1, miR-148A, miR-423, miR-92B, miR-611, miR-2110, and miR-548H4. To our knowledge this is the first report in which placental analysis has been used for determining the pathogenesis of and predicting VSD.
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Affiliation(s)
- Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, United States of America
- * E-mail:
| | - Samet Albayrak
- Department of Obstetrics and Gynaecology, Wayne State University School of Medicine, Detroit, Michigan, United States of America
| | - Rita Zafra
- Department of Obstetrics and Gynaecology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Alosh Baraa
- Department of Pathology, Wayne State University School of Medicine, Detroit, Michigan, United States of America
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, United States of America
| | - Avinash M. Veerappa
- Department of Studies in Genetics and Genomics, Laboratory of Genomic Sciences, University of Mysore, Mysore, India
| | - Deepthi Mahishi
- Department of Studies in Genetics and Genomics, Laboratory of Genomic Sciences, University of Mysore, Mysore, India
| | - Nazia Saiyed
- Biotechnology, Nirma Institute of Science, Nirma University, Ahmedabad, India
| | - Nitish K. Mishra
- Department of Genetics, Cell Biology & Anatomy, College of Medicine, University of Nebraska Medical Centre Omaha, Nebraska, United States of America
| | - Chittibabu Guda
- Department of Genetics, Cell Biology & Anatomy, College of Medicine, University of Nebraska Medical Centre Omaha, Nebraska, United States of America
| | - Rouba Ali-Fehmi
- Department of Pathology, Wayne State University School of Medicine, Detroit, Michigan, United States of America
| | - Ray O. Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, United States of America
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Krzyzewska IM, Alders M, Maas SM, Bliek J, Venema A, Henneman P, Rezwan FI, Lip KVD, Mul AN, Mackay DJG, Mannens MMAM. Genome-wide methylation profiling of Beckwith-Wiedemann syndrome patients without molecular confirmation after routine diagnostics. Clin Epigenetics 2019; 11:53. [PMID: 30898153 PMCID: PMC6429826 DOI: 10.1186/s13148-019-0649-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 03/06/2019] [Indexed: 11/16/2022] Open
Abstract
Beckwith-Wiedemann syndrome (BWS) is caused due to the disturbance of imprinted genes at chromosome 11p15. The molecular confirmation of this syndrome is possible in approximately 85% of the cases, whereas in the remaining 15% of the cases, the underlying defect remains unclear. The goal of our research was to identify new epigenetic loci related to BWS. We studied a group of 25 patients clinically diagnosed with BWS but without molecular conformation after DNA diagnostics and performed a whole genome methylation analysis using the HumanMethylation450 Array (Illumina).We found hypermethylation throughout the methylome in two BWS patients. The hypermethylated sites in these patients overlapped and included both non-imprinted and imprinted regions. This finding was not previously described in any BWS-diagnosed patient.Furthermore, one BWS patient exhibited aberrant methylation in four maternally methylated regions-IGF1R, NHP2L1, L3MBTL, and ZDBF2-that overlapped with the differentially methylated regions found in BWS patients with multi-locus imprinting disturbance (MLID). This finding suggests that the BWS phenotype can result from MLID without detectable methylation defects in the primarily disease-associated loci (11p15). Another patient manifested small but significant aberrant methylation in disease-associated loci at 11p near H19, possibly confirming the diagnosis in this patient.
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Affiliation(s)
- I M Krzyzewska
- Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction & Development, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - M Alders
- Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction & Development, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - S M Maas
- Amsterdam UMC, University of Amsterdam, Department of Pediatrics, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - J Bliek
- Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction & Development, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - A Venema
- Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction & Development, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - P Henneman
- Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction & Development, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - F I Rezwan
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - K V D Lip
- Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction & Development, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - A N Mul
- Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction & Development, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - D J G Mackay
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - M M A M Mannens
- Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction & Development, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
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31
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Epigenetically dysregulated genes and pathways implicated in the pathogenesis of non-syndromic high myopia. Sci Rep 2019; 9:4145. [PMID: 30858441 PMCID: PMC6411983 DOI: 10.1038/s41598-019-40299-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/30/2018] [Indexed: 12/20/2022] Open
Abstract
Myopia, commonly referred to as nearsightedness, is one of the most common causes of visual disability throughout the world. It affects more people worldwide than any other chronic visual impairment condition. Although the prevalence varies among various ethnic groups, the incidence of myopia is increasing in all populations across globe. Thus, it is considered a pressing public health problem. Both genetics and environment play a role in development of myopia. To elucidate the epigenetic mechanism(s) underlying the pathophysiology of high-myopia, we conducted methylation profiling in 18 cases and 18 matched controls (aged 4–12 years), using Illumina MethylationEPIC BeadChips array. The degree of myopia was variable among subjects, ranging from −6 to −15D. We identified 1541 hypermethylated CpGs, representing 1745 genes (2.0-fold or higher) (false discovery rate (FDR) p ≤ 0.05), multiple CpGs were p < 5 × 10−8 with a receiver operating characteristic area under the curve (ROC-AUC) ≥ 0.75 in high-myopia subjects compared to controls. Among these, 48 CpGs had excellent correlation (AUC ≥ 0.90). Herein, we present the first genome-wide DNA methylation analysis in a unique high-myopia cohort, showing extensive and discrete methylation changes relative to controls. The genes we identified hold significant potential as targets for novel therapeutic intervention either alone, or in combination.
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32
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Yu H, Bai L, Tang G, Wang X, Huang M, Cao G, Wang J, Luo Y. Novel Assay for Quantitative Analysis of DNA Methylation at Single-Base Resolution. Clin Chem 2019; 65:664-673. [PMID: 30737203 DOI: 10.1373/clinchem.2018.298570] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 01/22/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND The DNA methylation profile provides valuable biological information with potential clinical utility. Several methods, such as quantitative methylation-specific PCR (qMSP), have been developed to examine methylation of specific CpG sites. Existing qMSP-based techniques fail to examine the genomic methylation at a single-base resolution, particularly for loci in gene bodies or extensive CpG open seas lacking flanking CpGs. Therefore, we established a novel assay for quantitative analysis of single-base methylation. METHODS To achieve a robust single-base specificity, we developed a PCR-based method using paired probes following bisulfite treatment. The 6-carboxyfluorescein- and 2'-chloro-7'phenyl-1,4-dichloro-6-carboxy-fluorescein-labeled probes conjugated with minor groove binder were designed to specifically bind to the methylated and unmethylated allele of targeted single CpGs at their 3' half regions, respectively. The methylation percentage was calculated by values of methylation / (methylation + unmethylation). RESULTS In the detection of single CpGs within promoters or bodies of 4 human genes, the quantitative analysis of the single-base methylation assay showed a detection capability in the 1 to 1:10000 dilution experiments with linearity over 4 orders of magnitude (R 2 = 0.989-0.994; all P < 0.001). In a cohort of 10 colorectal cancer samples, the assay showed a comparable detection performance with bisulfite pyrosequencing (R 2 = 0.875-0.990; all P < 0.001), which was better than conventional qMSP methods normalized by input control reaction (R 2 = 0.841 vs 0.769; P = 0.002 vs 0.009). CONCLUSIONS This assay is highly specific and sensitive for determining single-base methylation and, thus, is potentially useful for methylation-based panels in diagnostic and prognostic applications.
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Affiliation(s)
- Huichuan Yu
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Liangliang Bai
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Guannan Tang
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaolin Wang
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Meijin Huang
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Guangwen Cao
- Department of Epidemiology, Second Military Medical University, Shanghai, China
| | - Jianping Wang
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.,Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yanxin Luo
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; .,Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
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33
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Porter LF, Saptarshi N, Fang Y, Rathi S, den Hollander AI, de Jong EK, Clark SJ, Bishop PN, Olsen TW, Liloglou T, Chavali VRM, Paraoan L. Whole-genome methylation profiling of the retinal pigment epithelium of individuals with age-related macular degeneration reveals differential methylation of the SKI, GTF2H4, and TNXB genes. Clin Epigenetics 2019; 11:6. [PMID: 30642396 PMCID: PMC6332695 DOI: 10.1186/s13148-019-0608-2] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 01/02/2019] [Indexed: 12/13/2022] Open
Abstract
Background Age-related macular degeneration (AMD) is a degenerative disorder of the central retina and the foremost cause of blindness. The retinal pigment epithelium (RPE) is a primary site of disease pathogenesis. The genetic basis of AMD is relatively well understood; however, this knowledge is yet to yield a treatment for the most prevalent non-neovascular disease forms. Therefore, tissue-specific epigenetic mechanisms of gene regulation are of considerable interest in AMD. We aimed to identify differentially methylated genes associated with AMD in the RPE and differentiate local DNA methylation aberrations from global DNA methylation changes, as local DNA methylation changes may be more amenable to therapeutic manipulation. Methods Epigenome-wide association study and targeted gene expression profiling were carried out in RPE cells from eyes of human donors. We performed genome-wide DNA methylation profiling (Illumina 450k BeadChip array) on RPE cells from 44 human donor eyes (25 AMD and 19 normal controls). We validated the findings using bisulfite pyrosequencing in 55 RPE samples (30 AMD and 25 normal controls) including technical (n = 38) and independent replicate samples (n = 17). Long interspersed nucleotide element 1 (LINE-1) analysis was then applied to assess global DNA methylation changes in the RPE. RT-qPCR on independent donor RPE samples was performed to assess gene expression changes. Results Genome-wide DNA methylation profiling identified differential methylation of multiple loci including the SKI proto-oncogene (SKI) (p = 1.18 × 10−9), general transcription factor IIH subunit H4 (GTF2H4) (p = 7.03 × 10−7), and Tenascin X (TNXB) (p = 6.30 × 10−6) genes in AMD. Bisulfite pyrosequencing validated the differentially methylated locus cg18934822 in SKI, and cg22508626 within GTF2H4, and excluded global DNA methylation changes in the RPE in AMD. We further demonstrated the differential expression of SKI, GTF2H4, and TNXB in the RPE of independent AMD donors. Conclusions We report the largest genome-wide methylation analysis of RPE in AMD along with associated gene expression changes to date, for the first-time reaching genome-wide significance, and identified novel targets for functional and future therapeutic intervention studies. The novel differentially methylated genes SKI and GTF2H4 have not been previously associated with AMD, and regulate disease pathways implicated in AMD, including TGF beta signaling (SKI) and transcription-dependent DNA repair mechanisms (GTF2H4). Electronic supplementary material The online version of this article (10.1186/s13148-019-0608-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Louise F Porter
- St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK. .,Department of Eye and Vision Science, William Duncan Building, University of Liverpool, Liverpool, UK.
| | - Neil Saptarshi
- Department of Eye and Vision Science, William Duncan Building, University of Liverpool, Liverpool, UK
| | - Yongxiang Fang
- Centre for Genomic Research, University of Liverpool, Liverpool, UK
| | - Sonika Rathi
- Department of Ophthalmology, University of Pennsylvania School of Medicine, Philadelphia, USA
| | - Anneke I den Hollander
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eiko K de Jong
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Simon J Clark
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Paul N Bishop
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.,Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | | | | | - Venkata R M Chavali
- Department of Ophthalmology, University of Pennsylvania School of Medicine, Philadelphia, USA
| | - Luminita Paraoan
- Department of Eye and Vision Science, William Duncan Building, University of Liverpool, Liverpool, UK
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Diseases and their clinical heterogeneity – Are we ignoring the SNiPers and micRomaNAgers? An illustration using Beta-thalassemia clinical spectrum and fetal hemoglobin levels. Genomics 2019; 111:67-75. [DOI: 10.1016/j.ygeno.2018.01.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 12/18/2017] [Accepted: 01/03/2018] [Indexed: 12/18/2022]
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35
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Sevane N, Martínez R, Bruford MW. Genome-wide differential DNA methylation in tropically adapted Creole cattle and their Iberian ancestors. Anim Genet 2018; 50:15-26. [DOI: 10.1111/age.12731] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2018] [Indexed: 12/15/2022]
Affiliation(s)
- N. Sevane
- School of Biosciences; Cardiff University; Cathays Park Cardiff CF10 3AX UK
| | - R. Martínez
- Corporación Colombiana De Investigación Agropecuaria (Corpoica); Centro de Investigaciones Tibaitatá; km 14 via Bogotá 250047 Mosquera Colombia
| | - M. W. Bruford
- School of Biosciences; Cardiff University; Cathays Park Cardiff CF10 3AX UK
- Sustainable Places Research Institute; Cardiff University; Cardiff CF10 3BA UK
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36
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Hu K, Li J. Detection and analysis of CpG sites with multimodal DNA methylation level distributions and their relationships with SNPs. BMC Proc 2018; 12:36. [PMID: 30275887 PMCID: PMC6157119 DOI: 10.1186/s12919-018-0141-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
DNA methylation levels at cytosine-phosphate-guanine (CpG) sites with multimodal distributions among different samples have been reported recently. One possible explanation for such variability is that genetic variants might affect epigenetic variation. One obvious case is that mutations such as single-nucleotide polymorphisms (SNPs) interrupt CpG sites, resulting in different DNA methylation levels for different genotypes. However, the relationship between genetic variations and epigenetic differences has not been studied thoroughly, partially because of the lack of powerful and robust methods to survey genome-wide CpG sites with multimodal methylation level distributions (mmCpGs). In this article, we develop a Gaussian mixture-model clustering (GMMC)-based approach to systematically detect all mmCpGs across the genome based on the GAW20 data set. In total, 3785 and 3847 mmCpGs have been identified in pre- and posttreatment data sets, respectively. Result analysis shows that approximately 68 to 70% of mmCpGs detected from unrelated individuals either have direct overlaps with SNPs or have associations with nearby SNPs, suggesting a strong correlation between SNPs and mmCpGs. Comparison with an existing approach illustrates that our GMMC-based method is more consistent when the number of samples decreases. In conclusion, mmCpGs may reveal important connections between genetics and epigenetics and they should be carefully identified and evaluated.
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Affiliation(s)
- Ke Hu
- Department of Electrical Engineering, Computer Science Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106 USA
| | - Jing Li
- Department of Electrical Engineering, Computer Science Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106 USA
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37
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Radhakrishna U, Vishweswaraiah S, Veerappa AM, Zafra R, Albayrak S, Sitharam PH, Saiyed NM, Mishra NK, Guda C, Bahado-Singh R. Newborn blood DNA epigenetic variations and signaling pathway genes associated with Tetralogy of Fallot (TOF). PLoS One 2018; 13:e0203893. [PMID: 30212560 PMCID: PMC6136787 DOI: 10.1371/journal.pone.0203893] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 08/29/2018] [Indexed: 12/31/2022] Open
Abstract
Tetralogy of Fallot (TOF) is the most common Critical Congenital Heart Defect (CCHD). The etiology of TOF is unknown in most cases. Preliminary data from our group and others suggest that epigenetic changes may play an important role in CHD. Epidemiologically, a significant percentage of CHD including TOF fail to be diagnosed in the prenatal and early newborn period which can negatively affect health outcomes. We performed genome-wide methylation assay in newborn blood in 24 non-syndromic TOF cases and 24 unaffected matched controls using Illumina Infinium HumanMethylation450 BeadChips. We identified 64 significantly differentially methylated CpG sites in TOF cases, of which 25 CpG sites had high predictive accuracy for TOF, based on the area under the receiver operating characteristics curve (AUC ROC) ≥ 0.90). The CpG methylation difference between TOF and controls was ≥10% in 51 CpG targets suggesting biological significance. Gene ontology analysis identified significant biological processes and functions related to these differentially methylated genes, including: CHD development, cardiomyopathy, diabetes, immunological, inflammation and other plausible pathways in CHD development. Multiple genes known or plausibly linked to heart development and post-natal heart disease were found to be differentially methylated in the blood DNA of newborns with TOF including: ABCB1, PPP2R5C, TLR1, SELL, SCN3A, CREM, RUNX and LHX9. We generated novel and highly accurate putative molecular markers for TOF detection using leucocyte DNA and thus provided information on pathogenesis of TOF.
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Affiliation(s)
- Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, United States of America
- * E-mail:
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, United States of America
| | - Avinash M. Veerappa
- Department of Studies in Genetics and Genomics, Laboratory of Genomic Sciences, University of Mysore, Mysore, Karnataka, India
| | - Rita Zafra
- Department of Obstetrics and Gynecology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Samet Albayrak
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, United States of America
| | - Prajna H. Sitharam
- Department of Studies in Genetics and Genomics, Laboratory of Genomic Sciences, University of Mysore, Mysore, Karnataka, India
| | - Nazia M. Saiyed
- Biotechnology, Nirma Institute of Science, Nirma University, Ahmedabad, Gujarat, India
| | - Nitish K. Mishra
- Department of Genetics, Cell Biology & Anatomy College of Medicine, University of Nebraska Medical Center Omaha, Omaha, Nebraska, United States of America
| | - Chittibabu Guda
- Department of Genetics, Cell Biology & Anatomy College of Medicine, University of Nebraska Medical Center Omaha, Omaha, Nebraska, United States of America
| | - Ray Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, United States of America
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Daca-Roszak P, Swierniak M, Jaksik R, Tyszkiewicz T, Oczko-Wojciechowska M, Zebracka-Gala J, Jarzab B, Witt M, Zietkiewicz E. Transcriptomic population markers for human population discrimination. BMC Genet 2018; 19:54. [PMID: 30086702 PMCID: PMC6081795 DOI: 10.1186/s12863-018-0663-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 07/30/2018] [Indexed: 12/27/2022] Open
Abstract
Background Numerous studies have demonstrated significant differences in the expression level across continental human populations. Most of published results were performed on B-cell lines materials examined under specific laboratory conditions, without further validation in a primary biological material. The goal of our study was to identify mRNA markers characterized by a significant and stable difference in the gene expression profile in Caucasian and Chinese populations, both in the commercially available B-lymphocyte cell lines and in the primary samples of the peripheral blood. Results The preliminary selection of population-differentiating transcripts was based on Illumina expression microarray analysis of the representative group of ethnically-specified B-lymphocyte cell lines. Twenty genes with the inter-population difference in the mean expression characterized by the at least 1.5-fold change and FDR < 0.05 were identified. Subsequently, a two-step validation procedure was carried out. In the first step, a subset of selected population- differentiating transcripts was tested in the independent set of B-lymphocyte cell lines, using TLDA cards. Based on TLDA analysis, three transcripts representing Fch > 2 were chosen for validation. The differentiating status was confirmed for all of them: UTS2, UGT2B17 and SLC7A7. The mean expression of UTS2 was higher in CHB (25.8-fold change compared to CEU), while the expression of UGT2B17 and SLC7A7 was higher in CEU (3.2- and 2.2-fold change, respectively). In the next validation step, two transcripts were verified in the primary biological material. As an ultimate result of our study, two mRNA markers (UTS2 and UGT2B17) exhibiting population differences in the expression level in both B-cell line and in the blood were identified. Further statistical analysis confirmed the discriminatory potential of these two markers. Conclusions An inter-population differences on the level of gene expression were identified in both B-cell lines and peripheral blood samples. These findings may have a practical application in the field of forensic science. In particular, these transcripts, targeted by specific probes, may be used as population-specific targets in the efforts aiming to separate mixture of blood from individuals of different populations. Notwithstanding, these results have to be confirmed on extended population group. Electronic supplementary material The online version of this article (10.1186/s12863-018-0663-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- P Daca-Roszak
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszynska 32, 60-479, Poznan, Poland
| | - M Swierniak
- Maria Sklodowska-Curie, Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland.,Present address: Laboratory of Human Cancer Genetics, Center of New Technologies, CENT, University of Warsaw, Warsaw, Poland.,Genomic Medicine, Medical University of Warsaw, Warsaw, Poland
| | - R Jaksik
- Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland
| | - T Tyszkiewicz
- Maria Sklodowska-Curie, Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - M Oczko-Wojciechowska
- Maria Sklodowska-Curie, Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - J Zebracka-Gala
- Maria Sklodowska-Curie, Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - B Jarzab
- Maria Sklodowska-Curie, Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - M Witt
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszynska 32, 60-479, Poznan, Poland
| | - E Zietkiewicz
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszynska 32, 60-479, Poznan, Poland.
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Trijau M, Asselman J, Armant O, Adam-Guillermin C, De Schamphelaere KAC, Alonzo F. Transgenerational DNA Methylation Changes in Daphnia magna Exposed to Chronic γ Irradiation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:4331-4339. [PMID: 29486114 DOI: 10.1021/acs.est.7b05695] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Our aim was to investigate epigenetic changes in Daphnia magna after a 25-day chronic external γ irradiation (generation F0 exposed to 6.5 μGy·h-1 or 41.3 mGy·h-1) and their potential inheritance by subsequent recovering generations, namely, F2 (exposed as germline cells in F1 embryos) and F3 (the first truly unexposed generation). Effects on survival, growth, and reproduction were observed and DNA was extracted for whole-genome bisulfite sequencing in all generations. Results showed effects on reproduction in F0 but no effect in the subsequent generations F1, F2, and F3. In contrast, we observed significant methylation changes at specific CpG positions in every generation independent of dose rate, with a majority of hypomethylation. Some of these changes were shared between dose rates and between generations. Associated gene functions included gene families and genes that were previously shown to play roles during exposure to ionizing radiation. Common methylation changes detected between generations F2 and F3 clearly showed that epigenetic modifications can be transmitted to unexposed generations, most likely through the germline, with potential implications for environmental risk.
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Affiliation(s)
- Marie Trijau
- Institut de Radioprotection et de Sûreté Nucléaire , PSE-ENV, SRTE, LECO, Cadarache, Saint-Paul-lèz-Durance 13115 , France
| | - Jana Asselman
- Laboratory for Environmental Toxicology and Aquatic Ecology , Ghent University , Ghent 9000 , Belgium
| | - Olivier Armant
- Institut de Radioprotection et de Sûreté Nucléaire , PSE-ENV, SRTE, LECO, Cadarache, Saint-Paul-lèz-Durance 13115 , France
| | - Christelle Adam-Guillermin
- Institut de Radioprotection et de Sûreté Nucléaire , PSE-ENV, SRTE, LECO, Cadarache, Saint-Paul-lèz-Durance 13115 , France
| | | | - Frédéric Alonzo
- Institut de Radioprotection et de Sûreté Nucléaire , PSE-ENV, SRTE, LECO, Cadarache, Saint-Paul-lèz-Durance 13115 , France
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Tian FY, Wang XM, Xie C, Zhao B, Niu Z, Fan L, Hivert MF, Chen WQ. Placental surface area mediates the association between FGFR2 methylation in placenta and full-term low birth weight in girls. Clin Epigenetics 2018; 10:39. [PMID: 29588807 PMCID: PMC5863829 DOI: 10.1186/s13148-018-0472-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 03/14/2018] [Indexed: 12/14/2022] Open
Abstract
Background Fibroblast growth factor receptor 2 (FGFR2) gene encodes a protein of the fibroblast growth factor receptor family. FGFR2 gene expression is associated with the regulation of implantation process of placenta which plays a vital role in fetal growth. DNA methylation is widely known as a mechanism of fetal growth. However, it is unclear whether and how DNA methylation of FGFR2 gene in the placenta is associated with full-term low birth weight. This case-control study aims to explore the links between FGFR2 methylation in placenta and full-term low birth weight and to further examine the mediation effect of placental surface area on this association. Results We conducted analyses for each of the five valid CpG sites at FGFR2 in 165 mother-baby pairs (86 FT-LBW vs. 79 FT-NBW) and found that per one standard deviation increase in the DNA methylation of CpG 11 at FGFR2 was associated with 1.64-fold higher risk of full-term low birth weight (OR = 1.64, 95% CI = [1.07, 2.52]) and 0.18 standard deviation decrease in placental surface area (β = - 0.18; standard error = 0.08, p = 0.02). The mediation effect of placental surface area on the association between DNA methylation and full-term low birth weight was significant in girls (OR = 1.38, 95% CI = [1.05, 1.80]) but not in boys. The estimated mediation proportion was 48.38%. Conclusion Our findings suggested that placental surface area mediated the association between DNA methylation of FGFR2 in placenta and full-term low birth weight in a sex-specific manner. Our study supported the importance of placental epigenetic changes in placental development and fetal growth.
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Affiliation(s)
- Fu-Ying Tian
- 1Department of Medical Statistics and Epidemiology, Guangzhou Key Laboratory of Environmental Pollution and Health Assessment, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Room 715, 74 Zhongshan Road 2, Guangzhou, 510080 Guangdong China
| | - Xi-Meng Wang
- 1Department of Medical Statistics and Epidemiology, Guangzhou Key Laboratory of Environmental Pollution and Health Assessment, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Room 715, 74 Zhongshan Road 2, Guangzhou, 510080 Guangdong China
| | - Chuanbo Xie
- Department of Cancer Prevention Research, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Bo Zhao
- 3Children's Hospital Boston and Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115 USA
| | - Zhongzheng Niu
- 4Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, State University of New York at Buffalo, 265 Farber Hall, Buffalo, NY 14214 USA
| | - Lijun Fan
- 1Department of Medical Statistics and Epidemiology, Guangzhou Key Laboratory of Environmental Pollution and Health Assessment, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Room 715, 74 Zhongshan Road 2, Guangzhou, 510080 Guangdong China
| | - Marie-France Hivert
- 5Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401, Boston, MA USA.,6Diabetes Center, Massachusetts General Hospital, 50 Staniford Street, Boston, MA USA.,7Department of Medicine, Université de Sherbrooke, 3001 12th Avenue North, Sherbrooke, Québec Canada.,8Centre de recherche du Centre Hospitalier Universitaire de Sherbrooke, 3001 12th Avenue North, wing 9, door 6, Sherbrooke, Québec Canada
| | - Wei-Qing Chen
- 1Department of Medical Statistics and Epidemiology, Guangzhou Key Laboratory of Environmental Pollution and Health Assessment, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Room 715, 74 Zhongshan Road 2, Guangzhou, 510080 Guangdong China.,9Department of Information Management, Xinhua College, Sun Yat-sen University, Guangzhou, Guangdong China
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Zhao N, Zhan X, Huang YT, Almli LM, Smith A, Epstein MP, Conneely K, Wu MC. Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies. Genet Epidemiol 2017; 42:156-167. [DOI: 10.1002/gepi.22100] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 09/26/2017] [Accepted: 10/27/2017] [Indexed: 12/22/2022]
Affiliation(s)
- Ni Zhao
- Department of Biostatistics; Johns Hopkins University; Baltimore Maryland 21205 United States of America
| | - Xiang Zhan
- Department of Public Health Sciences; Pennsylvania State University; Hershey Pennsylvania 17033 United States of America
| | - Yen-Tsung Huang
- Institute of Statistical Science; Academia Sinica; Taipei 11529 Taiwan
| | - Lynn M Almli
- Department of Psychiatry and Behavioral Sciences; Emory University; Atlanta Georgia 30322 United States of America
| | - Alicia Smith
- Department of Gynecology and Obstetrics; Emory University; Atlanta Georgia 30322 United States of America
| | - Michael P. Epstein
- Department of Human Genetics; Emory University; Atlanta Georgia 30322 United States of America
| | - Karen Conneely
- Department of Human Genetics; Emory University; Atlanta Georgia 30322 United States of America
| | - Michael C. Wu
- Public Health Sciences; Fred Hutchinson Cancer Research Center; Seattle Washington 98109 United States of America
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42
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Tomlinson MS, Bommarito PA, Martin EM, Smeester L, Fichorova RN, Onderdonk AB, Kuban KCK, O’Shea TM, Fry RC. Microorganisms in the human placenta are associated with altered CpG methylation of immune and inflammation-related genes. PLoS One 2017; 12:e0188664. [PMID: 29240761 PMCID: PMC5730116 DOI: 10.1371/journal.pone.0188664] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 11/10/2017] [Indexed: 12/26/2022] Open
Abstract
Microorganisms in the placenta have been linked to adverse pregnancy outcomes as well as neonatal illness. Inflammation in the placenta has been identified as a contributing factor in this association, but the underlying biological mechanisms are not yet fully understood. The placental epigenome may serve as an intermediate between placental microbes and inflammation, contributing to adverse outcomes in the offspring. In the present study, genome-wide DNA methylation (n = 486,428 CpG sites) of 84 placentas was analyzed in relation to 16 species of placental microorganisms using samples collected from the Extremely Low Gestation Age Newborns (ELGAN) cohort. A total of n = 1,789 CpG sites, corresponding to n = 1,079 genes, displayed differential methylation (q<0.1) in relation to microorganisms. The altered genes encode for proteins that are involved in immune/inflammatory responses, specifically the NF-κB signaling pathway. These data support bacteria-dependent epigenetic patterning in the placenta and provide potential insight into mechanisms that associate the presence of microorganisms in the placenta to pregnancy and neonatal outcomes. This study lays the foundation for investigations of the placental microbiome and its role in placental function.
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Affiliation(s)
- Martha Scott Tomlinson
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Paige A. Bommarito
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Elizabeth M. Martin
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Lisa Smeester
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Raina N. Fichorova
- Laboratory of Genital Tract Biology, Department of Obstetrics and Gynecology, Harvard Medical School and Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Andrew B. Onderdonk
- Department of Pathology, Harvard Medical School and Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Karl C. K. Kuban
- Division of Pediatric Neurology, Department of Pediatrics, Boston Medical Center, Boston, Massachusetts, United States of America
| | - T. Michael O’Shea
- Department of Pediatrics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Rebecca C. Fry
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
- * E-mail:
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43
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Chu AY, Tin A, Schlosser P, Ko YA, Qiu C, Yao C, Joehanes R, Grams ME, Liang L, Gluck CA, Liu C, Coresh J, Hwang SJ, Levy D, Boerwinkle E, Pankow JS, Yang Q, Fornage M, Fox CS, Susztak K, Köttgen A. Epigenome-wide association studies identify DNA methylation associated with kidney function. Nat Commun 2017; 8:1286. [PMID: 29097680 PMCID: PMC5668367 DOI: 10.1038/s41467-017-01297-7] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2017] [Accepted: 09/05/2017] [Indexed: 11/10/2022] Open
Abstract
Chronic kidney disease (CKD) is defined by reduced estimated glomerular filtration rate (eGFR). Previous genetic studies have implicated regulatory mechanisms contributing to CKD. Here we present epigenome-wide association studies of eGFR and CKD using whole-blood DNA methylation of 2264 ARIC Study and 2595 Framingham Heart Study participants to identify epigenetic signatures of kidney function. Of 19 CpG sites significantly associated (P < 1e-07) with eGFR/CKD and replicated, five also associate with renal fibrosis in biopsies from CKD patients and show concordant DNA methylation changes in kidney cortex. Lead CpGs at PTPN6/PHB2, ANKRD11, and TNRC18 map to active enhancers in kidney cortex. At PTPN6/PHB2 cg19942083, methylation in kidney cortex associates with lower renal PTPN6 expression, higher eGFR, and less renal fibrosis. The regions containing the 243 eGFR-associated (P < 1e-05) CpGs are significantly enriched for transcription factor binding sites of EBF1, EP300, and CEBPB (P < 5e-6). Our findings highlight kidney function associated epigenetic variation. Genome-wide association studies of kidney function show enrichment of associated genetic variants in regulatory regions. Here, the authors perform epigenome-wide association studies of kidney function and disease, identifying 19 CpG sites significantly associated with these.
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Affiliation(s)
- Audrey Y Chu
- The Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Bethesda, MD, 20892, USA.,NHLBI's Framingham Heart Study, Framingham, MA, 01702, USA
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center-University of Freiburg, 79106, Freiburg, Germany
| | - Yi-An Ko
- Renal Electrolyte and Hypertension Division, Department of Medicine, Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Chengxiang Qiu
- Renal Electrolyte and Hypertension Division, Department of Medicine, Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Chen Yao
- The Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Bethesda, MD, 20892, USA.,NHLBI's Framingham Heart Study, Framingham, MA, 01702, USA
| | - Roby Joehanes
- The Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Bethesda, MD, 20892, USA.,NHLBI's Framingham Heart Study, Framingham, MA, 01702, USA.,Institute of Aging Research, Hebrew Senior Life, Boston, MA, 02131, USA.,Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Liming Liang
- Department of Biostatistics, Harvard University School of Public Health, Boston, MA, 02115, USA
| | - Caroline A Gluck
- Renal Electrolyte and Hypertension Division, Department of Medicine, Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Chunyu Liu
- The Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Bethesda, MD, 20892, USA.,NHLBI's Framingham Heart Study, Framingham, MA, 01702, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Shih-Jen Hwang
- The Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Bethesda, MD, 20892, USA.,NHLBI's Framingham Heart Study, Framingham, MA, 01702, USA
| | - Daniel Levy
- The Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Bethesda, MD, 20892, USA.,NHLBI's Framingham Heart Study, Framingham, MA, 01702, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - James S Pankow
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, 55454, USA
| | - Qiong Yang
- NHLBI's Framingham Heart Study, Framingham, MA, 01702, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Myriam Fornage
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Caroline S Fox
- The Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Bethesda, MD, 20892, USA.,NHLBI's Framingham Heart Study, Framingham, MA, 01702, USA
| | - Katalin Susztak
- Renal Electrolyte and Hypertension Division, Department of Medicine, Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA. .,Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center-University of Freiburg, 79106, Freiburg, Germany.
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44
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Soozangar N, Sadeghi MR, Jeddi F, Somi MH, Shirmohamadi M, Samadi N. Comparison of genome‐wide analysis techniques to DNA methylation analysis in human cancer. J Cell Physiol 2017; 233:3968-3981. [DOI: 10.1002/jcp.26176] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 08/24/2017] [Indexed: 12/11/2022]
Affiliation(s)
- Narges Soozangar
- Liver and Gastrointestinal Diseases Research CenterTabriz University of Medical SciencesTabrizIran
- Department of Molecular Medicine, Faculty of Advanced Medical Sciences,Tabriz University of Medical SciencesTabrizIran
- Molecular Medicine Research CenterTabriz University of Medical SciencesTabrizIran
| | - Mohammad R. Sadeghi
- Liver and Gastrointestinal Diseases Research CenterTabriz University of Medical SciencesTabrizIran
- Department of Molecular Medicine, Faculty of Advanced Medical Sciences,Tabriz University of Medical SciencesTabrizIran
| | - Farhad Jeddi
- Liver and Gastrointestinal Diseases Research CenterTabriz University of Medical SciencesTabrizIran
- Department of Molecular Medicine, Faculty of Advanced Medical Sciences,Tabriz University of Medical SciencesTabrizIran
| | - Mohammad H. Somi
- Liver and Gastrointestinal Diseases Research CenterTabriz University of Medical SciencesTabrizIran
- Department of Molecular Medicine, Faculty of Advanced Medical Sciences,Tabriz University of Medical SciencesTabrizIran
| | - Masoud Shirmohamadi
- Liver and Gastrointestinal Diseases Research CenterTabriz University of Medical SciencesTabrizIran
| | - Nasser Samadi
- Department of Molecular Medicine, Faculty of Advanced Medical Sciences,Tabriz University of Medical SciencesTabrizIran
- Department of Biochemistry, Faculty of MedicineTabriz University of Medical SciencesTabrizIran
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Taskesen E, Mishra A, van der Sluis S, Ferrari R, Veldink JH, van Es MA, Smit AB, Posthuma D, Pijnenburg Y. Susceptible genes and disease mechanisms identified in frontotemporal dementia and frontotemporal dementia with Amyotrophic Lateral Sclerosis by DNA-methylation and GWAS. Sci Rep 2017; 7:8899. [PMID: 28827549 PMCID: PMC5567187 DOI: 10.1038/s41598-017-09320-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 07/26/2017] [Indexed: 12/13/2022] Open
Abstract
Frontotemporal dementia (FTD) is a neurodegenerative disorder predominantly affecting the frontal and temporal lobes. Genome-wide association studies (GWAS) on FTD identified only a few risk loci. One of the possible explanations is that FTD is clinically, pathologically, and genetically heterogeneous. An important open question is to what extent epigenetic factors contribute to FTD and whether these factors vary between FTD clinical subgroup. We compared the DNA-methylation levels of FTD cases (n = 128), and of FTD cases with Amyotrophic Lateral Sclerosis (FTD-ALS; n = 7) to those of unaffected controls (n = 193), which resulted in 14 and 224 candidate genes, respectively. Cluster analysis revealed significant class separation of FTD-ALS from controls. We could further specify genes with increased susceptibility for abnormal gene-transcript behavior by jointly analyzing DNA-methylation levels with the presence of mutations in a GWAS FTD-cohort. For FTD-ALS, this resulted in 9 potential candidate genes, whereas for FTD we detected 1 candidate gene (ELP2). Independent validation-sets confirmed the genes DLG1, METTL7A, KIAA1147, IGHMBP2, PCNX, UBTD2, WDR35, and ELP2/SLC39A6 among others. We could furthermore demonstrate that genes harboring mutations and/or displaying differential DNA-methylation, are involved in common pathways, and may therefore be critical for neurodegeneration in both FTD and FTD-ALS.
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Affiliation(s)
- E Taskesen
- VU University Amsterdam, Center for Neurogenomics and Cognitive Research, Complex Trait Genetics (CTG), Amsterdam Neuroscience, Amsterdam, The Netherlands.,VU University Medical Center (VUMC), Alzheimer Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - A Mishra
- VU University Amsterdam, Center for Neurogenomics and Cognitive Research, Complex Trait Genetics (CTG), Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - S van der Sluis
- VU University Amsterdam, Center for Neurogenomics and Cognitive Research, Complex Trait Genetics (CTG), Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - R Ferrari
- UCL London, Institute of Neurology, Department of Molecular Neuroscience, London, UK
| | | | - J H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M A van Es
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - A B Smit
- VU University Amsterdam, Center for Neurogenomics and Cognitive Research, Department of Molecular and Cellular Neurobiology (MCN), Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - D Posthuma
- VU University Amsterdam, Center for Neurogenomics and Cognitive Research, Complex Trait Genetics (CTG), Amsterdam Neuroscience, Amsterdam, The Netherlands.,VU University Medical Center (VUMC), Alzheimer Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Y Pijnenburg
- VU University Medical Center (VUMC), Alzheimer Center, Amsterdam Neuroscience, Amsterdam, The Netherlands.
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46
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Strategies for analyzing bisulfite sequencing data. J Biotechnol 2017; 261:105-115. [PMID: 28822795 DOI: 10.1016/j.jbiotec.2017.08.007] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 08/07/2017] [Accepted: 08/08/2017] [Indexed: 01/10/2023]
Abstract
DNA methylation is one of the main epigenetic modifications in the eukaryotic genome; it has been shown to play a role in cell-type specific regulation of gene expression, and therefore cell-type identity. Bisulfite sequencing is the gold-standard for measuring methylation over the genomes of interest. Here, we review several techniques used for the analysis of high-throughput bisulfite sequencing. We introduce specialized short-read alignment techniques as well as pre/post-alignment quality check methods to ensure data quality. Furthermore, we discuss subsequent analysis steps after alignment. We introduce various differential methylation methods and compare their performance using simulated and real bisulfite sequencing datasets. We also discuss the methods used to segment methylomes in order to pinpoint regulatory regions. We introduce annotation methods that can be used for further classification of regions returned by segmentation and differential methylation methods. Finally, we review software packages that implement strategies to efficiently deal with large bisulfite sequencing datasets locally and we discuss online analysis workflows that do not require any prior programming skills. The analysis strategies described in this review will guide researchers at any level to the best practices of bisulfite sequencing analysis.
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Lin X, Lim IY, Wu Y, Teh AL, Chen L, Aris IM, Soh SE, Tint MT, MacIsaac JL, Morin AM, Yap F, Tan KH, Saw SM, Kobor MS, Meaney MJ, Godfrey KM, Chong YS, Holbrook JD, Lee YS, Gluckman PD, Karnani N. Developmental pathways to adiposity begin before birth and are influenced by genotype, prenatal environment and epigenome. BMC Med 2017; 15:50. [PMID: 28264723 PMCID: PMC5340003 DOI: 10.1186/s12916-017-0800-1] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 01/21/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Obesity is an escalating health problem worldwide, and hence the causes underlying its development are of primary importance to public health. There is growing evidence that suboptimal intrauterine environment can perturb the metabolic programing of the growing fetus, thereby increasing the risk of developing obesity in later life. However, the link between early exposures in the womb, genetic susceptibility, and perturbed epigenome on metabolic health is not well understood. In this study, we shed more light on this aspect by performing a comprehensive analysis on the effects of variation in prenatal environment, neonatal methylome, and genotype on birth weight and adiposity in early childhood. METHODS In a prospective mother-offspring cohort (N = 987), we interrogated the effects of 30 variables that influence the prenatal environment, umbilical cord DNA methylation, and genotype on offspring weight and adiposity, over the period from birth to 48 months. This is an interim analysis on an ongoing cohort study. RESULTS Eleven of 30 prenatal environments, including maternal adiposity, smoking, blood glucose and plasma unsaturated fatty acid levels, were associated with birth weight. Polygenic risk scores derived from genetic association studies on adult adiposity were also associated with birth weight and child adiposity, indicating an overlap between the genetic pathways influencing metabolic health in early and later life. Neonatal methylation markers from seven gene loci (ANK3, CDKN2B, CACNA1G, IGDCC4, P4HA3, ZNF423 and MIRLET7BHG) were significantly associated with birth weight, with a subset of these in genes previously implicated in metabolic pathways in humans and in animal models. Methylation levels at three of seven birth weight-linked loci showed significant association with prenatal environment, but none were affected by polygenic risk score. Six of these birth weight-linked loci continued to show a longitudinal association with offspring size and/or adiposity in early childhood. CONCLUSIONS This study provides further evidence that developmental pathways to adiposity begin before birth and are influenced by environmental, genetic and epigenetic factors. These pathways can have a lasting effect on offspring size, adiposity and future metabolic outcomes, and offer new opportunities for risk stratification and prevention of obesity. CLINICAL TRIAL REGISTRATION This birth cohort is a prospective observational study, designed to study the developmental origins of health and disease, and was retrospectively registered on 1 July 2010 under the identifier NCT01174875 .
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Affiliation(s)
- Xinyi Lin
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Ives Yubin Lim
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Yonghui Wu
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Li Chen
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Izzuddin M Aris
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Shu E Soh
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Mya Thway Tint
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.,Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Julia L MacIsaac
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Alexander M Morin
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Fabian Yap
- KK Women's and Children's Hospital, Singapore, 229899, Singapore
| | - Kok Hian Tan
- KK Women's and Children's Hospital, Singapore, 229899, Singapore
| | - Seang Mei Saw
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore.,Singapore Eye Research Institute, Singapore, 169856, Singapore.,Duke NUS Medical School, Singapore, 169857, Singapore
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Ludmer Centre for Neuroinformatics and Mental Health, Douglas University Mental Health Institute, McGill University, Montreal, Quebec, H4H 1R3, Canada
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Joanna D Holbrook
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Yung Seng Lee
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.,Division of Paediatric Endocrinology and Diabetes, Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, 119228, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, 1142, New Zealand
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore. .,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.
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"Gap hunting" to characterize clustered probe signals in Illumina methylation array data. Epigenetics Chromatin 2016; 9:56. [PMID: 27980682 PMCID: PMC5142147 DOI: 10.1186/s13072-016-0107-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Accepted: 11/25/2016] [Indexed: 12/16/2022] Open
Abstract
Background The Illumina 450k array has been widely used in epigenetic association studies. Current quality-control (QC) pipelines typically remove certain sets of probes, such as those containing a SNP or with multiple mapping locations. An additional set of potentially problematic probes are those with DNA methylation distributions characterized by two or more distinct clusters separated by gaps. Data-driven identification of such probes may offer additional insights for downstream analyses. Results We developed a procedure, termed “gap hunting,” to identify probes showing clustered distributions. Among 590 peripheral blood samples from the Study to Explore Early Development, we identified 11,007 “gap probes.” The vast majority (9199) are likely attributed to an underlying SNP(s) or other variant in the probe, although SNP-affected probes exist that do not produce a gap signals. Specific factors predict which SNPs lead to gap signals, including type of nucleotide change, probe type, DNA strand, and overall methylation state. These expected effects are demonstrated in paired genotype and 450k data on the same samples. Gap probes can also serve as a surrogate for the local genetic sequence on a haplotype scale and can be used to adjust for population stratification. Conclusions The characteristics of gap probes reflect potentially informative biology. QC pipelines may benefit from an efficient data-driven approach that “flags” gap probes, rather than filtering such probes, followed by careful interpretation of downstream association analyses. Our results should translate directly to the recently released Illumina EPIC array given the similar chemistry and content design. Electronic supplementary material The online version of this article (doi:10.1186/s13072-016-0107-z) contains supplementary material, which is available to authorized users.
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Dirks RAM, Stunnenberg HG, Marks H. Genome-wide epigenomic profiling for biomarker discovery. Clin Epigenetics 2016; 8:122. [PMID: 27895806 PMCID: PMC5117701 DOI: 10.1186/s13148-016-0284-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Accepted: 11/02/2016] [Indexed: 12/24/2022] Open
Abstract
A myriad of diseases is caused or characterized by alteration of epigenetic patterns, including changes in DNA methylation, post-translational histone modifications, or chromatin structure. These changes of the epigenome represent a highly interesting layer of information for disease stratification and for personalized medicine. Traditionally, epigenomic profiling required large amounts of cells, which are rarely available with clinical samples. Also, the cellular heterogeneity complicates analysis when profiling clinical samples for unbiased genome-wide biomarker discovery. Recent years saw great progress in miniaturization of genome-wide epigenomic profiling, enabling large-scale epigenetic biomarker screens for disease diagnosis, prognosis, and stratification on patient-derived samples. All main genome-wide profiling technologies have now been scaled down and/or are compatible with single-cell readout, including: (i) Bisulfite sequencing to determine DNA methylation at base-pair resolution, (ii) ChIP-Seq to identify protein binding sites on the genome, (iii) DNaseI-Seq/ATAC-Seq to profile open chromatin, and (iv) 4C-Seq and HiC-Seq to determine the spatial organization of chromosomes. In this review we provide an overview of current genome-wide epigenomic profiling technologies and main technological advances that allowed miniaturization of these assays down to single-cell level. For each of these technologies we evaluate their application for future biomarker discovery. We will focus on (i) compatibility of these technologies with methods used for clinical sample preservation, including methods used by biobanks that store large numbers of patient samples, and (ii) automation of these technologies for robust sample preparation and increased throughput.
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Affiliation(s)
- René A M Dirks
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6500HB Nijmegen, The Netherlands
| | - Hendrik G Stunnenberg
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6500HB Nijmegen, The Netherlands
| | - Hendrik Marks
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6500HB Nijmegen, The Netherlands
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Boot A, Oosting J, de Miranda NFCC, Zhang Y, Corver WE, van de Water B, Morreau H, van Wezel T. Imprinted survival genes preclude loss of heterozygosity of chromosome 7 in cancer cells. J Pathol 2016; 240:72-83. [DOI: 10.1002/path.4756] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 05/21/2016] [Accepted: 05/24/2016] [Indexed: 12/19/2022]
Affiliation(s)
- Arnoud Boot
- Department of Pathology; Leiden University Medical Center; Leiden The Netherlands
| | - Jan Oosting
- Department of Pathology; Leiden University Medical Center; Leiden The Netherlands
| | - Noel FCC de Miranda
- Department of Pathology; Leiden University Medical Center; Leiden The Netherlands
| | - Yinghui Zhang
- Division of Toxicology, Leiden Academic Center for Drug Research; Leiden University; The Netherlands
| | - Willem E Corver
- Department of Pathology; Leiden University Medical Center; Leiden The Netherlands
| | - Bob van de Water
- Division of Toxicology, Leiden Academic Center for Drug Research; Leiden University; The Netherlands
| | - Hans Morreau
- Department of Pathology; Leiden University Medical Center; Leiden The Netherlands
| | - Tom van Wezel
- Department of Pathology; Leiden University Medical Center; Leiden The Netherlands
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