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Bell CG. Epigenomic insights into common human disease pathology. Cell Mol Life Sci 2024; 81:178. [PMID: 38602535 PMCID: PMC11008083 DOI: 10.1007/s00018-024-05206-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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2
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Taylor JY, Jones-Patten A, Prescott L, Potts-Thompson S, Joyce C, Tayo B, Saban K. The race-based stress reduction intervention (RiSE) study on African American women in NYC and Chicago: Design and methods for complex genomic analysis. PLoS One 2024; 19:e0295293. [PMID: 38598554 PMCID: PMC11006145 DOI: 10.1371/journal.pone.0295293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/19/2023] [Indexed: 04/12/2024] Open
Abstract
RiSE study aims to evaluate a race-based stress-reduction intervention as an effective strategy to improve coping and decrease stress-related symptoms, inflammatory burden, and modify DNA methylation of stress response-related genes in older AA women. This article will describe genomic analytic methods to be utilized in this longitudinal, randomized clinical trial of older adult AA women in Chicago and NYC that examines the effect of the RiSE intervention on DNAm pre- and post-intervention, and its overall influence on inflammatory burden. Salivary DNAm will be measured at baseline and 6 months following the intervention, using the Oragene-DNA kit. Measures of perceived stress, depressive symptoms, fatigue, sleep, inflammatory burden, and coping strategies will be assessed at 4 time points including at baseline, 4 weeks, 8 weeks, and 6 months. Genomic data analysis will include the use of pre-processed and quality-controlled methylation data expressed as beta (β) values. Association analyses will be performed to detect differentially methylated sites on the targeted candidate genes between the intervention and non-intervention groups using the Δβ (changes in methylation) with adjustment for age, health behaviors, early life adversity, hybridization batch, and top principal components of the probes as covariates. To account for multiple testing, we will use FDR adjustment with a corrected p-value of <0.05 regarded as statistically significant. To assess the relationship between inflammatory burden and Δβ among the study samples, we will repeat association analyses with the inclusion of individual inflammation protein measures. ANCOVA will be used because it is more statistically powerful to detect differences.
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Affiliation(s)
- Jacquelyn Y. Taylor
- Center for Research on People of Color, Columbia University School of Nursing, New York, New York, United States of America
| | - Alexandria Jones-Patten
- Center for Research on People of Color, Columbia University School of Nursing, New York, New York, United States of America
| | - Laura Prescott
- Center for Research on People of Color, Columbia University School of Nursing, New York, New York, United States of America
| | - Stephanie Potts-Thompson
- Center for Research on People of Color, Columbia University School of Nursing, New York, New York, United States of America
| | - Cara Joyce
- Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, Illinois, United States of America
| | - Bamidele Tayo
- Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, Illinois, United States of America
| | - Karen Saban
- Marcella Niehoff School of Nursing, Center for Translational Research and Education, Loyola University Chicago, Maywood, Illinois, United States of America
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3
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Bozack AK, Navas-Acien A, Cardenas A. DNA Methylation-Based Biomarkers of Protein Levels and Cardiovascular Disease Risk: Opportunities and Challenges for Precision Cardiology. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004571. [PMID: 38348680 PMCID: PMC11021153 DOI: 10.1161/circgen.124.004571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Affiliation(s)
- Anne K Bozack
- Department of Epidemiology and Population Health (A.K.B., A.C.), Stanford University School of Medicine, CA
| | - Ana Navas-Acien
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY (A.N.-A.)
| | - Andres Cardenas
- Department of Epidemiology and Population Health (A.K.B., A.C.), Stanford University School of Medicine, CA
- Department of Pediatrics (A.C.), Stanford University School of Medicine, CA
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4
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Sigurpalsdottir BD, Stefansson OA, Holley G, Beyter D, Zink F, Hardarson MÞ, Sverrisson SÞ, Kristinsdottir N, Magnusdottir DN, Magnusson OÞ, Gudbjartsson DF, Halldorsson BV, Stefansson K. A comparison of methods for detecting DNA methylation from long-read sequencing of human genomes. Genome Biol 2024; 25:69. [PMID: 38468278 DOI: 10.1186/s13059-024-03207-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 02/28/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Long-read sequencing can enable the detection of base modifications, such as CpG methylation, in single molecules of DNA. The most commonly used methods for long-read sequencing are nanopore developed by Oxford Nanopore Technologies (ONT) and single molecule real-time (SMRT) sequencing developed by Pacific Bioscience (PacBio). In this study, we systematically compare the performance of CpG methylation detection from long-read sequencing. RESULTS We demonstrate that CpG methylation detection from 7179 nanopore-sequenced DNA samples is highly accurate and consistent with 132 oxidative bisulfite-sequenced (oxBS) samples, isolated from the same blood draws. We introduce quality filters for CpGs that further enhance the accuracy of CpG methylation detection from nanopore-sequenced DNA, while removing at most 30% of CpGs. We evaluate the per-site performance of CpG methylation detection across different genomic features and CpG methylation rates and demonstrate how the latest R10.4 flowcell chemistry and base-calling algorithms improve methylation detection from nanopore sequencing. Additionally, we show how the methylation detection of 50 SMRT-sequenced genomes compares to nanopore sequencing and oxBS. CONCLUSIONS This study provides the first systematic comparison of CpG methylation detection tools for long-read sequencing methods. We compare two commonly used computational methods for the detection of CpG methylation in a large number of nanopore genomes, including samples sequenced using the latest R10.4 nanopore flowcell chemistry and 50 SMRT sequenced samples. We provide insights into the strengths and limitations of each sequencing method as well as recommendations for standardization and evaluation of tools designed for genome-scale modified base detection using long-read sequencing.
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Affiliation(s)
- Brynja D Sigurpalsdottir
- deCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
- School of Technology, Reykjavík University, Reykjavík, Iceland.
| | | | | | - Doruk Beyter
- deCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland
| | - Florian Zink
- deCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland
| | - Marteinn Þ Hardarson
- deCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland
- School of Technology, Reykjavík University, Reykjavík, Iceland
| | | | | | | | | | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavík, Iceland
| | - Bjarni V Halldorsson
- deCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
- School of Technology, Reykjavík University, Reykjavík, Iceland.
| | - Kari Stefansson
- deCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland
- Faculty of Medicine, School of Health Science, University of Iceland, Reykjavík, Iceland
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5
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Peters TJ, Meyer B, Ryan L, Achinger-Kawecka J, Song J, Campbell EM, Qu W, Nair S, Loi-Luu P, Stricker P, Lim E, Stirzaker C, Clark SJ, Pidsley R. Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling. BMC Genomics 2024; 25:251. [PMID: 38448820 PMCID: PMC10916044 DOI: 10.1186/s12864-024-10027-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/18/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND The Illumina family of Infinium Methylation BeadChip microarrays has been widely used over the last 15 years for genome-wide DNA methylation profiling, including large-scale and population-based studies, due to their ease of use and cost effectiveness. Succeeding the popular HumanMethylationEPIC BeadChip (EPICv1), the recently released Infinium MethylationEPIC v2.0 BeadChip (EPICv2) claims to extend genomic coverage to more than 935,000 CpG sites. Here, we comprehensively characterise the reproducibility, reliability and annotation of the EPICv2 array, based on bioinformatic analysis of both manifest data and new EPICv2 data from diverse biological samples. RESULTS We find a high degree of reproducibility with EPICv1, evidenced by comparable sensitivity and precision from empirical cross-platform comparison incorporating whole genome bisulphite sequencing (WGBS), and high correlation between technical sample replicates, including between samples with DNA input levels below the manufacturer's recommendation. We provide a full assessment of probe content, evaluating genomic distribution and changes from previous array versions. We characterise EPICv2's new feature of replicated probes and provide recommendations as to the superior probes. In silico analysis of probe sequences demonstrates that probe cross-hybridisation remains a significant problem in EPICv2. By mapping the off-target sites at single nucleotide resolution and comparing with WGBS we show empirical evidence for preferential off-target binding. CONCLUSIONS Overall, we find EPICv2 a worthy successor to the previous Infinium methylation microarrays, however some technical issues remain. To support optimal EPICv2 data analysis we provide an expanded version of the EPICv2 manifest to aid researchers in understanding probe design, data processing, choosing appropriate probes for analysis and for integration with methylation datasets from previous versions of the Infinium Methylation BeadChip.
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Affiliation(s)
- Timothy J Peters
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Braydon Meyer
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Lauren Ryan
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Joanna Achinger-Kawecka
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Jenny Song
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Elyssa M Campbell
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Wenjia Qu
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Shalima Nair
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Phuc Loi-Luu
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Phillip Stricker
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia
- Department of Urology, St. Vincent's Prostate Cancer Centre, Sydney, NSW, 2050, Australia
| | - Elgene Lim
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Clare Stirzaker
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Susan J Clark
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia.
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia.
| | - Ruth Pidsley
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia.
- St Vincent's Clinical School, UNSW Sydney, Sydney, NSW, 2010, Australia.
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6
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Satomi K, Ichimura K, Shibahara J. Decoding the DNA methylome of central nervous system tumors: An emerging modality for integrated diagnosis. Pathol Int 2024; 74:51-67. [PMID: 38224248 DOI: 10.1111/pin.13402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 01/16/2024]
Abstract
The definitive diagnosis and classification of individual cancers are crucial for patient care and cancer research. To achieve a robust diagnosis of central nervous system (CNS) tumors, a genotype-phenotype integrated diagnostic approach was introduced in recent versions of the World Health Organization classification, followed by the incorporation of a genome-wide DNA methylome-based classification. Microarray-based platforms are widely used to obtain DNA methylome data, and the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) has a webtool for a DNA methylation-based classifier (DKFZ classifier). Integration of DNA methylome will further enhance the precision of CNS tumor classification, especially in diagnostically challenging cases. However, in the clinical application of DNA methylome-based classification, challenges related to data interpretation persist, in addition to technical caveats, regulations, and limited accessibility. Dimensionality reduction (DMR) can complement integrated diagnosis by visualizing a profile and comparing it with other known samples. Therefore, DNA methylome-based classification is a highly useful research tool for auxiliary analysis in challenging diagnostic and rare disease cases, and for establishing novel tumor concepts. Decoding the DNA methylome, especially by DMR in addition to DKFZ classifier, emphasizes the capability of grasping the fundamental biological principles that provide new perspectives on CNS tumors.
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Affiliation(s)
- Kaishi Satomi
- Department of Pathology, Kyorin University Faculty of Medicine, Tokyo, Japan
| | - Koichi Ichimura
- Department of Brain Disease Translational Research, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Junji Shibahara
- Department of Pathology, Kyorin University Faculty of Medicine, Tokyo, Japan
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Ajithkumar P, Vasantharajan SS, Pattison S, McCall JL, Rodger EJ, Chatterjee A. Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis. Int J Mol Sci 2024; 25:874. [PMID: 38255946 PMCID: PMC10815915 DOI: 10.3390/ijms25020874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Metastatic progression is a complex, multistep process and the leading cause of cancer mortality. There is growing evidence that emphasises the significance of epigenetic modification, specifically DNA methylation and histone modifications, in influencing colorectal (CRC) metastasis. Epigenetic modifications influence the expression of genes involved in various cellular processes, including the pathways associated with metastasis. These modifications could contribute to metastatic progression by enhancing oncogenes and silencing tumour suppressor genes. Moreover, specific epigenetic alterations enable cancer cells to acquire invasive and metastatic characteristics by altering cell adhesion, migration, and invasion-related pathways. Exploring the involvement of DNA methylation and histone modification is crucial for identifying biomarkers that impact cancer prediction for metastasis in CRC. This review provides a summary of the potential epigenetic biomarkers associated with metastasis in CRC, particularly DNA methylation and histone modifications, and examines the pathways associated with these biomarkers.
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Affiliation(s)
- Priyadarshana Ajithkumar
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; (P.A.)
| | - Sai Shyam Vasantharajan
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; (P.A.)
| | - Sharon Pattison
- Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
| | - John L. McCall
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
| | - Euan J. Rodger
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; (P.A.)
| | - Aniruddha Chatterjee
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; (P.A.)
- School of Health Sciences and Technology, UPES University, Dehradun 248007, India
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Ndhlovu LC, Bendall ML, Dwaraka V, Pang APS, Dopkins N, Carreras N, Smith R, Nixon DF, Corley MJ. Retroelement-Age Clocks: Epigenetic Age Captured by Human Endogenous Retrovirus and LINE-1 DNA methylation states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570422. [PMID: 38106164 PMCID: PMC10723416 DOI: 10.1101/2023.12.06.570422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Human endogenous retroviruses (HERVs), the remnants of ancient viral infections embedded within the human genome, and long interspersed nuclear elements 1 (LINE-1), a class of autonomous retrotransposons, are silenced by host epigenetic mechanisms including DNA methylation. The resurrection of particular retroelements has been linked to biological aging. Whether the DNA methylation states of locus specific HERVs and LINEs can be used as a biomarker of chronological age in humans remains unclear. We show that highly predictive epigenetic clocks of chronological age can be constructed from retroelement DNA methylation states in the immune system, across human tissues, and pan-mammalian species. We found retroelement epigenetic clocks were reversed during transient epigenetic reprogramming, accelerated in people living with HIV-1, responsive to antiretroviral therapy, and accurate in estimating long-term culture ages of human brain organoids. Our findings support the hypothesis of epigenetic dysregulation of retroelements as a potential contributor to the biological hallmarks of aging.
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Affiliation(s)
- Lishomwa C. Ndhlovu
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Matthew L. Bendall
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | | | - Alina PS Pang
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Nicholas Dopkins
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | | | | | - Douglas F. Nixon
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Michael J. Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
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9
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Giuili E, Grolaux R, Macedo CZNM, Desmyter L, Pichon B, Neuens S, Vilain C, Olsen C, Van Dooren S, Smits G, Defrance M. Comprehensive evaluation of the implementation of episignatures for diagnosis of neurodevelopmental disorders (NDDs). Hum Genet 2023; 142:1721-1735. [PMID: 37889307 PMCID: PMC10676303 DOI: 10.1007/s00439-023-02609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023]
Abstract
Episignatures are popular tools for the diagnosis of rare neurodevelopmental disorders. They are commonly based on a set of differentially methylated CpGs used in combination with a support vector machine model. DNA methylation (DNAm) data often include missing values due to changes in data generation technology and batch effects. While many normalization methods exist for DNAm data, their impact on episignature performance have never been assessed. In addition, technologies to quantify DNAm evolve quickly and this may lead to poor transposition of existing episignatures generated on deprecated array versions to new ones. Indeed, probe removal between array versions, technologies or during preprocessing leads to missing values. Thus, the effect of missing data on episignature performance must also be carefully evaluated and addressed through imputation or an innovative approach to episignatures design. In this paper, we used data from patients suffering from Kabuki and Sotos syndrome to evaluate the influence of normalization methods, classification models and missing data on the prediction performances of two existing episignatures. We compare how six popular normalization methods for methylarray data affect episignature classification performances in Kabuki and Sotos syndromes and provide best practice suggestions when building new episignatures. In this setting, we show that Illumina, Noob or Funnorm normalization methods achieved higher classification performances on the testing sets compared to Quantile, Raw and Swan normalization methods. We further show that penalized logistic regression and support vector machines perform best in the classification of Kabuki and Sotos syndrome patients. Then, we describe a new paradigm to build episignatures based on the detection of differentially methylated regions (DMRs) and evaluate their performance compared to classical differentially methylated cytosines (DMCs)-based episignatures in the presence of missing data. We show that the performance of classical DMC-based episignatures suffers from the presence of missing data more than the DMR-based approach. We present a comprehensive evaluation of how the normalization of DNA methylation data affects episignature performance, using three popular classification models. We further evaluate how missing data affect those models' predictions. Finally, we propose a novel methodology to develop episignatures based on differentially methylated regions identification and show how this method slightly outperforms classical episignatures in the presence of missing data.
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Affiliation(s)
- Edoardo Giuili
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium
| | - Robin Grolaux
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium
| | - Catarina Z N M Macedo
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium
| | - Laurence Desmyter
- Center for Human Genetics, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Bruno Pichon
- Center for Human Genetics, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Sebastian Neuens
- Center for Human Genetics, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
- Department of Genetics, Hôpital Universitaire Des Enfants Reine Fabiola, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Catheline Vilain
- Center for Human Genetics, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
- Department of Genetics, Hôpital Universitaire Des Enfants Reine Fabiola, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Catharina Olsen
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium
- Clinical Sciences, Research Group Reproduction and Genetics, Brussels Interuniversity Genomics High Throughput Core (BRIGHTcore), Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Clinical Sciences, Research Group Reproduction and Genetics, Centre for Medical Genetics, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Sonia Van Dooren
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium
- Clinical Sciences, Research Group Reproduction and Genetics, Brussels Interuniversity Genomics High Throughput Core (BRIGHTcore), Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Clinical Sciences, Research Group Reproduction and Genetics, Centre for Medical Genetics, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Guillaume Smits
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium
- Center for Human Genetics, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
- Department of Genetics, Hôpital Universitaire Des Enfants Reine Fabiola, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Matthieu Defrance
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium.
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10
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Kaur D, Lee SM, Goldberg D, Spix NJ, Hinoue T, Li HT, Dwaraka VB, Smith R, Shen H, Liang G, Renke N, Laird PW, Zhou W. Comprehensive Evaluation of The Infinium Human MethylationEPIC v2 BeadChip. EPIGENETICS COMMUNICATIONS 2023; 3:6. [PMID: 38455390 PMCID: PMC10919401 DOI: 10.1186/s43682-023-00021-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/18/2023] [Indexed: 03/09/2024]
Abstract
Infinium Methylation BeadChips are widely used to profile DNA cytosine modifications in large cohort studies for reasons of cost-effectiveness, accurate quantification, and user-friendly data analysis in characterizing these canonical epigenetic marks. In this work, we conducted a comprehensive evaluation of the updated Infinium MethylationEPIC v2 BeadChip (EPICv2). Our evaluation revealed that EPICv2 offers significant improvements over its predecessors, including expanded enhancer coverage, applicability to diverse ancestry groups, support for low-input DNA down to one nanogram, coverage of existing epigenetic clocks, cell type deconvolution panels, and human trait associations, while maintaining accuracy and reproducibility. Using EPICv2, we were able to identify epigenome and sequence signatures in cell line models of DNMT and SETD2 loss and/or hypomorphism. Furthermore, we provided probe-wise evaluation and annotation to facilitate the use of new features on this array for studying the interplay between somatic mutations and epigenetic landscape in cancer genomics. In conclusion, EPICv2 provides researchers with a valuable tool for studying epigenetic modifications and their role in development and disease.
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Affiliation(s)
- Diljeet Kaur
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA, 19104, USA
- These authors contribute equally
| | - Sol Moe Lee
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA, 19104, USA
- These authors contribute equally
| | - David Goldberg
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA, 19104, USA
| | - Nathan J Spix
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Toshinori Hinoue
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Hong-Tao Li
- Department of Urology, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
| | | | - Ryan Smith
- TruDiagnostic Inc, Lexington, KY 40503, USA
| | - Hui Shen
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Gangning Liang
- Department of Urology, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Nicole Renke
- Illumina, Inc., Product Management Department, San Diego, CA 92122, USA
| | - Peter W Laird
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Wanding Zhou
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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11
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Pośpiech E, Pisarek A, Rudnicka J, Noroozi R, Boroń M, Masny A, Wysocka B, Migacz-Gruszka K, Lisman D, Pruszkowska-Przybylska P, Kobus M, Szargut M, Dowejko J, Stanisz K, Zacharczuk J, Zieliński P, Sitek A, Ossowski A, Spólnicka M, Branicki W. Introduction of a multiplex amplicon sequencing assay to quantify DNA methylation in target cytosine markers underlying four selected epigenetic clocks. Clin Epigenetics 2023; 15:128. [PMID: 37563670 PMCID: PMC10416531 DOI: 10.1186/s13148-023-01545-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/02/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND DNA methylation analysis has proven to be a powerful tool for age assessment. However, the implementation of epigenetic age prediction in diagnostics or routine forensic casework requires appropriate laboratory methods. In this study, we aimed to compare the performance of large-scale DNA methylation analysis protocols that show promise in terms of accuracy, throughput, multiplexing capacity, and high sensitivity. RESULTS The protocols were designed to target a predefined panel of 161 genomic CG/CA sites from four known estimators of epigenetic age-related parameters, optimized and validated using artificially methylated controls or blood samples. We successfully targeted 96% of these loci using two enrichment protocols: Ion AmpliSeq™, an amplicon-based method integrated with Ion Torrent S5, and SureSelectXT Methyl-Seq, a hybridization-based method followed by MiSeq FGx sequencing. Both protocols demonstrated high accuracy and robustness. Although hybridization assays have greater multiplexing capabilities, the best overall performance was observed for the amplicon-based protocol with the lowest variability in DNA methylation at 25 ng of starting DNA, mean observed marker coverage of ~ 6.7 k reads, and accuracy of methylation quantification with a mean absolute difference between observed and expected methylation beta value of 0.054. The Ion AmpliSeq method correlated strongly with genome-scale EPIC microarray data (R = 0.91) and showed superiority in terms of methylation measurement accuracy. Method-to-method bias was accounted for by the use of linear transformation, which provided a highly accurate prediction of calendar age with a mean absolute error of less than 5 years for the VISAGE and Hannum age clocks used. The pace of aging (PoAm) and the mortality risk score (MRS) estimators included in our panel represent next-generation clocks, were found to have low to moderate correlations with the VISAGE and Hannum models (R < 0.75), and thus may capture different aspects of epigenetic aging. CONCLUSIONS We propose a laboratory tool that allows the quantification of DNA methylation in cytosines underlying four different clocks, thus providing broad information on epigenetic aging while maintaining a reasonable number of CpG markers, opening the way to a wide range of applications in forensics, medicine, and healthcare.
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Affiliation(s)
- Ewelina Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland.
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland.
| | - Aleksandra Pisarek
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Krakow, Poland
| | - Joanna Rudnicka
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
| | - Rezvan Noroozi
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
| | - Michał Boroń
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | | | - Bożena Wysocka
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Kamila Migacz-Gruszka
- Department of Dermatology, Collegium Medicum of the Jagiellonian University, Krakow, Poland
| | - Dagmara Lisman
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | | | - Magdalena Kobus
- Institute of Biological Sciences, Faculty of Biology and Environmental Sciences, Cardinal Stefan Wyszynski University in Warsaw, Warsaw, Poland
| | - Maria Szargut
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Joanna Dowejko
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Kamila Stanisz
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Julia Zacharczuk
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Piotr Zieliński
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Krakow, Poland
| | - Aneta Sitek
- Department of Anthropology, Faculty of Biology and Environmental Protection, University of Łódź, Łódź, Poland
| | - Andrzej Ossowski
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | | | - Wojciech Branicki
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Krakow, Poland
- Institute of Forensic Research, Krakow, Poland
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