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Seale K, Teschendorff A, Reiner AP, Voisin S, Eynon N. A comprehensive map of the aging blood methylome in humans. Genome Biol 2024; 25:240. [PMID: 39242518 PMCID: PMC11378482 DOI: 10.1186/s13059-024-03381-w] [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: 03/19/2024] [Accepted: 08/28/2024] [Indexed: 09/09/2024] Open
Abstract
BACKGROUND During aging, the human methylome undergoes both differential and variable shifts, accompanied by increased entropy. The distinction between variably methylated positions (VMPs) and differentially methylated positions (DMPs), their contribution to epigenetic age, and the role of cell type heterogeneity remain unclear. RESULTS We conduct a comprehensive analysis of > 32,000 human blood methylomes from 56 datasets (age range = 6-101 years). We find a significant proportion of the blood methylome that is differentially methylated with age (48% DMPs; FDR < 0.005) and variably methylated with age (37% VMPs; FDR < 0.005), with considerable overlap between the two groups (59% of DMPs are VMPs). Bivalent and Polycomb regions become increasingly methylated and divergent between individuals, while quiescent regions lose methylation more uniformly. Both chronological and biological clocks, but not pace-of-aging clocks, show a strong enrichment for CpGs undergoing both mean and variance changes during aging. The accumulation of DMPs shifting towards a methylation fraction of 50% drives the increase in entropy, smoothening the epigenetic landscape. However, approximately a quarter of DMPs exhibit anti-entropic effects, opposing this direction of change. While changes in cell type composition minimally affect DMPs, VMPs and entropy measurements are moderately sensitive to such alterations. CONCLUSION This study represents the largest investigation to date of genome-wide DNA methylation changes and aging in a single tissue, providing valuable insights into primary molecular changes relevant to chronological and biological aging.
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Affiliation(s)
- Kirsten Seale
- Institute for Health and Sport (iHeS), Victoria University, Footscray, VIC, 3011, Australia
| | - Andrew Teschendorff
- CAS Key Lab of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
- UCL Cancer Institute, University College London, London, UK
| | | | - Sarah Voisin
- Institute for Health and Sport (iHeS), Victoria University, Footscray, VIC, 3011, Australia
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Nir Eynon
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, 3800, Australia.
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2
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Grolaux R, Jones-Freeman B, Jacques M, Eynon N. The benefits of exercise on aging: focus on muscle biomarkers. Aging (Albany NY) 2024; 16:11482-11483. [PMID: 39120582 PMCID: PMC11346794 DOI: 10.18632/aging.206064] [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: 07/16/2024] [Accepted: 08/04/2024] [Indexed: 08/10/2024]
Abstract
The focus on maintaining health and vitality (e.g., good healthspan) in later life has become increasingly important as the world's population is getting older. In the last decade, advances in aging research have identified biomarkers like DNA methylation (DNAm) and gene expression, offering insights into both chronological and biological aging. This understanding opens up possibilities for interventions that can slow down molecular aspects of the aging process. Exploring the impact of exercise on these biomarkers in human skeletal muscle (a critical tissue for metabolism, thermogenesis and movement) reveals its potential to foster healthier aging.
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Affiliation(s)
- Robin Grolaux
- Australian Regenerative Medicine Institute, Monash University, Melbourne, Australia
| | | | - Macsue Jacques
- Australian Regenerative Medicine Institute, Monash University, Melbourne, Australia
| | - Nir Eynon
- Australian Regenerative Medicine Institute, Monash University, Melbourne, Australia
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3
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Berglund A, Yamoah K, Eschrich SA, Falahat R, Mulé JJ, Kim S, Matta J, Dutil J, Ruiz‐Deya G, Ortiz Sanchez C, Wang L, Park H, Banerjee HN, Lotan T, Barry KH, Putney RM, Kim SJ, Gwede C, Kresovich JK, Kim Y, Lin H, Dhillon J, Chakrabarti R, Park JY. Epigenome-wide association study of prostate cancer in African American men identified differentially methylated genes. Cancer Med 2024; 13:e70044. [PMID: 39162297 PMCID: PMC11334050 DOI: 10.1002/cam4.70044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/13/2024] [Accepted: 07/12/2024] [Indexed: 08/21/2024] Open
Abstract
INTRODUCTION Men with African ancestry have the highest incidence and mortality rates of prostate cancer (PCa) worldwide. METHODS This study aimed to identify differentially methylated genes between tumor vs. adjacent normal and aggressive vs. indolent PCa in 121 African American patients. Epigenome-wide DNA methylation patterns in tumor DNA were assessed using the human Illumina Methylation EPIC V1 array. RESULTS Around 5,139 differentially methylated CpG-sites (q < 0.01, lΔβl > 0.2) were identified when comparing normal vs. tumor, with an overall trend of hypermethylation in prostate tumors. Multiple representative differentially methylated regions (DMRs), including immune-related genes, such as CD40, Galectin3, OX40L, and STING, were detected in prostate tumors when compared to adjacent normal tissues. Based on an epigenetic clock model, we observed that tumors' total number of stem cell divisions and the stem cell division rate were significantly higher than adjacent normal tissues. Regarding PCa aggressiveness, 2,061 differentially methylated CpG-sites (q < 0.05, lΔβl > .05) were identified when the grade group (GG)1 was compared with GG4/5. Among these 2,061 CpG sites, 155 probes were consistently significant in more than one comparison. Among these genes, several immune system genes, such as COL18A1, S100A2, ITGA4, HLA-C, and ADCYAP1, have previously been linked to tumor progression in PCa. CONCLUSION Several differentially methylated genes involved in immune-oncologic pathways associated with disease risk or aggressiveness were identified. In addition, 261 African American-specific differentially methylated genes related to the risk of PCa were identified. These results can shedlight on potential mechanisms contributing to PCa disparities in the African American Population.
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Affiliation(s)
- Anders Berglund
- Department of Biostatistics and BioinformaticsH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Kosj Yamoah
- Department of Radiation OncologyH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Steven A. Eschrich
- Department of Biostatistics and BioinformaticsH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Rana Falahat
- Department of ImmunologyH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - James J. Mulé
- Department of ImmunologyH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Sungjune Kim
- Department of Radiation OncologyMayo Clinic Alix College of Medicine and Health SciencesJacksonvilleFloridaUSA
| | - Jaime Matta
- Department of Basic SciencesPonce Research Institute, Ponce Health Sciences University‐School of MedicinePoncePuerto Rico
| | - Julie Dutil
- Department of Basic SciencesPonce Research Institute, Ponce Health Sciences University‐School of MedicinePoncePuerto Rico
| | - Gilberto Ruiz‐Deya
- Department of Basic SciencesPonce Research Institute, Ponce Health Sciences University‐School of MedicinePoncePuerto Rico
| | - Carmen Ortiz Sanchez
- Department of Basic SciencesPonce Research Institute, Ponce Health Sciences University‐School of MedicinePoncePuerto Rico
| | - Liang Wang
- Department of Tumor BiologyH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Hyun Park
- Department of Cancer EpidemiologyH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Hirendra N. Banerjee
- Natural, Pharmacy and Health SciencesElizabeth City State UniversityElizabeth CityNorth CarolinaUSA
| | | | - Kathryn Hughes Barry
- Department of Epidemiology and Public HealthUniversity of Maryland School of MedicineBaltimoreMarylandUSA
- Program in OncologyUniversity of Maryland Greenebaum Comprehensive Cancer CenterBaltimoreMarylandUSA
| | - Ryan M. Putney
- Department of Biostatistics and BioinformaticsH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Seung Joon Kim
- Division of Pulmonology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of MedicineThe Catholic University of KoreaSeoulRepublic of Korea
| | - Clement Gwede
- Department of Health Outcome and BehaviorH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Jacob K. Kresovich
- Department of Cancer EpidemiologyH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Youngchul Kim
- Department of Biostatistics and BioinformaticsH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Hui‐Yi Lin
- Biostatistics and Data Science Program, School of Public HealthLouisiana State University School of MedicineNew OrleansLouisianaUSA
| | - Jasreman Dhillon
- Department of PathologyH. Lee Moffitt Cancer CenterTampaFloridaUSA
| | - Ratna Chakrabarti
- Burnett School of Biomedical SciencesUniversity of Central FloridaOrlandoFloridaUSA
| | - Jong Y. Park
- Department of Cancer EpidemiologyH. Lee Moffitt Cancer CenterTampaFloridaUSA
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4
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Shokhirev MN, Torosin NS, Kramer DJ, Johnson AA, Cuellar TL. CheekAge: a next-generation buccal epigenetic aging clock associated with lifestyle and health. GeroScience 2024; 46:3429-3443. [PMID: 38441802 PMCID: PMC11009193 DOI: 10.1007/s11357-024-01094-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 02/05/2024] [Indexed: 04/13/2024] Open
Abstract
Epigenetic aging clocks are computational models that predict age using DNA methylation information. Initially, first-generation clocks were developed to make predictions using CpGs that change with age. Over time, next-generation clocks were created using CpGs that relate to both age and health. Since existing next-generation clocks were constructed in blood, we sought to develop a next-generation clock optimized for prediction in cheek swabs, which are non-invasive and easy to collect. To do this, we collected MethylationEPIC data as well as lifestyle and health information from 8045 diverse adults. Using a novel simulated annealing approach that allowed us to incorporate lifestyle and health factors into training as well as a combination of CpG filtering, CpG clustering, and clock ensembling, we constructed CheekAge, an epigenetic aging clock that has a strong correlation with age, displays high test-retest reproducibility across replicates, and significantly associates with a plethora of lifestyle and health factors, such as BMI, smoking status, and alcohol intake. We validated CheekAge in an internal dataset and multiple publicly available datasets, including samples from patients with progeria or meningioma. In addition to exploring the underlying biology of the data and clock, we provide a free online tool that allows users to mine our methylomic data and predict epigenetic age.
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Meyer DH, Schumacher B. Aging clocks based on accumulating stochastic variation. NATURE AGING 2024; 4:871-885. [PMID: 38724736 PMCID: PMC11186771 DOI: 10.1038/s43587-024-00619-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 03/28/2024] [Indexed: 05/15/2024]
Abstract
Aging clocks have provided one of the most important recent breakthroughs in the biology of aging, and may provide indicators for the effectiveness of interventions in the aging process and preventive treatments for age-related diseases. The reproducibility of accurate aging clocks has reinvigorated the debate on whether a programmed process underlies aging. Here we show that accumulating stochastic variation in purely simulated data is sufficient to build aging clocks, and that first-generation and second-generation aging clocks are compatible with the accumulation of stochastic variation in DNA methylation or transcriptomic data. We find that accumulating stochastic variation is sufficient to predict chronological and biological age, indicated by significant prediction differences in smoking, calorie restriction, heterochronic parabiosis and partial reprogramming. Although our simulations may not explicitly rule out a programmed aging process, our results suggest that stochastically accumulating changes in any set of data that have a ground state at age zero are sufficient for generating aging clocks.
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Affiliation(s)
- David H Meyer
- Institute for Genome Stability in Aging and Disease, University Hospital and University of Cologne, Cologne, Germany.
- Cologne Excellence Cluster for Cellular Stress Responses in Aging-Associated Diseases (CECAD), Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.
| | - Björn Schumacher
- Institute for Genome Stability in Aging and Disease, University Hospital and University of Cologne, Cologne, Germany.
- Cologne Excellence Cluster for Cellular Stress Responses in Aging-Associated Diseases (CECAD), Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.
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6
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Tarkhov AE, Lindstrom-Vautrin T, Zhang S, Ying K, Moqri M, Zhang B, Tyshkovskiy A, Levy O, Gladyshev VN. Nature of epigenetic aging from a single-cell perspective. NATURE AGING 2024; 4:854-870. [PMID: 38724733 DOI: 10.1038/s43587-024-00616-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/26/2024] [Indexed: 05/15/2024]
Abstract
Age-related changes in DNA methylation (DNAm) form the basis of the most robust predictors of age-epigenetic clocks-but a clear mechanistic understanding of exactly which aspects of aging are quantified by these clocks is lacking. Here, to clarify the nature of epigenetic aging, we juxtapose the dynamics of tissue and single-cell DNAm in mice. We compare these changes during early development with those observed during adult aging in mice, and corroborate our analyses with a single-cell RNA sequencing analysis within the same multiomics dataset. We show that epigenetic aging involves co-regulated changes as well as a major stochastic component, and this is consistent with transcriptional patterns. We further support the finding of stochastic epigenetic aging by direct tissue and single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated and stochastic CpG clusters showing consistent transcriptomic coordination patterns. Together, our analyses increase our understanding of the basis of epigenetic clocks and highlight potential opportunities for targeting aging and evaluating longevity interventions.
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Affiliation(s)
- Andrei E Tarkhov
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Retro Biosciences Inc., Redwood City, CA, USA.
| | - Thomas Lindstrom-Vautrin
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sirui Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Obstetrics & Gynecology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Bohan Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Orr Levy
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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7
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Tong H, Dwaraka VB, Chen Q, Luo Q, Lasky-Su JA, Smith R, Teschendorff AE. Quantifying the stochastic component of epigenetic aging. NATURE AGING 2024; 4:886-901. [PMID: 38724732 PMCID: PMC11186785 DOI: 10.1038/s43587-024-00600-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/21/2024] [Indexed: 05/15/2024]
Abstract
DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock's predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66-75% of the accuracy underpinning Horvath's clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang's clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath's age acceleration in males and PhenoAge's age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.
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Affiliation(s)
- Huige Tong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | | | - Qingwen Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
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8
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Ravaioli F, Stagni F, Guidi S, Pirazzini C, Garagnani P, Silvani A, Zoccoli G, Bartesaghi R, Bacalini MG. Increased hippocampal epigenetic age in the Ts65Dn mouse model of Down Syndrome. Front Aging Neurosci 2024; 16:1401109. [PMID: 38836050 PMCID: PMC11148439 DOI: 10.3389/fnagi.2024.1401109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/06/2024] [Indexed: 06/06/2024] Open
Abstract
Down syndrome (DS) is a segmental progeroid genetic disorder associated with multi-systemic precocious aging phenotypes, which are particularly evident in the immune and nervous systems. Accordingly, people with DS show an increased biological age as measured by epigenetic clocks. The Ts65Dn trisomic mouse, which harbors extra-numerary copies of chromosome 21 (Hsa21)-syntenic regions, was shown to recapitulate several progeroid features of DS, but no biomarkers of age have been applied to it so far. In this pilot study, we used a mouse-specific epigenetic clock to measure the epigenetic age of hippocampi from Ts65Dn and euploid mice at 20 weeks. Ts65Dn mice showed an increased epigenetic age in comparison with controls, and the observed changes in DNA methylation partially recapitulated those observed in hippocampi from people with DS. Collectively, our results support the use of the Ts65Dn model to decipher the molecular mechanisms underlying the progeroid DS phenotypes.
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Affiliation(s)
| | - Fiorenza Stagni
- Department for Life Quality Studies, University of Bologna, Rimini, Italy
| | - Sandra Guidi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Chiara Pirazzini
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
| | - Paolo Garagnani
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Alessandro Silvani
- PRISM Lab, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giovanna Zoccoli
- PRISM Lab, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Renata Bartesaghi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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9
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Sun W, Wang Z, Wen S, Huang A, Li H, Jiang L, Feng Q, Fan D, Tian Q, Han D, Liu X. Technical strategy for monozygotic twin discrimination by single-nucleotide variants. Int J Legal Med 2024; 138:767-779. [PMID: 38197923 DOI: 10.1007/s00414-023-03150-7] [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: 03/08/2023] [Accepted: 12/11/2023] [Indexed: 01/11/2024]
Abstract
Monozygotic (MZ) twins are theoretically genetically identical. Although they are revealed to accumulate mutations after the zygote splits, discriminating between twin genomes remains a formidable challenge in the field of forensic genetics. Single-nucleotide variants (SNVs) are responsible for a substantial portion of genetic variation, thus potentially serving as promising biomarkers for the identification of MZ twins. In this study, we sequenced the whole genome of a pair of female MZ twins when they were 27 and 33 years old to approximately 30 × coverage using peripheral blood on an Illumina NovaSeq 6000 Sequencing System. Potentially discordant SNVs supported by whole-genome sequencing were validated extensively by amplicon-based targeted deep sequencing and Sanger sequencing. In total, we found nine bona fide post-twinning SNVs, all of which were identified in the younger genomes and found in the older genomes. None of the SNVs occurred within coding exons, three of which were observed in introns, supported by whole-exome sequencing results. A double-blind test was employed, and the reliability of MZ twin discrimination by discordant SNVs was endorsed. All SNVs were successfully detected when input DNA amounts decreased to 0.25 ng, and reliable detection was limited to seven SNVs below 0.075 ng input. This comprehensive analysis confirms that SNVs could serve as cost-effective biomarkers for MZ twin discrimination.
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Affiliation(s)
- Weifen Sun
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ziwei Wang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
- Department of Forensic Science, Medical School of Soochow University, Suzhou, 215123, China
| | - Shubo Wen
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
- Department of Forensic Science, Medical School of Soochow University, Suzhou, 215123, China
| | - Ao Huang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
- Department of Forensic Science, Medical School of Soochow University, Suzhou, 215123, China
| | - Hui Li
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
| | - Lei Jiang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
| | - Qi Feng
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center of Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200233, China
| | - Danlin Fan
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center of Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200233, China
| | - Qilin Tian
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center of Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200233, China
| | - Dingding Han
- Department of Clinical Laboratory, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200062, China
| | - Xiling Liu
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China.
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10
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Teschendorff AE. On epigenetic stochasticity, entropy and cancer risk. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230054. [PMID: 38432318 PMCID: PMC10909509 DOI: 10.1098/rstb.2023.0054] [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: 04/01/2023] [Accepted: 09/26/2023] [Indexed: 03/05/2024] Open
Abstract
Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers. I also describe the mounting evidence that a significant proportion of DNAm changes in ageing and cancer development are related to cell proliferation, reflecting tissue-turnover and the opportunity this offers for predicting cancer risk via the development of epigenetic mitotic-like clocks. Finally, I describe how age-associated DNAm changes may be causally implicated in cancer development via an irreversible suppression of tissue-specific transcription factors that increases epigenetic and transcriptomic entropy, promoting a more plastic yet aberrant cancer stem-cell state. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Andrew E. Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, People's Republic of China
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11
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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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12
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Laubach ZM, Bozack A, Aris IM, Slopen N, Tiemeier H, Hivert MF, Cardenas A, Perng W. Maternal prenatal social experiences and offspring epigenetic age acceleration from birth to mid-childhood. Ann Epidemiol 2024; 90:28-34. [PMID: 37839726 PMCID: PMC10842218 DOI: 10.1016/j.annepidem.2023.10.003] [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: 07/12/2023] [Revised: 09/27/2023] [Accepted: 10/10/2023] [Indexed: 10/17/2023]
Abstract
PURPOSE Investigate associations of maternal social experiences with offspring epigenetic age acceleration (EAA) from birth through mid-childhood among 205 mother-offspring dyads of minoritized racial and ethnic groups. METHODS We used linear regression to examine associations of maternal experiences of racial bias or discrimination (0 = none, 1-2 = intermediate, or 3+ = high), social support (tertile 1 = low, 2 = intermediate, 3 = high), and socioeconomic status index (tertile 1 = low, 2 = intermediate, 3 = high) during the prenatal period with offspring EAA according to Horvath's Pan-Tissue, Horvath's Skin and Blood, and Intrinsic EAA clocks at birth, 3 years, and 7 years. RESULTS In comparison to children of women who did not experience any racial bias or discrimination, those whose mothers reported highest levels of racial bias or discrimination had lower Pan-Tissue clock EAA in early (-0.50 years; 90% CI: -0.91, -0.09) and mid-childhood (-0.75 years; -1.41, -0.08). We observed similar associations for the Skin and Blood clock and Intrinsic EAA. Maternal experiences of discrimination were not associated with Pan-Tissue EAA at birth. Neither maternal social support nor socioeconomic status predicted offspring EAA. CONCLUSIONS Children whose mothers experienced higher racial bias or discrimination exhibited slower EAA. Future studies are warranted to confirm these findings and establish associations of early-life EAA with long-term health outcomes.
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Affiliation(s)
- Zachary M Laubach
- Department of Ecology and Evolutionary Biology (EBIO), University of Colorado Boulder
| | - Anne Bozack
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA
| | - Izzuddin M Aris
- Division of Chronic Disease Research Across the Lifecourse (CORAL), Department of Population Medicine, Harvard Medical School, Boston, MA
| | - Natalie Slopen
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Henning Tiemeier
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Marie-France Hivert
- Division of Chronic Disease Research Across the Lifecourse (CORAL), Department of Population Medicine, Harvard Medical School, Boston, MA; Diabetes Unit, Massachusetts General Hospital, Boston, MA
| | - Andres Cardenas
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA
| | - Wei Perng
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD Center), Department of Epidemiology, Colorado School of Public Health, Aurora, CO.
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13
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Lê BM, Hatch D, Yang Q, Shah N, Luyster FS, Garrett ME, Tanabe P, Ashley-Koch AE, Knisely MR. Characterizing epigenetic aging in an adult sickle cell disease cohort. Blood Adv 2024; 8:47-55. [PMID: 37967379 PMCID: PMC10784677 DOI: 10.1182/bloodadvances.2023011188] [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: 08/10/2023] [Revised: 10/20/2023] [Accepted: 11/09/2023] [Indexed: 11/17/2023] Open
Abstract
ABSTRACT Sickle cell disease (SCD) affects ∼100 000 predominantly African American individuals in the United States, causing significant cellular damage, increased disease complications, and premature death. However, the contribution of epigenetic factors to SCD pathophysiology remains relatively unexplored. DNA methylation (DNAm), a primary epigenetic mechanism for regulating gene expression in response to the environment, is an important driver of normal cellular aging. Several DNAm epigenetic clocks have been developed to serve as a proxy for cellular aging. We calculated the epigenetic ages of 89 adults with SCD (mean age, 30.64 years; 60.64% female) using 5 published epigenetic clocks: Horvath, Hannum, PhenoAge, GrimAge, and DunedinPACE. We hypothesized that in chronic disease, such as SCD, individuals would demonstrate epigenetic age acceleration, but the results differed depending on the clock used. Recently developed clocks more consistently demonstrated acceleration (GrimAge, DunedinPACE). Additional demographic and clinical phenotypes were analyzed to explore their association with epigenetic age estimates. Chronological age was significantly correlated with epigenetic age in all clocks (Horvath, r = 0.88; Hannum, r = 0.89; PhenoAge, r = 0.85; GrimAge, r = 0.88; DunedinPACE, r = 0.34). The SCD genotype was associated with 2 clocks (PhenoAge, P = .02; DunedinPACE, P < .001). Genetic ancestry, biological sex, β-globin haplotypes, BCL11A rs11886868, and SCD severity were not associated. These findings, among the first to interrogate epigenetic aging in adults with SCD, demonstrate epigenetic age acceleration with recently developed epigenetic clocks but not older-generation clocks. Further development of epigenetic clocks may improve their predictive ability and utility for chronic diseases such as SCD.
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Affiliation(s)
- Brandon M. Lê
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC
| | | | - Qing Yang
- School of Nursing, Duke University, Durham, NC
| | - Nirmish Shah
- Department of Medicine, Division of Pediatric Hematology/Oncology, Duke University, Durham, NC
| | | | - Melanie E. Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC
| | | | | | - Allison E. Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC
- Department of Medicine, Duke University Medical Center, Durham, NC
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14
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Harvanek ZM, Boks MP, Vinkers CH, Higgins-Chen AT. The Cutting Edge of Epigenetic Clocks: In Search of Mechanisms Linking Aging and Mental Health. Biol Psychiatry 2023; 94:694-705. [PMID: 36764569 PMCID: PMC10409884 DOI: 10.1016/j.biopsych.2023.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/11/2023]
Abstract
Individuals with psychiatric disorders are at increased risk of age-related diseases and early mortality. Recent studies demonstrate that this link between mental health and aging is reflected in epigenetic clocks, aging biomarkers based on DNA methylation. The reported relationships between epigenetic clocks and mental health are mostly correlational, and the mechanisms are poorly understood. Here, we review recent progress concerning the molecular and cellular processes underlying epigenetic clocks as well as novel technologies enabling further studies of the causes and consequences of epigenetic aging. We then review the current literature on how epigenetic clocks relate to specific aspects of mental health, such as stress, medications, substance use, health behaviors, and symptom clusters. We propose an integrated framework where mental health and epigenetic aging are each broken down into multiple distinct processes, which are then linked to each other, using stress and schizophrenia as examples. This framework incorporates the heterogeneity and complexity of both mental health conditions and aging, may help reconcile conflicting results, and provides a basis for further hypothesis-driven research in humans and model systems to investigate potentially causal mechanisms linking aging and mental health.
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Affiliation(s)
- Zachary M Harvanek
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Marco P Boks
- Department of Psychiatry, University Medical Center Utrecht Brain Center, University of Utrecht, Utrecht, the Netherlands
| | - Christiaan H Vinkers
- Department of Psychiatry, Amsterdam University Medical Center, location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Department of Pathology, Yale University School of Medicine, New Haven, Connecticut.
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15
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Liu R, Zhao E, Yu H, Yuan C, Abbas MN, Cui H. Methylation across the central dogma in health and diseases: new therapeutic strategies. Signal Transduct Target Ther 2023; 8:310. [PMID: 37620312 PMCID: PMC10449936 DOI: 10.1038/s41392-023-01528-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 08/26/2023] Open
Abstract
The proper transfer of genetic information from DNA to RNA to protein is essential for cell-fate control, development, and health. Methylation of DNA, RNAs, histones, and non-histone proteins is a reversible post-synthesis modification that finetunes gene expression and function in diverse physiological processes. Aberrant methylation caused by genetic mutations or environmental stimuli promotes various diseases and accelerates aging, necessitating the development of therapies to correct the disease-driver methylation imbalance. In this Review, we summarize the operating system of methylation across the central dogma, which includes writers, erasers, readers, and reader-independent outputs. We then discuss how dysregulation of the system contributes to neurological disorders, cancer, and aging. Current small-molecule compounds that target the modifiers show modest success in certain cancers. The methylome-wide action and lack of specificity lead to undesirable biological effects and cytotoxicity, limiting their therapeutic application, especially for diseases with a monogenic cause or different directions of methylation changes. Emerging tools capable of site-specific methylation manipulation hold great promise to solve this dilemma. With the refinement of delivery vehicles, these new tools are well positioned to advance the basic research and clinical translation of the methylation field.
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Affiliation(s)
- Ruochen Liu
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
- Jinfeng Laboratory, Chongqing, 401329, China
- Chongqing Engineering and Technology Research Center for Silk Biomaterials and Regenerative Medicine, Chongqing, 400716, China
- Engineering Research Center for Cancer Biomedical and Translational Medicine, Southwest University, Chongqing, 400715, China
| | - Erhu Zhao
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
- Jinfeng Laboratory, Chongqing, 401329, China
- Chongqing Engineering and Technology Research Center for Silk Biomaterials and Regenerative Medicine, Chongqing, 400716, China
- Engineering Research Center for Cancer Biomedical and Translational Medicine, Southwest University, Chongqing, 400715, China
| | - Huijuan Yu
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
| | - Chaoyu Yuan
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
| | - Muhammad Nadeem Abbas
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China
- Jinfeng Laboratory, Chongqing, 401329, China
- Chongqing Engineering and Technology Research Center for Silk Biomaterials and Regenerative Medicine, Chongqing, 400716, China
- Engineering Research Center for Cancer Biomedical and Translational Medicine, Southwest University, Chongqing, 400715, China
| | - Hongjuan Cui
- State Key Laboratory of Resource Insects, Medical Research Institute, Southwest University, Chongqing, 400715, China.
- Jinfeng Laboratory, Chongqing, 401329, China.
- Chongqing Engineering and Technology Research Center for Silk Biomaterials and Regenerative Medicine, Chongqing, 400716, China.
- Engineering Research Center for Cancer Biomedical and Translational Medicine, Southwest University, Chongqing, 400715, China.
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16
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Liu Y, Sinke L, Jonkman TH, Slieker RC, van Zwet EW, Daxinger L, Heijmans BT. The inactive X chromosome accumulates widespread epigenetic variability with age. Clin Epigenetics 2023; 15:135. [PMID: 37626340 PMCID: PMC10464315 DOI: 10.1186/s13148-023-01549-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Loss of epigenetic control is a hallmark of aging. Among the most prominent roles of epigenetic mechanisms is the inactivation of one of two copies of the X chromosome in females through DNA methylation. Hence, age-related disruption of X-chromosome inactivation (XCI) may contribute to the aging process in women. METHODS We analyzed 9,777 CpGs on the X chromosome in whole blood samples from 2343 females and 1688 males (Illumina 450k methylation array) and replicated findings in duplicate using one whole blood and one purified monocyte data set (in total, 991/924 females/males). We used double generalized linear models to detect age-related differentially methylated CpGs (aDMCs), whose mean methylation level differs with age, and age-related variably methylated CpGs (aVMCs), whose methylation level becomes more variable with age. RESULTS In females, aDMCs were relatively uncommon (n = 33) and preferentially occurred in regions known to escape XCI. In contrast, many CpGs (n = 987) were found to display an increased variance with age (aVMCs). Of note, the replication rate of aVMCs was also high in purified monocytes (94%), indicating an independence of cell composition. aVMCs accumulated in CpG islands and regions subject to XCI suggesting that they stemmed from the inactive X. In males, carrying an active copy of the X chromosome only, aDMCs (n = 316) were primarily driven by cell composition, while aVMCs replicated well (95%) but were infrequent (n = 37). CONCLUSIONS Our results imply that age-related DNA methylation differences at the inactive X chromosome are dominated by the accumulation of variability.
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Affiliation(s)
- Yunfeng Liu
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Postzone S-5-P, 2333 ZC, Leiden, The Netherlands
| | - Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Postzone S-5-P, 2333 ZC, Leiden, The Netherlands
| | - Thomas H Jonkman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Postzone S-5-P, 2333 ZC, Leiden, The Netherlands
| | - Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Erik W van Zwet
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Lucia Daxinger
- Department of Human Genetics, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Postzone S-5-P, 2333 ZC, Leiden, The Netherlands.
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17
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Natoli V, Charras A, Hofmann SR, Northey S, Russ S, Schulze F, McCann L, Abraham S, Hedrich CM. DNA methylation patterns in CD4 + T-cells separate psoriasis patients from healthy controls, and skin psoriasis from psoriatic arthritis. Front Immunol 2023; 14:1245876. [PMID: 37662940 PMCID: PMC10472451 DOI: 10.3389/fimmu.2023.1245876] [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: 06/23/2023] [Accepted: 07/18/2023] [Indexed: 09/05/2023] Open
Abstract
Background Psoriasis is an autoimmune/inflammatory disorder primarily affecting the skin. Chronic joint inflammation triggers the diagnosis of psoriatic arthritis (PsA) in approximately one-third of psoriasis patients. Although joint disease typically follows the onset of skin psoriasis, in around 15% of cases it is the initial presentation, which can result in diagnostic delays. The pathophysiological mechanisms underlying psoriasis and PsA are not yet fully understood, but there is evidence pointing towards epigenetic dysregulation involving CD4+ and CD8+ T-cells. Objectives The aim of this study was to investigate disease-associated DNA methylation patterns in CD4+ T-cells from psoriasis and PsA patients that may represent potential diagnostic and/or prognostic biomarkers. Methods PBMCs were collected from 12 patients with chronic plaque psoriasis and 8 PsA patients, and 8 healthy controls. CD4+ T-cells were separated through FACS sorting, and DNA methylation profiling was performed (Illumina EPIC850K arrays). Bioinformatic analyses, including gene ontology (GO) and KEGG pathway analysis, were performed using R. To identify genes under the control of interferon (IFN), the Interferome database was consulted, and DNA Methylation Scores were calculated. Results Numbers and proportions of CD4+ T-cell subsets (naïve, central memory, effector memory, CD45RA re-expressing effector memory cells) did not vary between controls, skin psoriasis and PsA patients. 883 differentially methylated positions (DMPs) affecting 548 genes were identified between controls and "all" psoriasis patients. Principal component and partial least-squares discriminant analysis separated controls from skin psoriasis and PsA patients. GO analysis considering promoter DMPs delivered hypermethylation of genes involved in "regulation of wound healing, spreading of epidermal cells", "negative regulation of cell-substrate junction organization" and "negative regulation of focal adhesion assembly". Comparing controls and "all" psoriasis, a majority of DMPs mapped to IFN-related genes (69.2%). Notably, DNA methylation profiles also distinguished skin psoriasis from PsA patients (2,949 DMPs/1,084 genes) through genes affecting "cAMP-dependent protein kinase inhibitor activity" and "cAMP-dependent protein kinase regulator activity". Treatment with cytokine inhibitors (IL-17/TNF) corrected DNA methylation patterns of IL-17/TNF-associated genes, and methylation scores correlated with skin disease activity scores (PASI). Conclusion DNA methylation profiles in CD4+ T-cells discriminate between skin psoriasis and PsA. DNA methylation signatures may be applied for quantification of disease activity and patient stratification towards individualized treatment.
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Affiliation(s)
- Valentina Natoli
- Department of Women’s & Children’s Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
- Università degli Studi di Genova, Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-infantili (DINOGMI), Genoa, Italy
| | - Amandine Charras
- Department of Women’s & Children’s Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Sigrun R. Hofmann
- Klinik und Poliklinik für Kinder- und Jugendmedizin, Universitätsklinikum Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Sarah Northey
- Department of Women’s & Children’s Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Susanne Russ
- Klinik und Poliklinik für Kinder- und Jugendmedizin, Universitätsklinikum Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Felix Schulze
- Klinik und Poliklinik für Kinder- und Jugendmedizin, Universitätsklinikum Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Liza McCann
- Department of Paediatric Rheumatology, Alder Hey Children’s NHS Foundation Trust Hospital, Liverpool, United Kingdom
| | - Susanne Abraham
- Department of Dermatology, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Christian M. Hedrich
- Department of Women’s & Children’s Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
- Department of Paediatric Rheumatology, Alder Hey Children’s NHS Foundation Trust Hospital, Liverpool, United Kingdom
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18
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Walton E, Baltramonaityte V, Calhoun V, Heijmans BT, Thompson PM, Cecil CAM. A systematic review of neuroimaging epigenetic research: calling for an increased focus on development. Mol Psychiatry 2023; 28:2839-2847. [PMID: 37185958 PMCID: PMC10615743 DOI: 10.1038/s41380-023-02067-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/03/2023] [Accepted: 04/03/2023] [Indexed: 05/17/2023]
Abstract
Epigenetic mechanisms, such as DNA methylation (DNAm), have gained increasing attention as potential biomarkers and mechanisms underlying risk for neurodevelopmental, psychiatric and other brain-based disorders. Yet, surprisingly little is known about the extent to which DNAm is linked to individual differences in the brain itself, and how these associations may unfold across development - a time of life when many of these disorders emerge. Here, we systematically review evidence from the nascent field of Neuroimaging Epigenetics, combining structural or functional neuroimaging measures with DNAm, and the extent to which the developmental period (birth to adolescence) is represented in these studies. We identified 111 articles published between 2011-2021, out of which only a minority (21%) included samples under 18 years of age. Most studies were cross-sectional (85%), employed a candidate-gene approach (67%), and examined DNAm-brain associations in the context of health and behavioral outcomes (75%). Nearly half incorporated genetic data, and a fourth investigated environmental influences. Overall, studies support a link between peripheral DNAm and brain imaging measures, but there is little consistency in specific findings and it remains unclear whether DNAm markers present a cause, correlate or consequence of brain alterations. Overall, there is large heterogeneity in sample characteristics, peripheral tissue and brain outcome examined as well as the methods used. Sample sizes were generally low to moderate (median nall = 98, ndevelopmental = 80), and attempts at replication or meta-analysis were rare. Based on the strengths and weaknesses of existing studies, we propose three recommendations on how advance the field of Neuroimaging Epigenetics. We advocate for: (1) a greater focus on developmentally oriented research (i.e. pre-birth to adolescence); (2) the analysis of large, prospective, pediatric cohorts with repeated measures of DNAm and imaging to assess directionality; and (3) collaborative, interdisciplinary science to identify robust signals, triangulate findings and enhance translational potential.
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Affiliation(s)
- Esther Walton
- Department of Psychology, University of Bath, Bath, UK.
| | | | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Dept. of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Charlotte A M Cecil
- Molecular Epidemiology, Dept. of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
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19
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Okada D, Cheng JH, Zheng C, Kumaki T, Yamada R. Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging. Hum Genomics 2023; 17:8. [PMID: 36774528 PMCID: PMC9922449 DOI: 10.1186/s40246-023-00453-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 01/26/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown. RESULT We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age. CONCLUSION DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.
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Affiliation(s)
- Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Jian Hao Cheng
- grid.258799.80000 0004 0372 2033Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Cheng Zheng
- grid.258799.80000 0004 0372 2033Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tatsuro Kumaki
- grid.258799.80000 0004 0372 2033Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryo Yamada
- grid.258799.80000 0004 0372 2033Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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20
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A new blood based epigenetic age predictor for adolescents and young adults. Sci Rep 2023; 13:2303. [PMID: 36759656 PMCID: PMC9911637 DOI: 10.1038/s41598-023-29381-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/03/2023] [Indexed: 02/11/2023] Open
Abstract
Children have special rights for protection compared to adults in our society. However, more than 1/4 of children globally have no documentation of their date of birth. Hence, there is a pressing need to develop biological methods for chronological age prediction, robust to differences in genetics, psychosocial events and physical living conditions. At present, DNA methylation is the most promising biological biomarker applied for age assessment. The human genome contains around 28 million DNA methylation sites, many of which change with age. Several epigenetic clocks accurately predict chronological age using methylation levels at age associated GpG-sites. However, variation in DNA methylation increases with age, and there is no epigenetic clock specifically designed for adolescents and young adults. Here we present a novel age Predictor for Adolescents and Young Adults (PAYA), using 267 CpG methylation sites to assess the chronological age of adolescents and young adults. We compared different preprocessing approaches and investigated the effect on prediction performance of the epigenetic clock. We evaluated performance using an independent validation data set consisting of 18-year-old individuals, where we obtained a median absolute deviation of just below 0.7 years. This tool may be helpful in age assessment of adolescents and young adults. However, there is a need to investigate the robustness of the age predictor across geographical and disease populations as well as environmental effects.
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21
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Saddiki H, Colicino E, Lesseur C. Assessing Differential Variability of High-Throughput DNA Methylation Data. Curr Environ Health Rep 2022; 9:625-630. [PMID: 36040576 DOI: 10.1007/s40572-022-00374-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2022] [Indexed: 01/31/2023]
Abstract
PURPOSE OF REVIEW DNA methylation (DNAm) is essential to human development and plays an important role as a biomarker due to its susceptibility to environmental exposures. This article reviews the current state of statistical methods developed for differential variability analysis focusing on DNAm data. RECENT FINDINGS With the advent of high-throughput technologies allowing for highly reliable and cost-effective measurements of DNAm, many epigenome studies have analyzed DNAm levels to uncover biological mechanisms underlying past environmental exposures and subsequent health outcomes. These studies typically focused on detecting sites or regions which differ in their mean DNAm levels among exposure groups. However, more recent studies highlighted the importance of identifying differentially variable sites or regions as biologically relevant features. Currently, the analysis of differentially variable DNAm sites has not yet gained widespread adoption in environmental studies; yet, it is important to examine the effects of environmental exposures on inter-individual epigenetic variability. In this article, we describe six of the most widely used statistical approaches for analyzing differential variability of DNAm levels and provide a discussion of their advantages and current limitations.
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Affiliation(s)
- Hachem Saddiki
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elena Colicino
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Corina Lesseur
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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22
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Song J, Kuan PF. A systematic assessment of cell type deconvolution algorithms for DNA methylation data. Brief Bioinform 2022; 23:bbac449. [PMID: 36242584 PMCID: PMC9947552 DOI: 10.1093/bib/bbac449] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/11/2022] [Accepted: 09/20/2022] [Indexed: 12/14/2022] Open
Abstract
We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.
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Affiliation(s)
- Junyan Song
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY
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23
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Wang K, Liu H, Hu Q, Wang L, Liu J, Zheng Z, Zhang W, Ren J, Zhu F, Liu GH. Epigenetic regulation of aging: implications for interventions of aging and diseases. Signal Transduct Target Ther 2022; 7:374. [PMID: 36336680 PMCID: PMC9637765 DOI: 10.1038/s41392-022-01211-8] [Citation(s) in RCA: 134] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/14/2022] [Accepted: 09/28/2022] [Indexed: 11/09/2022] Open
Abstract
Aging is accompanied by the decline of organismal functions and a series of prominent hallmarks, including genetic and epigenetic alterations. These aging-associated epigenetic changes include DNA methylation, histone modification, chromatin remodeling, non-coding RNA (ncRNA) regulation, and RNA modification, all of which participate in the regulation of the aging process, and hence contribute to aging-related diseases. Therefore, understanding the epigenetic mechanisms in aging will provide new avenues to develop strategies to delay aging. Indeed, aging interventions based on manipulating epigenetic mechanisms have led to the alleviation of aging or the extension of the lifespan in animal models. Small molecule-based therapies and reprogramming strategies that enable epigenetic rejuvenation have been developed for ameliorating or reversing aging-related conditions. In addition, adopting health-promoting activities, such as caloric restriction, exercise, and calibrating circadian rhythm, has been demonstrated to delay aging. Furthermore, various clinical trials for aging intervention are ongoing, providing more evidence of the safety and efficacy of these therapies. Here, we review recent work on the epigenetic regulation of aging and outline the advances in intervention strategies for aging and age-associated diseases. A better understanding of the critical roles of epigenetics in the aging process will lead to more clinical advances in the prevention of human aging and therapy of aging-related diseases.
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Affiliation(s)
- Kang Wang
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Huicong Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Qinchao Hu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
- Hospital of Stomatology, Sun Yat-sen University, 510060, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, 510060, Guangzhou, China
| | - Lingna Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Jiaqing Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Zikai Zheng
- University of Chinese Academy of Sciences, 100049, Beijing, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
| | - Weiqi Zhang
- University of Chinese Academy of Sciences, 100049, Beijing, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 100101, Beijing, China
| | - Jie Ren
- University of Chinese Academy of Sciences, 100049, Beijing, China.
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 100101, Beijing, China.
| | - Fangfang Zhu
- School of Biomedical Engineering, Shanghai Jiao Tong University, 200030, Shanghai, China.
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 100101, Beijing, China.
- Advanced Innovation Center for Human Brain Protection, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, 100053, Beijing, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, 100101, Beijing, China.
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24
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Roopnarinesingh XR, Porter H, Giles C, Brown C, Georgescu C, Wren J. Multi-tissue DNA methylation microarray signature is predictive of gene function. Epigenetics 2022; 17:1404-1418. [PMID: 35152835 PMCID: PMC9586602 DOI: 10.1080/15592294.2022.2036411] [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: 10/19/2021] [Revised: 01/10/2022] [Accepted: 01/25/2022] [Indexed: 11/03/2022] Open
Abstract
Background Transcriptional correlation networks derived from publicly available gene expression microarrays have been previously shown to be predictive of known gene functions, but less is known about the predictive capacity of correlated DNA methylation at CpG sites. Guilt-by-association co-expression methods can adapted for use with DNA methylation when a representative methylation value is created for each gene. We examine how methylation compares to expression in predicting Gene Ontology terms using both co-methylation and traditional machine learning approaches across different types of representative methylation values per gene. Methods We perform guilt-by-association gene function prediction with a suite of models called Methylation Array Network Analysis, using a network of correlated methylation values derived from over 24,000 samples. In generating the correlation matrix, the performance of different methods of collapsing probe-level data effect on the resulting gene function predictions was compared, along with the use of different regions surrounding the gene of interest. Results Using mean comethylation of a given gene to its annotated term had an overall highest prediction macro-AUC of 0.60 using mean gene body methylation, across all Gene Ontology terms. This was increased using the logistic regression approach with the highest macro-AUC of 0.82 using mean gene body methylation, compared to the naive predictor of 0.72. Conclusion Genes correlated in their methylation state are functionally related. Genes clustered in co-methylation space were enriched for chromatin state, PRC2, immune response, and development-related terms.
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Affiliation(s)
- Xiavan Renaldo Roopnarinesingh
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
- Biochemistry and Molecular Biology Dept, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Hunter Porter
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
- Oklahoma Center for Neuroscience, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Cory Giles
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Chase Brown
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
- Oklahoma Center for Neuroscience, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Constantin Georgescu
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Jonathan Wren
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
- Biochemistry and Molecular Biology Dept, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
- Oklahoma Center for Neuroscience, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
- Stephenson Cancer Center, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
- Department of Geriatric Medicine, University of Oklahoma Health Sciences Center, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
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25
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Li N, Zhu X, Nian W, Li Y, Sun Y, Yuan G, Zhang Z, Yang W, Xu J, Lizaso A, Li B, Zhang Z, Wu L, Zhang Y. Blood-based DNA methylation profiling for the detection of ovarian cancer. Gynecol Oncol 2022; 167:295-305. [PMID: 36096974 DOI: 10.1016/j.ygyno.2022.07.008] [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: 02/27/2022] [Revised: 06/15/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Ovarian cancer is a fatal gynecological cancer due to the lack of effective screening strategies at early stage. This study explored the utility of DNA methylation profiling of blood samples for the detection of ovarian cancer. METHODS Targeted bisulfite sequencing was performed on tissue (n = 152) and blood samples (n = 373) obtained from healthy women, women with benign ovarian tumors, or malignant epithelial ovarian tumors. Based on the tissue-derived differentially-methylated regions, a supervised machine learning algorithm was implemented and cross-validated using the blood-derived DNA methylation profiles of the training cohort (n = 178) to predict and classify each blood sample as malignant or non-malignant. The model was further evaluated using an independent test cohort (n = 184). RESULTS Comparison of the DNA methylation profiles of normal/benign and malignant tumor samples identified 1272 differentially-methylated regions, with 49.4% hypermethylated regions and 50.6% hypomethylated regions. Five-fold cross-validation of the model using the training dataset yielded an area under the curve of 0.94. Using the test dataset, the model accurately predicted non-malignancy in 96.2% of healthy women (n = 53) and 93.5% of women with benign tumors (n = 46). For patients with malignant tumors, the model accurately predicted malignancy in 44.4% of stage I-II (n = 9), 86.4% of stage III (n = 59), 100.0% of stage IV tumors (n = 6), and 81.8% of tumors with unknown stage (n = 11). Overall, the model yielded a predictive accuracy of 89.5%. CONCLUSIONS Our study demonstrates the potential clinical application of blood-based DNA methylation profiling for the detection of ovarian cancer.
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Affiliation(s)
- Ning Li
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Xin Zhu
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China; Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha 410008, China
| | - Weiqi Nian
- Chongqing University Cancer Hospital, Chongqing 400030, China
| | - Yifan Li
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Yangchun Sun
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Guangwen Yuan
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Zhenjing Zhang
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Wenqing Yang
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China; Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha 410008, China
| | - Jiayue Xu
- Burning Rock Biotech, Guangzhou 510300, China
| | | | - Bingsi Li
- Burning Rock Biotech, Guangzhou 510300, China
| | | | - Lingying Wu
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China.
| | - Yu Zhang
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China; Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China.
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26
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Higham J, Kerr L, Zhang Q, Walker RM, Harris SE, Howard DM, Hawkins EL, Sandu AL, Steele JD, Waiter GD, Murray AD, Evans KL, McIntosh AM, Visscher PM, Deary IJ, Cox SR, Sproul D. Local CpG density affects the trajectory and variance of age-associated DNA methylation changes. Genome Biol 2022; 23:216. [PMID: 36253871 PMCID: PMC9575273 DOI: 10.1186/s13059-022-02787-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND DNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown. RESULTS We analyze longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation. CONCLUSIONS Our results, which we reproduce in two independent groups of individuals, demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.
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Affiliation(s)
- Jonathan Higham
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Lyndsay Kerr
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Qian Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Present address: Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Present address: School of Psychology, University of Exeter, Edinburgh, UK
| | - Sarah E Harris
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, Edinburgh, UK
| | - David M Howard
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Emma L Hawkins
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Anca-Larisa Sandu
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - J Douglas Steele
- Division of Imaging Science and Technology, Medical School, University of Dundee, Dundee, UK
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Ian J Deary
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, Edinburgh, UK
| | - Duncan Sproul
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
- CRUK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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27
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Bergstedt J, Azzou SAK, Tsuo K, Jaquaniello A, Urrutia A, Rotival M, Lin DTS, MacIsaac JL, Kobor MS, Albert ML, Duffy D, Patin E, Quintana-Murci L. The immune factors driving DNA methylation variation in human blood. Nat Commun 2022; 13:5895. [PMID: 36202838 PMCID: PMC9537159 DOI: 10.1038/s41467-022-33511-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/21/2022] [Indexed: 11/08/2022] Open
Abstract
Epigenetic changes are required for normal development, yet the nature and respective contribution of factors that drive epigenetic variation in humans remain to be fully characterized. Here, we assessed how the blood DNA methylome of 884 adults is affected by DNA sequence variation, age, sex and 139 factors relating to life habits and immunity. Furthermore, we investigated whether these effects are mediated or not by changes in cellular composition, measured by deep immunophenotyping. We show that DNA methylation differs substantially between naïve and memory T cells, supporting the need for adjustment on these cell-types. By doing so, we find that latent cytomegalovirus infection drives DNA methylation variation and provide further support that the increased dispersion of DNA methylation with aging is due to epigenetic drift. Finally, our results indicate that cellular composition and DNA sequence variation are the strongest predictors of DNA methylation, highlighting critical factors for medical epigenomics studies.
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Affiliation(s)
- Jacob Bergstedt
- Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris, France.
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Sadoune Ait Kaci Azzou
- Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris, France
| | - Kristin Tsuo
- Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris, France
| | - Anthony Jaquaniello
- Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris, France
| | | | - Maxime Rotival
- Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris, France
| | - David T S Lin
- Edwin S.H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Julia L MacIsaac
- Edwin S.H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Michael S Kobor
- Edwin S.H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, Canada
| | | | - Darragh Duffy
- Institut Pasteur, Université Paris Cité, Translational Immunology Unit, Institut Pasteur, Paris, France
| | - Etienne Patin
- Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris, France.
| | - Lluís Quintana-Murci
- Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris, France.
- Chair of Human Genomics and Evolution, Collège de France, Paris, France.
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28
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Zhou A, Wu Z, Zaw Phyo AZ, Torres D, Vishwanath S, Ryan J. Epigenetic aging as a biomarker of dementia and related outcomes: a systematic review. Epigenomics 2022; 14:1125-1138. [PMID: 36154448 DOI: 10.2217/epi-2022-0209] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background: Biological aging may be a robust biomarker of dementia or cognitive performance. This systematic review synthesized the evidence for an association between epigenetic aging and dementia, mild cognitive impairment and cognitive function. Methods: A systematic search was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: 30 eligible articles were included. There was no strong evidence that accelerated epigenetic aging was associated with dementia/mild cognitive impairment (n = 7). There was some evidence of an association with poorer cognition (n = 20), particularly with GrimAge acceleration, but this was inconsistent and varied across cognitive domains. A meta-analysis was not performed due to high study heterogeneity. Conclusion: There is insufficient evidence to indicate that current epigenetic aging clocks can be clinically useful biomarkers of dementia or cognitive aging.
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Affiliation(s)
- Aoshuang Zhou
- Division of Epidemiology, Jockey Club School of Public Health & Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | - Zimu Wu
- Biological Neuropsychiatry & Dementia Unit, School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Aung Zaw Zaw Phyo
- Biological Neuropsychiatry & Dementia Unit, School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Daniel Torres
- Biological Neuropsychiatry & Dementia Unit, School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Swarna Vishwanath
- Biological Neuropsychiatry & Dementia Unit, School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Joanne Ryan
- Biological Neuropsychiatry & Dementia Unit, School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
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29
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Okada D, Zheng C, Cheng JH. Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data. Comput Struct Biotechnol J 2022; 20:4850-4859. [PMID: 36147671 PMCID: PMC9474327 DOI: 10.1016/j.csbj.2022.08.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Differential expression analysis is a standard approach in molecular biology. For example, genes whose expression levels differ between diseased and non-diseased samples are considered to be associated with that disease. On the other hand, differential variability analysis focuses on the differences of the variances of gene expression between sample groups. Although differential variability is also known to capture biological information, its interpretation remains unclear and controversial. Recent single-cell analyses have revealed that differences between sample groups can affect gene expression in a cellular subset-specific manner or by altering the proportion of a particular cellular subset. The aim of this study is to clarify the interpretation of mean and variance of bulk gene expression data. METHOD We developed a mathematical model in which the bulk gene expression value is proportional to the mean value of the single-cell gene expression profile. Based on this model, we performed theoretical, simulated and real single-cell RNA-seq data analyses. RESULT AND CONCLUSION We identified how differences in single-cell gene expression profiles affect the differences in the mean and the variance of bulk gene expression. It is shown that differential expression analysis of bulk expression data can overlook significant changes in gene expression at the single-cell level. Further, differential variability analysis capture the complex feature affected by different gene expression shifts for each subset, changes in the proportions of cellular subsets, and variation in single-cell distribution parameters among samples.
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Affiliation(s)
- Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto 6068507, Kyoto, Japan
| | - Cheng Zheng
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto 6068507, Kyoto, Japan
| | - Jian Hao Cheng
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto 6068507, Kyoto, Japan
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30
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Schaffner SL, Kobor MS. DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome. Front Genet 2022; 13:971298. [PMID: 36061205 PMCID: PMC9437223 DOI: 10.3389/fgene.2022.971298] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/25/2022] [Indexed: 12/15/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.
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Affiliation(s)
- Samantha L. Schaffner
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Michael S. Kobor
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
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31
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Derakhshan M, Kessler NJ, Ishida M, Demetriou C, Brucato N, Moore G, Fall CHD, Chandak GR, Ricaut FX, Prentice A, Hellenthal G, Silver M. Tissue- and ethnicity-independent hypervariable DNA methylation states show evidence of establishment in the early human embryo. Nucleic Acids Res 2022; 50:6735-6752. [PMID: 35713545 PMCID: PMC9749461 DOI: 10.1093/nar/gkac503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/06/2022] [Accepted: 05/27/2022] [Indexed: 12/24/2022] Open
Abstract
We analysed DNA methylation data from 30 datasets comprising 3474 individuals, 19 tissues and 8 ethnicities at CpGs covered by the Illumina450K array. We identified 4143 hypervariable CpGs ('hvCpGs') with methylation in the top 5% most variable sites across multiple tissues and ethnicities. hvCpG methylation was influenced but not determined by genetic variation, and was not linked to probe reliability, epigenetic drift, age, sex or cell heterogeneity effects. hvCpG methylation tended to covary across tissues derived from different germ-layers and hvCpGs were enriched for proximity to ERV1 and ERVK retrovirus elements. hvCpGs were also enriched for loci previously associated with periconceptional environment, parent-of-origin-specific methylation, and distinctive methylation signatures in monozygotic twins. Together, these properties position hvCpGs as strong candidates for studying how stochastic and/or environmentally influenced DNA methylation states which are established in the early embryo and maintained stably thereafter can influence life-long health and disease.
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Affiliation(s)
| | - Noah J Kessler
- Department of Genetics, University of Cambridge,
Cambridge CB2 3EH, UK
| | - Miho Ishida
- UCL Great Ormond Street Institute of Child Health, UK
| | | | - Nicolas Brucato
- Laboratoire Évolution and Diversité Biologique (EDB UMR 5174), Université
de Toulouse Midi-Pyrénées, CNRS, IRD, UPS,Toulouse, France
| | | | - Caroline H D Fall
- MRC Lifecourse Epidemiology Unit, University of Southampton,
Southampton, UK
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC Group), CSIR-Centre for Cellular
and Molecular Biology,Hyderabad, India
| | - Francois-Xavier Ricaut
- Laboratoire Évolution and Diversité Biologique (EDB UMR 5174), Université
de Toulouse Midi-Pyrénées, CNRS, IRD, UPS,Toulouse, France
| | - Andrew M Prentice
- Medical Research Council Unit The Gambia at the London School of Hygiene
and Tropical Medicine, The Gambia
| | - Garrett Hellenthal
- UCL Genetics Institute, University College London,
Gower Street, London WC1E 6BT, UK
| | - Matt J Silver
- London School of Hygiene and Tropical Medicine, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene
and Tropical Medicine, The Gambia
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Epigenetic Studies for Evaluation of NPS Toxicity: Focus on Synthetic Cannabinoids and Cathinones. Biomedicines 2022; 10:biomedicines10061398. [PMID: 35740419 PMCID: PMC9219842 DOI: 10.3390/biomedicines10061398] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 11/26/2022] Open
Abstract
In the recent decade, numerous new psychoactive substances (NPSs) have been added to the illicit drug market. These are synthetized to mimic the effects of classic drugs of abuse (i.e., cannabis, cocaine, etc.), with the purpose of bypassing substance legislations and increasing the pharmacotoxicological effects. To date, research into the acute pharmacological effects of new NPSs is ongoing and necessary in order to provide an appropriate contribution to public health. In fact, multiple examples of NPS-related acute intoxication and mortality have been recorded in the literature. Accordingly, several in vitro and in vivo studies have investigated the pharmacotoxicological profiles of these compounds, revealing that they can cause adverse effects involving various organ systems (i.e., cardiovascular, respiratory effects) and highlighting their potential increased consumption risks. In this sense, NPSs should be regarded as a complex issue that requires continuous monitoring. Moreover, knowledge of long-term NPS effects is lacking. Because genetic and environmental variables may impact NPS responses, epigenetics may aid in understanding the processes behind the harmful events induced by long-term NPS usage. Taken together, “pharmacoepigenomics” may provide a new field of combined study on genetic differences and epigenetic changes in drug reactions that might be predictive in forensic implications.
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Seale K, Horvath S, Teschendorff A, Eynon N, Voisin S. Making sense of the ageing methylome. Nat Rev Genet 2022; 23:585-605. [PMID: 35501397 DOI: 10.1038/s41576-022-00477-6] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2022] [Indexed: 12/22/2022]
Abstract
Over time, the human DNA methylation landscape accrues substantial damage, which has been associated with a broad range of age-related diseases, including cardiovascular disease and cancer. Various age-related DNA methylation changes have been described, including at the level of individual CpGs, such as differential and variable methylation, and at the level of the whole methylome, including entropy and correlation networks. Here, we review these changes in the ageing methylome as well as the statistical tools that can be used to quantify them. We detail the evidence linking DNA methylation to ageing phenotypes and the longevity strategies aimed at altering both DNA methylation patterns and machinery to extend healthspan and lifespan. Lastly, we discuss theories on the mechanistic causes of epigenetic ageing.
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Affiliation(s)
- Kirsten Seale
- Institute for Health and Sport (iHeS), Victoria University, Footscray, Melbourne, Victoria, Australia
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Altos Labs, San Diego, CA, USA
| | - Andrew Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.,UCL Cancer Institute, University College London, London, UK
| | - Nir Eynon
- Institute for Health and Sport (iHeS), Victoria University, Footscray, Melbourne, Victoria, Australia.
| | - Sarah Voisin
- Institute for Health and Sport (iHeS), Victoria University, Footscray, Melbourne, Victoria, Australia.
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Ravaioli F, Zampieri M, Morandi L, Pirazzini C, Pellegrini C, De Fanti S, Gensous N, Pirazzoli GL, Sambati L, Ghezzo A, Ciccarone F, Reale A, Monti D, Salvioli S, Caiafa P, Capri M, Bürkle A, Moreno-Villanueva M, Garagnani P, Franceschi C, Bacalini MG. DNA Methylation Analysis of Ribosomal DNA in Adults With Down Syndrome. Front Genet 2022; 13:792165. [PMID: 35571061 PMCID: PMC9094685 DOI: 10.3389/fgene.2022.792165] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 03/18/2022] [Indexed: 01/08/2023] Open
Abstract
Control of ribosome biogenesis is a critical aspect of the regulation of cell metabolism. As ribosomal genes (rDNA) are organized in repeated clusters on chromosomes 13, 14, 15, 21, and 22, trisomy of chromosome 21 confers an excess of rDNA copies to persons with Down syndrome (DS). Previous studies showed an alteration of ribosome biogenesis in children with DS, but the epigenetic regulation of rDNA genes has not been investigated in adults with DS so far. In this study, we used a targeted deep-sequencing approach to measure DNA methylation (DNAm) of rDNA units in whole blood from 69 adults with DS and 95 euploid controls. We further evaluated the expression of the precursor of ribosomal RNAs (RNA45S) in peripheral blood mononuclear cells (PBMCs) from the same subjects. We found that the rDNA promoter tends to be hypermethylated in DS concerning the control group. The analysis of epihaplotypes (the combination of methylated and unmethylated CpG sites along the same DNA molecule) showed a significantly lower intra-individual diversity in the DS group, which at the same time was characterized by a higher interindividual variability. Finally, we showed that RNA45S expression is lower in adults with DS. Collectively, our results suggest a rearrangement of the epigenetic profile of rDNA in DS, possibly to compensate for the extranumerary rDNA copies. Future studies should assess whether the regulation of ribosome biogenesis can contribute to the pathogenesis of DS and explain the clinical heterogeneity characteristic of the syndrome.
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Affiliation(s)
- Francesco Ravaioli
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Michele Zampieri
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Luca Morandi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto Delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Chiara Pirazzini
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Bologna, Italy
| | | | - Sara De Fanti
- Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy
- Interdepartmental Centre Alma Mater Research Institute on Global Challenges and Climate Change, University of Bologna, Bologna, Italy
| | - Noémie Gensous
- Department of Internal Medicine and Clinical Immunology, CHU Bordeaux (Groupe Hospitalier Saint-André), Bordeaux, France
- UMR/CNRS 5164, ImmunoConcEpT, CNRS, University of Bordeaux, Bordeaux, France
| | | | - Luisa Sambati
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, U.O.C. Clinica Neurologica Rete Neurologica Metropolitana (NEUROMET), Bologna, Italy
| | | | - Fabio Ciccarone
- IRCCS San Raffaele Roma, Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, Rome, Italy
| | - Anna Reale
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Daniela Monti
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Florence, Italy
| | - Stefano Salvioli
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Paola Caiafa
- Department of Cellular Biotechnologies and Haematology, Sapienza University of Rome, Rome, Italy
| | - Miriam Capri
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Alexander Bürkle
- Molecular Toxicology Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Maria Moreno-Villanueva
- Molecular Toxicology Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
- Applied Biomedical Research Center (CRBA), S. Orsola-Malpighi Polyclinic, Bologna, Italy
- CNR Institute of Molecular Genetics “Luigi Luca Cavalli-Sforza”—Unit of Bologna, Bologna, Italy
- Department of Laboratory Medicine, Clinical Chemistry, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden
| | - Claudio Franceschi
- Laboratory of Systems Medicine of Healthy Aging, Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Maria Giulia Bacalini
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Bologna, Italy
- *Correspondence: Maria Giulia Bacalini,
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Dietary Restriction and Rapamycin Affect Brain Aging in Mice by Attenuating Age-Related DNA Methylation Changes. Genes (Basel) 2022; 13:genes13040699. [PMID: 35456505 PMCID: PMC9030181 DOI: 10.3390/genes13040699] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/02/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023] Open
Abstract
The fact that dietary restriction (DR) and long-term rapamycin treatment (RALL) can ameliorate the aging process has been reported by many researchers. As the interface between external and genetic factors, epigenetic modification such as DNA methylation may have latent effects on the aging rate at the molecular level. To understand the mechanism behind the impacts of dietary restriction and rapamycin on aging, DNA methylation and gene expression changes were measured in the hippocampi of different-aged mice. Examining the single-base resolution of DNA methylation, we discovered that both dietary restriction and rapamycin treatment can maintain DNA methylation in a younger state compared to normal-aged mice. Through functional enrichment analysis of genes in which DNA methylation or gene expression can be affected by DR/RALL, we found that DR/RALL may retard aging through a relationship in which DNA methylation and gene expression work together not only in the same gene but also in the same biological process. This study is instructive for understanding the maintenance of DNA methylation by DR/RALL in the aging process, as well as the role of DR and RALL in the amelioration of aging.
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Mozhui K, Lu AT, Li CZ, Haghani A, Sandoval-Sierra JV, Wu Y, Williams RW, Horvath S. Genetic loci and metabolic states associated with murine epigenetic aging. eLife 2022; 11:e75244. [PMID: 35389339 PMCID: PMC9049972 DOI: 10.7554/elife.75244] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/01/2022] [Indexed: 11/25/2022] Open
Abstract
Changes in DNA methylation (DNAm) are linked to aging. Here, we profile highly conserved CpGs in 339 predominantly female mice belonging to the BXD family for which we have deep longevity and genomic data. We use a 'pan-mammalian' microarray that provides a common platform for assaying the methylome across mammalian clades. We computed epigenetic clocks and tested associations with DNAm entropy, diet, weight, metabolic traits, and genetic variation. We describe the multifactorial variance of methylation at these CpGs and show that high-fat diet augments the age-related changes. Entropy increases with age. The progression to disorder, particularly at CpGs that gain methylation over time, was predictive of genotype-dependent life expectancy. The longer-lived BXD strains had comparatively lower entropy at a given age. We identified two genetic loci that modulate epigenetic age acceleration (EAA): one on chromosome (Chr) 11 that encompasses the Erbb2/Her2 oncogenic region, and the other on Chr19 that contains a cytochrome P450 cluster. Both loci harbor genes associated with EAA in humans, including STXBP4, NKX2-3, and CUTC. Transcriptome and proteome analyses revealed correlations with oxidation-reduction, metabolic, and immune response pathways. Our results highlight concordant loci for EAA in humans and mice, and demonstrate a tight coupling between the metabolic state and epigenetic aging.
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Affiliation(s)
- Khyobeni Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, College of MedicineMemphisUnited States
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of MedicineMemphisUnited States
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California Los AngelesLos AngelesUnited States
| | - Caesar Z Li
- Department of Human Genetics, David Geffen School of Medicine, University of California Los AngelesLos AngelesUnited States
| | - Amin Haghani
- Department of Biostatistics, Fielding School of Public Health, University of California Los AngelesLos AngelesUnited States
| | - Jose Vladimir Sandoval-Sierra
- Department of Preventive Medicine, University of Tennessee Health Science Center, College of MedicineMemphisUnited States
| | - Yibo Wu
- YCI Laboratory for Next-Generation Proteomics, RIKEN Center for Integrative Medical SciencesYokohamaJapan
- University of GenevaGenevaSwitzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of MedicineMemphisUnited States
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los AngelesLos AngelesUnited States
- Department of Biostatistics, Fielding School of Public Health, University of California Los AngelesLos AngelesUnited States
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Jonkman TH, Dekkers KF, Slieker RC, Grant CD, Ikram MA, van Greevenbroek MMJ, Franke L, Veldink JH, Boomsma DI, Slagboom PE, Consortium BIOS, Heijmans BT. Functional genomics analysis identifies T and NK cell activation as a driver of epigenetic clock progression. Genome Biol 2022; 23:24. [PMID: 35031073 PMCID: PMC8759260 DOI: 10.1186/s13059-021-02585-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 12/20/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Epigenetic clocks use DNA methylation (DNAm) levels of specific sets of CpG dinucleotides to accurately predict individual chronological age. A popular application of these clocks is to explore whether the deviation of predicted age from chronological age is associated with disease phenotypes, where this deviation is interpreted as a potential biomarker of biological age. This wide application, however, contrasts with the limited insight in the processes that may drive the running of epigenetic clocks. RESULTS We perform a functional genomics analysis on four epigenetic clocks, including Hannum's blood predictor and Horvath's multi-tissue predictor, using blood DNA methylome and transcriptome data from 3132 individuals. The four clocks result in similar predictions of individual chronological age, and their constituting CpGs are correlated in DNAm level and are enriched for similar histone modifications and chromatin states. Interestingly, DNAm levels of CpGs from the clocks are commonly associated with gene expression in trans. The gene sets involved are highly overlapping and enriched for T cell processes. Further analysis of the transcriptome and methylome of sorted blood cell types identifies differences in DNAm between naive and activated T and NK cells as a probable contributor to the clocks. Indeed, within the same donor, the four epigenetic clocks predict naive cells to be up to 40 years younger than activated cells. CONCLUSIONS The ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells, an established feature of immuno-senescence. This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes.
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Affiliation(s)
- Thomas H Jonkman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Koen F Dekkers
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Crystal D Grant
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Marleen M J van Greevenbroek
- Department of Internal Medicine and School for Cardiovascular Diseases, Maastricht University Medical Center, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Centre Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | | | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands.
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Berglund A, Matta J, Encarnación-Medina J, Ortiz-Sanchéz C, Dutil J, Linares R, Marcial J, Abreu-Takemura C, Moreno N, Putney R, Chakrabarti R, Lin HY, Yamoah K, Osterman CD, Wang L, Dhillon J, Kim Y, Kim SJ, Ruiz-Deya G, Park JY. Dysregulation of DNA Methylation and Epigenetic Clocks in Prostate Cancer among Puerto Rican Men. Biomolecules 2021; 12:2. [PMID: 35053153 PMCID: PMC8773891 DOI: 10.3390/biom12010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 12/02/2022] Open
Abstract
In 2021, approximately 248,530 new prostate cancer (PCa) cases are estimated in the United States. Hispanic/Latinos (H/L) are the second largest racial/ethnic group in the US. The objective of this study was to assess DNA methylation patterns between aggressive and indolent PCa along with ancestry proportions in 49 H/L men from Puerto Rico (PR). Prostate tumors were classified as aggressive (n = 17) and indolent (n = 32) based on the Gleason score. Genomic DNA samples were extracted by macro-dissection. DNA methylation patterns were assessed using the Illumina EPIC DNA methylation platform. We used ADMIXTURE to estimate global ancestry proportions. We identified 892 differentially methylated genes in prostate tumor tissues as compared with normal tissues. Based on an epigenetic clock model, we observed that the total number of stem cell divisions (TNSC) and stem cell division rate (SCDR) were significantly higher in tumor than adjacent normal tissues. Regarding PCa aggressiveness, 141 differentially methylated genes were identified. Ancestry proportions of PR men were estimated as African, European, and Indigenous American; these were 24.1%, 64.2%, and 11.7%, respectively. The identification of DNA methylation profiles associated with risk and aggressiveness of PCa in PR H/L men will shed light on potential mechanisms contributing to PCa disparities in PR population.
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Affiliation(s)
- Anders Berglund
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA; (A.B.); (R.P.); (Y.K.)
| | - Jaime Matta
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Jarline Encarnación-Medina
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Carmen Ortiz-Sanchéz
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Julie Dutil
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Raymond Linares
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Joshua Marcial
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Caren Abreu-Takemura
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Natasha Moreno
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Ryan Putney
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA; (A.B.); (R.P.); (Y.K.)
| | - Ratna Chakrabarti
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL 32816, USA;
| | - Hui-Yi Lin
- Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA;
| | - Kosj Yamoah
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA;
| | - Carlos Diaz Osterman
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Liang Wang
- Department of Molecular Biology, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA;
| | - Jasreman Dhillon
- Department of Pathology, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA;
| | - Youngchul Kim
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA; (A.B.); (R.P.); (Y.K.)
| | - Seung Joon Kim
- Department of Internal Medicine, Catholic University of Korea, Seoul 06591, Korea;
| | - Gilberto Ruiz-Deya
- Department of Basic Sciences, Ponce Research Institute, School of Medicine, Ponce Health Sciences University, Ponce 00716-2347, Puerto Rico; (J.M.); (J.E.-M.); (C.O.-S.); (J.D.); (R.L.); (J.M.); (C.A.-T.); (C.D.O.); (G.R.-D.)
| | - Jong Y. Park
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA
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Porter HL, Brown CA, Roopnarinesingh X, Giles CB, Georgescu C, Freeman WM, Wren JD. Many chronological aging clocks can be found throughout the epigenome: Implications for quantifying biological aging. Aging Cell 2021; 20:e13492. [PMID: 34655509 PMCID: PMC8590098 DOI: 10.1111/acel.13492] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 08/19/2021] [Accepted: 09/09/2021] [Indexed: 01/01/2023] Open
Abstract
Epigenetic alterations are a hallmark of aging and age-related diseases. Computational models using DNA methylation data can create "epigenetic clocks" which are proposed to reflect "biological" aging. Thus, it is important to understand the relationship between predictive clock sites and aging biology. To do this, we examined over 450,000 methylation sites from 9,699 samples. We found ~20% of the measured genomic cytosines can be used to make many different epigenetic clocks whose age prediction performance surpasses that of telomere length. Of these predictive sites, the average methylation change over a lifetime was small (~1.5%) and these sites were under-represented in canonical regions of epigenetic regulation. There was only a weak association between "accelerated" epigenetic aging and disease. We also compare tissue-specific and pan-tissue clock performance. This is critical to applying clocks both to new sample sets in basic research, as well as understanding if clinically available tissues will be feasible samples to evaluate "epigenetic aging" in unavailable tissues (e.g., brain). Despite the reproducible and accurate age predictions from DNA methylation data, these findings suggest they may have limited utility as currently designed in understanding the molecular biology of aging and may not be suitable as surrogate endpoints in studies of anti-aging interventions. Purpose-built clocks for specific tissues age ranges or phenotypes may perform better for their specific purpose. However, if purpose-built clocks are necessary for meaningful predictions, then the utility of clocks and their application in the field needs to be considered in that context.
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Affiliation(s)
- Hunter L. Porter
- Oklahoma Medical Research FoundationOklahomaOKUSA
- University of Oklahoma Health Sciences CenterOklahomaOKUSA
- Oklahoma Center for Geroscience and Healthy Brain AgingOklahomaOKUSA
| | - Chase A. Brown
- Oklahoma Medical Research FoundationOklahomaOKUSA
- University of Oklahoma Health Sciences CenterOklahomaOKUSA
| | - Xiavan Roopnarinesingh
- Oklahoma Medical Research FoundationOklahomaOKUSA
- University of Oklahoma Health Sciences CenterOklahomaOKUSA
| | - Cory B. Giles
- Oklahoma Medical Research FoundationOklahomaOKUSA
- University of Oklahoma Health Sciences CenterOklahomaOKUSA
- Oklahoma Center for Geroscience and Healthy Brain AgingOklahomaOKUSA
| | | | - Willard M. Freeman
- Oklahoma Medical Research FoundationOklahomaOKUSA
- University of Oklahoma Health Sciences CenterOklahomaOKUSA
| | - Jonathan D. Wren
- Oklahoma Medical Research FoundationOklahomaOKUSA
- University of Oklahoma Health Sciences CenterOklahomaOKUSA
- Oklahoma Center for Geroscience and Healthy Brain AgingOklahomaOKUSA
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40
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van Dongen J, Gordon SD, McRae AF, Odintsova VV, Mbarek H, Breeze CE, Sugden K, Lundgren S, Castillo-Fernandez JE, Hannon E, Moffitt TE, Hagenbeek FA, van Beijsterveldt CEM, Jan Hottenga J, Tsai PC, Min JL, Hemani G, Ehli EA, Paul F, Stern CD, Heijmans BT, Slagboom PE, Daxinger L, van der Maarel SM, de Geus EJC, Willemsen G, Montgomery GW, Reversade B, Ollikainen M, Kaprio J, Spector TD, Bell JT, Mill J, Caspi A, Martin NG, Boomsma DI. Identical twins carry a persistent epigenetic signature of early genome programming. Nat Commun 2021; 12:5618. [PMID: 34584077 PMCID: PMC8479069 DOI: 10.1038/s41467-021-25583-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 07/19/2021] [Indexed: 02/08/2023] Open
Abstract
Monozygotic (MZ) twins and higher-order multiples arise when a zygote splits during pre-implantation stages of development. The mechanisms underpinning this event have remained a mystery. Because MZ twinning rarely runs in families, the leading hypothesis is that it occurs at random. Here, we show that MZ twinning is strongly associated with a stable DNA methylation signature in adult somatic tissues. This signature spans regions near telomeres and centromeres, Polycomb-repressed regions and heterochromatin, genes involved in cell-adhesion, WNT signaling, cell fate, and putative human metastable epialleles. Our study also demonstrates a never-anticipated corollary: because identical twins keep a lifelong molecular signature, we can retrospectively diagnose if a person was conceived as monozygotic twin.
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Affiliation(s)
- Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
| | - Scott D Gordon
- Queensland Institute of Medical Research Berghofer, Brisbane, QLD, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Hamdi Mbarek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | | | - Karen Sugden
- Department of Psychology and Neuroscience and Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Sara Lundgren
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | | | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience and Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Catharina E M van Beijsterveldt
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Josine L Min
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Erik A Ehli
- Avera Institute for Human Genetics, Sioux Falls, SD, USA
| | - Franziska Paul
- Institute of Molecular and Cellular Biology, A*STAR, Singapore, Singapore
| | - Claudio D Stern
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Lucia Daxinger
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Bruno Reversade
- Institute of Molecular and Cellular Biology, A*STAR, Singapore, Singapore
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
- Medical Genetics Department, KOC University, School of Medicine, Istanbul, Turkey
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Avshalom Caspi
- Department of Psychology and Neuroscience and Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Nicholas G Martin
- Queensland Institute of Medical Research Berghofer, Brisbane, QLD, Australia
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
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41
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Gharipour M, Mani A, Amini Baghbahadorani M, de Souza Cardoso CK, Jahanfar S, Sarrafzadegan N, de Oliveira C, Silveira EA. How Are Epigenetic Modifications Related to Cardiovascular Disease in Older Adults? Int J Mol Sci 2021; 22:9949. [PMID: 34576113 PMCID: PMC8470616 DOI: 10.3390/ijms22189949] [Citation(s) in RCA: 9] [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: 08/17/2021] [Revised: 09/08/2021] [Accepted: 09/09/2021] [Indexed: 12/16/2022] Open
Abstract
The rate of aging has increased globally during recent decades and has led to a rising burden of age-related diseases such as cardiovascular disease (CVD). At the molecular level, epigenetic modifications have been shown recently to alter gene expression during the life course and impair cellular function. In this regard, several CVD risk factors, such as lifestyle and environmental factors, have emerged as key factors in epigenetic modifications within the cardiovascular system. In this study, we attempted to summarized recent evidence related to epigenetic modification, inflammation response, and CVD in older adults as well as the effect of lifestyle modification as a preventive strategy in this age group. Recent evidence showed that lifestyle and environmental factors may affect epigenetic mechanisms, such as DNA methylation, histone acetylation, and miRNA expression. Several substances or nutrients such as selenium, magnesium, curcumin, and caffeine (present in coffee and some teas) could regulate epigenetics. Similarly, physical inactivity, alcohol consumption, air pollutants, psychological stress, and shift working are well-known modifiers of epigenetic patterns. Understanding the exact ways that lifestyle and environmental factors could affect the expression of genes could help to influence the time of incidence and severity of aging-associated diseases. This review highlighted that a healthy lifestyle throughout the life course, such as a healthy diet rich in fibers, vitamins, and essential elements, and specific fatty acids, adequate physical activity and sleep, smoking cessation, and stress control, could be useful tools in preventing epigenetic changes that lead to impaired cardiovascular function.
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Affiliation(s)
- Mojgan Gharipour
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan 8158388994, Iran;
| | - Arya Mani
- Cardiovascular Research Center, Department of Internal Medicine, and Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA;
| | - Mona Amini Baghbahadorani
- Interventional Cardiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan 8158388994, Iran;
| | - Camila Kellen de Souza Cardoso
- School of Social Sciences and Health, Nutrition Course, Pontifical Catholic University of Goias, Goiânia 74605-010, Brazil;
| | - Shayesteh Jahanfar
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MI 02111, USA;
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan 8158388994, Iran;
- Faculty of Medicine, School of Population and Public Health, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Cesar de Oliveira
- Department of Epidemiology & Public Health, Institute of Epidemiology & Health Care, University College London, London WC1E 6BT, UK;
| | - Erika Aparecida Silveira
- Department of Epidemiology & Public Health, Institute of Epidemiology & Health Care, University College London, London WC1E 6BT, UK;
- Graduate Program in Health Sciences, Faculty of Medicine, Federal University of Goiás, Goiânia 74690-900, Brazil
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42
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Johnson AA, Shokhirev MN. Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease. Rejuvenation Res 2021; 24:377-389. [PMID: 34486398 DOI: 10.1089/rej.2021.0012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of genes for age prediction, we found that the following six genes were prioritized in all five tissues: CHI3L2, CIDEC, FCGR3A, RPS4Y1, SLC11A1, and VTCN1. Since being selected for age prediction in multiple tissues is unique, we decided to explore these pan-tissue clock genes in greater detail. In the present study, we began by performing over-representation and network topology-based enrichment analyses in the Gene Ontology Biological Process database. These analyses revealed that the immunological terms "response to protozoan," "immune response," and "positive regulation of immune system process" were significantly enriched by these clock inputs. Expression analyses in mouse and human tissues identified that these inputs are frequently upregulated or downregulated with age. A detailed literature search showed that all six genes had noteworthy connections to age-related disease. For example, mice deficient in Cidec are protected against various metabolic defects, while suppressing VTCN1 inhibits age-related cancers in mouse models. Using a large multitissue transcriptomic dataset, we additionally generate a novel, minimalistic aging clock that can predict human age using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging.
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Affiliation(s)
| | - Maxim N Shokhirev
- Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, La Jolla, California, USA
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43
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Tan Q, Li S, Sørensen M, Nygaard M, Mengel‐From J, Christensen K. Age patterns of intra-pair DNA methylation discordance in twins: Sex difference in epigenomic instability and implication on survival. Aging Cell 2021; 20:e13460. [PMID: 34427971 PMCID: PMC8441297 DOI: 10.1111/acel.13460] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 07/22/2021] [Accepted: 08/06/2021] [Indexed: 12/19/2022] Open
Abstract
Aging is a biological process linked to specific patterns and changes in the epigenome. We hypothesize that age‐related variation in the DNA methylome could reflect cumulative environmental modulation to the epigenome which could impact epigenomic instability and survival differentially by sex. To test the hypothesis, we performed sex‐stratified epigenome‐wide association studies on age‐related intra‐pair DNA methylation discordance in 492 twins aged 56–80 years. We identified 3084 CpGs showing increased methylation variability with age (FDR < 0.05, 7 CpGs with p < 1e‐07) in male twins but no significant site found in female twins. The results were replicated in an independent cohort of 292 twins aged 30–74 years with 37% of the discovery CpGs successfully replicated in male twins. Functional annotation showed that genes linked to the identified CpGs were significantly enriched in signaling pathways, neurological functions, extracellular matrix assembly, and cancer. We further explored the implication of discovery CpGs on individual survival in an old cohort of 224 twins (220 deceased). In total, 264 CpGs displayed significant association with risk of death in male twins. In female twins, 175 of the male discovery CpGs also showed non‐random correlation with mortality. Intra‐pair comparison showed that majority of the discovery CpGs have higher methylation in the longer‐lived twins suggesting that loss of DNA methylation during aging contributes to increased risk of death which is more pronounced in male twins. In conclusion, age‐related epigenomic instability in the DNA methylome is more evident in males than in females and could impact individual survival and contribute to sex difference in human lifespan.
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Affiliation(s)
- Qihua Tan
- Epidemiology, Biostatistics and Biodemography Department of Public Health University of Southern Denmark Odense Denmark
- Unit of Human Genetics Department of Clinical Research University of Southern Denmark Odense Denmark
| | - Shuxia Li
- Epidemiology, Biostatistics and Biodemography Department of Public Health University of Southern Denmark Odense Denmark
| | - Mette Sørensen
- Epidemiology, Biostatistics and Biodemography Department of Public Health University of Southern Denmark Odense Denmark
| | - Marianne Nygaard
- Epidemiology, Biostatistics and Biodemography Department of Public Health University of Southern Denmark Odense Denmark
| | - Jonas Mengel‐From
- Epidemiology, Biostatistics and Biodemography Department of Public Health University of Southern Denmark Odense Denmark
| | - Kaare Christensen
- Epidemiology, Biostatistics and Biodemography Department of Public Health University of Southern Denmark Odense Denmark
- Unit of Human Genetics Department of Clinical Research University of Southern Denmark Odense Denmark
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44
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Palla G, Pollner P, Börcsök J, Major A, Molnár B, Csabai I. Hierarchy and control of ageing-related methylation networks. PLoS Comput Biol 2021; 17:e1009327. [PMID: 34534207 PMCID: PMC8480875 DOI: 10.1371/journal.pcbi.1009327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 09/29/2021] [Accepted: 08/05/2021] [Indexed: 11/28/2022] Open
Abstract
DNA methylation provides one of the most widely studied biomarkers of ageing. Since the methylation of CpG dinucleotides function as switches in cellular mechanisms, it is plausible to assume that by proper adjustment of these switches age may be tuned. Though, adjusting hundreds of CpG methylation levels coherently may never be feasible and changing just a few positions may lead to biologically unstable state. A prominent example of methylation-based age estimators is provided by Horvath's clock, based on 353 CpG dinucleotides, showing a high correlation (not necessarily causation) with chronological age across multiple tissue types. On this small subset of CpG dinucleotides we demonstrate how the adjustment of one methylation level leads to a cascade of changes at other sites. Among the studied subset, we locate the most important CpGs (and related genes) that may have a large influence on the rest of the sub-system. According to our analysis, the structure of this network is way more hierarchical compared to what one would expect based on ensembles of uncorrelated connections. Therefore, only a handful of CpGs is enough to modify the system towards a desired state. When propagation of the change over the network is taken into account, the resulting modification in the predicted age can be significantly larger compared to the effect of isolated CpG perturbations. By adjusting the most influential single CpG site and following the propagation of methylation level changes we can reach up to 5.74 years in virtual age reduction, significantly larger than without taking into account of the network control. Extending our approach to the whole methylation network may identify key nodes that have controller role in the ageing process.
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Affiliation(s)
- Gergely Palla
- Health Services Management Training Centre, Semmelweis University, Budapest, Hungary
- MTA-ELTE Statistical and Biological Physics Research Group, Dept. of Biological Physics, Eötvös University, Budapest, Hungary
| | - Péter Pollner
- Health Services Management Training Centre, Semmelweis University, Budapest, Hungary
- MTA-ELTE Statistical and Biological Physics Research Group, Dept. of Biological Physics, Eötvös University, Budapest, Hungary
| | - Judit Börcsök
- Danish Cancer Society Research Center, Copenhagen, Denmark
| | - András Major
- Dept. of Physics of Complex Systems, ELTE Eötvös University, Budapest, Hungary
| | - Béla Molnár
- Molecular Medicine Research Group, Hungarian Academy of Sciences, Budapest, Hungary
| | - István Csabai
- Dept. of Physics of Complex Systems, ELTE Eötvös University, Budapest, Hungary
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45
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Yang T, Niu J, Chen H, Wei P. Estimation of total mediation effect for high-dimensional omics mediators. BMC Bioinformatics 2021; 22:414. [PMID: 34425752 PMCID: PMC8381496 DOI: 10.1186/s12859-021-04322-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 08/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Environmental exposures can regulate intermediate molecular phenotypes, such as gene expression, by different mechanisms and thereby lead to various health outcomes. It is of significant scientific interest to unravel the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposure and traits. Mediation analysis is an important tool for investigating such relationships. However, it has mainly focused on low-dimensional settings, and there is a lack of a good measure of the total mediation effect. Here, we extend an R-squared (R[Formula: see text]) effect size measure, originally proposed in the single-mediator setting, to the moderate- and high-dimensional mediator settings in the mixed model framework. RESULTS Based on extensive simulations, we compare our measure and estimation procedure with several frequently used mediation measures, including product, proportion, and ratio measures. Our R[Formula: see text]-based second-moment measure has small bias and variance under the correctly specified model. To mitigate potential bias induced by non-mediators, we examine two variable selection procedures, i.e., iterative sure independence screening and false discovery rate control, to exclude the non-mediators. We establish the consistency of the proposed estimation procedures and introduce a resampling-based confidence interval. By applying the proposed estimation procedure, we found that 38% of the age-related variations in systolic blood pressure can be explained by gene expression profiles in the Framingham Heart Study of 1711 individuals. An R package "RsqMed" is available on CRAN. CONCLUSION R-squared (R[Formula: see text]) is an effective and efficient measure for total mediation effect especially under high-dimensional setting.
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Affiliation(s)
- Tianzhong Yang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, USA
- Division of Biostatistics, University of Minnesota, Minneapolis, USA
| | - Jingbo Niu
- Section of Nephrology, Baylor College of Medicine, Houston, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, USA
- Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA.
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46
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Al-Ghanmy HS, Al-Rashedi NA, Ayied AY. Age estimation by DNA methylation levels in Iraqi subjects. GENE REPORTS 2021. [DOI: 10.1016/j.genrep.2021.101022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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47
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Acton RJ, Yuan W, Gao F, Xia Y, Bourne E, Wozniak E, Bell J, Lillycrop K, Wang J, Dennison E, Harvey NC, Mein CA, Spector TD, Hysi PG, Cooper C, Bell CG. The genomic loci of specific human tRNA genes exhibit ageing-related DNA hypermethylation. Nat Commun 2021; 12:2655. [PMID: 33976121 PMCID: PMC8113476 DOI: 10.1038/s41467-021-22639-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 03/05/2021] [Indexed: 02/03/2023] Open
Abstract
The epigenome has been shown to deteriorate with age, potentially impacting on ageing-related disease. tRNA, while arising from only ˜46 kb (<0.002% genome), is the second most abundant cellular transcript. tRNAs also control metabolic processes known to affect ageing, through core translational and additional regulatory roles. Here, we interrogate the DNA methylation state of the genomic loci of human tRNA. We identify a genomic enrichment for age-related DNA hypermethylation at tRNA loci. Analysis in 4,350 MeDIP-seq peripheral-blood DNA methylomes (16-82 years), identifies 44 and 21 hypermethylating specific tRNAs at study-and genome-wide significance, respectively, contrasting with none hypomethylating. Validation and replication (450k array and independent targeted Bisuphite-sequencing) supported the hypermethylation of this functional unit. Tissue-specificity is a significant driver, although the strongest consistent signals, also independent of major cell-type change, occur in tRNA-iMet-CAT-1-4 and tRNA-Ser-AGA-2-6. This study presents a comprehensive evaluation of the genomic DNA methylation state of human tRNA genes and reveals a discreet hypermethylation with advancing age.
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Affiliation(s)
- Richard J Acton
- William Harvey Research Institute, Barts & The London School of Medicine and Dentistry, Charterhouse Square, Queen Mary University of London, London, UK
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- Human Development and Health, Institute of Developmental Sciences, University of Southampton, Southampton, UK
| | - Wei Yuan
- Department of Twin Research & Genetic Epidemiology, St Thomas Hospital, King's College London, London, UK
- Institute of Cancer Research, Sutton, UK
| | - Fei Gao
- BGI-Shenzhen, Shenzhen, China
| | | | - Emma Bourne
- Barts & The London Genome Centre, Blizard Institute, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Eva Wozniak
- Barts & The London Genome Centre, Blizard Institute, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Jordana Bell
- Department of Twin Research & Genetic Epidemiology, St Thomas Hospital, King's College London, London, UK
| | - Karen Lillycrop
- Human Development and Health, Institute of Developmental Sciences, University of Southampton, Southampton, UK
| | - Jun Wang
- Shenzhen Digital Life Institute, Shenzhen, Guangdong, China
- iCarbonX, Zhuhai, Guangdong, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau, China
| | - Elaine Dennison
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Charles A Mein
- Barts & The London Genome Centre, Blizard Institute, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, St Thomas Hospital, King's College London, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, St Thomas Hospital, King's College London, London, UK
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Christopher G Bell
- William Harvey Research Institute, Barts & The London School of Medicine and Dentistry, Charterhouse Square, Queen Mary University of London, London, UK.
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48
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Vershinina O, Bacalini MG, Zaikin A, Franceschi C, Ivanchenko M. Disentangling age-dependent DNA methylation: deterministic, stochastic, and nonlinear. Sci Rep 2021; 11:9201. [PMID: 33911141 PMCID: PMC8080842 DOI: 10.1038/s41598-021-88504-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 04/06/2021] [Indexed: 12/30/2022] Open
Abstract
DNA methylation variability arises due to concurrent genetic and environmental influences. Each of them is a mixture of regular and noisy sources, whose relative contribution has not been satisfactorily understood yet. We conduct a systematic assessment of the age-dependent methylation by the signal-to-noise ratio and identify a wealth of "deterministic" CpG probes (about 90%), whose methylation variability likely originates due to genetic and general environmental factors. The remaining 10% of "stochastic" CpG probes are arguably governed by the biological noise or incidental environmental factors. Investigating the mathematical functional relationship between methylation levels and variability, we find that in about 90% of the age-associated differentially methylated positions, the variability changes as the square of the methylation level, whereas in the most of the remaining cases the dependence is linear. Furthermore, we demonstrate that the methylation level itself in more than 15% cases varies nonlinearly with age (according to the power law), in contrast to the previously assumed linear changes. Our findings present ample evidence of the ubiquity of strong DNA methylation regulation, resulting in the individual age-dependent and nonlinear methylation trajectories, whose divergence explains the cross-sectional variability. It may also serve a basis for constructing novel nonlinear epigenetic clocks.
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Affiliation(s)
- O. Vershinina
- grid.28171.3d0000 0001 0344 908XDepartment of Applied Mathematics, Mathematics of Future Technologies Center, Laboratory of Systems Medicine of Healthy Aging, Lobachevsky University, Nizhny Novgorod, Russia 603950
| | - M. G. Bacalini
- grid.492077.fUniversity of Bologna and IRCCS Istituto delle Scienze Neurologiche di Bologna (ISNB), 40139 Bologna, Italy
| | - A. Zaikin
- grid.28171.3d0000 0001 0344 908XDepartment of Applied Mathematics, Mathematics of Future Technologies Center, Laboratory of Systems Medicine of Healthy Aging, Lobachevsky University, Nizhny Novgorod, Russia 603950 ,grid.83440.3b0000000121901201Department of Mathematics and Institute for Women’s Health, University College London, London, WC1H 0AY UK ,grid.448878.f0000 0001 2288 8774Centre for Analysis of Complex Systems, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia 119992
| | - C. Franceschi
- grid.28171.3d0000 0001 0344 908XDepartment of Applied Mathematics, Mathematics of Future Technologies Center, Laboratory of Systems Medicine of Healthy Aging, Lobachevsky University, Nizhny Novgorod, Russia 603950 ,grid.492077.fUniversity of Bologna and IRCCS Istituto delle Scienze Neurologiche di Bologna (ISNB), 40139 Bologna, Italy
| | - M. Ivanchenko
- grid.28171.3d0000 0001 0344 908XDepartment of Applied Mathematics, Mathematics of Future Technologies Center, Laboratory of Systems Medicine of Healthy Aging, Lobachevsky University, Nizhny Novgorod, Russia 603950
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49
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Ageing affects subtelomeric DNA methylation in blood cells from a large European population enrolled in the MARK-AGE study. GeroScience 2021; 43:1283-1302. [PMID: 33870444 PMCID: PMC8190237 DOI: 10.1007/s11357-021-00347-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 02/23/2021] [Indexed: 11/23/2022] Open
Abstract
Ageing leaves characteristic traces in the DNA methylation make-up of the genome. However, the importance of DNA methylation in ageing remains unclear. The study of subtelomeric regions could give promising insights into this issue. Previously reported associations between susceptibility to age-related diseases and epigenetic instability at subtelomeres suggest that the DNA methylation profile of subtelomeres undergoes remodelling during ageing. In the present work, this hypothesis has been tested in the context of the European large-scale project MARK-AGE. In this cross-sectional study, we profiled the DNA methylation of chromosomes 5 and 21 subtelomeres, in more than 2000 age-stratified women and men recruited in eight European countries. The study included individuals from the general population as well as the offspring of nonagenarians and Down syndrome subjects, who served as putative models of delayed and accelerated ageing, respectively. Significant linear changes of subtelomeric DNA methylation with increasing age were detected in the general population, indicating that subtelomeric DNA methylation changes are typical signs of ageing. Data also show that, compared to the general population, the dynamics of age-related DNA methylation changes are attenuated in the offspring of centenarian, while they accelerate in Down syndrome individuals. This result suggests that subtelomeric DNA methylation changes reflect the rate of ageing progression. We next attempted to trace the age-related changes of subtelomeric methylation back to the influence of diverse variables associated with methylation variations in the population, including demographics, dietary/health habits and clinical parameters. Results indicate that the effects of age on subtelomeric DNA methylation are mostly independent of all other variables evaluated.
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A New Perspective on the Origin of DNA Double-Strand Breaks and Its Implications for Ageing. Genes (Basel) 2021; 12:genes12020163. [PMID: 33530310 PMCID: PMC7912064 DOI: 10.3390/genes12020163] [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: 11/11/2020] [Revised: 01/19/2021] [Accepted: 01/19/2021] [Indexed: 02/07/2023] Open
Abstract
It is estimated that 10-50 DNA double-strand breaks (DSBs) occur in a nucleated human cell per cell cycle. We reviewed the present state of knowledge and hypothesized that the currently accepted mechanisms cannot explain such high frequency of DSBs occurring daily under normal physiological conditions. We propose an alternative model that implicates illegitimate genomic integration into healthy cells of cell-free chromatin (cfCh) particles released from the billions of cells that die in the body every day. Repeated genomic integration of cfCh may have catastrophic consequences for the cell, such as DSBs, their faulty repair by nonhomologous end joining (NHEJ) followed by apoptosis with release of more cfCh which would integrate into genomes of surrounding cells. This can creates a vicious cycle of cfCh integration, DSBs, NHEJ, and more apoptosis, thereby providing a potential explanation as to why so many billions of cells die in the body on a daily basis. We also recount the recent observation that cfCh integration and the resulting DSBs activate inflammatory cytokines. This leads us to propose that concurrent DSBs and induction of inflammation occurring throughout life may be the underlying cause of ageing, degenerative disorders, and cancer. Finally, we discuss the prospect that agents that can inactivate/degrade cfCh may hold the key to making healthy ageing a realizable goal.
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