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Yusipov I, Kalyakulina A, Trukhanov A, Franceschi C, Ivanchenko M. Map of epigenetic age acceleration: A worldwide analysis. Ageing Res Rev 2024; 100:102418. [PMID: 39002646 DOI: 10.1016/j.arr.2024.102418] [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: 04/17/2024] [Revised: 07/03/2024] [Accepted: 07/08/2024] [Indexed: 07/15/2024]
Abstract
We present a systematic analysis of epigenetic age acceleration based on by far the largest collection of publicly available DNA methylation data for healthy samples (93 datasets, 23 K samples), focusing on the geographic (25 countries) and ethnic (31 ethnicities) aspects around the world. We employed the most popular epigenetic tools for assessing age acceleration and examined their quality metrics and ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models. In most cases, the models proved to be inconsistent with each other and showed different signs of age acceleration, with the PhenoAge model tending to systematically underestimate and different versions of the GrimAge model tending to systematically overestimate the age prediction of healthy subjects. Referring to data availability and consistency, most countries and populations are still not represented in GEO, moreover, different datasets use different criteria for determining healthy controls. Because of this, it is difficult to fully isolate the contribution of "geography/environment", "ethnicity" and "healthiness" to epigenetic age acceleration. Among the explored metrics, only the DunedinPACE, which measures aging rate, appears to adequately reflect the standard of living and socioeconomic indicators in countries, although it has a limited application to blood methylation data only. Invariably, by epigenetic age acceleration, males age faster than females in most of the studied countries and populations.
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Affiliation(s)
- Igor Yusipov
- Artificial Intelligence Research Center, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
| | - Alena Kalyakulina
- Artificial Intelligence Research Center, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
| | - Arseniy Trukhanov
- Mriya Life Institute, National Academy of Active Longevity, Moscow 124489, Russia.
| | - Claudio Franceschi
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
| | - Mikhail Ivanchenko
- Artificial Intelligence Research Center, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
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2
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Corveleyn L, Sen P, Adams P, Sidoli S. Linking Aging to Cancer: The Role of Chromatin Biology. J Gerontol A Biol Sci Med Sci 2024; 79:glae133. [PMID: 38761362 PMCID: PMC11170291 DOI: 10.1093/gerona/glae133] [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: 02/20/2024] [Indexed: 05/20/2024] Open
Abstract
Epigenetic changes have been established to be a hallmark of aging, which implies that aging science requires collaborating with the field of chromatin biology. DNA methylation patterns, changes in relative abundance of histone post-translational modifications, and chromatin remodeling are the central players in modifying chromatin structure. Aging is commonly associated with an overall increase in chromatin instability, loss of homeostasis, and decondensation. However, numerous publications have highlighted that the link between aging and chromatin changes is not nearly as linear as previously expected. This complex interplay of these epigenetic elements during the lifetime of an organism likely contributes to cellular senescence, genomic instability, and disease susceptibility. Yet, the causal links between these phenomena still need to be fully unraveled. In this perspective article, we discuss potential future directions of aging chromatin biology.
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Affiliation(s)
- Laura Corveleyn
- Laboratory of Pharmaceutical Biotechnology, Department of Pharmaceutics, Ghent University, Ghent, Belgium
| | - Payel Sen
- Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, Maryland, USA
| | - Peter Adams
- Sanford Burnham Prebys Medical Discovery Institute, San Diego, California, USA
| | - Simone Sidoli
- Department of Biochemistry, Albert Einstein College of Medicine, New York, New York, USA
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3
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Yokomizo T, Oshima M, Iwama A. Epigenetics of hematopoietic stem cell aging. Curr Opin Hematol 2024; 31:207-216. [PMID: 38640057 DOI: 10.1097/moh.0000000000000818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
PURPOSE OF REVIEW The development of new antiaging medicines is of great interest to the current elderly and aging population. Aging of the hematopoietic system is attributed to the aging of hematopoietic stem cells (HSCs), and epigenetic alterations are the key effectors driving HSC aging. Understanding the epigenetics of HSC aging holds promise of providing new insights for combating HSC aging and age-related hematological malignancies. RECENT FINDINGS Aging is characterized by the progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death. During aging, the HSCs undergo both quantitative and qualitative changes. These functional changes in HSCs cause dysregulated hematopoiesis, resulting in anemia, immune dysfunction, and an increased risk of hematological malignancies. Various cell-intrinsic and cell-extrinsic effectors influencing HSC aging have also been identified. Epigenetic alterations are one such mechanism. SUMMARY Cumulative epigenetic alterations in aged HSCs affect their fate, leading to aberrant self-renewal, differentiation, and function of aged HSCs. In turn, these factors provide an opportunity for aged HSCs to expand by modulating their self-renewal and differentiation balance, thereby contributing to the development of hematological malignancies.
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Affiliation(s)
- Takako Yokomizo
- Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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Jeong H, Mendizabal I, Yi SV. Human Brain Aging is Associated with Dysregulation of Cell-Type Epigenetic Identity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.596981. [PMID: 38895434 PMCID: PMC11185522 DOI: 10.1101/2024.06.01.596981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Significant links between aging and DNA methylation are emerging from recent studies. On the one hand, DNA methylation undergoes changes with age, a process termed as epigenetic drift. On the other hand, DNA methylation serves as a readily accessible and accurate biomarker for aging. A key missing piece of information, however, is the molecular mechanisms underlying these processes, and how they are related, if any. Addressing the limitations of previous research due to the limited number of investigated CpGs and the heterogeneous nature of tissue samples, here we have examined DNA methylation of over 20 million CpGs across a broad age span in neurons and non-neuronal cells, primarily oligodendrocytes. We show that aging is a primary predictor of DNA methylation variation, surpassing the influence of factors such as sex and schizophrenia diagnosis, among others. On the genome-wide scale, epigenetic drift manifests as significant yet subtle trends that are influenced by the methylation level of individual CpGs. We reveal that CpGs that are highly differentiated between cell types are especially prone to age-associated DNA methylation alterations, leading to the divergence of epigenetic cell type identities as individuals age. On the other hand, CpGs that are included in commonly used epigenetic clocks tend to be those sites that are not highly cell type differentiated. Therefore, dysregulation of epigenetic cell-type identities and current DNA epigenetic clocks represent distinct features of age-associated DNA methylation alterations.
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Affiliation(s)
- Hyeonsoo Jeong
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Current Address: Altos Labs, San Diego, CA, USA
| | - Isabel Mendizabal
- Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, Building 801A, 48160, Derio, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Translational prostate cancer Research lab, CIC bioGUNE-Basurto, Biocruces Bizkaia Health Research Institute, Derio, Spain
| | - Soojin V Yi
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Ecology, Evolution, and Marine Biology, Department of Molecular, Cellular, and Cell Biology, Neuroscience Research Institute, University of California, Santa Barbara, California 93106, USA
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5
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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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6
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Bell CG. Quantifying stochasticity in the aging DNA methylome. NATURE AGING 2024; 4:755-758. [PMID: 38755436 DOI: 10.1038/s43587-024-00634-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.
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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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Niimi P, Gould V, Thrush-Evensen K, Levine ME. The Latent Aging of Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596284. [PMID: 38854054 PMCID: PMC11160607 DOI: 10.1101/2024.05.28.596284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
As epigenetic clocks have evolved from powerful estimators of chronological aging to predictors of mortality and disease risk, it begs the question of what role DNA methylation plays in the aging process. We hypothesize that while it has the potential to serve as an informative biomarker, DNA methylation could also be a key to understanding the biology entangled between aging, (de)differentiation, and epigenetic reprogramming. Here we use an unsupervised approach to analyze time associated DNA methylation from both in vivo and in vitro samples to measure an underlying signal that ties these phenomena together. We identify a methylation pattern shared across all three, as well as a signal that tracks aging in tissues but appears refractory to reprogramming, suggesting that aging and reprogramming may not be fully mirrored processes.
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Affiliation(s)
- Peter Niimi
- Program in Experimental Pathology, Yale University, New Haven, CT, USA
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - Victoria Gould
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | | | - Morgan E Levine
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
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9
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Gilchrist JJ, Fang H, Danielli S, Tomkova M, Nassiri I, Ng E, Tong O, Taylor C, Muldoon D, Cohen LRZ, Al-Mossawi H, Lau E, Neville M, Schuster-Boeckler B, Knight JC, Fairfax BP. Characterization of the genetic determinants of context-specific DNA methylation in primary monocytes. CELL GENOMICS 2024; 4:100541. [PMID: 38663408 PMCID: PMC11099345 DOI: 10.1016/j.xgen.2024.100541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/24/2023] [Accepted: 03/27/2024] [Indexed: 05/12/2024]
Abstract
To better understand inter-individual variation in sensitivity of DNA methylation (DNAm) to immune activity, we characterized effects of inflammatory stimuli on primary monocyte DNAm (n = 190). We find that monocyte DNAm is site-dependently sensitive to lipopolysaccharide (LPS), with LPS-induced demethylation occurring following hydroxymethylation. We identify 7,359 high-confidence immune-modulated CpGs (imCpGs) that differ in genomic localization and transcription factor usage according to whether they represent a gain or loss in DNAm. Demethylated imCpGs are profoundly enriched for enhancers and colocalize to genes enriched for disease associations, especially cancer. DNAm is age associated, and we find that 24-h LPS exposure triggers approximately 6 months of gain in epigenetic age, directly linking epigenetic aging with innate immune activity. By integrating LPS-induced changes in DNAm with genetic variation, we identify 234 imCpGs under local genetic control. Exploring shared causal loci between LPS-induced DNAm responses and human disease traits highlights examples of disease-associated loci that modulate imCpG formation.
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Affiliation(s)
- James J Gilchrist
- Department of Paediatrics, University of Oxford, Oxford OX3 9DU, UK; MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Hai Fang
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Sara Danielli
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Marketa Tomkova
- Ludwig Cancer Research Oxford, University of Oxford, Oxford OX3 7DQ, UK
| | - Isar Nassiri
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK; Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Esther Ng
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Orion Tong
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Chelsea Taylor
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Dylan Muldoon
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Lea R Z Cohen
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Hussein Al-Mossawi
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Evelyn Lau
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Matt Neville
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LE, UK
| | | | - Julian C Knight
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Benjamin P Fairfax
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK; Department of Oncology, University of Oxford, Oxford OX3 9DS, UK.
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10
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Wang K, Sartor MA, Colacino JA, Dolinoy DC, Svoboda LK. Sex-Specific Deflection of Age-Related DNA Methylation and Gene Expression in Mouse Heart by Perinatal Toxicant Exposures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.25.591125. [PMID: 38712146 PMCID: PMC11071472 DOI: 10.1101/2024.04.25.591125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Global and site-specific changes in DNA methylation and gene expression are associated with cardiovascular aging and disease, but how toxicant exposures during early development influence the normal trajectory of these age-related molecular changes, and whether there are sex differences, has not yet been investigated. Objectives We used an established mouse model of developmental exposures to investigate the effects of perinatal exposure to either lead (Pb) or diethylhexyl phthalate (DEHP), two ubiquitous environmental contaminants strongly associated with CVD, on age-related cardiac DNA methylation and gene expression. Methods Dams were randomly assigned to receive human physiologically relevant levels of Pb (32 ppm in water), DEHP (25 mg/kg chow), or control water and chow. Exposures started two weeks prior to mating and continued until weaning at postnatal day 21 (3 weeks of age). Approximately one male and one female offspring per litter were followed to 3 weeks, 5 months, or 10 months of age, at which time whole hearts were collected (n ≥ 5 per sex per exposure). Enhanced reduced representation bisulfite sequencing (ERRBS) was used to assess the cardiac DNA methylome at 3 weeks and 10 months, and RNA-seq was conducted at all 3 time points. MethylSig and edgeR were used to identify age-related differentially methylated regions (DMRs) and differentially expressed genes (DEGs), respectively, within each sex and exposure group. Cell type deconvolution of bulk RNA-seq data was conducted using the MuSiC algorithm and publicly available single cell RNA-seq data. Results Thousands of DMRs and hundreds of DEGs were identified in control, DEHP, and Pb-exposed hearts across time between 3 weeks and 10 months of age. A closer look at the genes and pathways showing differential DNA methylation revealed that the majority were unique to each sex and exposure group. Overall, pathways governing development and differentiation were most frequently altered with age in all conditions. A small number of genes in each group showed significant changes in DNA methylation and gene expression with age, including several that were altered by both toxicants but were unchanged in control. We also observed subtle, but significant changes in the proportion of several cell types due to age, sex, and developmental exposure. Discussion Together these data show that perinatal Pb or DEHP exposures deflect normal age-related gene expression, DNA methylation programs, and cellular composition across the life course, long after cessation of exposure, and highlight potential biomarkers of developmental toxicant exposures. Further studies are needed to investigate how these epigenetic and transcriptional changes impact cardiovascular health across the life course.
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11
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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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12
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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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13
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Jin K, McCoy BM, Goldman EA, Usova V, Tkachev V, Chitsazan AD, Kakebeen A, Jeffery U, Creevy KE, Wills A, Snyder‐Mackler N, Promislow DEL. DNA methylation and chromatin accessibility predict age in the domestic dog. Aging Cell 2024; 23:e14079. [PMID: 38263575 PMCID: PMC11019125 DOI: 10.1111/acel.14079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 01/25/2024] Open
Abstract
Across mammals, the epigenome is highly predictive of chronological age. These "epigenetic clocks," most of which have been built using DNA methylation (DNAm) profiles, have gained traction as biomarkers of aging and organismal health. While the ability of DNAm to predict chronological age has been repeatedly demonstrated, the ability of other epigenetic features to predict age remains unclear. Here, we use two types of epigenetic information-DNAm, and chromatin accessibility as measured by ATAC-seq-to develop age predictors in peripheral blood mononuclear cells sampled from a population of domesticated dogs. We measured DNAm and ATAC-seq profiles for 71 dogs, building separate predictive clocks from each, as well as the combined dataset. We also use fluorescence-assisted cell sorting to quantify major lymphoid populations for each sample. We found that chromatin accessibility can accurately predict chronological age (R2 ATAC = 26%), though less accurately than the DNAm clock (R2 DNAm = 33%), and the clock built from the combined datasets was comparable to both (R2 combined = 29%). We also observed various populations of CD62L+ T cells significantly correlated with dog age. Finally, we found that all three clocks selected features that were in or near at least two protein-coding genes: BAIAP2 and SCARF2, both previously implicated in processes related to cognitive or neurological impairment. Taken together, these results highlight the potential of chromatin accessibility as a complementary epigenetic resource for modeling and investigating biologic age.
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Affiliation(s)
- Kelly Jin
- Department of Laboratory Medicine & PathologyUniversity of WashingtonSeattleWashingtonUSA
| | - Brianah M. McCoy
- Center for Evolution and MedicineArizona State UniversityTempeArizonaUSA
- School of Life SciencesArizona State UniversityTempeArizonaUSA
| | | | - Viktoria Usova
- Department of Laboratory Medicine & PathologyUniversity of WashingtonSeattleWashingtonUSA
| | - Victor Tkachev
- Division of Pediatric Hematology/OncologyBoston Children's HospitalBostonMassachusettsUSA
- Dana Farber Cancer InstituteBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Alex D. Chitsazan
- Department of BiochemistryUniversity of WashingtonSeattleWashingtonUSA
| | - Anneke Kakebeen
- Department of BiochemistryUniversity of WashingtonSeattleWashingtonUSA
| | - Unity Jeffery
- College of Veterinary MedicineTexas A & M UniversityCollege StationTexasUSA
| | - Kate E. Creevy
- College of Veterinary MedicineTexas A & M UniversityCollege StationTexasUSA
| | - Andrea Wills
- Department of BiochemistryUniversity of WashingtonSeattleWashingtonUSA
| | - Noah Snyder‐Mackler
- Center for Evolution and MedicineArizona State UniversityTempeArizonaUSA
- School of Life SciencesArizona State UniversityTempeArizonaUSA
| | - Daniel E. L. Promislow
- Department of Laboratory Medicine & PathologyUniversity of WashingtonSeattleWashingtonUSA
- Department of BiologyUniversity of WashingtonSeattleWashingtonUSA
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14
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Wang X, Dong Y, Zhang H, Zhao Y, Miao T, Mohseni G, Du L, Wang C. DNA methylation drives a new path in gastric cancer early detection: Current impact and prospects. Genes Dis 2024; 11:847-860. [PMID: 37692483 PMCID: PMC10491876 DOI: 10.1016/j.gendis.2023.02.038] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 02/24/2023] [Indexed: 03/31/2023] Open
Abstract
Gastric cancer (GC) is one of the most common and deadly cancers worldwide. Early detection offers the best chance for curative treatment and reducing its mortality. However, the optimal population-based early screening for GC remains unmet. Aberrant DNA methylation occurs in the early stage of GC, exhibiting cancer-specific genetic and epigenetic changes, and can be detected in the media such as blood, gastric juice, and feces, constituting a valuable biomarker for cancer early detection. Furthermore, DNA methylation is a stable epigenetic alteration, and many innovative methods have been developed to quantify it rapidly and accurately. Nonetheless, large-scale clinical validation of DNA methylation serving as tumor biomarkers is still lacking, precluding their implementation in clinical practice. In conclusion, after a critical analysis of the recent existing literature, we summarized the evolving roles of DNA methylation during GC occurrence, expounded the newly discovered noninvasive DNA methylation biomarkers for early detection of GC, and discussed its challenges and prospects in clinical applications.
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Affiliation(s)
- Xinhui Wang
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, Shandong 250033, China
| | - Yaqi Dong
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, Shandong 250033, China
| | - Hong Zhang
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, Shandong 250033, China
- Department of Clinical Laboratory, Fuling Hospital, Chongqing University, Chongqing 402774, China
| | - Yinghui Zhao
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, Shandong 250033, China
- Suzhou Research Institute of Shandong University, Suzhou, Jiangsu 215123, China
| | - Tianshu Miao
- Department of Biochemistry and Molecular Biology, Shandong University School of Basic Medical Sciences, Jinan, Shandong 250012, China
| | - Ghazal Mohseni
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, Shandong 250033, China
| | - Lutao Du
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, Shandong 250033, China
- Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, Shandong 250033, China
- Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, Shandong 250033, China
| | - Chuanxin Wang
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, Shandong 250033, China
- Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, Shandong 250033, China
- Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, Shandong 250033, China
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15
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Zoller JA, Parasyraki E, Lu AT, Haghani A, Niehrs C, Horvath S. DNA methylation clocks for clawed frogs reveal evolutionary conservation of epigenetic aging. GeroScience 2024; 46:945-960. [PMID: 37270437 PMCID: PMC10828168 DOI: 10.1007/s11357-023-00840-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/25/2023] [Indexed: 06/05/2023] Open
Abstract
To address how conserved DNA methylation-based epigenetic aging is in diverse branches of the tree of life, we generated DNA methylation data from African clawed frogs (Xenopus laevis) and Western clawed frogs (Xenopus tropicalis) and built multiple epigenetic clocks. Dual species clocks were developed that apply to both humans and frogs (human-clawed frog clocks), supporting that epigenetic aging processes are evolutionary conserved outside mammals. Highly conserved positively age-related CpGs are located in neural-developmental genes such as uncx, tfap2d as well as nr4a2 implicated in age-associated disease. We conclude that signatures of epigenetic aging are evolutionary conserved between frogs and mammals and that the associated genes relate to neural processes, altogether opening opportunities to employ Xenopus as a model organism to study aging.
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Affiliation(s)
- Joseph A Zoller
- Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego, CA, USA
| | - Amin Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego, CA, USA
| | - Christof Niehrs
- Institute of Molecular Biology (IMB), Mainz, Germany.
- German Cancer Research Center (DKFZ), Division of Molecular Embryology, DKFZ-ZMBH Alliance, Heidelberg, Germany.
| | - Steve Horvath
- Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Altos Labs, San Diego, CA, USA.
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16
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Conde-Torres D, Blanco-González A, Seco-González A, Suárez-Lestón F, Cabezón A, Antelo-Riveiro P, Piñeiro Á, García-Fandiño R. Unraveling lipid and inflammation interplay in cancer, aging and infection for novel theranostic approaches. Front Immunol 2024; 15:1320779. [PMID: 38361953 PMCID: PMC10867256 DOI: 10.3389/fimmu.2024.1320779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024] Open
Abstract
The synergistic relationships between Cancer, Aging, and Infection, here referred to as the CAIn Triangle, are significant determinants in numerous health maladies and mortality rates. The CAIn-related pathologies exhibit close correlations with each other and share two common underlying factors: persistent inflammation and anomalous lipid concentration profiles in the membranes of affected cells. This study provides a comprehensive evaluation of the most pertinent interconnections within the CAIn Triangle, in addition to examining the relationship between chronic inflammation and specific lipidic compositions in cellular membranes. To tackle the CAIn-associated diseases, a suite of complementary strategies aimed at diagnosis, prevention, and treatment is proffered. Our holistic approach is expected to augment the understanding of the fundamental mechanisms underlying these diseases and highlight the potential of shared features to facilitate the development of novel theranostic strategies.
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Affiliation(s)
- Daniel Conde-Torres
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Alexandre Blanco-González
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- MD.USE Innovations S.L., Edificio Emprendia, Santiago de Compostela, Spain
| | - Alejandro Seco-González
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Fabián Suárez-Lestón
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- MD.USE Innovations S.L., Edificio Emprendia, Santiago de Compostela, Spain
| | - Alfonso Cabezón
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Paula Antelo-Riveiro
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Ángel Piñeiro
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Rebeca García-Fandiño
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
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17
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Morandini F, Seluanov A, Gorbunova V. Slow and steady lives the longest. NATURE AGING 2024; 4:7-9. [PMID: 38191688 DOI: 10.1038/s43587-023-00554-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Affiliation(s)
| | - Andrei Seluanov
- Department of Biology, University of Rochester, Rochester, NY, USA
- Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Vera Gorbunova
- Department of Biology, University of Rochester, Rochester, NY, USA.
- Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA.
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18
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Dutta S, Goodrich JM, Dolinoy DC, Ruden DM. Biological Aging Acceleration Due to Environmental Exposures: An Exciting New Direction in Toxicogenomics Research. Genes (Basel) 2023; 15:16. [PMID: 38275598 PMCID: PMC10815440 DOI: 10.3390/genes15010016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024] Open
Abstract
Biological clock technologies are designed to assess the acceleration of biological age (B-age) in diverse cell types, offering a distinctive opportunity in toxicogenomic research to explore the impact of environmental stressors, social challenges, and unhealthy lifestyles on health impairment. These clocks also play a role in identifying factors that can hinder aging and promote a healthy lifestyle. Over the past decade, researchers in epigenetics have developed testing methods that predict the chronological and biological age of organisms. These methods rely on assessing DNA methylation (DNAm) levels at specific CpG sites, RNA levels, and various biomolecules across multiple cell types, tissues, and entire organisms. Commonly known as 'biological clocks' (B-clocks), these estimators hold promise for gaining deeper insights into the pathways contributing to the development of age-related disorders. They also provide a foundation for devising biomedical or social interventions to prevent, reverse, or mitigate these disorders. This review article provides a concise overview of various epigenetic clocks and explores their susceptibility to environmental stressors.
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Affiliation(s)
- Sudipta Dutta
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA;
| | - Jaclyn M. Goodrich
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA; (J.M.G.); (D.C.D.)
| | - Dana C. Dolinoy
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA; (J.M.G.); (D.C.D.)
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Douglas M. Ruden
- C. S. Mott Center for Human Health and Development, Department of Obstetrics and Gynecology, Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48202, USA
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19
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Refn MR, Andersen MM, Kampmann ML, Tfelt-Hansen J, Sørensen E, Larsen MH, Morling N, Børsting C, Pereira V. Longitudinal changes and variation in human DNA methylation analysed with the Illumina MethylationEPIC BeadChip assay and their implications on forensic age prediction. Sci Rep 2023; 13:21658. [PMID: 38066081 PMCID: PMC10709620 DOI: 10.1038/s41598-023-49064-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
DNA methylation, a pivotal epigenetic modification, plays a crucial role in regulating gene expression and is known to undergo dynamic changes with age. The present study investigated epigenome-wide methylation profiles in 64 individuals over two time points, 15 years apart, using the Illumina EPIC850k arrays. A mixed-effects model identified 2821 age-associated differentially methylated CpG positions (aDMPs) with a median rate of change of 0.18% per year, consistent with a 10-15% change during a human lifespan. Significant variation in the baseline DNA methylation levels between individuals of similar ages as well as inconsistent direction of change with time across individuals were observed for all the aDMPs. Twenty-three of the 2821 aDMPs were previously incorporated into forensic age prediction models. These markers displayed larger changes in DNA methylation with age compared to all the aDMPs and less variation among individuals. Nevertheless, the forensic aDMPs also showed inter-individual variations in the direction of DNA methylation changes. Only cg16867657 in ELOVL2 exhibited a uniform direction of the age-related change among the investigated individuals, which supports the current knowledge that CpG sites in ELOVL2 are the best markers for age prediction.
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Affiliation(s)
- Mie Rath Refn
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark.
| | - Mikkel Meyer Andersen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
- The Department of Mathematical Sciences, Aalborg University, 9220, Aalborg, Denmark
| | - Marie-Louise Kampmann
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Jacob Tfelt-Hansen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
- The Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Margit Hørup Larsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Claus Børsting
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Vania Pereira
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
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20
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Horvath S, Haghani A, Zoller JA, Lu AT, Ernst J, Pellegrini M, Jasinska AJ, Mattison JA, Salmon AB, Raj K, Horvath M, Paul KC, Ritz BR, Robeck TR, Spriggs M, Ehmke EE, Jenkins S, Li C, Nathanielsz PW. Pan-primate studies of age and sex. GeroScience 2023; 45:3187-3209. [PMID: 37493860 PMCID: PMC10643767 DOI: 10.1007/s11357-023-00878-3] [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: 06/08/2023] [Accepted: 07/16/2023] [Indexed: 07/27/2023] Open
Abstract
Age and sex have a profound effect on cytosine methylation levels in humans and many other species. Here we analyzed DNA methylation profiles of 2400 tissues derived from 37 primate species including 11 haplorhine species (baboons, marmosets, vervets, rhesus macaque, chimpanzees, gorillas, orangutan, humans) and 26 strepsirrhine species (suborders Lemuriformes and Lorisiformes). From these we present here, pan-primate epigenetic clocks which are highly accurate for all primates including humans (age correlation R = 0.98). We also carried out in-depth analysis of baboon DNA methylation profiles and generated five epigenetic clocks for baboons (Olive-yellow baboon hybrid), one of which, the pan-tissue epigenetic clock, was trained on seven tissue types (fetal cerebral cortex, adult cerebral cortex, cerebellum, adipose, heart, liver, and skeletal muscle) with ages ranging from late fetal life to 22.8 years of age. Using the primate data, we characterize the effect of age and sex on individual cytosines in highly conserved regions. We identify 11 sex-related CpGs on autosomes near genes (POU3F2, CDYL, MYCL, FBXL4, ZC3H10, ZXDC, RRAS, FAM217A, RBM39, GRIA2, UHRF2). Low overlap can be observed between age- and sex-related CpGs. Overall, this study advances our understanding of conserved age- and sex-related epigenetic changes in primates, and provides biomarkers of aging for all primates.
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Affiliation(s)
- Steve Horvath
- Altos Labs, San Diego, CA, USA.
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
| | | | - Joseph A Zoller
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Jason Ernst
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Matteo Pellegrini
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Anna J Jasinska
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and BiobehavioralSciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Julie A Mattison
- Translational Gerontology Branch, National Institute On Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Adam B Salmon
- The Sam and Ann Barshop Institute for Longevity and Aging Studies, and Department of Molecular Medicine, UT Health San Antonio, and the Geriatric Research Education and Clinical Center, South Texas Veterans Healthcare System, San Antonio, TX, USA
| | | | | | - Kimberly C Paul
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Beate R Ritz
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Epidemiology, UCLA Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Todd R Robeck
- Corporate Zoological Operations, SeaWorld Parks, Orlando, FL, USA
| | - Maria Spriggs
- Busch Gardens Tampa, SeaWorld Parks, Tampa, FL, 33612, USA
| | | | - Susan Jenkins
- Texas Pregnancy & Life-Course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources Department, Laramie, WY, USA
| | - Cun Li
- Texas Pregnancy & Life-Course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources Department, Laramie, WY, USA
| | - Peter W Nathanielsz
- Texas Pregnancy & Life-Course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources Department, Laramie, WY, USA
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21
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Lu YR, Tian X, Sinclair DA. The Information Theory of Aging. NATURE AGING 2023; 3:1486-1499. [PMID: 38102202 DOI: 10.1038/s43587-023-00527-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 10/02/2023] [Indexed: 12/17/2023]
Abstract
Information storage and retrieval is essential for all life. In biology, information is primarily stored in two distinct ways: the genome, comprising nucleic acids, acts as a foundational blueprint and the epigenome, consisting of chemical modifications to DNA and histone proteins, regulates gene expression patterns and endows cells with specific identities and functions. Unlike the stable, digital nature of genetic information, epigenetic information is stored in a digital-analog format, susceptible to alterations induced by diverse environmental signals and cellular damage. The Information Theory of Aging (ITOA) states that the aging process is driven by the progressive loss of youthful epigenetic information, the retrieval of which via epigenetic reprogramming can improve the function of damaged and aged tissues by catalyzing age reversal.
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Affiliation(s)
- Yuancheng Ryan Lu
- Paul F. Glenn Center for Biology of Aging Research, Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xiao Tian
- Paul F. Glenn Center for Biology of Aging Research, Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - David A Sinclair
- Paul F. Glenn Center for Biology of Aging Research, Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
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22
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Yang T, Xiao Y, Cheng Y, Huang J, Wei Q, Li C, Shang H. Epigenetic clocks in neurodegenerative diseases: a systematic review. J Neurol Neurosurg Psychiatry 2023; 94:1064-1070. [PMID: 36963821 DOI: 10.1136/jnnp-2022-330931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/03/2023] [Indexed: 03/26/2023]
Abstract
BACKGROUND Biological ageing is one of the principal risk factors for neurodegenerative diseases. It is becoming increasingly clear that acceleration of DNA methylation age, as measured by the epigenetic clock, is closely associated with many age-related diseases. METHODS We searched the PubMed and Web of Science databases to identify eligible studies reporting epigenetic clocks in several neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD). RESULTS Twenty-three studies (12 for AD, 4 for PD, 5 for ALS, and 2 for HD) were included. We systematically summarised the clinical utility of 11 epigenetic clocks (based on blood and brain tissues) in assessing the risk factors, age of onset, diagnosis, progression, prognosis and pathology of AD, PD, ALS and HD. We also critically described our current understandings to these evidences, and further discussed key challenges, potential mechanisms and future perspectives of epigenetic ageing in neurodegenerative diseases. CONCLUSIONS Epigenetic clocks hold great potential in neurodegenerative diseases. Further research is encouraged to evaluate the clinical utility and promote the application. PROSPERO REGISTRATION NUMBER CRD42022365233.
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Affiliation(s)
- Tianmi Yang
- Department of Neurology, Sichuan University, Chengdu, Sichuan, China
| | - Yi Xiao
- Department of Neurology, Sichuan University, Chengdu, Sichuan, China
| | - Yangfan Cheng
- Department of Neurology, Sichuan University, Chengdu, Sichuan, China
| | - Jingxuan Huang
- Department of Neurology, Sichuan University, Chengdu, Sichuan, China
| | - Qianqian Wei
- Department of Neurology, Sichuan University, Chengdu, Sichuan, China
| | - Chunyu Li
- Department of Neurology, Sichuan University, Chengdu, Sichuan, China
| | - Huifang Shang
- Department of Neurology, Sichuan University, Chengdu, Sichuan, China
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23
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Nehar-Belaid D, Sokolowski M, Ravichandran S, Banchereau J, Chaussabel D, Ucar D. Baseline immune states (BIS) associated with vaccine responsiveness and factors that shape the BIS. Semin Immunol 2023; 70:101842. [PMID: 37717525 DOI: 10.1016/j.smim.2023.101842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/11/2023] [Indexed: 09/19/2023]
Abstract
Vaccines are among the greatest inventions in medicine, leading to the elimination or control of numerous diseases, including smallpox, polio, measles, rubella, and, most recently, COVID-19. Yet, the effectiveness of vaccines varies among individuals. In fact, while some recipients mount a robust response to vaccination that protects them from the disease, others fail to respond. Multiple clinical and epidemiological factors contribute to this heterogeneity in responsiveness. Systems immunology studies fueled by advances in single-cell biology have been instrumental in uncovering pre-vaccination immune cell types and genomic features (i.e., the baseline immune state, BIS) that have been associated with vaccine responsiveness. Here, we review clinical factors that shape the BIS, and the characteristics of the BIS associated with responsiveness to frequently studied vaccines (i.e., influenza, COVID-19, bacterial pneumonia, malaria). Finally, we discuss potential strategies to enhance vaccine responsiveness in high-risk groups, focusing specifically on older adults.
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Affiliation(s)
| | - Mark Sokolowski
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA
| | | | | | - Damien Chaussabel
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA
| | - Duygu Ucar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA; Institute for Systems Genomics, University of Connecticut Health Center, Farmington, CT, USA.
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24
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Naue J. Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications. Genes Genomics 2023; 45:1239-1261. [PMID: 37253906 PMCID: PMC10504122 DOI: 10.1007/s13258-023-01392-8] [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: 02/28/2023] [Accepted: 04/15/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals. OBJECTIVE This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application. METHODS The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics. CONCLUSION The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.
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Affiliation(s)
- Jana Naue
- Institute of Forensic Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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25
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Gupta MK, Peng H, Li Y, Xu CJ. The role of DNA methylation in personalized medicine for immune-related diseases. Pharmacol Ther 2023; 250:108508. [PMID: 37567513 DOI: 10.1016/j.pharmthera.2023.108508] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Epigenetics functions as a bridge between host genetic & environmental factors, aiding in human health and diseases. Many immune-related diseases, including infectious and allergic diseases, have been linked to epigenetic mechanisms, particularly DNA methylation. In this review, we summarized an updated overview of DNA methylation and its importance in personalized medicine, and demonstrated that DNA methylation has excellent potential for disease prevention, diagnosis, and treatment in a personalized manner. The future implications and limitations of the DNA methylation study have also been well-discussed.
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Affiliation(s)
- Manoj Kumar Gupta
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany; TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - He Peng
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany; TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Yang Li
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany; TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany; Department of Internal Medicine and Radboud Institute for Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Cheng-Jian Xu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany; TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany; Department of Internal Medicine and Radboud Institute for Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
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26
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Allegra A, Caserta S, Mirabile G, Gangemi S. Aging and Age-Related Epigenetic Drift in the Pathogenesis of Leukemia and Lymphomas: New Therapeutic Targets. Cells 2023; 12:2392. [PMID: 37830606 PMCID: PMC10572300 DOI: 10.3390/cells12192392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/24/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
One of the traits of cancer cells is abnormal DNA methylation patterns. The idea that age-related epigenetic changes may partially explain the increased risk of cancer in the elderly is based on the observation that aging is also accompanied by comparable changes in epigenetic patterns. Lineage bias and decreased stem cell function are signs of hematopoietic stem cell compartment aging. Additionally, aging in the hematopoietic system and the stem cell niche have a role in hematopoietic stem cell phenotypes linked with age, such as leukemia and lymphoma. Understanding these changes will open up promising pathways for therapies against age-related disorders because epigenetic mechanisms are reversible. Additionally, the development of high-throughput epigenome mapping technologies will make it possible to identify the "epigenomic identity card" of every hematological disease as well as every patient, opening up the possibility of finding novel molecular biomarkers that can be used for diagnosis, prediction, and prognosis.
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Affiliation(s)
- Alessandro Allegra
- Division of Hematology, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria, 98125 Messina, Italy; (S.C.); (G.M.)
| | - Santino Caserta
- Division of Hematology, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria, 98125 Messina, Italy; (S.C.); (G.M.)
| | - Giuseppe Mirabile
- Division of Hematology, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria, 98125 Messina, Italy; (S.C.); (G.M.)
| | - Sebastiano Gangemi
- Allergy and Clinical Immunology Unit, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria, 98125 Messina, Italy;
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27
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Varshavsky M, Harari G, Glaser B, Dor Y, Shemer R, Kaplan T. Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm. CELL REPORTS METHODS 2023; 3:100567. [PMID: 37751697 PMCID: PMC10545910 DOI: 10.1016/j.crmeth.2023.100567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 06/18/2023] [Accepted: 08/03/2023] [Indexed: 09/28/2023]
Abstract
Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.
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Affiliation(s)
- Miri Varshavsky
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel; The Center for Computational Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gil Harari
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel; Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Benjamin Glaser
- Department of Endocrinology and Metabolism, Hadassah Medical Center and Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Dor
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel; The Center for Computational Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ruth Shemer
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Tommy Kaplan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel; Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel; The Center for Computational Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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28
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López-Gil L, Pascual-Ahuir A, Proft M. Genomic Instability and Epigenetic Changes during Aging. Int J Mol Sci 2023; 24:14279. [PMID: 37762580 PMCID: PMC10531692 DOI: 10.3390/ijms241814279] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/15/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
Aging is considered the deterioration of physiological functions along with an increased mortality rate. This scientific review focuses on the central importance of genomic instability during the aging process, encompassing a range of cellular and molecular changes that occur with advancing age. In particular, this revision addresses the genetic and epigenetic alterations that contribute to genomic instability, such as telomere shortening, DNA damage accumulation, and decreased DNA repair capacity. Furthermore, the review explores the epigenetic changes that occur with aging, including modifications to histones, DNA methylation patterns, and the role of non-coding RNAs. Finally, the review discusses the organization of chromatin and its contribution to genomic instability, including heterochromatin loss, chromatin remodeling, and changes in nucleosome and histone abundance. In conclusion, this review highlights the fundamental role that genomic instability plays in the aging process and underscores the need for continued research into these complex biological mechanisms.
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Affiliation(s)
- Lucía López-Gil
- Department of Biotechnology, Instituto de Biología Molecular y Celular de Plantas, Universitat Politècnica de València, Ingeniero Fausto Elio s/n, 46022 Valencia, Spain;
- Department of Molecular and Cellular Pathology and Therapy, Instituto de Biomedicina de Valencia IBV-CSIC, Consejo Superior de Investigaciones Científicas CSIC, Jaime Roig 11, 46010 Valencia, Spain
| | - Amparo Pascual-Ahuir
- Department of Biotechnology, Instituto de Biología Molecular y Celular de Plantas, Universitat Politècnica de València, Ingeniero Fausto Elio s/n, 46022 Valencia, Spain;
| | - Markus Proft
- Department of Molecular and Cellular Pathology and Therapy, Instituto de Biomedicina de Valencia IBV-CSIC, Consejo Superior de Investigaciones Científicas CSIC, Jaime Roig 11, 46010 Valencia, Spain
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29
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Lu AT, Fei Z, Haghani A, Robeck TR, Zoller JA, Li CZ, Lowe R, Yan Q, Zhang J, Vu H, Ablaeva J, Acosta-Rodriguez VA, Adams DM, Almunia J, Aloysius A, Ardehali R, Arneson A, Baker CS, Banks G, Belov K, Bennett NC, Black P, Blumstein DT, Bors EK, Breeze CE, Brooke RT, Brown JL, Carter GG, Caulton A, Cavin JM, Chakrabarti L, Chatzistamou I, Chen H, Cheng K, Chiavellini P, Choi OW, Clarke SM, Cooper LN, Cossette ML, Day J, DeYoung J, DiRocco S, Dold C, Ehmke EE, Emmons CK, Emmrich S, Erbay E, Erlacher-Reid C, Faulkes CG, Ferguson SH, Finno CJ, Flower JE, Gaillard JM, Garde E, Gerber L, Gladyshev VN, Gorbunova V, Goya RG, Grant MJ, Green CB, Hales EN, Hanson MB, Hart DW, Haulena M, Herrick K, Hogan AN, Hogg CJ, Hore TA, Huang T, Izpisua Belmonte JC, Jasinska AJ, Jones G, Jourdain E, Kashpur O, Katcher H, Katsumata E, Kaza V, Kiaris H, Kobor MS, Kordowitzki P, Koski WR, Krützen M, Kwon SB, Larison B, Lee SG, Lehmann M, Lemaitre JF, Levine AJ, Li C, Li X, Lim AR, Lin DTS, Lindemann DM, Little TJ, Macoretta N, Maddox D, Matkin CO, Mattison JA, McClure M, Mergl J, Meudt JJ, Montano GA, Mozhui K, Munshi-South J, Naderi A, Nagy M, Narayan P, Nathanielsz PW, Nguyen NB, Niehrs C, O'Brien JK, O'Tierney Ginn P, Odom DT, Ophir AG, Osborn S, Ostrander EA, Parsons KM, Paul KC, Pellegrini M, Peters KJ, Pedersen AB, Petersen JL, Pietersen DW, Pinho GM, Plassais J, Poganik JR, Prado NA, Reddy P, Rey B, Ritz BR, Robbins J, Rodriguez M, Russell J, Rydkina E, Sailer LL, Salmon AB, Sanghavi A, Schachtschneider KM, Schmitt D, Schmitt T, Schomacher L, Schook LB, Sears KE, Seifert AW, Seluanov A, Shafer ABA, Shanmuganayagam D, Shindyapina AV, Simmons M, Singh K, Sinha I, Slone J, Snell RG, Soltanmaohammadi E, Spangler ML, Spriggs MC, Staggs L, Stedman N, Steinman KJ, Stewart DT, Sugrue VJ, Szladovits B, Takahashi JS, Takasugi M, Teeling EC, Thompson MJ, Van Bonn B, Vernes SC, Villar D, Vinters HV, Wallingford MC, Wang N, Wayne RK, Wilkinson GS, Williams CK, Williams RW, Yang XW, Yao M, Young BG, Zhang B, Zhang Z, Zhao P, Zhao Y, Zhou W, Zimmermann J, Ernst J, Raj K, Horvath S. Universal DNA methylation age across mammalian tissues. NATURE AGING 2023; 3:1144-1166. [PMID: 37563227 PMCID: PMC10501909 DOI: 10.1038/s43587-023-00462-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 06/21/2023] [Indexed: 08/12/2023]
Abstract
Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.
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Affiliation(s)
- A T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - Z Fei
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Statistics, University of California, Riverside, Riverside, CA, USA
| | - A Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - T R Robeck
- Zoological SeaWorld Parks and Entertainment, Orlando, FL, USA
| | - J A Zoller
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Z Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - R Lowe
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - Q Yan
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - J Zhang
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - H Vu
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - J Ablaeva
- Department of Biology, University of Rochester, Rochester, NY, USA
| | - V A Acosta-Rodriguez
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - D M Adams
- Department of Biology, University of Maryland, College Park, MD, USA
| | - J Almunia
- Loro Parque Fundacion, Puerto de la Cruz, Spain
| | - A Aloysius
- Department of Biology, University of Kentucky, Lexington, KY, USA
| | - R Ardehali
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - A Arneson
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - C S Baker
- Marine Mammal Institute, Oregon State University, Newport, OR, USA
| | - G Banks
- School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham, UK
| | - K Belov
- School of Life and Environmental Sciences, the University of Sydney, Sydney, New South Wales, Australia
| | - N C Bennett
- Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - P Black
- Busch Gardens Tampa, Tampa, FL, USA
| | - D T Blumstein
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
- Rocky Mountain Biological Laboratory, Crested Butte, CO, USA
| | - E K Bors
- Marine Mammal Institute, Oregon State University, Newport, OR, USA
| | - C E Breeze
- Altius Institute for Biomedical Sciences, Seattle, WA, USA
| | - R T Brooke
- Epigenetic Clock Development Foundation, Los Angeles, CA, USA
| | - J L Brown
- Center for Species Survival, Smithsonian Conservation Biology Institute, Front Royal, VA, USA
| | - G G Carter
- Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, OH, USA
| | - A Caulton
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - J M Cavin
- Gulf World, Dolphin Company, Panama City Beach, FL, USA
| | - L Chakrabarti
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK
| | - I Chatzistamou
- Department of Pathology, Microbiology and Immunology, School of Medicine, University of South Carolina, Columbia, SC, USA
| | - H Chen
- Department of Pharmacology, Addiction Science and Toxicology, the University of Tennessee Health Science Center, Memphis, TN, USA
| | - K Cheng
- Medical Informatics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - P Chiavellini
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - O W Choi
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - S M Clarke
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand
| | - L N Cooper
- Department of Anatomy and Neurobiology, Northeast Ohio Medical University, Rootstown, OH, USA
| | - M L Cossette
- Department of Environmental and Life Sciences, Trent University, Peterborough, Ontario, Canada
| | - J Day
- Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, New South Wales, Australia
| | - J DeYoung
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - S DiRocco
- SeaWorld of Florida, Orlando, FL, USA
| | - C Dold
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA
| | | | - C K Emmons
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - S Emmrich
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - E Erbay
- Altos Labs, San Francisco, CA, USA
| | - C Erlacher-Reid
- SeaWorld of Florida, Orlando, FL, USA
- SeaWorld Orlando, Orlando, FL, USA
| | - C G Faulkes
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - S H Ferguson
- Fisheries and Oceans Canada, Freshwater Institute, Winnipeg, Manitoba, Canada
- Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - C J Finno
- Department of Population Health and Reproduction, University of California, Davis School of Veterinary Medicine, Davis, CA, USA
| | | | - J M Gaillard
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - E Garde
- Greenland Institute of Natural Resources, Nuuk, Greenland
| | - L Gerber
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, New South Wales, Australia
| | - V N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - V Gorbunova
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - R G Goya
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - M J Grant
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - C B Green
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - E N Hales
- Department of Population Health and Reproduction, University of California, Davis School of Veterinary Medicine, Davis, CA, USA
| | - M B Hanson
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - D W Hart
- Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - M Haulena
- Vancouver Aquarium, Vancouver, British Columbia, Canada
| | - K Herrick
- SeaWorld of California, San Diego, CA, USA
| | - A N Hogan
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - C J Hogg
- School of Life and Environmental Sciences, the University of Sydney, Sydney, New South Wales, Australia
| | - T A Hore
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - T Huang
- Division of Human Genetics, Department of Pediatrics, University at Buffalo, Buffalo, NY, USA
- Division of Genetics and Metabolism, Oishei Children's Hospital, Buffalo, NY, USA
| | | | - A J Jasinska
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - G Jones
- School of Biological Sciences, University of Bristol, Bristol, UK
| | | | - O Kashpur
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
| | - H Katcher
- Yuvan Research, Mountain View, CA, USA
| | | | - V Kaza
- Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA
| | - H Kiaris
- Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M S Kobor
- Edwin S.H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada
| | - P Kordowitzki
- Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn, Poland
- Institute for Veterinary Medicine, Nicolaus Copernicus University, Torun, Poland
| | - W R Koski
- LGL Limited, King City, Ontario, Canada
| | - M Krützen
- Evolutionary Genetics Group, Department of Evolutionary Anthropology, University of Zurich, Zurich, Switzerland
| | - S B Kwon
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - B Larison
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
- Center for Tropical Research, Institute for the Environment and Sustainability, UCLA, Los Angeles, CA, USA
| | - S G Lee
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - M Lehmann
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - J F Lemaitre
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - A J Levine
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Li
- Texas Pregnancy and Life-course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources, Laramie, WY, USA
| | - X Li
- Technology Center for Genomics and Bioinformatics, Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - A R Lim
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - D T S Lin
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - T J Little
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - N Macoretta
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - D Maddox
- White Oak Conservation, Yulee, FL, USA
| | - C O Matkin
- North Gulf Oceanic Society, Homer, AK, USA
| | - J A Mattison
- Translational Gerontology Branch, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | | | - J Mergl
- Marineland of Canada, Niagara Falls, Ontario, Canada
| | - J J Meudt
- Biomedical and Genomic Research Group, Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - G A Montano
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA
| | - K Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
| | - J Munshi-South
- Louis Calder Center-Biological Field Station, Department of Biological Sciences, Fordham University, Armonk, NY, USA
| | - A Naderi
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M Nagy
- Museum fur Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
| | - P Narayan
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - P W Nathanielsz
- Texas Pregnancy and Life-course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources, Laramie, WY, USA
| | - N B Nguyen
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Niehrs
- Institute of Molecular Biology, Mainz, Germany
- Division of Molecular Embryology, DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - J K O'Brien
- Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, New South Wales, Australia
| | - P O'Tierney Ginn
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
- Department of Obstetrics and Gynecology, Tufts University School of Medicine, Boston, MA, USA
| | - D T Odom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Division of Regulatory Genomics and Cancer Evolution, Deutsches Krebsforschungszentrum, Heidelberg, Germany
| | - A G Ophir
- Department of Psychology, Cornell University, Ithaca, NY, USA
| | - S Osborn
- SeaWorld of Texas, San Antonio, TX, USA
| | - E A Ostrander
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - K M Parsons
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - K C Paul
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - M Pellegrini
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - K J Peters
- Evolutionary Genetics Group, Department of Evolutionary Anthropology, University of Zurich, Zurich, Switzerland
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia
| | - A B Pedersen
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - J L Petersen
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | - D W Pietersen
- Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - G M Pinho
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - J Plassais
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - J R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Prado
- Department of Biology, College of Arts and Science, Adelphi University, Garden City, NY, USA
| | - P Reddy
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | - B Rey
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - B R Ritz
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
- Department of Environmental Health Sciences, UCLA Fielding School of Public Health, Los Angeles, CA, USA
- Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - J Robbins
- Center for Coastal Studies, Provincetown, MA, USA
| | | | - J Russell
- SeaWorld of California, San Diego, CA, USA
| | - E Rydkina
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - L L Sailer
- Department of Psychology, Cornell University, Ithaca, NY, USA
| | - A B Salmon
- The Sam and Ann Barshop Institute for Longevity and Aging Studies and Department of Molecular Medicine, UT Health San Antonio and the Geriatric Research Education and Clinical Center, South Texas Veterans Healthcare System, San Antonio, TX, USA
| | | | - K M Schachtschneider
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago, Chicago, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - D Schmitt
- College of Agriculture, Missouri State University, Springfield, MO, USA
| | - T Schmitt
- SeaWorld of California, San Diego, CA, USA
| | | | - L B Schook
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - K E Sears
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - A W Seifert
- Department of Biology, University of Kentucky, Lexington, KY, USA
| | - A Seluanov
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - A B A Shafer
- Department of Forensic Science, Environmental and Life Sciences, Trent University, Peterborough, Ontario, Canada
| | - D Shanmuganayagam
- Biomedical and Genomic Research Group, Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - A V Shindyapina
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - K Singh
- Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM'S NMIMS University, Mumbai, India
| | - I Sinha
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - J Slone
- Division of Human Genetics, Department of Pediatrics, University at Buffalo, Buffalo, NY, USA
| | - R G Snell
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - E Soltanmaohammadi
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M L Spangler
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | | | - L Staggs
- SeaWorld of Florida, Orlando, FL, USA
| | | | - K J Steinman
- Species Preservation Laboratory, SeaWorld San Diego, San Diego, CA, USA
| | - D T Stewart
- Biology Department, Acadia University, Wolfville, Nova Scotia, Canada
| | - V J Sugrue
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - B Szladovits
- Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, UK
| | - J S Takahashi
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Howard Hughes Medical Institute, Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - M Takasugi
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - E C Teeling
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - M J Thompson
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - B Van Bonn
- John G. Shedd Aquarium, Chicago, IL, USA
| | - S C Vernes
- School of Biology, the University of St Andrews, Fife, UK
- Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - D Villar
- Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - H V Vinters
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - M C Wallingford
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
- Division of Obstetrics and Gynecology, Tufts University School of Medicine, Boston, MA, USA
| | - N Wang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - R K Wayne
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - G S Wilkinson
- Department of Biology, University of Maryland, College Park, MD, USA
| | - C K Williams
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - R W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
| | - X W Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - M Yao
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - B G Young
- Fisheries and Oceans Canada, Winnipeg, Manitoba, Canada
| | - B Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Z Zhang
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - P Zhao
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA, USA
| | - Y Zhao
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - W Zhou
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - J Zimmermann
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, Koblenz, Germany
| | - J Ernst
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - K Raj
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - S Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA.
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
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Jones JD, Martinez S, Gonzalez I, Odom GJ, Comer SD. No evidence of accelerated epigenetic aging among black heroin users: A case vs control analysis. ADDICTION NEUROSCIENCE 2023; 7:100096. [PMID: 37388854 PMCID: PMC10305791 DOI: 10.1016/j.addicn.2023.100096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
This study sought to assess the association between illicit opioid use and accelerated epigenetic aging (A.K.A. DNAm Age) among people of African ancestry who use heroin. DNA was obtained from participants with opioid use disorder (OUD) who confirmed heroin as their primary drug of choice. Clinical inventories of drug use included: the Addiction Severity Index (ASI) Drug-Composite Score (range: 0-1), and Drug Abuse Screening Test (DAST-10; range: 0-10). A control group of participants of African ancestry who did not use heroin was recruited and matched to heroin users on sex, age, socioeconomic level, and smoking status. Methylation data were assessed in an epigenetic clock to determined and compare Epigenetic Age to Chronological Age (i.e., age acceleration or deceleration). Data were obtained from 32 controls [mean age 36.3 (±7.5) years] and 64 heroin users [mean age 48.1 (±6.6) years]. The experimental group used heroin for an average of 18.1 (±10.6) years, reported use of 6.4 (±6.1) bags of heroin/day, with a mean DAST-10 score of 7.0 (±2.6) and ASI Score of 0.33 (±0.19). Mean age acceleration for heroin users [+0.56 (± 9.5) years] was significantly (p< 0.05) lower than controls [+5.19 (± 9.1) years]. This study did not find evidence that heroin use causes epigenetic age acceleration.
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Affiliation(s)
- Jermaine D. Jones
- Department of Psychiatry, Division on Substance Use Disorders, New York State Psychiatric Institute, and Columbia University Irving Medical Center, 1051 Riverside Drive, New York, NY 10032, USA
| | - Suky Martinez
- Department of Psychiatry, Division on Substance Use Disorders, New York State Psychiatric Institute, and Columbia University Irving Medical Center, 1051 Riverside Drive, New York, NY 10032, USA
| | - Ingrid Gonzalez
- Department of Biostatistics, Robert Stempel College of Public Health, Florida International University, 1200 SW 8th St, Miami, FL 33174, USA
| | - Gabriel J. Odom
- Department of Biostatistics, Robert Stempel College of Public Health, Florida International University, 1200 SW 8th St, Miami, FL 33174, USA
| | - Sandra D. Comer
- Department of Psychiatry, Division on Substance Use Disorders, New York State Psychiatric Institute, and Columbia University Irving Medical Center, 1051 Riverside Drive, New York, NY 10032, USA
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Vasileva D, Greenwood CMT, Daley D. A Review of the Epigenetic Clock: Emerging Biomarkers for Asthma and Allergic Disease. Genes (Basel) 2023; 14:1724. [PMID: 37761864 PMCID: PMC10531327 DOI: 10.3390/genes14091724] [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: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
DNA methylation (DNAm) is a dynamic, age-dependent epigenetic modification that can be used to study interactions between genetic and environmental factors. Environmental exposures during critical periods of growth and development may alter DNAm patterns, leading to increased susceptibility to diseases such as asthma and allergies. One method to study the role of DNAm is the epigenetic clock-an algorithm that uses DNAm levels at select age-informative Cytosine-phosphate-Guanine (CpG) dinucleotides to predict epigenetic age (EA). The difference between EA and calendar age (CA) is termed epigenetic age acceleration (EAA) and reveals information about the biological capacity of an individual. Associations between EAA and disease susceptibility have been demonstrated for a variety of age-related conditions and, more recently, phenotypes such as asthma and allergic diseases, which often begin in childhood and progress throughout the lifespan. In this review, we explore different epigenetic clocks and how they have been applied, particularly as related to childhood asthma. We delve into how in utero and early life exposures (e.g., smoking, air pollution, maternal BMI) result in methylation changes. Furthermore, we explore the potential for EAA to be used as a biomarker for asthma and allergic diseases and identify areas for further study.
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Affiliation(s)
- Denitsa Vasileva
- Centre for Heart Lung Innovation, University of British Columbia and Saint Paul’s Hospital, Vancouver, BC V6Z 1Y6, Canada;
| | - Celia M. T. Greenwood
- Lady Davis Institute for Medical Research, Montreal, QC H3T 1E2, Canada;
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 0G4, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H3A 0G4, Canada
- Department of Human Genetics, McGill University, Montreal, QC H3A 0G4, Canada
| | - Denise Daley
- Centre for Heart Lung Innovation, University of British Columbia and Saint Paul’s Hospital, Vancouver, BC V6Z 1Y6, Canada;
- Department of Medicine, Respiratory Division, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
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Senapati P, Miyano M, Sayaman RW, Basam M, Leung A, LaBarge MA, Schones DE. Loss of epigenetic suppression of retrotransposons with oncogenic potential in aging mammary luminal epithelial cells. Genome Res 2023; 33:1229-1241. [PMID: 37463750 PMCID: PMC10547379 DOI: 10.1101/gr.277511.122] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 06/23/2023] [Indexed: 07/20/2023]
Abstract
A primary function of DNA methylation in mammalian genomes is to repress transposable elements (TEs). The widespread methylation loss that is commonly observed in cancer cells results in the loss of epigenetic repression of TEs. The aging process is similarly characterized by changes to the methylome. However, the impact of these epigenomic alterations on TE silencing and the functional consequences of this have remained unclear. To assess the epigenetic regulation of TEs in aging, we profiled DNA methylation in human mammary luminal epithelial cells (LEps)-a key cell lineage implicated in age-related breast cancers-from younger and older women. We report here that several TE subfamilies function as regulatory elements in normal LEps, and a subset of these display consistent methylation changes with age. Methylation changes at these TEs occurred at lineage-specific transcription factor binding sites, consistent with loss of lineage specificity. Whereas TEs mainly showed methylation loss, CpG islands (CGIs) that are targets of the Polycomb repressive complex 2 (PRC2) show a gain of methylation in aging cells. Many TEs with methylation loss in aging LEps have evidence of regulatory activity in breast cancer samples. We furthermore show that methylation changes at TEs impact the regulation of genes associated with luminal breast cancers. These results indicate that aging leads to DNA methylation changes at TEs that undermine the maintenance of lineage specificity, potentially increasing susceptibility to breast cancer.
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Affiliation(s)
- Parijat Senapati
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, California 91010, USA
| | - Masaru Miyano
- Department of Population Sciences, Beckman Research Institute, City of Hope, Duarte, California 91010, USA
| | - Rosalyn W Sayaman
- Department of Population Sciences, Beckman Research Institute, City of Hope, Duarte, California 91010, USA
- Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94143-0981, USA
| | - Mudaser Basam
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, California 91010, USA
- Department of Population Sciences, Beckman Research Institute, City of Hope, Duarte, California 91010, USA
| | - Amy Leung
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, California 91010, USA
| | - Mark A LaBarge
- Department of Population Sciences, Beckman Research Institute, City of Hope, Duarte, California 91010, USA
- Irell & Manella Graduate School of Biological Sciences, City of Hope, Duarte, California 91010, USA
- Center for Cancer Biomarker Research, University of Bergen, 5021 Bergen, Norway
| | - Dustin E Schones
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, California 91010, USA;
- Irell & Manella Graduate School of Biological Sciences, City of Hope, Duarte, California 91010, USA
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33
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Simpson DJ, Zhao Q, Olova NN, Dabrowski J, Xie X, Latorre‐Crespo E, Chandra T. Region-based epigenetic clock design improves RRBS-based age prediction. Aging Cell 2023; 22:e13866. [PMID: 37170475 PMCID: PMC10410054 DOI: 10.1111/acel.13866] [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: 02/08/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/13/2023] Open
Abstract
Recent studies suggest that epigenetic rejuvenation can be achieved using drugs that mimic calorie restriction and techniques such as reprogramming-induced rejuvenation. To effectively test rejuvenation in vivo, mouse models are the safest alternative. However, we have found that the recent epigenetic clocks developed for mouse reduced-representation bisulphite sequencing (RRBS) data have significantly poor performance when applied to external datasets. We show that the sites captured and the coverage of key CpGs required for age prediction vary greatly between datasets, which likely contributes to the lack of transferability in RRBS clocks. To mitigate these coverage issues in RRBS-based age prediction, we present two novel design strategies that use average methylation over large regions rather than individual CpGs, whereby regions are defined by sliding windows (e.g. 5 kb), or density-based clustering of CpGs. We observe improved correlation and error in our regional blood clocks (RegBCs) compared to published individual-CpG-based techniques when applied to external datasets. The RegBCs are also more robust when applied to low coverage data and detect a negative age acceleration in mice undergoing calorie restriction. Our RegBCs offer a proof of principle that age prediction of RRBS datasets can be improved by accounting for multiple CpGs over a region, which negates the lack of read depth currently hindering individual-CpG-based approaches.
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Affiliation(s)
- Daniel J. Simpson
- MRC Human Genetics Unit, MRC Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
| | - Qian Zhao
- MRC Human Genetics Unit, MRC Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
| | - Nelly N. Olova
- MRC Human Genetics Unit, MRC Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
| | - Jan Dabrowski
- MRC Human Genetics Unit, MRC Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
| | - Xiaoxiao Xie
- MRC Human Genetics Unit, MRC Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
| | - Eric Latorre‐Crespo
- MRC Human Genetics Unit, MRC Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
| | - Tamir Chandra
- MRC Human Genetics Unit, MRC Institute of Genetics and CancerUniversity of EdinburghEdinburghUK
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Jeong S, Cho S, Yang SK, Oh SA, Kang YK. Parallel shift of DNA methylation and gene expression toward the mean in mouse spleen with aging. Aging (Albany NY) 2023; 15:6690-6709. [PMID: 37494662 PMCID: PMC10415566 DOI: 10.18632/aging.204903] [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: 04/19/2023] [Accepted: 07/06/2023] [Indexed: 07/28/2023]
Abstract
Age-associated DNA-methylation drift (AMD) manifests itself in two ways in mammals: global decrease (hypomethylation) and local increase of DNA methylation (hypermethylation). To comprehend the principle behind this bidirectional AMD, we studied methylation states of spatially clustered CpG dinucleotides in mouse splenic DNA using reduced-representation-bisulfite-sequencing (RRBS). The mean methylation levels of whole CpGs declined with age. Promoter-resident CpGs, generally weakly methylated (<5%) in young mice, became hypermethylated in old mice, whereas CpGs in gene-body and intergenic regions, initially moderately (~33%) and extensively (>80%) methylated, respectively, were hypomethylated in the old. Chromosome-wise analysis of methylation revealed that inter-individual heterogeneities increase with age. The density of nearby CpGs was used to classify individual CpGs, which found hypermethylation in CpG-rich regions and hypomethylation in CpG-poor regions. When genomic regions were grouped by methylation level, high-methylation regions tended to become hypomethylated whereas low-methylation regions tended to become hypermethylated, regardless of genomic structure/function. Data analysis revealed that while methylation level and CpG density were interdependent, methylation level was a better predictor of the AMD pattern representing a shift toward the mean. Further analysis of gene-expression data showed a decrease in the expression of highly-expressed genes and an increase in the expression of lowly-expressed genes with age. This shift towards the mean in gene-expression changes was correlated with that of methylation changes, indicating a potential link between the two age-associated changes. Our findings suggest that age-associated hyper- and hypomethylation events are stochastic and attributed to malfunctioning intrinsic mechanisms for methylation maintenance in low- and high-methylation regions, respectively.
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Affiliation(s)
- Sangkyun Jeong
- Medical Research Division, Korea Institute of Oriental Medicine (KIOM), Yuseong-gu, Daejeon 34054, South Korea
- Genomics Department, Keyomics Co. Ltd., Yuseong-gu, Daejeon 34013, South Korea
| | - Sunwha Cho
- Genomics Department, Keyomics Co. Ltd., Yuseong-gu, Daejeon 34013, South Korea
| | - Seung Kyoung Yang
- Genomics Department, Keyomics Co. Ltd., Yuseong-gu, Daejeon 34013, South Korea
| | - Soo A. Oh
- Medical Research Division, Korea Institute of Oriental Medicine (KIOM), Yuseong-gu, Daejeon 34054, South Korea
| | - Yong-Kook Kang
- Development and Differentiation Research Center, Aging Convergence Research Center (ACRC), Korea Research Institute of Bioscience Biotechnology (KRIBB), Yuseong-gu, Daejeon 34141, South Korea
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35
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 77] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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36
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Abstract
Ageing is inherent to all human beings, yet why we age remains a hotly contested topic. Most mechanistic explanations of ageing posit that ageing is caused by the accumulation of one or more forms of molecular damage. Here, I propose that we age not because of inevitable damage to the hardware but rather because of intrinsic design flaws in the software, defined as the DNA code that orchestrates how a single cell develops into an adult organism. As the developmental software runs, its sequence of events is reflected in shifting cellular epigenetic states. Overall, I suggest that to understand ageing we need to decode our software and the flow of epigenetic information throughout the life course.
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Affiliation(s)
- João Pedro de Magalhães
- Genomics of Ageing and Rejuvenation Lab, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, B15 2WB, UK.
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37
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Dec E, Clement J, Cheng K, Church GM, Fossel MB, Rehkopf DH, Rosero-Bixby L, Kobor MS, Lin DT, Lu AT, Fei Z, Guo W, Chew YC, Yang X, Putra SED, Reiner AP, Correa A, Vilalta A, Pirazzini C, Passarino G, Monti D, Arosio B, Garagnani P, Franceschi C, Horvath S. Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity. GeroScience 2023:10.1007/s11357-023-00731-7. [PMID: 36964402 PMCID: PMC10400760 DOI: 10.1007/s11357-023-00731-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/09/2023] [Indexed: 03/26/2023] Open
Abstract
Claims surrounding exceptional longevity are sometimes disputed or dismissed for lack of credible evidence. Here, we present three DNA methylation-based age estimators (epigenetic clocks) for verifying age claims of centenarians. The three centenarian clocks were developed based on n = 7039 blood and saliva samples from individuals older than 40, including n = 184 samples from centenarians, 122 samples from semi-supercentenarians (aged 105 +), and 25 samples from supercentenarians (aged 110 +). The oldest individual was 115 years old. Our most accurate centenarian clock resulted from applying a neural network model to a training set composed of individuals older than 40. An epigenome-wide association study of age in different age groups revealed that age effects in young individuals (age < 40) are correlated (r = 0.55) with age effects in old individuals (age > 90). We present a chromatin state analysis of age effects in centenarians. The centenarian clocks are expected to be useful for validating claims surrounding exceptional old age.
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Affiliation(s)
- Eric Dec
- Department of Pediatrics, Division of Genetics and Genomic Medicine, University of California, Irvine, USA.
| | - James Clement
- Betterhumans Inc., Gainesville, FL, USA
- Department of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, Ferrara, Italy
| | - Kaiyang Cheng
- Medical Informatics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA
| | | | - David H Rehkopf
- Epidemiology & Population Health and Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Luis Rosero-Bixby
- Centro Centroamericano de Población, Universidad de Costa Rica, San Pedro, Costa Rica
| | - Michael S Kobor
- Edwin S.H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - David Ts Lin
- Edwin S.H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Ake T Lu
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego, CA, USA
| | - Zhe Fei
- Dept. of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Wei Guo
- Zymo Research Corp, Irvine, CA, USA
| | | | | | - Sulistyo E Dwi Putra
- Department of Biology, Faculty of Biotechnology, University of Surabaya, Surabaya, 60293, Indonesia
| | - Alex P Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Adolfo Correa
- Medicine and Population Health Science, University of Mississippi Medical Center, Jackson, USA
| | | | - Chiara Pirazzini
- IRCCS Istituto Delle Scienze Neurologiche Di Bologna, Bologna, Italy
| | - Giuseppe Passarino
- Department of Biology Ecology and Earth Science, University of Calabria, Rende, Italy
| | - Daniela Monti
- Department of Experimental and Clinical, Biomedical Sciences "Mario Serio" University of Florence, Florence, Italy
| | - Beatrice Arosio
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Paolo Garagnani
- Department of Medical and Surgical Sciences, University of Bologna, 40126, Bologna, Italy
- Alma Mater Research Institute On Global Challenges and Climate Change (Alma Climate), University of Bologna, Bologna, Italy
- Department of Laboratory Medicine, Clinical Chemistry, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- CNR Institute of Molecular Genetics "Luigi Luca Cavalli-Sforza", Unit of Bologna, Bologna, Italy
| | - Claudio Franceschi
- Department of Medical and Surgical Sciences, University of Bologna, 40126, Bologna, Italy
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod, Russia
| | - Steve Horvath
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Altos Labs, San Diego, CA, USA.
- Dept. of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
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D’Egidio F, Castelli V, Cimini A, d’Angelo M. Cell Rearrangement and Oxidant/Antioxidant Imbalance in Huntington's Disease. Antioxidants (Basel) 2023; 12:571. [PMID: 36978821 PMCID: PMC10045781 DOI: 10.3390/antiox12030571] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Huntington's Disease (HD) is a hereditary neurodegenerative disorder caused by the expansion of a CAG triplet repeat in the HTT gene, resulting in the production of an aberrant huntingtin (Htt) protein. The mutant protein accumulation is responsible for neuronal dysfunction and cell death. This is due to the involvement of oxidative damage, excitotoxicity, inflammation, and mitochondrial impairment. Neurons naturally adapt to bioenergetic alteration and oxidative stress in physiological conditions. However, this dynamic system is compromised when a neurodegenerative disorder occurs, resulting in changes in metabolism, alteration in calcium signaling, and impaired substrates transport. Thus, the aim of this review is to provide an overview of the cell's answer to the stress induced by HD, focusing on the role of oxidative stress and its balance with the antioxidant system.
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Affiliation(s)
| | | | | | - Michele d’Angelo
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
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39
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Devall M, Soanes DM, Smith AR, Dempster EL, Smith RG, Burrage J, Iatrou A, Hannon E, Troakes C, Moore K, O'Neill P, Al-Sarraj S, Schalkwyk L, Mill J, Weedon M, Lunnon K. Genome-wide characterization of mitochondrial DNA methylation in human brain. Front Endocrinol (Lausanne) 2023; 13:1059120. [PMID: 36726473 PMCID: PMC9885148 DOI: 10.3389/fendo.2022.1059120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/05/2022] [Indexed: 01/17/2023] Open
Abstract
Background There is growing interest in the role of DNA methylation in regulating the transcription of mitochondrial genes, particularly in brain disorders characterized by mitochondrial dysfunction. Here, we present a novel approach to interrogate the mitochondrial DNA methylome at single base resolution using targeted bisulfite sequencing. We applied this method to investigate mitochondrial DNA methylation patterns in post-mortem superior temporal gyrus and cerebellum brain tissue from seven human donors. Results We show that mitochondrial DNA methylation patterns are relatively low but conserved, with peaks in DNA methylation at several sites, such as within the D-LOOP and the genes MT-ND2, MT-ATP6, MT-ND4, MT-ND5 and MT-ND6, predominantly in a non-CpG context. The elevated DNA methylation we observe in the D-LOOP we validate using pyrosequencing. We identify loci that show differential DNA methylation patterns associated with age, sex and brain region. Finally, we replicate previously reported differentially methylated regions between brain regions from a methylated DNA immunoprecipitation sequencing study. Conclusions We have annotated patterns of DNA methylation at single base resolution across the mitochondrial genome in human brain samples. Looking to the future this approach could be utilized to investigate the role of mitochondrial epigenetic mechanisms in disorders that display mitochondrial dysfunction.
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Affiliation(s)
- Matthew Devall
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Darren M Soanes
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Adam R Smith
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Emma L Dempster
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Rebecca G Smith
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Joe Burrage
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Artemis Iatrou
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Eilis Hannon
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Claire Troakes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Karen Moore
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Paul O'Neill
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Safa Al-Sarraj
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Leonard Schalkwyk
- School of Biological Sciences, University of Essex, Essex, United Kingdom
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Michael Weedon
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Katie Lunnon
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
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Selvestrel D, Stocco G, Aloi M, Arrigo S, Cardile S, Cecchin E, Congia M, Curci D, Gatti S, Graziano F, Langefeld CD, Lucafò M, Martelossi S, Martinelli M, Pagarin S, Scarallo L, Stacul EF, Strisciuglio C, Thompson S, Zuin G, Decorti G, Bramuzzo M. DNA methylation of the TPMT gene and azathioprine pharmacokinetics in children with very early onset inflammatory bowel disease. Biomed Pharmacother 2023; 157:113901. [PMID: 36462311 DOI: 10.1016/j.biopha.2022.113901] [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: 06/20/2022] [Revised: 10/12/2022] [Accepted: 10/17/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Thiopurine methyltransferase (TPMT) is a crucial enzyme for azathioprine biotransformation and its activity is higher in very early onset inflammatory bowel disease (VEO-IBD) patients than in adolescents with IBD (aIBD). AIMS The aims of this pharmacoepigenetic study were to evaluate differences in peripheral blood DNA methylation of the TPMT gene and in azathioprine pharmacokinetics in patients with VEO-IBD compared to aIBD. METHODS The association of age with whole genome DNA methylation profile was evaluated in a pilot group of patients and confirmed by a meta-analysis on 3 cohorts of patients available on the public functional genomics data repository. Effects of candidate CpG sites in the TPMT gene were validated in a larger cohort using pyrosequencing. TPMT activity and azathioprine metabolites (TGN) were measured in patients' erythrocytes by HPLC and associated with patients' age group and TPMT DNA methylation. RESULTS Whole genome DNA methylation pilot analysis, combined with the meta-analysis revealed cg22736354, located on TPMT downstream neighboring region, as the only statistically significant CpG whose methylation increases with age, resulting lower in VEO-IBD patients compared to aIBD (median 9.6% vs 12%, p = 0.029). Pyrosequencing confirmed lower cg22736354 methylation in VEO-IBD patients (median 4.0% vs 6.0%, p = 4.6 ×10-5). No differences in TPMT promoter methylation were found. Reduced cg22736354 methylation was associated with lower TGN concentrations (rho = 0.31, p = 0.01) in patients with VEO-IBD and aIBD. CONCLUSION Methylation of cg22736354 in TPMT gene neighborhood is lower in patients with VEO-IBD and is associated with reduced azathioprine inactivation and increased TGN concentrations.
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Affiliation(s)
| | - Gabriele Stocco
- Institute for Maternal and Child Health, IRCCS "Burlo Garofolo", Trieste, Italy; Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Marina Aloi
- Women's and Children's Health Department, Pediatric Gastroenterology and Hepatology Unit, Sapienza University of Rome, Rome, Italy
| | - Serena Arrigo
- Pediatric Gastroenterology and Endoscopy Unit, Institute 'Giannina Gaslini', Genoa, Italy
| | - Sabrina Cardile
- Hepatology and Gastroenterology Unit, Bambino Gesù Hospital, Rome, Italy
| | - Erika Cecchin
- Experimental and Clinical Pharmacology, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy
| | - Mauro Congia
- Pediatric Clinic and Rare Diseases, Microcitemic Pediatric Hospital Antonio Cao, Azienda Ospedaliera Brotzu, Cagliari, Italy
| | - Debora Curci
- Institute for Maternal and Child Health, IRCCS "Burlo Garofolo", Trieste, Italy
| | - Simona Gatti
- Department of Pediatrics, Università Politecnica delle Marche, Ancona, Italy
| | | | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Marianna Lucafò
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | | | - Massimo Martinelli
- Department of Translational Medical Science, Section of Pediatrics, University of Naples "Federico II", Naples, Italy
| | - Sofia Pagarin
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Luca Scarallo
- University of Florence-Meyer Hospital, Florence, Italy
| | | | - Caterina Strisciuglio
- Departement of Woman, Child and General and Specialistic Surgery, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Susan Thompson
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Giovanna Zuin
- Department of Pediatrics, University of Milano-Bicocca, Foundation MBBM/San Gerardo Hospital, Monza, Italy
| | - Giuliana Decorti
- Institute for Maternal and Child Health, IRCCS "Burlo Garofolo", Trieste, Italy; Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy.
| | - Matteo Bramuzzo
- Gastroenterology, Digestive Endoscopy and Nutrition Unit, Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy
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41
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Regulation, functions and transmission of bivalent chromatin during mammalian development. Nat Rev Mol Cell Biol 2023; 24:6-26. [PMID: 36028557 DOI: 10.1038/s41580-022-00518-2] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2022] [Indexed: 12/25/2022]
Abstract
Cells differentiate and progress through development guided by a dynamic chromatin landscape that mediates gene expression programmes. During development, mammalian cells display a paradoxical chromatin state: histone modifications associated with gene activation (trimethylated histone H3 Lys4 (H3K4me3)) and with gene repression (trimethylated H3 Lys27 (H3K27me3)) co-occur at promoters of developmental genes. This bivalent chromatin modification state is thought to poise important regulatory genes for expression or repression during cell-lineage specification. In this Review, we discuss recent work that has expanded our understanding of the molecular basis of bivalent chromatin and its contributions to mammalian development. We describe the factors that establish bivalency, especially histone-lysine N-methyltransferase 2B (KMT2B) and Polycomb repressive complex 2 (PRC2), and consider evidence indicating that PRC1 shapes bivalency and may contribute to its transmission between generations. We posit that bivalency is a key feature of germline and embryonic stem cells, as well as other types of stem and progenitor cells. Finally, we discuss the relevance of bivalent chromtin to human development and cancer, and outline avenues of future research.
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42
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Aminzadeh-Gohari S, Kofler B, Herzog C. Dietary restriction in senolysis and prevention and treatment of disease. Crit Rev Food Sci Nutr 2022; 64:5242-5268. [PMID: 36484738 PMCID: PMC7616065 DOI: 10.1080/10408398.2022.2153355] [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] [Indexed: 12/13/2022]
Abstract
Aging represents a key risk factor for a plethora of diseases. Targeting detrimental processes which occur during aging, especially before onset of age-related disease, could provide drastic improvements in healthspan. There is increasing evidence that dietary restriction (DR), including caloric restriction, fasting, or fasting-mimicking diets, extend both lifespan and healthspan. This has sparked interest in the use of dietary regimens as a non-pharmacological means to slow aging and prevent disease. Here, we review the current evidence on the molecular mechanisms underlying DR-induced health improvements, including removal of senescent cells, metabolic reprogramming, and epigenetic rejuvenation.
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Affiliation(s)
- Sepideh Aminzadeh-Gohari
- Research Program for Receptor Biochemistry and Tumor Metabollism, Department of Pediatrics, University Hospital of the Paracelsus Medical University, Salzburg, Austria
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
- Research Institute for Biomedical Ageing, Universität Innsbruck, Innsbruck, Austria
| | - Barbara Kofler
- Research Program for Receptor Biochemistry and Tumor Metabollism, Department of Pediatrics, University Hospital of the Paracelsus Medical University, Salzburg, Austria
| | - Chiara Herzog
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
- Research Institute for Biomedical Ageing, Universität Innsbruck, Innsbruck, Austria
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43
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Libertini G, Corbi G, Shubernetskaya O, Ferrara N. Is Human Aging a Form of Phenoptosis? BIOCHEMISTRY. BIOKHIMIIA 2022; 87:1446-1464. [PMID: 36717439 DOI: 10.1134/s0006297922120033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
A much debated question is whether aging is the cumulative consequence of degenerative factors insufficiently opposed by natural selection, or, on the contrary, an ordered process, genetically determined and regulated, modeled by natural selection, and for which the definition of phenoptotic phenomenon would be entirely appropriate. In this review, theoretical arguments and empirical data about the two hypotheses are exposed, with more evidence in support of the thesis of aging as a form of phenoptosis. However, as the thesis of aging as an adaptive and programmed phenomenon necessarily requires the existence of specific mechanisms that determine to age, such as the subtelomere-telomere theory proposed for this purpose, the evidence supporting the mechanisms described by this theory is reported. In particular, it is highlighted that the recent interpretation of the role of TERRA sequences in the context of subtelomere-telomere theory is a fundamental point in supporting the hypothesized mechanisms. Furthermore, some characteristics of the mechanisms proposed by the theory, such as epigenetic modifications in aging, gradual cell senescence, cell senescence, limits in cell duplications, and fixed size of the telomeric heterochromatin hood, are exposed in their compatibility with both the thesis of aging as phenoptotic phenomenon and the opposite thesis. In short, aging as a form of phenoptosis appears a scientifically sound hypothesis while the opposite thesis should clarify the meaning of various phenomena that appear to invalidate it.
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Affiliation(s)
- Giacinto Libertini
- Italian Society for Evolutionary Biology (SIBE), Asti, 14100, Italy. .,Department of Translational Medical Sciences, Federico II University of Naples, Naples, 80131, Italy
| | - Graziamaria Corbi
- Department of Medicine and Health Sciences, University of Molise, Campobasso, 86100, Italy. .,Italian Society of Gerontology and Geriatrics (SIGG), Firenze, 50129, Italy
| | - Olga Shubernetskaya
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
| | - Nicola Ferrara
- Department of Translational Medical Sciences, Federico II University of Naples, Naples, 80131, Italy. .,Istituti Clinici Scientifici Maugeri SPA - Società Benefit, IRCCS, Telese Terme, BN, 82037, Italy
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44
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Ambroa-Conde A, Girón-Santamaría L, Mosquera-Miguel A, Phillips C, Casares de Cal M, Gómez-Tato A, Álvarez-Dios J, de la Puente M, Ruiz-Ramírez J, Lareu M, Freire-Aradas A. Epigenetic age estimation in saliva and in buccal cells. Forensic Sci Int Genet 2022; 61:102770. [DOI: 10.1016/j.fsigen.2022.102770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/22/2022] [Accepted: 08/24/2022] [Indexed: 11/04/2022]
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45
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Epigenetic clock: A promising biomarker and practical tool in aging. Ageing Res Rev 2022; 81:101743. [PMID: 36206857 DOI: 10.1016/j.arr.2022.101743] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 09/13/2022] [Accepted: 09/30/2022] [Indexed: 01/31/2023]
Abstract
As a complicated process, aging is characterized by various changes at the cellular, subcellular and nuclear levels, one of which is epigenetic aging. With increasing awareness of the critical role that epigenetic alternations play in aging, DNA methylation patterns have been employed as a measure of biological age, currently referred to as the epigenetic clock. This review provides a comprehensive overview of the epigenetic clock as a biomarker of aging and a useful tool to manage healthy aging. In this burgeoning scientific field, various kinds of epigenetic clocks continue to emerge, including Horvath's clock, Hannum's clock, DNA PhenoAge, and DNA GrimAge. We hereby present the most classic epigenetic clocks, as well as their differences. Correlations of epigenetic age with morbidity, mortality and other factors suggest the potential of epigenetic clocks for risk prediction and identification in the context of aging. In particular, we summarize studies on promising age-reversing interventions, with epigenetic clocks employed as a practical tool in the efficacy evaluation. We also discuss how the lack of higher-quality information poses a major challenge, and offer some suggestions to address existing obstacles. Hopefully, our review will help provide an appropriate understanding of the epigenetic clocks, thereby enabling novel insights into the aging process and how it can be manipulated to promote healthy aging.
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46
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Issah I, Arko-Mensah J, Rozek LS, Rentschler K, Agyekum TP, Dwumoh D, Batterman S, Robins TG, Fobil JN. Association between global DNA methylation (LINE-1) and occupational particulate matter exposure among informal electronic-waste recyclers in Ghana. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2022; 32:2406-2424. [PMID: 34404291 DOI: 10.1080/09603123.2021.1969007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/12/2021] [Indexed: 06/13/2023]
Abstract
This study examined the associations between PM (2.5 and 10) and global DNA methylation among 100 e-waste workers and 51 non-e-waste workers serving as controls. Long interspersed nucleotide repetitive elements-1 (LINE-1) was measured by pyrosequencing. Personal PM2.5 and PM10 were measured over a 4-hour work-shift using real-time particulate monitors incorporated into a backpack . Linear regression models were used to assess the association between PM and LINE-1 DNA methylation. The concentrations of PM2.5 and PM10 were significantly higher among the e-waste workers than the controls (77.32 vs 34.88, p < 0.001 and 210.21 vs 121.92, p < 0.001, respectively). PM2.5 exposure was associated with increased LINE-1 CpG2 DNA methylation (β = 0.003; 95% CI; 0.001, 0.006; p = 0.022) but not with the average of all 4 CpG sites of LINE-1. In summary, high levels of PM2.5 exposure was associated with increased levels of global DNA methylation in a site-specific manner.
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Affiliation(s)
- Ibrahim Issah
- Department of Biological, Environmental and Occupational Health Sciences, University of Ghana, School of Public Health, Accra, Ghana
| | - John Arko-Mensah
- Department of Biological, Environmental and Occupational Health Sciences, University of Ghana, School of Public Health, Accra, Ghana
| | - Laura S Rozek
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Katie Rentschler
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Thomas P Agyekum
- Department of Biological, Environmental and Occupational Health Sciences, University of Ghana, School of Public Health, Accra, Ghana
| | - Duah Dwumoh
- Department of Biostatistics, University of Ghana School of Public Health, Legon, Ghana
| | - Stuart Batterman
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Thomas G Robins
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Julius N Fobil
- Department of Biological, Environmental and Occupational Health Sciences, University of Ghana, School of Public Health, Accra, Ghana
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47
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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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48
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Pérez RF, Tejedor JR, Fernández AF, Fraga MF. Aging and cancer epigenetics: Where do the paths fork? Aging Cell 2022; 21:e13709. [PMID: 36103298 PMCID: PMC9577950 DOI: 10.1111/acel.13709] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 08/29/2022] [Indexed: 01/25/2023] Open
Abstract
Aging and cancer are clearly associated processes, at both the epidemiological and molecular level. Epigenetic mechanisms are good candidates to explain the molecular links between the two phenomena, but recent reports have also revealed considerable differences, particularly regarding the loss of DNA methylation in the two processes. The large-scale generation and availability of genome-wide epigenetic data now permits systematic studies to be undertaken which may help clarify the similarities and differences between aging and cancer epigenetic alterations. In addition, the development of epigenetic clocks provides a new dimension in which to investigate diseases at the molecular level. Here, we examine current and future questions about the roles of DNA methylation mechanisms as causal factors in the processes of aging and cancer so that we may better understand if and how aging-associated epigenetic alterations lead to tumorigenesis. It seems certain that comprehending the molecular mechanisms underlying epigenetic clocks, especially with regard to somatic stem cell aging, combined with applying single-cell epigenetic-age profiling technologies to aging and cancer cohorts, and the integration of existing and upcoming epigenetic evidence within the genetic damage models of aging will prove to be crucial to improving understanding of these two interrelated phenomena.
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Affiliation(s)
- Raúl Fernández Pérez
- Cancer Epigenetics and Nanomedicine LaboratoryNanomaterials and Nanotechnology Research Center (CINN‐CSIC)El EntregoSpain,Health Research Institute of Asturias (ISPA‐FINBA)Institute of Oncology of Asturias (IUOPA) and Department of Organisms and Systems Biology (BOS)University of OviedoOviedoSpain,Rare Diseases CIBER (CIBERER)Carlos III Health Institute (ISCIII)MadridSpain
| | - Juan Ramón Tejedor
- Cancer Epigenetics and Nanomedicine LaboratoryNanomaterials and Nanotechnology Research Center (CINN‐CSIC)El EntregoSpain,Health Research Institute of Asturias (ISPA‐FINBA)Institute of Oncology of Asturias (IUOPA) and Department of Organisms and Systems Biology (BOS)University of OviedoOviedoSpain,Rare Diseases CIBER (CIBERER)Carlos III Health Institute (ISCIII)MadridSpain
| | - Agustín Fernández Fernández
- Cancer Epigenetics and Nanomedicine LaboratoryNanomaterials and Nanotechnology Research Center (CINN‐CSIC)El EntregoSpain,Health Research Institute of Asturias (ISPA‐FINBA)Institute of Oncology of Asturias (IUOPA) and Department of Organisms and Systems Biology (BOS)University of OviedoOviedoSpain,Rare Diseases CIBER (CIBERER)Carlos III Health Institute (ISCIII)MadridSpain
| | - Mario Fernández Fraga
- Cancer Epigenetics and Nanomedicine LaboratoryNanomaterials and Nanotechnology Research Center (CINN‐CSIC)El EntregoSpain,Health Research Institute of Asturias (ISPA‐FINBA)Institute of Oncology of Asturias (IUOPA) and Department of Organisms and Systems Biology (BOS)University of OviedoOviedoSpain,Rare Diseases CIBER (CIBERER)Carlos III Health Institute (ISCIII)MadridSpain
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Li M, Bao L, Zhu P, Wang S. Effect of metformin on the epigenetic age of peripheral blood in patients with diabetes mellitus. Front Genet 2022; 13:955835. [PMID: 36226195 PMCID: PMC9548538 DOI: 10.3389/fgene.2022.955835] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Metformin has been proven to have an antiaging effect. However, studies on how metformin affects global epigenetic regulation and its effect on the epigenetic clock in diabetes mellitus (DM) patients are limited. This study aims to investigate the impact of metformin on the epigenetic age in subjects with type 2 DM. Results: We collected the peripheral blood of the metformin group and the no-metformin group of the 32 DM patients. Three previously established epigenetic clocks (Hannum, Horvath, and DNAmPhenoAge) were used to estimate the epigenetic age acceleration of the two groups. We defined biological age acceleration for each group by comparing the estimated biological age with the chronological age. Results were presented as follows: 1) all three epigenetic clocks were strongly correlated with chronological age. 2) We found a strong association between metformin intake and slower epigenetic aging by Horvath’s clock and Hannum’s clock. Conclusions: Here, we found an association between metformin intake and slower epigenetic aging.
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Affiliation(s)
- Man Li
- Department of Geriatrics, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Litao Bao
- Institute of Gerontology, Second Medical Center, PLA General Hospital, Beijing, China
| | - Ping Zhu
- Department of Geriatrics, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Shuxia Wang
- Department of Geriatrics, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
- *Correspondence: Shuxia Wang,
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50
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Kim B, Vasanthakumar A, Li QS, Nudelman KN, Risacher SL, Davis JW, Idler K, Lee J, Seo SW, Waring JF, Saykin AJ, Nho K. Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12354. [PMID: 36187194 PMCID: PMC9489162 DOI: 10.1002/dad2.12354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022]
Abstract
Introduction The acceleration of biological aging is a risk factor for Alzheimer's disease (AD). Here, we performed weighted gene co-expression network analysis (WGCNA) to identify modules and dysregulated genesinvolved in biological aging in AD. Methods We performed WGCNA to identify modules associated with biological clocks and hub genes of the module with the highest module significance. In addition, we performed differential expression analysis and association analysis with AD biomarkers. Results WGCNA identified five modules associated with biological clocks, with the module designated as "purple" showing the strongest association. Functional enrichment analysis revealed that the purple module was related to cell migration and death. Ten genes were identified as hub genes in purple modules, of which CX3CR1 was downregulated in AD and low levels of CX3CR1 expression were associated with AD biomarkers. Conclusion Network analysis identified genes associated with biological clocks, which suggests the genetic architecture underlying biological aging in AD. Highlights Examine links between Alzheimer's disease (AD) peripheral transcriptome and biological aging changes.Weighted gene co-expression network analysis (WGCNA) found five modules related to biological aging.Among the hub genes of the module, CX3CR1 was downregulated in AD.The CX3CR1 expression level was associated with cognitive performance and brain atrophy.
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Affiliation(s)
- Bo‐Hyun Kim
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Samsung Alzheimer Research CenterSamsung Medical CenterSeoulRepublic of Korea
- Department of Health Sciences and TechnologySHAISTSungkyunkwan UniversitySeoulRepublic of Korea
| | | | - Qingqin S. Li
- Neuroscience Therapeutic AreaJanssen Research & Development, LLCTitusvilleNew JerseyUSA
| | - Kelly N.H. Nudelman
- National Centralized Repository for Alzheimer's Disease and Related DementiasIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Shannon L. Risacher
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Kenneth Idler
- Genomics Research CenterAbbVieNorth ChicagoIllinoisUSA
| | - Jong‐Min Lee
- Department of Biomedical EngineeringHanyang UniversitySeoulRepublic of Korea
| | - Sang Won Seo
- Samsung Alzheimer Research CenterSamsung Medical CenterSeoulRepublic of Korea
- Department of NeurologySamsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea
- Department of Health Sciences and TechnologySHAISTSungkyunkwan UniversitySeoulRepublic of Korea
| | | | - Andrew J. Saykin
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Kwangsik Nho
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
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