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Yuen ZWS, Shanmuganandam S, Stanley M, Jiang S, Hein N, Daniel R, McNevin D, Jack C, Eyras E. Profiling age and body fluid DNA methylation markers using nanopore adaptive sampling. Forensic Sci Int Genet 2024; 71:103048. [PMID: 38640705 DOI: 10.1016/j.fsigen.2024.103048] [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: 12/11/2023] [Revised: 04/07/2024] [Accepted: 04/11/2024] [Indexed: 04/21/2024]
Abstract
DNA methylation plays essential roles in regulating physiological processes, from tissue and organ development to gene expression and aging processes and has emerged as a widely used biomarker for the identification of body fluids and age prediction. Currently, methylation markers are targeted independently at specific CpG sites as part of a multiplexed assay rather than through a unified assay. Methylation detection is also dependent on divergent methodologies, ranging from enzyme digestion and affinity enrichment to bisulfite treatment, alongside various technologies for high-throughput profiling, including microarray and sequencing. In this pilot study, we test the simultaneous identification of age-associated and body fluid-specific methylation markers using a single technology, nanopore adaptive sampling. This innovative approach enables the profiling of multiple CpG marker sites across entire gene regions from a single sample without the need for specialized DNA preparation or additional biochemical treatments. Our study demonstrates that adaptive sampling achieves sufficient coverage in regions of interest to accurately determine the methylation status, shows a robust consistency with whole-genome bisulfite sequencing data, and corroborates known CpG markers of age and body fluids. Our work also resulted in the identification of new sites strongly correlated with age, suggesting new possible age methylation markers. This study lays the groundwork for the systematic development of nanopore-based methodologies in both age prediction and body fluid identification, highlighting the feasibility and potential of nanopore adaptive sampling while acknowledging the need for further validation and expansion in future research.
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Affiliation(s)
- Zaka Wing-Sze Yuen
- EMBL Australia Partner Laboratory Network, John Curtin School of Medical Research, The Australian National University, Canberra, Australia; The Shine-Dalgarno Centre for RNA Innovation, John Curtin School of Medical Research, The Australian National University, Canberra, Australia; The Centre for Computational Biomedical Sciences, John Curtin School of Medical Research, The Australian National University, Canberra, Australia
| | - Somasundhari Shanmuganandam
- Department of Immunity, Inflammation and Infection, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia; Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Australian National University, Canberra, ACT 2601, Australia
| | - Maurice Stanley
- Department of Immunity, Inflammation and Infection, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia; Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Australian National University, Canberra, ACT 2601, Australia
| | - Simon Jiang
- Department of Immunity, Inflammation and Infection, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia; Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Australian National University, Canberra, ACT 2601, Australia; Department of Renal Medicine, The Canberra Hospital, Canberra, ACT 2605, Australia
| | - Nadine Hein
- ACRF Department of Cancer Biology and Therapeutics and Division of Genome Sciences and Cancer, John Curtin School of Medical Research, Australian National University, Acton, Canberra, Australia
| | - Runa Daniel
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, Queensland, Australia
| | - Dennis McNevin
- Centre for Forensic Science, School of Mathematical & Physical Sciences, Faculty of Science, University of Technology Sydney, Sydney, Australia
| | - Cameron Jack
- ANU Bioinformatics Consultancy, John Curtin School of Medical Research, The Australian National University, Canberra, Australia
| | - Eduardo Eyras
- EMBL Australia Partner Laboratory Network, John Curtin School of Medical Research, The Australian National University, Canberra, Australia; The Shine-Dalgarno Centre for RNA Innovation, John Curtin School of Medical Research, The Australian National University, Canberra, Australia; The Centre for Computational Biomedical Sciences, John Curtin School of Medical Research, The Australian National University, Canberra, Australia.
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2
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Balard A, Baltazar-Soares M, Eizaguirre C, Heckwolf MJ. An epigenetic toolbox for conservation biologists. Evol Appl 2024; 17:e13699. [PMID: 38832081 PMCID: PMC11146150 DOI: 10.1111/eva.13699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 06/05/2024] Open
Abstract
Ongoing climatic shifts and increasing anthropogenic pressures demand an efficient delineation of conservation units and accurate predictions of populations' resilience and adaptive potential. Molecular tools involving DNA sequencing are nowadays routinely used for these purposes. Yet, most of the existing tools focusing on sequence-level information have shortcomings in detecting signals of short-term ecological relevance. Epigenetic modifications carry valuable information to better link individuals, populations, and species to their environment. Here, we discuss a series of epigenetic monitoring tools that can be directly applied to various conservation contexts, complementing already existing molecular monitoring frameworks. Focusing on DNA sequence-based methods (e.g. DNA methylation, for which the applications are readily available), we demonstrate how (a) the identification of epi-biomarkers associated with age or infection can facilitate the determination of an individual's health status in wild populations; (b) whole epigenome analyses can identify signatures of selection linked to environmental conditions and facilitate estimating the adaptive potential of populations; and (c) epi-eDNA (epigenetic environmental DNA), an epigenetic-based conservation tool, presents a non-invasive sampling method to monitor biological information beyond the mere presence of individuals. Overall, our framework refines conservation strategies, ensuring a comprehensive understanding of species' adaptive potential and persistence on ecologically relevant timescales.
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Affiliation(s)
- Alice Balard
- School of Biological and Behavioural Sciences Queen Mary University of London London UK
| | | | - Christophe Eizaguirre
- School of Biological and Behavioural Sciences Queen Mary University of London London UK
| | - Melanie J Heckwolf
- Department of Ecology Leibniz Centre for Tropical Marine Research Bremen Germany
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3
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Filoglu G, Sımsek SZ, Ersoy G, Can K, Bulbul O. Epigenetic-based age prediction in blood samples: Model development. J Forensic Sci 2024; 69:869-879. [PMID: 38308398 DOI: 10.1111/1556-4029.15478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
Aging is a complex process influenced by genetic, epigenetic, and environmental factors that lead to tissue deterioration and frailty. Epigenetic mechanisms, such as DNA methylation, play a significant role in gene expression regulation and aging. This study presents a new age estimation model developed for the Turkish population using blood samples. Eight CpG sites in loci TOM1L1, ELOVL2, ASPA, FHL2, C1orf132, CCDC102B, cg07082267, and RASSF5 were selected based on their correlation with age. Methylation patterns of these sites were analyzed in blood samples from 100 volunteers, grouped into age categories (20-35, 36-55, and ≥56). Sensitivity analysis indicated a reliable performance with DNA inputs ≥1 ng. Statistical modeling, utilizing Multiple Linear Regression, underscores the reliability of the primary 6-CpG model, excluding cg07082267 and TOM1L1. This model demonstrates strong correlations with chronological age (r = 0.941) and explains 88% of the age variance with low error rates (MAE = 4.07, RMSE = 5.73 years). Validation procedures, including a training-test split and fivefold cross-validation, consistently confirm the model's accuracy and consistency. The study indicates minimal variation in error scores across age cohorts and no significant gender differences. The developed model showed strong predictive accuracy, with the ability to estimate age within certain prediction intervals. This study contributes to the age prediction by using DNA methylation patterns, which can have disparate applications, including forensic and clinical assessments.
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Affiliation(s)
- Gonul Filoglu
- Department of Science, Institute of Forensic Sciences and Legal Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Sumeyye Zulal Sımsek
- Department of Science, Institute of Forensic Sciences and Legal Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Gokhan Ersoy
- Department of Forensic Medicine, Institute of Forensic Sciences and Legal Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Kadriye Can
- Department of Science, Institute of Forensic Sciences and Legal Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Ozlem Bulbul
- Department of Science, Institute of Forensic Sciences and Legal Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
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Castagnola MJ, Medina-Paz F, Zapico SC. Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation. Int J Mol Sci 2024; 25:4917. [PMID: 38732129 PMCID: PMC11084977 DOI: 10.3390/ijms25094917] [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: 03/25/2024] [Revised: 04/27/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024] Open
Abstract
Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.
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Affiliation(s)
- María Josefina Castagnola
- Department of Chemistry and Environmental Sciences, New Jersey Institute of Technology, Tiernan Hall 365, Newark, NJ 07102, USA; (M.J.C.); (F.M.-P.)
| | - Francisco Medina-Paz
- Department of Chemistry and Environmental Sciences, New Jersey Institute of Technology, Tiernan Hall 365, Newark, NJ 07102, USA; (M.J.C.); (F.M.-P.)
| | - Sara C. Zapico
- Department of Chemistry and Environmental Sciences, New Jersey Institute of Technology, Tiernan Hall 365, Newark, NJ 07102, USA; (M.J.C.); (F.M.-P.)
- Department of Anthropology and Laboratories of Analytical Biology, National Museum of Natural History, MRC 112, Smithsonian Institution, Washington, DC 20560, USA
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Gutiérrez-Hurtado IA, Sánchez-Méndez AD, Becerra-Loaiza DS, Rangel-Villalobos H, Torres-Carrillo N, Gallegos-Arreola MP, Aguilar-Velázquez JA. Loss of the Y Chromosome: A Review of Molecular Mechanisms, Age Inference, and Implications for Men's Health. Int J Mol Sci 2024; 25:4230. [PMID: 38673816 PMCID: PMC11050192 DOI: 10.3390/ijms25084230] [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: 03/05/2024] [Revised: 03/29/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Until a few years ago, it was believed that the gradual mosaic loss of the Y chromosome (mLOY) was a normal age-related process. However, it is now known that mLOY is associated with a wide variety of pathologies in men, such as cardiovascular diseases, neurodegenerative disorders, and many types of cancer. Nevertheless, the mechanisms that generate mLOY in men have not been studied so far. This task is of great importance because it will allow focusing on possible methods of prophylaxis or therapy for diseases associated with mLOY. On the other hand, it would allow better understanding of mLOY as a possible marker for inferring the age of male samples in cases of human identification. Due to the above, in this work, a comprehensive review of the literature was conducted, presenting the most relevant information on the possible molecular mechanisms by which mLOY is generated, as well as its implications for men's health and its possible use as a marker to infer age.
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Affiliation(s)
- Itzae Adonai Gutiérrez-Hurtado
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Guadalajara 44340, Jalisco, Mexico
| | - Astrid Desireé Sánchez-Méndez
- Laboratorio de Ciencias Morfológico Forenses y Medicina Molecular, Departamento de Morfología, Centro Universitario de Ciencias de la Salud, Guadalajara 44340, Jalisco, Mexico
- Doctorado en Genética Humana, Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | | | - Héctor Rangel-Villalobos
- Instituto de Investigación en Genética Molecular, Departamento de Ciencias Médicas y de la Vida, Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47820, Jalisco, Mexico
| | - Norma Torres-Carrillo
- Departamento de Microbiología y Patología, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Martha Patricia Gallegos-Arreola
- División de Genética, Centro de Investigación Biomédica de Occidente (CIBO), Instituto Mexicano del Seguro Social (IMSS), Guadalajara 44340, Jalisco, Mexico
| | - José Alonso Aguilar-Velázquez
- Laboratorio de Ciencias Morfológico Forenses y Medicina Molecular, Departamento de Morfología, Centro Universitario de Ciencias de la Salud, Guadalajara 44340, Jalisco, Mexico
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Noroozi R, Rudnicka J, Pisarek A, Wysocka B, Masny A, Boroń M, Migacz-Gruszka K, Pruszkowska-Przybylska P, Kobus M, Lisman D, Zielińska G, Iljin A, Wiktorska JA, Michalczyk M, Kaczka P, Krzysztofik M, Sitek A, Ossowski A, Spólnicka M, Branicki W, Pośpiech E. Analysis of epigenetic clocks links yoga, sleep, education, reduced meat intake, coffee, and a SOCS2 gene variant to slower epigenetic aging. GeroScience 2024; 46:2583-2604. [PMID: 38103096 PMCID: PMC10828238 DOI: 10.1007/s11357-023-01029-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/23/2023] [Indexed: 12/17/2023] Open
Abstract
DNA methylation (DNAm) clocks hold promise for measuring biological age, useful for guiding clinical interventions and forensic identification. This study compared the commonly used DNAm clocks, using DNA methylation and SNP data generated from nearly 1000 human blood or buccal swab samples. We evaluated different preprocessing methods for age estimation, investigated the association of epigenetic age acceleration (EAA) with various lifestyle and sociodemographic factors, and undertook a series of novel genome-wide association analyses for different EAA measures to find associated genetic variants. Our results highlighted the Skin&Blood clock with ssNoob normalization as the most accurate predictor of chronological age. We provided novel evidence for an association between the practice of yoga and a reduction in the pace of aging (DunedinPACE). Increased sleep and physical activity were associated with lower mortality risk score (MRS) in our dataset. University degree, vegetable consumption, and coffee intake were associated with reduced levels of epigenetic aging, whereas smoking, higher BMI, meat consumption, and manual occupation correlated well with faster epigenetic aging, with FitAge, GrimAge, and DunedinPACE clocks showing the most robust associations. In addition, we found a novel association signal for SOCS2 rs73218878 (p = 2.87 × 10-8) and accelerated GrimAge. Our study emphasizes the importance of an optimized DNAm analysis workflow for accurate estimation of epigenetic age, which may influence downstream analyses. The results support the influence of genetic background on EAA. The associated SOCS2 is a member of the suppressor of cytokine signaling family known for its role in human longevity. The reported association between various risk factors and EAA has practical implications for the development of health programs to improve quality of life and reduce premature mortality associated with age-related diseases.
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Affiliation(s)
- Rezvan Noroozi
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joanna Rudnicka
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - Aleksandra Pisarek
- Institute of Zoology and Biomedical Research of the Jagiellonian University, Krakow, Poland
| | - Bożena Wysocka
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | | | - Michał Boroń
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | | | | | - Magdalena Kobus
- Institute of Biological Sciences, Faculty of Biology and Environmental Sciences, Cardinal Stefan Wyszynski University in Warsaw, Warsaw, Poland
| | - Dagmara Lisman
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Grażyna Zielińska
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Aleksandra Iljin
- Department of Plastic, Reconstructive and Aesthetic Surgery, Medical University of Lodz, Lodz, Poland
| | | | - Małgorzata Michalczyk
- Department of Sport Nutrition, The Jerzy Kukuczka Academy of Physical Education in Katowice, Katowice, Poland
| | - Piotr Kaczka
- Department of Sport Nutrition, The Jerzy Kukuczka Academy of Physical Education in Katowice, Katowice, Poland
| | - Michał Krzysztofik
- Department of Sport Nutrition, The Jerzy Kukuczka Academy of Physical Education in Katowice, Katowice, Poland
| | - Aneta Sitek
- Department of Anthropology, University of Lodz, Lodz, Poland
| | - Andrzej Ossowski
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | | | - Wojciech Branicki
- Institute of Zoology and Biomedical Research of the Jagiellonian University, Krakow, Poland
- Institute of Forensic Research, Krakow, Poland
| | - Ewelina Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland.
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland.
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7
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Kampmann ML, Fleckhaus J, Børsting C, Jurtikova H, Piters A, Papin J, Gauthier Q, Ghemrawi M, Doutremepuich C, McCord B, Schneider PM, Drabek J, Morling N. Collaborative exercise: analysis of age estimation using a QIAGEN protocol and the PyroMark Q48 platform. Forensic Sci Res 2024; 9:owad055. [PMID: 38567377 PMCID: PMC10986743 DOI: 10.1093/fsr/owad055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Human age estimation from trace samples may give important leads early in a police investigation by contributing to the description of the perpetrator. Several molecular biomarkers are available for the estimation of chronological age, and currently, DNA methylation patterns are the most promising. In this study, a QIAGEN age protocol for age estimation was tested by five forensic genetic laboratories. The assay comprised bisulfite treatment of the extracted DNA, amplification of five CpG loci (in the genes of ELOVL2, C1orf132, TRIM59, KLF14, and FHL2), and sequencing of the amplicons using the PyroMark Q48 platform. Blood samples from 49 individuals with ages ranging from 18 to 64 years as well as negative and methylation controls were analyzed. An existing age estimation model was applied to display a mean absolute deviation of 3.62 years within the reference data set. Key points Age determination as an intelligence tool during investigations can be a powerful tool in forensic genetics.In this study, five laboratories ran 49 samples and obtained a mean absolute deviation of 3.62 years.Five markers were analyzed on a PyroMark Q48 platform.
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Affiliation(s)
- Marie-Louise Kampmann
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagen, Denmark
| | - Jan Fleckhaus
- Institute of Legal Medicine, Faculty of Medicine and University Clinic, University of Cologne, Cologne, Germany
| | - Claus Børsting
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagen, Denmark
| | - Helena Jurtikova
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc and the University Hospital Olomouc, Olomouc, the Czech Republic
| | - Alice Piters
- Laboratoire d’Hématologie Médico-Légale, Bordeaux Cedex, France
| | - Julien Papin
- Laboratoire d’Hématologie Médico-Légale, Bordeaux Cedex, France
| | - Quentin Gauthier
- Department of Chemistry and Biochemistry, Florida International University, Miami, FL, USA
| | - Mirna Ghemrawi
- Department of Chemistry and Biochemistry, Florida International University, Miami, FL, USA
| | | | - Bruce McCord
- Department of Chemistry and Biochemistry, Florida International University, Miami, FL, USA
| | - Peter M Schneider
- Institute of Legal Medicine, Faculty of Medicine and University Clinic, University of Cologne, Cologne, Germany
| | - Jiri Drabek
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc and the University Hospital Olomouc, Olomouc, the Czech Republic
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagen, Denmark
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Shiga M, Asari M, Takahashi Y, Isozaki S, Hoshina C, Mori K, Namba R, Okuda K, Shimizu K. DNA methylation-based age estimation and quantification of the degradation levels of bisulfite-converted DNA. Leg Med (Tokyo) 2024; 67:102336. [PMID: 37923589 DOI: 10.1016/j.legalmed.2023.102336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/07/2023]
Abstract
DNA methylation modifications are known to influence epigenetic phenomena and have been a focus of forensic science research for some time. Degraded DNA after bisulfite treatment is widely used in DNA methylation analysis. In this study, we analyzed methylation levels at 12 CpG sites of four selected genomic regions by pyrosequencing after bisulfite treatment. DNA was extracted from buccal swab samples collected from 102 Japanese individuals who were 21-77 years old. We also developed a simple method to quantify the degradation levels of bisulfite-converted DNA by real-time PCR, and evaluated the effect of DNA degradation on age estimation. We found that the methylation levels and chronological ages were highly correlated in the four selected regions, and the mean absolute deviation (MAD) between chronological and estimated ages was low at 3.88 years. These results indicated that pyrosequencing analysis at the 12 CpGs was useful for age estimation in the Japanese population. To develop a sensitive quantification method, we analyzed the amplification efficiency of short and long fragments from 10 regions by real-time PCR. The amplification efficiency was highest for CCDC102B, and the degradation levels of bisulfite-converted DNA for the 102 samples were categorized as moderately or heavily degraded. For the younger age groups (20-49 years), the MADs were lower for moderately degraded DNA than they were for heavily degraded DNA. This finding indicates that degradation levels affected the accuracy of age estimation in most of the samples; the exception was the samples from the 50-77 years age group.
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Affiliation(s)
- Mihiro Shiga
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan; Department of Orthopaedic Surgery, Keiyukai Medical Foundation Yoshida Hospital, Asahikawa 070-0054, Japan
| | - Masaru Asari
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan.
| | - Yuta Takahashi
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Shotaro Isozaki
- Department of Forensic Medicine, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Chisato Hoshina
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Kanae Mori
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Ryo Namba
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Katsuhiro Okuda
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Keiko Shimizu
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
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Campisi M, Cannella L, Pavanello S. Cosmic chronometers: Is spaceflight a catalyst for biological ageing? Ageing Res Rev 2024; 95:102227. [PMID: 38346506 DOI: 10.1016/j.arr.2024.102227] [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: 08/21/2023] [Revised: 01/05/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024]
Abstract
Astronauts returning from space missions often exhibit health issues mirroring age-related conditions, suggesting spaceflight as a potential driver of biological ageing and age-related diseases. To unravel the underlying mechanisms of these conditions, this comprehensive review explores the impact of the space "exposome" on the twelve hallmarks of ageing. Through a meticulous analysis encompassing both space environments and terrestrial analogs, we aim to decipher how different conditions influence ageing hallmarks. Utilizing PubMed, we identified 189 studies and 60 meet screening criteria. Research on biological ageing in space has focused on genomic instability, chronic inflammation, and deregulated nutrient sensing. Spaceflight consistently induces genomic instability, linked to prolonged exposure to ionizing radiation, triggers pro-inflammatory and immune alterations, resembling conditions in isolated simulations. Nutrient sensing pathways reveal increased systemic insulin-like growth-factor-1. Microbiome studies indicate imbalances favoring opportunistic species during spaceflight. Telomere dynamics present intriguing patterns, with lengthening during missions and rapid shortening upon return. Despite a pro-ageing trend, some protective mechanisms emerge. Countermeasures, encompassing dietary adjustments, prebiotics, postbiotics, symbiotics, tailored exercises, meditation, and anti-inflammatory supplements, exhibit potential. Spaceflight's impact on ageing is intricate, with diverse findings challenging established beliefs. Multidisciplinary studies provide guidance for future research in this field.
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Affiliation(s)
- Manuela Campisi
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy
| | - Luana Cannella
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy
| | - Sofia Pavanello
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy.
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Yamagishi T, Sakurai W, Watanabe K, Toyomane K, Akutsu T. Development and comparison of forensic interval age prediction models by statistical and machine learning methods based on the methylation rates of ELOVL2 in blood DNA. Forensic Sci Int Genet 2024; 69:103004. [PMID: 38160598 DOI: 10.1016/j.fsigen.2023.103004] [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: 10/05/2023] [Revised: 12/06/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
Age estimation can be useful information for narrowing down candidates of unidentified donors in criminal investigations. Various age estimation models based on DNA methylation biomarkers have been developed for forensic usage in the past decade. However, many of these models using ordinary least squares regression cannot generate an appropriate estimation due to the deterioration in prediction accuracy caused by an increased prediction error in older age groups. In the present study, to address this problem, we developed age estimation models that set an appropriate prediction interval for all age groups by two approaches: a statistical method using quantile regression (QR) and a machine learning method using an artificial neural network (ANN). Methylation datasets (n = 1280, age 0-91 years) of the promoter for the gene encoding ELOVL fatty acid elongase 2 were used to develop the QR and ANN models. By validation using several test datasets, both models were shown to enlarge prediction intervals in accordance with aging and have a high level of correct prediction (>90 %) for older age groups. The QR and ANN models also generated a point age prediction with high accuracy. The ANN model enabled a prediction with a mean absolute error (MAE) of 5.3 years and root mean square error (RMSE) of 7.3 years for the test dataset (n = 549), which were comparable to those of the QR model (MAE = 5.6 years, RMSE = 7.8 years). Their applicability to casework was also confirmed using bloodstain samples stored for various periods of time (1-14 years), indicating the stability of the models for aged bloodstain samples. From these results, it was considered that the proposed models can provide more useful and effective age estimation in forensic settings.
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Affiliation(s)
- Takayuki Yamagishi
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan.
| | - Wataru Sakurai
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
| | - Ken Watanabe
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
| | - Kochi Toyomane
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
| | - Tomoko Akutsu
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
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11
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Kalyakulina A, Yusipov I, Moskalev A, Franceschi C, Ivanchenko M. eXplainable Artificial Intelligence (XAI) in aging clock models. Ageing Res Rev 2024; 93:102144. [PMID: 38030090 DOI: 10.1016/j.arr.2023.102144] [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: 09/26/2023] [Revised: 11/07/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023]
Abstract
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.
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Affiliation(s)
- Alena Kalyakulina
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
| | - Igor Yusipov
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Alexey Moskalev
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Claudio Franceschi
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Mikhail Ivanchenko
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
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12
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Turiello R, Nouwairi RL, Keller J, Cunha LL, Dignan LM, Landers JP. A rotationally-driven dynamic solid phase sodium bisulfite conversion disc for forensic epigenetic sample preparation. LAB ON A CHIP 2023; 24:97-112. [PMID: 38019115 DOI: 10.1039/d3lc00867c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The approaches to forensic human identification (HID) are largely comparative in nature, relying upon the comparison of short tandem repeat profiles to known reference materials and/or database profiles. However, many profiles are generated from evidence materials that either do not have a reference material for comparison or do not produce a database hit. As an alternative to individualizing analysis for HID, researchers of forensic DNA have demonstrated that the human epigenome can provide a wealth of information. However, epigenetic analysis requires sodium b̲is̲ulfite c̲onversion (BSC), a sample preparation method that is time-consuming, labor-intensive, prone to contamination, and characterized by DNA loss and fragmentation. To provide an alternative method for BSC that is more amenable to integration with the forensic DNA workflow, we describe a rotationally-driven, microfluidic method for dynamic solid phase-BSC (dSP-BSC) that streamlines the sample preparation process in an automated format, capable of preparing up to four samples in parallel. The method permitted decreased incubation intervals by ∼36% and was assessed for relative DNA recovery and conversion efficiency and compared to gold-standard and enzymatic approaches.
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Affiliation(s)
- R Turiello
- Department of Chemistry, University of Virginia, Charlottesville, VA, USA.
| | - R L Nouwairi
- Department of Chemistry, University of Virginia, Charlottesville, VA, USA.
| | - J Keller
- Department of Chemistry, University of Virginia, Charlottesville, VA, USA.
| | - L L Cunha
- Department of Chemistry, University of Virginia, Charlottesville, VA, USA.
| | - L M Dignan
- Department of Chemistry, University of Virginia, Charlottesville, VA, USA.
| | - J P Landers
- Department of Chemistry, University of Virginia, Charlottesville, VA, USA.
- Department of Mechanical Engineering, University of Virginia, Charlottesville, VA, USA
- Department of Pathology, University of Virginia, Charlottesville, VA, USA
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13
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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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14
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Song J, Zhang X, Lv S, Liu M, Hua X, Yue L, Wang S, He W. Age-related promoter-switch regulates Runx1 expression in adult rat hearts. BMC Cardiovasc Disord 2023; 23:541. [PMID: 37936072 PMCID: PMC10631011 DOI: 10.1186/s12872-023-03583-3] [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: 02/23/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Runt-related transcription factor-1 (RUNX1), a key member of the core-binding factor family of transcription factors, has emerged as a novel therapeutic target for cardiovascular disease. There is an urgent need to fully understand the expression pattern of Runx1 in the heart and the mechanisms by which it is controlled under normal conditions and in response to disease. The expression of Runx1 is regulated at the transcriptional level by two promoters designated P1 and P2. Alternative usage of these two promoters creates differential mRNA transcripts diversified in distribution and translational potential. While the significance of P1/P2 promoter-switch in the transcriptional control of Runx1 has been highlighted in the embryogenic process, very little is known about the level of P1- and P2-specific transcripts in adult hearts, and the underlying mechanisms controlling the promoter-switch. METHODS To amplify P1/P2 specific sequences in the heart, we used two different sense primers complementary to either P1 or P2 5'-regions to monitor the expression of P1/P2 transcripts. DNA methylation levels were assessed at the Runx1 promoter regions. Rats were grouped by age. RESULTS The expression levels of both P1- and P2-derived Runx1 transcripts were decreased in older rats when compared with that in young adults, paralleled with an age-dependent decline in Runx1 protein level. Furthermore, older rats demonstrated a higher degree of DNA methylation at Runx1 promoter regions. Alternative promoter usage was observed in hearts with increased age, as reflected by altered P1:P2 mRNA ratio. CONCLUSION Our data demonstrate that the expression of Runx1 in the heart is age-dependent and underscore the importance of gene methylation in the promoter-mediated transcriptional control of Runx1, thereby providing new insights to the role of epigenetic regulation in the heart.
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Affiliation(s)
- Jiawei Song
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Xiaoling Zhang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Sinan Lv
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Meng Liu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xing Hua
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Limin Yue
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Si Wang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Weihong He
- Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, 610041, China.
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15
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Kotková L, Drábek J. Age-related changes in sperm DNA methylation and their forensic and clinical implications. Epigenomics 2023; 15:1157-1173. [PMID: 38031735 DOI: 10.2217/epi-2023-0307] [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: 12/01/2023] Open
Abstract
As a link between a stable genome and a dynamic environment, epigenetics is a promising tool for mapping age-related changes in human DNA. Methylated cytosine changes at specific loci are generally less studied in sperm DNA than in somatic cell DNA. Age-related methylation changes can be connected to various reproductive health problems and multiple disorders in offspring. In addition, they can be helpful in forensic fields, where testing of specific loci in semen samples found at sexual assault crime scenes can predict a perpetrator's age and narrow down the police investigation. This review focuses on age-related methylation changes in sperm. It covers the biological role of methylation, methylation testing techniques and the implications of methylation changes in forensics and clinical practice.
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Affiliation(s)
- Lucie Kotková
- Institute of Molecular & Translational Medicine, Faculty of Medicine & Dentistry, Palacky University Olomouc and University Hospital Olomouc, 77900, Czech Republic
| | - Jiří Drábek
- Institute of Molecular & Translational Medicine, Faculty of Medicine & Dentistry, Palacky University Olomouc and University Hospital Olomouc, 77900, Czech Republic
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16
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Fleckhaus J, Bugert P, Al-Rashedi NAM, Rothschild MA. Investigation of the impact of biogeographic ancestry on DNA methylation based age predictions comparing a Middle East and a Central European population. Forensic Sci Int Genet 2023; 67:102923. [PMID: 37598451 DOI: 10.1016/j.fsigen.2023.102923] [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: 04/13/2023] [Revised: 07/10/2023] [Accepted: 08/01/2023] [Indexed: 08/22/2023]
Abstract
DNA methylation based age prediction is a new method in the toolbox of forensic genetics. Typically, the method is applied in the course of police investigation e.g. to predict the age of an unknown person that has left a biological trace at a crime scene. The method can also be used to answer other forensic questions, for example to estimate the age of unknown human bodies in the course of the identification process. In the present study, we tested for a potential impact of biogeographic ancestry (BGA) on age predictions using five age dependent methylated CpG sites within the genetic regions of ELOVL2, MIR29B2CHG, FHL2, KLF14 and TRIM59. We collected 102 blood samples each from donors living in Iraq, Middle East (ME) and Germany, Central Europe (EU). Both sample sets were matched in sex and age ranging from 18 to 68 years with exactly one male and female sample per year of age. All samples were analyzed by bisulfite pyrosequencing applying a multiplex pre-amplification strategy based on a single input of 35 ng converted DNA in the PCR. For the CpGs in MIR29B2CHG, FHL2 and KLF14, we observed significantly different methylation levels between the two populations. While we were able to train two highly accurate prediction models for the respective population with mean absolute deviations between predicted and actual ages (MAD) of 3.34 years for the ME model, and 2.72 years for the EU model, we found an absolute prediction difference between the two population specific models of more than 4 years. A combined model for both populations compensated the methylation difference between the two populations, providing MADs of prediction of only 3.81 years for ME and 3.31 years for EU samples. In total, the results of the present study strongly support the benefit of BGA information for more reliable methylation based age predictions.
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Affiliation(s)
- J Fleckhaus
- Institute of Legal Medicine, Medical Faculty, University Hospital and University of Cologne, Melatenguertel 60-62, D-50823 Cologne, Germany.
| | - P Bugert
- Institute of Transfusion Medicine and Immunology, Heidelberg University, Medical Faculty Mannheim, German Red Cross Blood Service of Baden-Württemberg - Hessen gGmbH, Mannheim, Germany
| | - N A M Al-Rashedi
- Department of Biology, College of Science, Al Muthanna University, Samawah, Iraq
| | - M A Rothschild
- Institute of Legal Medicine, Medical Faculty, University Hospital and University of Cologne, Melatenguertel 60-62, D-50823 Cologne, Germany
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17
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Xiao C, Li Y, Chen M, Yi S, Huang D. Improved age estimation from semen using sperm-specific age-related CpG markers. Forensic Sci Int Genet 2023; 67:102941. [PMID: 37820545 DOI: 10.1016/j.fsigen.2023.102941] [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: 05/10/2023] [Revised: 08/25/2023] [Accepted: 10/02/2023] [Indexed: 10/13/2023]
Abstract
Accurate age estimation from semen has the potential to greatly narrow the pool of unidentified suspects in sexual assault investigations. However, previous efforts utilizing semen age-related CpG (AR-CpG) markers have shown lower accuracy compared to blood AR-CpG-based methods. This discrepancy may be attributed to DNA methylation (DNAm) interferences from "round cells" such as leukocytes and immature sperm cells in semen. This study aimed to develop age calculators based on sperm-specific AR-CpG markers and to achieve performance-improved age estimates from sperm DNA. Through an analysis of publicly available MethylationEPIC microarray data from 90 sperm samples of healthy males aged 22-51 years, we identified 31 sperm-specific AR-CpG markers with absolute Pearson's R values > 0.5 and Benjamini-Hochberg adjusted p values < 0.013. The top 19 AR-CpG markers with the largest absolute R values and beta ranges > 0.10, along with 3 reported semen AR-CpG markers (cg06304190, cg06979108, and cg12837463), were integrated into two methylation SNaPshot panels (Ⅰ and Ⅱ), each containing 11 markers. The 21 qualified AR-CpG markers showed absolute R values ≥ 0.427 in an independent validation cohort of 253 sperm DNA samples (22-67 years), with cg21843517 exhibiting the strongest age correlation (R = 0.853). The optimal models, constructed using sperm DNAm data of the training set (n = 214, 22-67 years) and markers from panel Ⅰ (n = 11), panel Ⅱ (n = 10), or both panels, achieved mean absolute errors (MAEs) of 2.526-4.746, 3.890-5.715, and > 9.800 years on the test sets of sperm (n = 39, 23-64 years), semen (same donors as the sperm test set), and whole blood (n = 40, 22-65 years), respectively. The simplified models incorporating 3, 5, 9, or 14 AR-CpG markers (MAE = 2.918-4.139 years for sperm) still outperformed the Lee et al. original model (MAE = 6.444 years for semen) and the reconstructed panel Lee model (MAE = 6.011 years for sperm). The final models, utilizing all sperm DNAm data (n = 253) and markers from panel Ⅰ, panel Ⅱ, or both panels, yielded mean MAEs of 2.587, 2.766, and 2.200 years, respectively, on the 50 test sets generated by 5 repeats of 10-fold cross-validations. Additionally, multiple markers in both panels demonstrated the ability to discern sperm or semen from blood with 100% accuracy. In summary, our study substantiates the potential of sperm-specific AR-CpG markers for precise age estimation from sperm DNA, providing an improved toolset for forensic investigations.
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Affiliation(s)
- Chao Xiao
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China; Hubei Key Laboratory of the Forensic Science, Hubei University of Police, Wuhan, Hubei 430035, PR China.
| | - Ya Li
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Maomin Chen
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Shaohua Yi
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Daixin Huang
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
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18
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Freire-Aradas A, Tomsia M, Piniewska-Róg D, Ambroa-Conde A, Casares de Cal MA, Pisarek A, Gómez-Tato A, Álvarez-Dios J, Pośpiech E, Parson W, Kayser M, Phillips C, Branicki W. Development of an epigenetic age predictor for costal cartilage with a simultaneous somatic tissue differentiation system. Forensic Sci Int Genet 2023; 67:102936. [PMID: 37783021 DOI: 10.1016/j.fsigen.2023.102936] [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: 04/14/2023] [Revised: 09/13/2023] [Accepted: 09/27/2023] [Indexed: 10/04/2023]
Abstract
Age prediction from DNA has been a topic of interest in recent years due to the promising results obtained when using epigenetic markers. Since DNA methylation gradually changes across the individual's lifetime, prediction models have been developed accordingly for age estimation. The tissue-dependence for this biomarker usually necessitates the development of tissue-specific age prediction models, in this way, multiple models for age inference have been constructed for the most commonly encountered forensic tissues (blood, oral mucosa, semen). The analysis of skeletal remains has also been attempted and prediction models for bone have now been reported. Recently, the VISAGE Enhanced Tool was developed for the simultaneous DNA methylation analysis of 8 age-correlated loci using targeted high-throughput sequencing. It has been shown that this method is compatible with epigenetic age estimation models for blood, buccal cells, and bone. Since when dealing with decomposed cadavers or postmortem samples, cartilage samples are also an important biological source, an age prediction model for cartilage has been generated in the present study based on methylation data collected using the VISAGE Enhanced Tool. In this way, we have developed a forensic cartilage age prediction model using a training set composed of 109 samples (19-74 age range) based on DNA methylation levels from three CpGs in FHL2, TRIM59 and KLF14, using multivariate quantile regression which provides a mean absolute error (MAE) of ± 4.41 years. An independent testing set composed of 72 samples (19-75 age range) was also analyzed and provided an MAE of ± 4.26 years. In addition, we demonstrate that the 8 VISAGE markers, comprising EDARADD, TRIM59, ELOVL2, MIR29B2CHG, PDE4C, ASPA, FHL2 and KLF14, can be used as tissue prediction markers which provide reliable blood, buccal cells, bone, and cartilage differentiation using a developed multinomial logistic regression model. A training set composed of 392 samples (n = 87 blood, n = 86 buccal cells, n = 110 bone and n = 109 cartilage) was used for building the model (correct classifications: 98.72%, sensitivity: 0.988, specificity: 0.996) and validation was performed using a testing set composed of 192 samples (n = 38 blood, n = 36 buccal cells, n = 46 bone and n = 72 cartilage) showing similar predictive success to the training set (correct classifications: 97.4%, sensitivity: 0.968, specificity: 0.991). By developing both a new cartilage age model and a tissue differentiation model, our study significantly expands the use of the VISAGE Enhanced Tool while increasing the amount of DNA methylation-based information obtained from a single sample and a single forensic laboratory analysis. Both models have been placed in the open-access Snipper forensic classification website.
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Affiliation(s)
- A Freire-Aradas
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain.
| | - M Tomsia
- Department of Forensic Medicine and Forensic Toxicology, Medical University of Silesia, Katowice, Poland
| | - D Piniewska-Róg
- Department of Forensic Medicine, Jagiellonian University Medical College, Kraków, Poland
| | - A Ambroa-Conde
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - M A Casares de Cal
- CITMAga (Center for Mathematical Research and Technology of Galicia), University of Santiago de Compostela, Spain
| | - A Pisarek
- Institute of Zoology and Biomedical Research, Jagiellonian University, Kraków, Poland
| | - A Gómez-Tato
- CITMAga (Center for Mathematical Research and Technology of Galicia), University of Santiago de Compostela, Spain
| | - J Álvarez-Dios
- Faculty of Mathematics, University of Santiago de Compostela, Spain
| | - E Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland; Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Poland
| | - W Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Austria; Forensic Science Program, Pennsylvania State University, PA, USA
| | - M Kayser
- Department of Forensic Molecular Biology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - C Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - W Branicki
- Institute of Zoology and Biomedical Research, Jagiellonian University, Kraków, Poland; Institute of Forensic Research, Kraków, Poland.
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19
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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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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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21
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Tangili M, Slettenhaar AJ, Sudyka J, Dugdale HL, Pen I, Palsbøll PJ, Verhulst S. DNA methylation markers of age(ing) in non-model animals. Mol Ecol 2023; 32:4725-4741. [PMID: 37401200 DOI: 10.1111/mec.17065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/05/2023]
Abstract
Inferring the chronological and biological age of individuals is fundamental to population ecology and our understanding of ageing itself, its evolution, and the biological processes that affect or even cause ageing. Epigenetic clocks based on DNA methylation (DNAm) at specific CpG sites show a strong correlation with chronological age in humans, and discrepancies between inferred and actual chronological age predict morbidity and mortality. Recently, a growing number of epigenetic clocks have been developed in non-model animals and we here review these studies. We also conduct a meta-analysis to assess the effects of different aspects of experimental protocol on the performance of epigenetic clocks for non-model animals. Two measures of performance are usually reported, the R2 of the association between the predicted and chronological age, and the mean/median absolute deviation (MAD) of estimated age from chronological age, and we argue that only the MAD reflects accuracy. R2 for epigenetic clocks based on the HorvathMammalMethylChip4 was higher and the MAD scaled to age range lower, compared with other DNAm quantification approaches. Scaled MAD tended to be lower among individuals in captive populations, and decreased with an increasing number of CpG sites. We conclude that epigenetic clocks can predict chronological age with relatively high accuracy, suggesting great potential in ecological epigenetics. We discuss general aspects of epigenetic clocks in the hope of stimulating further DNAm-based research on ageing, and perhaps more importantly, other key traits.
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Affiliation(s)
- Marianthi Tangili
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Annabel J Slettenhaar
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Joanna Sudyka
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Hannah L Dugdale
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
- Faculty of Biological Sciences, School of Biology, University of Leeds, Leeds, UK
| | - Ido Pen
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Per J Palsbøll
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
- Center for Coastal Studies, Provincetown, Massachusetts, USA
| | - Simon Verhulst
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
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22
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Pośpiech E, Pisarek A, Rudnicka J, Noroozi R, Boroń M, Masny A, Wysocka B, Migacz-Gruszka K, Lisman D, Pruszkowska-Przybylska P, Kobus M, Szargut M, Dowejko J, Stanisz K, Zacharczuk J, Zieliński P, Sitek A, Ossowski A, Spólnicka M, Branicki W. Introduction of a multiplex amplicon sequencing assay to quantify DNA methylation in target cytosine markers underlying four selected epigenetic clocks. Clin Epigenetics 2023; 15:128. [PMID: 37563670 PMCID: PMC10416531 DOI: 10.1186/s13148-023-01545-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/02/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND DNA methylation analysis has proven to be a powerful tool for age assessment. However, the implementation of epigenetic age prediction in diagnostics or routine forensic casework requires appropriate laboratory methods. In this study, we aimed to compare the performance of large-scale DNA methylation analysis protocols that show promise in terms of accuracy, throughput, multiplexing capacity, and high sensitivity. RESULTS The protocols were designed to target a predefined panel of 161 genomic CG/CA sites from four known estimators of epigenetic age-related parameters, optimized and validated using artificially methylated controls or blood samples. We successfully targeted 96% of these loci using two enrichment protocols: Ion AmpliSeq™, an amplicon-based method integrated with Ion Torrent S5, and SureSelectXT Methyl-Seq, a hybridization-based method followed by MiSeq FGx sequencing. Both protocols demonstrated high accuracy and robustness. Although hybridization assays have greater multiplexing capabilities, the best overall performance was observed for the amplicon-based protocol with the lowest variability in DNA methylation at 25 ng of starting DNA, mean observed marker coverage of ~ 6.7 k reads, and accuracy of methylation quantification with a mean absolute difference between observed and expected methylation beta value of 0.054. The Ion AmpliSeq method correlated strongly with genome-scale EPIC microarray data (R = 0.91) and showed superiority in terms of methylation measurement accuracy. Method-to-method bias was accounted for by the use of linear transformation, which provided a highly accurate prediction of calendar age with a mean absolute error of less than 5 years for the VISAGE and Hannum age clocks used. The pace of aging (PoAm) and the mortality risk score (MRS) estimators included in our panel represent next-generation clocks, were found to have low to moderate correlations with the VISAGE and Hannum models (R < 0.75), and thus may capture different aspects of epigenetic aging. CONCLUSIONS We propose a laboratory tool that allows the quantification of DNA methylation in cytosines underlying four different clocks, thus providing broad information on epigenetic aging while maintaining a reasonable number of CpG markers, opening the way to a wide range of applications in forensics, medicine, and healthcare.
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Affiliation(s)
- Ewelina Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland.
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland.
| | - Aleksandra Pisarek
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Krakow, Poland
| | - Joanna Rudnicka
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
| | - Rezvan Noroozi
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
| | - Michał Boroń
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | | | - Bożena Wysocka
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Kamila Migacz-Gruszka
- Department of Dermatology, Collegium Medicum of the Jagiellonian University, Krakow, Poland
| | - Dagmara Lisman
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | | | - Magdalena Kobus
- Institute of Biological Sciences, Faculty of Biology and Environmental Sciences, Cardinal Stefan Wyszynski University in Warsaw, Warsaw, Poland
| | - Maria Szargut
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Joanna Dowejko
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Kamila Stanisz
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Julia Zacharczuk
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Piotr Zieliński
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Krakow, Poland
| | - Aneta Sitek
- Department of Anthropology, Faculty of Biology and Environmental Protection, University of Łódź, Łódź, Poland
| | - Andrzej Ossowski
- Department of Forensic Genetics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | | | - Wojciech Branicki
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Krakow, Poland
- Institute of Forensic Research, Krakow, Poland
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Mongelli A, Mengozzi A, Geiger M, Gorica E, Mohammed SA, Paneni F, Ruschitzka F, Costantino S. Mitochondrial epigenetics in aging and cardiovascular diseases. Front Cardiovasc Med 2023; 10:1204483. [PMID: 37522089 PMCID: PMC10382027 DOI: 10.3389/fcvm.2023.1204483] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/29/2023] [Indexed: 08/01/2023] Open
Abstract
Mitochondria are cellular organelles which generate adenosine triphosphate (ATP) molecules for the maintenance of cellular energy through the oxidative phosphorylation. They also regulate a variety of cellular processes including apoptosis and metabolism. Of interest, the inner part of mitochondria-the mitochondrial matrix-contains a circular molecule of DNA (mtDNA) characterised by its own transcriptional machinery. As with genomic DNA, mtDNA may also undergo nucleotide mutations that have been shown to be responsible for mitochondrial dysfunction. During physiological aging, the mitochondrial membrane potential declines and associates with enhanced mitophagy to avoid the accumulation of damaged organelles. Moreover, if the dysfunctional mitochondria are not properly cleared, this could lead to cellular dysfunction and subsequent development of several comorbidities such as cardiovascular diseases (CVDs), diabetes, respiratory and cardiovascular diseases as well as inflammatory disorders and psychiatric diseases. As reported for genomic DNA, mtDNA is also amenable to chemical modifications, namely DNA methylation. Changes in mtDNA methylation have shown to be associated with altered transcriptional programs and mitochondrial dysfunction during aging. In addition, other epigenetic signals have been observed in mitochondria, in particular the interaction between mtDNA methylation and non-coding RNAs. Mitoepigenetic modifications are also involved in the pathogenesis of CVDs where oxygen chain disruption, mitochondrial fission, and ROS formation alter cardiac energy metabolism leading to hypertrophy, hypertension, heart failure and ischemia/reperfusion injury. In the present review, we summarize current evidence on the growing importance of epigenetic changes as modulator of mitochondrial function in aging. A better understanding of the mitochondrial epigenetic landscape may pave the way for personalized therapies to prevent age-related diseases.
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Affiliation(s)
- Alessia Mongelli
- Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, Zurich University Hospital and University of Zürich, Zurich, Switzerland
| | - Alessandro Mengozzi
- Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, Zurich University Hospital and University of Zürich, Zurich, Switzerland
| | - Martin Geiger
- Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, Zurich University Hospital and University of Zürich, Zurich, Switzerland
| | - Era Gorica
- Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, Zurich University Hospital and University of Zürich, Zurich, Switzerland
| | - Shafeeq Ahmed Mohammed
- Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, Zurich University Hospital and University of Zürich, Zurich, Switzerland
| | - Francesco Paneni
- Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, Zurich University Hospital and University of Zürich, Zurich, Switzerland
- Department of Cardiology, University Heart Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Frank Ruschitzka
- Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, Zurich University Hospital and University of Zürich, Zurich, Switzerland
- Department of Cardiology, University Heart Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sarah Costantino
- Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, Zurich University Hospital and University of Zürich, Zurich, Switzerland
- Department of Cardiology, University Heart Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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24
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Fokias K, Dierckx L, Van de Voorde W, Bekaert B. Improving the age estimation model for toenails. Forensic Sci Int Genet 2023; 66:102911. [PMID: 37454509 DOI: 10.1016/j.fsigen.2023.102911] [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: 04/26/2023] [Revised: 06/23/2023] [Accepted: 06/24/2023] [Indexed: 07/18/2023]
Abstract
In a previous study, we have provided the first proof that chronological age can be estimated through DNA methylation (DNAm) patterns in fingernails and toenails. DNAm data of 15 CpGs located in 4 genetic markers (ASPA, EDARADD, ELOVL2 and PDE4C) were evaluated, of which variable selection yielded age prediction models with a mean absolute deviation (MAD) ranging from 7.68 to 9.36 years, depending on the sampling location. Three additional age-associated markers (KLF14, MIR29B2CHG and TRIM59) were assessed in the current study with the goal of increasing the prediction accuracy of the model initially constructed for toenails. This new and improved age estimation assay yielded an MAD of 4.82 and 5.61 years for the training and test set, respectively. The feasibility of the application for post-mortem cases was also demonstrated through testing a limited set of samples collected from deceased individuals.
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Affiliation(s)
- Kristina Fokias
- KU Leuven, Forensic Biomedical Sciences, Department of Imaging & Pathology, Leuven, Belgium
| | - Lotte Dierckx
- KU Leuven, Forensic Biomedical Sciences, Department of Imaging & Pathology, Leuven, Belgium
| | - Wim Van de Voorde
- KU Leuven, Forensic Biomedical Sciences, Department of Imaging & Pathology, Leuven, Belgium; UZ Leuven, Laboratory of Forensic Genetics, Leuven, Belgium
| | - Bram Bekaert
- KU Leuven, Forensic Biomedical Sciences, Department of Imaging & Pathology, Leuven, Belgium; UZ Leuven, Laboratory of Forensic Genetics, Leuven, Belgium.
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25
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Bonzini M, Comotti A, Fattori A, Serra D, Laurino M, Mastorci F, Bufano P, Ciocan C, Ferrari L, Bollati V, Di Tecco C. Promoting health and productivity among ageing workers: a longitudinal study on work ability, biological and cognitive age in modern workplaces (PROAGEING study). BMC Public Health 2023; 23:1115. [PMID: 37308919 PMCID: PMC10258929 DOI: 10.1186/s12889-023-16010-1] [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: 05/04/2023] [Accepted: 05/29/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Large changes in ageing population and in retirement age are increasing the number of older people in the workforce, raising many challenges for policymakers in promoting employment opportunities and health for older workers. In this respect, longitudinal assessments of workability, well-being perception and cognitive skills over time may allow to detect factors influencing workers' health. Moreover, new available molecular markers permit the measurement of biological age and age-related changes. Most studies analysed one aspect at time (psychological, biological, labour productivity), without considering their interaction. Aims of the study are to evaluate the relationship between workability, cognitive skills, and biological age in a population of ageing workers; to conduct a cross-sectional analysis to assess the impact of occupational exposures on workability, cognitive skills, and biological age; to evaluate inter-individuals changes in a prospective analysis with a re-evaluation of each worker. METHODS Our study plans to enrol 1000 full-time workers, aged over 50, undergoing the medical surveillance required by the current Italian Legislation. Data collection includes information about: (a) work ability and psychosocial risk factors (work ability index, HSE Management Standard-21 item, Utrecht Work Engagement Scale, World Health Organisation-Five, Well-Being Index, job satisfaction, general well-being, technostress); (b) cognitive skills (Stroop Color and Word test, Simon task, Corsi's block-tapping test, Digit span test); (c) sleep habits and psychological well-being (Pittsburgh Sleep Quality Index, Insomnia Severity Index, Ford Insomnia Response to Stress Test; Symptom Check List 90, Psychological Well-Being Index, Profile of Mood State, Beck Depression Inventory, Beck Anxiety Inventory, Perceived Stress Scale, Brief COPE); (d) biological age (telomere length, DNA methylation) for 500 workers. All workers will repeat the evaluation after one year. DISCUSSION This study aims to increase our knowledge about interactions between work ability, cognitive ability, well-being perception and psychological status also by including molecular markers, with a longitudinal and multidisciplinary approach. By bringing better insights into the relationship between risk factors and their impact on perceived and biological health, this study also aims at identifying possible interventions and protective measures to ensure aged workers' well-being, consistent with all the eminent calls for actions promoted by key International and European labour organizations.
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Affiliation(s)
- Matteo Bonzini
- Occupational Medicine Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
- Department of Clinical Science and Community Health, University of Milan, Milan, Italy.
| | - Anna Comotti
- Occupational Medicine Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alice Fattori
- Occupational Medicine Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Science and Community Health, University of Milan, Milan, Italy
| | - Daniele Serra
- School of Medicine, University of Milan, Milan, Italy
| | - Marco Laurino
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Francesca Mastorci
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Pasquale Bufano
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Catalina Ciocan
- Department of Public Health and Pediatrics, University of Turin, Turin, Italy
| | - Luca Ferrari
- Department of Clinical Science and Community Health, University of Milan, Milan, Italy
| | - Valentina Bollati
- Department of Clinical Science and Community Health, University of Milan, Milan, Italy
| | - Cristina Di Tecco
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers' Compensation Authority (INAIL), Rome, Italy
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26
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Refn MR, Kampmann ML, Morling N, Tfelt-Hansen J, Børsting C, Pereira V. Prediction of chronological age and its applications in forensic casework: methods, current practices, and future perspectives. Forensic Sci Res 2023; 8:85-97. [PMID: 37621446 PMCID: PMC10445583 DOI: 10.1093/fsr/owad021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/28/2023] [Indexed: 08/26/2023] Open
Abstract
Estimating an individual's age can be relevant in several areas primarily related to the clinical and forensic fields. In the latter, estimation of an individual's chronological age from biological material left by the perpetrator at a crime scene may provide helpful information for police investigation. Estimation of age is also beneficial in immigration cases, where age can affect the person's protection status under the law, or in disaster victim identification to narrow the list of potential missing persons. In the last decade, research has focused on establishing new approaches for age prediction in the forensic field. From the first forensic age estimations based on morphological inspections of macroscopic changes in bone and teeth, the focus has shifted to molecular methods for age estimation. These methods allow the use of samples from human biological material that does not contain morphological age features and can, in theory, be investigated in traces containing only small amounts of biological material. Molecular methods involving DNA analyses are the primary choice and estimation of DNA methylation levels at specific sites in the genome is the most promising tool. This review aims to provide an overview of the status of forensic age prediction using molecular methods, with particular focus in DNA methylation. The frequent challenges that impact forensic age prediction model development will be addressed, together with the importance of validation efforts within the forensic community.
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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, Copenhagen, Denmark
| | - Marie-Louise Kampmann
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Tfelt-Hansen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen , Denmark
| | - Claus Børsting
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vania Pereira
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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27
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Milicic L, Porter T, Vacher M, Laws SM. Utility of DNA Methylation as a Biomarker in Aging and Alzheimer's Disease. J Alzheimers Dis Rep 2023; 7:475-503. [PMID: 37313495 PMCID: PMC10259073 DOI: 10.3233/adr-220109] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/23/2023] [Indexed: 06/15/2023] Open
Abstract
Epigenetic mechanisms such as DNA methylation have been implicated in a number of diseases including cancer, heart disease, autoimmune disorders, and neurodegenerative diseases. While it is recognized that DNA methylation is tissue-specific, a limitation for many studies is the ability to sample the tissue of interest, which is why there is a need for a proxy tissue such as blood, that is reflective of the methylation state of the target tissue. In the last decade, DNA methylation has been utilized in the design of epigenetic clocks, which aim to predict an individual's biological age based on an algorithmically defined set of CpGs. A number of studies have found associations between disease and/or disease risk with increased biological age, adding weight to the theory of increased biological age being linked with disease processes. Hence, this review takes a closer look at the utility of DNA methylation as a biomarker in aging and disease, with a particular focus on Alzheimer's disease.
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Affiliation(s)
- Lidija Milicic
- Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia
- Collaborative Genomics and Translation Group, Edith Cowan University, Joondalup, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Tenielle Porter
- Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia
- Collaborative Genomics and Translation Group, Edith Cowan University, Joondalup, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Curtin Medical School, Curtin University, Bentley, Western Australia, Australia
| | - Michael Vacher
- Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia
- CSIRO Health and Biosecurity, Australian e-Health Research Centre, Floreat, Western Australia
| | - Simon M. Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia
- Collaborative Genomics and Translation Group, Edith Cowan University, Joondalup, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Curtin Medical School, Curtin University, Bentley, Western Australia, Australia
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28
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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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Kayser M, Branicki W, Parson W, Phillips C. Recent advances in Forensic DNA Phenotyping of appearance, ancestry and age. Forensic Sci Int Genet 2023; 65:102870. [PMID: 37084623 DOI: 10.1016/j.fsigen.2023.102870] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/04/2023] [Indexed: 04/09/2023]
Abstract
Forensic DNA Phenotyping (FDP) comprises the prediction of a person's externally visible characteristics regarding appearance, biogeographic ancestry and age from DNA of crime scene samples, to provide investigative leads to help find unknown perpetrators that cannot be identified with forensic STR-profiling. In recent years, FDP has advanced considerably in all of its three components, which we summarize in this review article. Appearance prediction from DNA has broadened beyond eye, hair and skin color to additionally comprise other traits such as eyebrow color, freckles, hair structure, hair loss in men, and tall stature. Biogeographic ancestry inference from DNA has progressed from continental ancestry to sub-continental ancestry detection and the resolving of co-ancestry patterns in genetically admixed individuals. Age estimation from DNA has widened beyond blood to more somatic tissues such as saliva and bones as well as new markers and tools for semen. Technological progress has allowed forensically suitable DNA technology with largely increased multiplex capacity for the simultaneous analysis of hundreds of DNA predictors with targeted massively parallel sequencing (MPS). Forensically validated MPS-based FDP tools for predicting from crime scene DNA i) several appearance traits, ii) multi-regional ancestry, iii) several appearance traits together with multi-regional ancestry, and iv) age from different tissue types, are already available. Despite recent advances that will likely increase the impact of FDP in criminal casework in the near future, moving reliable appearance, ancestry and age prediction from crime scene DNA to the level of detail and accuracy police investigators may desire, requires further intensified scientific research together with technical developments and forensic validations as well as the necessary funding.
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Affiliation(s)
- Manfred Kayser
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Wojciech Branicki
- Institute of Zoology and Biomedical Research, Jagiellonian University, Kraków, Poland,; Institute of Forensic Research, Kraków, Poland
| | - Walther Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria; Forensic Science Program, The Pennsylvania State University, PA, USA
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
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Mongelli A, Panunzi S, Nesta M, Gottardi Zamperla M, Atlante S, Barbi V, Mongiardini V, Ferraro F, De Martino S, Cis L, Re A, Maltese S, Bachetti T, La Rovere MT, Martelli F, Pesce M, Nanni S, Massetti M, Pontecorvi A, Farsetti A, Gaetano C. Distinguishable DNA methylation defines a cardiac-specific epigenetic clock. Clin Epigenetics 2023; 15:53. [PMID: 36991505 PMCID: PMC10053964 DOI: 10.1186/s13148-023-01467-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 03/18/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND The present study investigates whether epigenetic differences emerge in the heart of patients undergoing cardiac surgery for an aortic valvular replacement (AVR) or coronary artery bypass graft (CABG). An algorithm is also established to determine how the pathophysiological condition might influence the human biological cardiac age. RESULTS Blood samples and cardiac auricles were collected from patients who underwent cardiac procedures: 94 AVR and 289 CABG. The CpGs from three independent blood-derived biological clocks were selected to design a new blood- and the first cardiac-specific clocks. Specifically, 31 CpGs from six age-related genes, ELOVL2, EDARADD, ITGA2B, ASPA, PDE4C, and FHL2, were used to construct the tissue-tailored clocks. The best-fitting variables were combined to define new cardiac- and blood-tailored clocks validated through neural network analysis and elastic regression. In addition, telomere length (TL) was measured by qPCR. These new methods revealed a similarity between chronological and biological age in the blood and heart; the average TL was significantly higher in the heart than in the blood. In addition, the cardiac clock discriminated well between AVR and CABG and was sensitive to cardiovascular risk factors such as obesity and smoking. Moreover, the cardiac-specific clock identified an AVR patient's subgroup whose accelerated bioage correlated with the altered ventricular parameters, including left ventricular diastolic and systolic volume. CONCLUSION This study reports on applying a method to evaluate the cardiac biological age revealing epigenetic features that separate subgroups of AVR and CABG.
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Affiliation(s)
- A Mongelli
- Laboratorio di Epigenetica, Istituti Clinici Scientifici (ICS) Maugeri IRCCS, 27100, Pavia, Italy
- Center for Translational and Experimental Cardiology (CTEC), University of Zurich, 8952, Schlieren, Switzerland
| | - S Panunzi
- National Research Council (CNR)-IASI, 00185, Rome, Italy
| | - M Nesta
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| | - M Gottardi Zamperla
- Laboratorio di Epigenetica, Istituti Clinici Scientifici (ICS) Maugeri IRCCS, 27100, Pavia, Italy
| | - S Atlante
- Laboratorio di Epigenetica, Istituti Clinici Scientifici (ICS) Maugeri IRCCS, 27100, Pavia, Italy
| | - V Barbi
- Laboratorio di Epigenetica, Istituti Clinici Scientifici (ICS) Maugeri IRCCS, 27100, Pavia, Italy
| | - V Mongiardini
- Laboratorio di Epigenetica, Istituti Clinici Scientifici (ICS) Maugeri IRCCS, 27100, Pavia, Italy
- Molecular Medicine, Istituto Italiano di Tecnologia, Genoa, Italy
| | - F Ferraro
- Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | - S De Martino
- Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | - L Cis
- Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | - A Re
- National Research Council (CNR)-IASI, 00185, Rome, Italy
- Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | - S Maltese
- National Research Council (CNR)-IRIB, 90146, Palermo, Italy
| | - T Bachetti
- Direzione Scientifica Centrale ICS Maugeri IRCCS, Pavia, Italy
| | - M T La Rovere
- Dipartimento di Cardiologia ICS Maugeri and Direzione Scientifica ICS Maugeri Montescano IRCCS, Pavia, Italy
| | - F Martelli
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - M Pesce
- Unità di Ingegneria Tissutale Cardiovascolare, Centro Cardiologico Monzino IRCCS, 20138, Milan, Italy
| | - S Nanni
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
- Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | - M Massetti
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
- Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | - A Pontecorvi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
- Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | - A Farsetti
- National Research Council (CNR)-IASI, 00185, Rome, Italy.
| | - C Gaetano
- Laboratorio di Epigenetica, Istituti Clinici Scientifici (ICS) Maugeri IRCCS, 27100, Pavia, Italy.
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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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32
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Campisi M, Mastrangelo G, Mielżyńska-Švach D, Hoxha M, Bollati V, Baccarelli AA, Carta A, Porru S, Pavanello S. The effect of high polycyclic aromatic hydrocarbon exposure on biological aging indicators. Environ Health 2023; 22:27. [PMID: 36927494 PMCID: PMC10022060 DOI: 10.1186/s12940-023-00975-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Aging represents a serious health and socioeconomic concern for our society. However, not all people age in the same way and air pollution has been shown to largely impact this process. We explored whether polycyclic aromatic hydrocarbons (PAHs), excellent fossil and wood burning tracers, accelerate biological aging detected by lymphocytes DNA methylation age (DNAmAge) and telomere length (TL), early nuclear DNA (nDNA) hallmarks of non-mitotic and mitotic cellular aging, and mitochondrial DNA copy number (mtDNAcn). METHODS The study population consisted of 49 male noncurrent-smoking coke-oven workers and 44 matched controls. Occupational and environmental sources of PAH exposures were evaluated by structured questionnaire and internal dose (urinary 1-pyrenol). We estimated Occup_PAHs, the product of 1-pyrenol and years of employment as coke-oven workers, and Environ_PAHs, from multiple items (diet, indoor and outdoor). Biological aging was determined by DNAmAge, via pyrosequencing, and by TL and mtDNAcn, via quantitative polymerase chain reaction. Genomic instability markers in lymphocytes as target dose [anti-benzo[a]pyrene diolepoxide (anti-BPDE)-DNA adduct], genetic instability (micronuclei), gene-specific (p53, IL6 and HIC1) and global (Alu and LINE-1 repeats) DNA methylation, and genetic polymorphisms (GSTM1) were also evaluated in the latent variable nDNA_changes. Structural equation modelling (SEM) analysis evaluated these multifaceted relationships. RESULTS In univariate analysis, biological aging was higher in coke-oven workers than controls as detected by higher percentage of subjects with biological age older than chronological age (AgeAcc ≥ 0, p = 0.007) and TL (p = 0.038), mtDNAcn was instead similar. Genomic instability, i.e., genotoxic and epigenetic alterations (LINE-1, p53 and Alu) and latent variable nDNA_changes were higher in workers (p < 0.001). In SEM analysis, DNAmAge and TL were positively correlated with Occup_PAHs (p < 0.0001). Instead, mtDNAcn is positively correlated with the latent variable nDNA_changes (p < 0.0001) which is in turn triggered by Occup_PAHs and Environ_PAHs. CONCLUSIONS Occupational PAHs exposure influences DNAmAge and TL, suggesting that PAHs target both non-mitotic and mitotic mechanisms and made coke-oven workers biologically older. Also, differences in mtDNAcn, which is modified through nDNA alterations, triggered by environmental and occupational PAH exposure, suggested a nuclear-mitochondrial core-axis of aging. By decreasing this risky gerontogenic exposure, biological aging and the consequent age-related diseases could be prevented.
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Affiliation(s)
- Manuela Campisi
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy
| | - Giuseppe Mastrangelo
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy
| | | | - Mirjam Hoxha
- Epidemiology, Epigenetics and Toxicology Lab, Dipartimento Di Scienze Cliniche E Di Comunità, Università Degli Studi Di Milano, Milan, Italia
| | - Valentina Bollati
- Epidemiology, Epigenetics and Toxicology Lab, Dipartimento Di Scienze Cliniche E Di Comunità, Università Degli Studi Di Milano, Milan, Italia
- UO Epidemiologia, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italia
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Angela Carta
- Department of Diagnostics and Public Health, University of Verona and Clinical Unit of Occupational Medicine, University Hospital of Verona, 37134, Verona, Italy
| | - Stefano Porru
- Department of Diagnostics and Public Health, University of Verona and Clinical Unit of Occupational Medicine, University Hospital of Verona, 37134, Verona, Italy
| | - Sofia Pavanello
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy.
- Padua Hospital, Occupational Medicine Unit, Padua, Italy.
- University Center for Space Studies and Activities "Giuseppe Colombo" - CISAS. University of Padua, Padua, Italy.
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33
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Forensic Age Estimation through a DNA Methylation-Based Age Prediction Model in the Italian Population: A Pilot Study. Int J Mol Sci 2023; 24:ijms24065381. [PMID: 36982454 PMCID: PMC10049185 DOI: 10.3390/ijms24065381] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 03/14/2023] Open
Abstract
DNA methylation is one of the epigenetic marks which has been studied intensively in recent years for age predicting purposes in the forensic area. In order to integrate age prediction into routine forensic workflow, the purpose of this study was to standardize and optimize a DNA methylation-based protocol tailored to the Italian context. A previously published protocol and age-predictive method was implemented for the analysis of 84 blood samples originating from Central Italy. The study here presented is based on the Single Base Extension method, considering five genes: ELOVL2, FHL2, KLF14, C1orf132, now identified as MIR29B2C, and TRIM59. The precise and specific steps consist of DNA extraction and quantification, bisulfite conversion, amplification of converted DNA, first purification, single base extension, second purification, capillary electrophoresis, and analysis of the results to train and test the tool. The prediction error obtained, expressed as mean absolute deviation, showed a value of 3.12 years in the training set and 3.01 years in the test set. Given that population-based differences in DNA methylation patterns have been previously reported in the literature, it would be useful to further improve the study implementing additional samples representative of the entire Italian population.
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34
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Carlsen L, Holländer O, Danzer MF, Vennemann M, Augustin C. DNA methylation-based age estimation for adults and minors: considering sex-specific differences and non-linear correlations. Int J Legal Med 2023; 137:635-643. [PMID: 36811674 PMCID: PMC10085938 DOI: 10.1007/s00414-023-02967-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/31/2023] [Indexed: 02/24/2023]
Abstract
DNA methylation patterns change during human lifetime; thus, they can be used to estimate an individual's age. It is known, however, that correlation between DNA methylation and aging might not be linear and that the sex might influence the methylation status. In this study, we conducted a comparative evaluation of linear and several non-linear regressions, as well as sex-specific versus unisex models. Buccal swab samples from 230 donors aged 1 to 88 years were analyzed using a minisequencing multiplex array. Samples were divided into a training set (n = 161) and a validation set (n = 69). The training set was used for a sequential replacement regression and a simultaneous 10-fold cross-validation. The resulting model was improved by including a cut-off of 20 years, dividing the younger individuals with non-linear from the older individuals with linear dependence between age and methylation status. Sex-specific models were developed and improved prediction accuracy in females but not in males, which might be explained by a small sample set. We finally established a non-linear, unisex model combining the markers EDARADD, KLF14, ELOVL2, FHL2, C1orf132, and TRIM59. While age- and sex-adjustments did not generally improve the performance of our model, we discuss how other models and large cohorts might benefit from such adjustments. Our model showed a cross-validated MAD and RMSE of 4.680 and 6.436 years in the training set and of 4.695 and 6.602 years in the validation set, respectively. We briefly explain how to apply the model for age prediction.
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Affiliation(s)
- Laura Carlsen
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Olivia Holländer
- Institute of Legal Medicine, University of Münster, Röntgenstraße 23, 48149, Münster, Germany
| | - Moritz Fabian Danzer
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Marielle Vennemann
- Institute of Legal Medicine, University of Münster, Röntgenstraße 23, 48149, Münster, Germany
| | - Christa Augustin
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.
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35
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Yang F, Qian J, Qu H, Ji Z, Li J, Hu W, Cheng F, Fang X, Yan J. DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression. Electrophoresis 2023; 44:835-844. [PMID: 36739525 DOI: 10.1002/elps.202200250] [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: 10/12/2022] [Revised: 12/08/2022] [Accepted: 01/24/2023] [Indexed: 02/06/2023]
Abstract
The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.
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Affiliation(s)
- Fenglong Yang
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Jialin Qian
- Beijing Center for Physical and Chemical Analysis, Beijing, P. R. China
| | - Hongzhu Qu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, P. R. China
| | - Zhimin Ji
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Junli Li
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Wenjing Hu
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Feng Cheng
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Xiangdong Fang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, P. R. China
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
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Fernandez-Tejero N, Gauthier Q, Cho S, McCord BR. High-resolution melt analysis for the detection of skin/sweat via DNA methylation. Electrophoresis 2023; 44:371-377. [PMID: 36480207 DOI: 10.1002/elps.202200120] [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: 05/05/2022] [Revised: 11/22/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022]
Abstract
The determination of tissue type is important when reconstructing a crime scene as skin cells may indicate innocent contact, whereas other types of cells, such as blood and semen, may indicate foul play. Up to now, there has been no specific DNA methylation-based marker to distinguish skin cell DNA from other body fluids. The goal of this study is to develop a DNA methylation-based assay to detect and identify skin cells collected at forensic crime scenes for use in DNA typing. For this reason, we have utilized a DNA methylation chip array-based genome-wide association study to identify skin-specific DNA methylation markers. DNA obtained from skin along with other body fluids, such as semen, saliva, blood, and vaginal epithelia, were tested using five genes that were identified as sites for potential new epigenetic skin markers. Samples were collected, bisulfite converted, and subjected to real-time polymerase chain reaction (PCR) with high-resolution melt analysis. In our studies, when using WDR11, PON2, and NHSL1 assays with bisulfite-modified PCR, skin/sweat amplicons melted at lower temperatures compared to blood, saliva, semen, and vaginal epithelia. One-way analysis of variance demonstrates that these three skin/sweat markers are significantly different when compared with other body fluids (p < 0.05). These results demonstrate that high-resolution melt analysis is a promising technology to detect and identify skin/sweat DNA from other body fluids.
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Affiliation(s)
- Nicole Fernandez-Tejero
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
| | - Quentin Gauthier
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
| | - Sohee Cho
- Institute of Forensic Science, Seoul National University College of Medicine, Seoul, South Korea
| | - Bruce R McCord
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
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An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review. Int J Mol Sci 2023; 24:ijms24032254. [PMID: 36768576 PMCID: PMC9916975 DOI: 10.3390/ijms24032254] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
The prediction of chronological age from methylation-based biomarkers represents one of the most promising applications in the field of forensic sciences. Age-prediction models developed so far are not easily applicable for forensic caseworkers. Among the several attempts to pursue this objective, the formulation of single-locus models might represent a good strategy. The present work aimed to develop an accurate single-locus model for age prediction exploiting ELOVL2, a gene for which epigenetic alterations are most highly correlated with age. We carried out a systematic review of different published pyrosequencing datasets in which methylation of the ELOVL2 promoter was analysed to formulate age prediction models. Nine of these, with available datasets involving 2298 participants, were selected. We found that irrespective of which model was adopted, a very strong relationship between ELOVL2 methylation levels and age exists. In particular, the model giving the best age-prediction accuracy was the gradient boosting regressor with a prediction error of about 5.5 years. The findings reported here strongly support the use of ELOVL2 for the formulation of a single-locus epigenetic model, but the inclusion of additional, non-redundant markers is a fundamental requirement to apply a molecular model to forensic applications with more robust results.
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38
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Advancement in Human Face Prediction Using DNA. Genes (Basel) 2023; 14:genes14010136. [PMID: 36672878 PMCID: PMC9858985 DOI: 10.3390/genes14010136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/15/2022] [Accepted: 12/21/2022] [Indexed: 01/05/2023] Open
Abstract
The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed.
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Takahashi Y, Asari M, Isozaki S, Hoshina C, Okuda K, Mori K, Namba R, Ochiai W, Shimizu K. Age prediction by methylation analysis of small amounts of DNA using locked nucleic acids. J Forensic Sci 2023; 68:267-274. [PMID: 36151731 DOI: 10.1111/1556-4029.15144] [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: 07/28/2022] [Revised: 09/02/2022] [Accepted: 09/13/2022] [Indexed: 12/31/2022]
Abstract
Age prediction based on methylation analysis has been reported in many populations, with 10 ng or more of DNA usually required for each determination. In this study, we designed thermostable locked nucleic acid (LNA) primers by replacing a small number of DNA bases in standard DNA primers with LNAs. We evaluated these primer sets by single-base extension analysis using 10, 5, or 2 ng of DNA that would be less than template DNA used in standard methylation testing, and determined sensitivity and accuracy. We analyzed EDARADD, SST, and KLF14 genes, targeting one CpG site in each gene. Melting temperature values of most LNA primers were 4°C higher than those of DNA primers. The intensities of signals from the EDARADD and SST genes were significantly improved by the LNA primers, by 3.3 times and 1.4 times, respectively, compared with the DNA primers using 2 ng of DNA. Coefficient of variation (CV) analysis was used to assess the accuracy of the determined methylation levels. CVs were increased using small amounts of DNA, but lower CVs were detected using LNA primers. We also showed high accuracy of age prediction for 51 individuals using LNA primers. The lowest mean absolute deviation was obtained using 10 ng of DNA and was 3.88 years with the LNA primers. Thermostable PCR primers were simply designed, and the LNAs improved the sensitivity and accuracy of methylation analysis for 10 ng or less of DNA.
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Affiliation(s)
- Yuta Takahashi
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa, Japan.,Department of Pharmacokinetics, School of Pharmacy and Pharmaceutical Sciences, Hoshi University, Shinagawa, Japan
| | - Masaru Asari
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa, Japan
| | - Shotaro Isozaki
- Department of Forensic Medicine, Tokai University School of Medicine, Isehara, Japan
| | - Chisato Hoshina
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa, Japan
| | - Katsuhiro Okuda
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa, Japan
| | - Kanae Mori
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa, Japan
| | - Ryo Namba
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa, Japan
| | - Wataru Ochiai
- Department of Pharmacokinetics, School of Pharmacy and Pharmaceutical Sciences, Hoshi University, Shinagawa, Japan
| | - Keiko Shimizu
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa, Japan
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Ghemrawi M, Tejero NF, Duncan G, McCord B. Pyrosequencing: Current forensic methodology and future applications-a review. Electrophoresis 2023; 44:298-312. [PMID: 36168852 DOI: 10.1002/elps.202200177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 02/01/2023]
Abstract
The recent development of small, single-amplicon-based benchtop systems for pyrosequencing has opened up a host of novel procedures for applications in forensic science. Pyrosequencing is a sequencing by synthesis technique, based on chemiluminescent inorganic pyrophosphate detection. This review explains the pyrosequencing workflow and illustrates the step-by-step chemistry, followed by a description of the assay design and factors to keep in mind for an exemplary assay. Existing and potential forensic applications are highlighted using this technology. Current applications include identifying species, identifying bodily fluids, and determining smoking status. We also review progress in potential applications for the future, including research on distinguishing monozygotic twins, detecting alcohol and drug abuse, and other phenotypic characteristics such as diet and body mass index. Overall, the versatility of the pyrosequencing technologies renders it a useful tool in forensic genomics.
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Affiliation(s)
- Mirna Ghemrawi
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
| | - Nicole Fernandez Tejero
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
| | - George Duncan
- Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Dania Beach, Florida, USA
| | - Bruce McCord
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
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Hong SR, Shin KJ. Can we integrate method-specific age-predictive models?: Analysis method-induced differences in detected DNA methylation status. Forensic Sci Int Genet 2023; 62:102805. [PMID: 36379153 DOI: 10.1016/j.fsigen.2022.102805] [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: 04/15/2022] [Revised: 11/02/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022]
Abstract
Forensic research surrounding the use of DNA methylation (DNAm) markers to predict age suggests that accurate prediction of chronological age can be achieved with just several DNAm markers. Several age-prediction models are based on DNAm levels that are detectable by a diverse range of DNAm analysis methods. Among the many DNAm analysis methods, targeted amplicon-based massively parallel sequencing (MPS) and single-base extension (SBE) methods have been widely studied owing to their practicality, including their multiplex capabilities. Since these two DNAm analysis methods share an identical amplification step during their experimental processes, several studies have compared the differences between the methods to construct integrated age-prediction models based on both MPS and SBE data. In this study, we compared the specific differences in DNAm levels between these two commonly exploited analysis methods by analyzing the identical PCR amplicons from the same samples and quantifying the actual bisulfite-converted DNA amount involved in the PCR step. The DNAm levels of five well-studied age-associated markers-CpGs on the ELOVL2, FHL2, KLF14, MIR29B2CHG, and TRIM59 genes-were obtained from blood samples of 250 Koreans using both DNAm analysis methods. The results showed that only ELOVL2 is interchangeable between the MPS and SBE methods, while the rest of the markers showed significant differences in DNAm values. These differences may result in high errors and consequential lowered accuracy in age estimates. Therefore, a DNAm analysis method-specific approach that considers method-induced DNAm differences is recommended to improve the overall accuracy and reliability of age-prediction methods.
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Affiliation(s)
- Sae Rom Hong
- Department of Forensic Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, 50-1 Yonsei-ro, 03722 Seoul, Republic of Korea
| | - Kyoung-Jin Shin
- Department of Forensic Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, 50-1 Yonsei-ro, 03722 Seoul, Republic of Korea.
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Li Piani L, Vigano' P, Somigliana E. Epigenetic clocks and female fertility timeline: A new approach to an old issue? Front Cell Dev Biol 2023; 11:1121231. [PMID: 37025178 PMCID: PMC10070683 DOI: 10.3389/fcell.2023.1121231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/03/2023] [Indexed: 04/08/2023] Open
Abstract
Worldwide increase in life expectancy has boosted research on aging. Overcoming the concept of chronological age, higher attention has been addressed to biological age, which reflects a person's real health state, and which may be the resulting combination of both intrinsic and environmental factors. As epigenetics may exert a pivotal role in the biological aging, epigenetic clocks were developed. They are based on mathematical models aimed at identifying DNA methylation patterns that can define the biological age and that can be adopted for different clinical scopes (i.e., estimation of the risks of developing age-related disorders or predicting lifespan). Recently, epigenetic clocks have gained a peculiar attention in the fertility research field, in particular in the female counterpart. The insight into the possible relations between epigenetic aging and women's infertility might glean additional information about certain conditions that are still not completely understood. Moreover, they could disclose significant implications for health promotion programs in infertile women. Of relevance here is that the impact of biological age and epigenetics may not be limited to fertility status but could translate into pregnancy issues. Indeed, epigenetic alterations of the mother may transfer into the offspring, and pregnancy itself as well as related complications could contribute to epigenetic modifications in both the mother and newborn. However, even if the growing interest has culminated in the conspicuous production of studies on these topics, a global overview and the availability of validated instruments for diagnosis is still missing. The present narrative review aims to explore the possible bonds between epigenetic aging and fertility timeline. In the "infertility" section, we will discuss the advances on epigenetic clocks focusing on the different tissues examined (endometrium, peripheral blood, ovaries). In the "pregnancy" section, we will discuss the results obtained from placenta, umbilical cord and peripheral blood. The possible role of epigenetic aging on infertility mechanisms and pregnancy outcomes represents a question that may configure epigenetic clock as a bond between two apparently opposite worlds: infertility and pregnancy.
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Affiliation(s)
- Letizia Li Piani
- Department of Clinical Sciences and Community Health, Università Degli Studi di Milano, Milan, Italy
- *Correspondence: Letizia Li Piani,
| | - Paola Vigano'
- Infertility Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Edgardo Somigliana
- Department of Clinical Sciences and Community Health, Università Degli Studi di Milano, Milan, Italy
- Infertility Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
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Paparazzo E, Geracitano S, Lagani V, Bartolomeo D, Aceto MA, D’Aquila P, Citrigno L, Bellizzi D, Passarino G, Montesanto A. A Blood-Based Molecular Clock for Biological Age Estimation. Cells 2022; 12:cells12010032. [PMID: 36611826 PMCID: PMC9818068 DOI: 10.3390/cells12010032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/05/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
In the last decade, extensive efforts have been made to identify biomarkers of biological age. DNA methylation levels of ELOVL fatty acid elongase 2 (ELOVL2) and the signal joint T-cell receptor rearrangement excision circles (sjTRECs) represent the most promising candidates. Although these two non-redundant biomarkers echo important biological aspects of the ageing process in humans, a well-validated molecular clock exploiting these powerful candidates has not yet been formulated. The present study aimed to develop a more accurate molecular clock in a sample of 194 Italian individuals by re-analyzing the previously obtained EVOLV2 methylation data together with the amount of sjTRECs in the same blood samples. The proposed model showed a high prediction accuracy both in younger individuals with an error of about 2.5 years and in older subjects where a relatively low error was observed if compared with those reported in previously published studies. In conclusion, an easy, cost-effective and reliable model to measure the individual rate and the quality of aging in human population has been proposed. Further studies are required to validate the model and to extend its use in an applicative context.
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Affiliation(s)
- Ersilia Paparazzo
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
| | - Silvana Geracitano
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
| | - Vincenzo Lagani
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology KAUST, Thuwal 23952, Saudi Arabia
- Institute of Chemical Biology, Ilia State University, 0162 Tbilisi, Georgia
- SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence, Thuwal 23952, Saudi Arabia
| | - Denise Bartolomeo
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
| | - Mirella Aurora Aceto
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
| | - Patrizia D’Aquila
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
| | - Luigi Citrigno
- National Research Council (CNR)—Institute for Biomedical Research and Innovation—(IRIB), 87050 Mangone, Italy
| | - Dina Bellizzi
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
| | - Giuseppe Passarino
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
- Correspondence: (G.P.); (A.M.)
| | - Alberto Montesanto
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
- Correspondence: (G.P.); (A.M.)
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Gensous N, Sala C, Pirazzini C, Ravaioli F, Milazzo M, Kwiatkowska KM, Marasco E, De Fanti S, Giuliani C, Pellegrini C, Santoro A, Capri M, Salvioli S, Monti D, Castellani G, Franceschi C, Bacalini MG, Garagnani P. A Targeted Epigenetic Clock for the Prediction of Biological Age. Cells 2022; 11:cells11244044. [PMID: 36552808 PMCID: PMC9777448 DOI: 10.3390/cells11244044] [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: 11/05/2022] [Revised: 11/28/2022] [Accepted: 12/06/2022] [Indexed: 12/15/2022] Open
Abstract
Epigenetic clocks were initially developed to track chronological age, but accumulating evidence indicates that they can also predict biological age. They are usually based on the analysis of DNA methylation by genome-wide methods, but targeted approaches, based on the assessment of a small number of CpG sites, are advisable in several settings. In this study, we developed a targeted epigenetic clock purposely optimized for the measurement of biological age. The clock includes six genomic regions mapping in ELOVL2, NHLRC1, AIM2, EDARADD, SIRT7 and TFAP2E genes, selected from a re-analysis of existing microarray data, whose DNA methylation is measured by EpiTYPER assay. In healthy subjects (n = 278), epigenetic age calculated using the targeted clock was highly correlated with chronological age (Spearman correlation = 0.89). Most importantly, and in agreement with previous results from genome-wide clocks, epigenetic age was significantly higher and lower than expected in models of increased (persons with Down syndrome, n = 62) and decreased (centenarians, n = 106; centenarians' offspring, n = 143; nutritional intervention in elderly, n = 233) biological age, respectively. These results support the potential of our targeted epigenetic clock as a new marker of biological age and open its evaluation in large cohorts to further promote the assessment of biological age in healthcare practice.
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Affiliation(s)
- Noémie Gensous
- Department of Internal Medicine and Clinical Immunology, CHU Bordeaux (Groupe Hospitalier Saint-André), 33077 Bordeaux, France
- UMR/CNRS 5164, ImmunoConcEpT, CNRS, University of Bordeaux, 33076 Bordeaux, France
| | - Claudia Sala
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Chiara Pirazzini
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Via Altura 3, 40139 Bologna, Italy
| | - Francesco Ravaioli
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Maddalena Milazzo
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | | | - Elena Marasco
- Personal Genomics S.R.L., Via Roveggia, 43/B, 37134 Verona, Italy
| | - Sara De Fanti
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Via Altura 3, 40139 Bologna, Italy
| | - Cristina Giuliani
- Laboratory of Molecular Anthropology, Centre for Genome Biology, Department of Biological, Geological and Environmental Sciences, University of Bologna, 40126 Bologna, Italy
| | - Camilla Pellegrini
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Via Altura 3, 40139 Bologna, Italy
| | - Aurelia Santoro
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
- Interdepartmental Center, “Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate)”, University of Bologna, 40126 Bologna, Italy
| | - Miriam Capri
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
- Interdepartmental Center, “Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate)”, University of Bologna, 40126 Bologna, Italy
| | - Stefano Salvioli
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
- Interdepartmental Center, “Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate)”, University of Bologna, 40126 Bologna, Italy
| | - Daniela Monti
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50139 Florence, Italy
| | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Claudio Franceschi
- Laboratory of Systems Medicine of Healthy Aging, Department of Applied Mathematics, Lobachevsky University, 603105 Nizhny Novgorod, Russia
| | - Maria Giulia Bacalini
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Via Altura 3, 40139 Bologna, Italy
- Correspondence: ; Tel.: +39-051-6225977
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
- Interdepartmental Center, “Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate)”, University of Bologna, 40126 Bologna, Italy
- Applied Biomedical Research Center (CRBA), S. Orsola-Malpighi Polyclinic, 40138 Bologna, Italy
- Department of Laboratory Medicine, Clinical Chemistry, Karolinska Institutet, Karolinska University Hospital, 14152 Huddinge, Sweden
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Li A, Koch Z, Ideker T. Epigenetic aging: Biological age prediction and informing a mechanistic theory of aging. J Intern Med 2022; 292:733-744. [PMID: 35726002 DOI: 10.1111/joim.13533] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Numerous studies have shown that epigenetic age-an individual's degree of aging based on patterns of DNA methylation-can be computed and is associated with an array of factors including diet, lifestyle, genetics, and disease. One can expect that still further associations will emerge with additional aging research, but to what end? Prediction of age was an important first step, but-in our view-the focus must shift from chasing increasingly accurate age computations to understanding the links between the epigenome and the mechanisms and physiological changes of aging. Here, we outline emerging areas of epigenetic aging research that prioritize biological understanding and clinical application. First, we survey recent progress in epigenetic clocks, which are beginning to predict not only chronological age but aging outcomes such as all-cause mortality and onset of disease, or which integrate aging signals across multiple biological processes. Second, we discuss research that exemplifies how investigation of the epigenome is building a mechanistic theory of aging and informing clinical practice. Such examples include identifying methylation sites and the genes most strongly predictive of aging-a subset of which have shown strong potential as biomarkers of neurodegenerative disease and cancer; relating epigenetic clock predictions to hallmarks of aging; and using longitudinal studies of DNA methylation to characterize human disease, resulting in the discovery of epigenetic indications of type 1 diabetes and the propensity for psychotic experiences.
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Affiliation(s)
- Adam Li
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Zane Koch
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, California, USA
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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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Daunay A, Hardy LM, Bouyacoub Y, Sahbatou M, Touvier M, Blanché H, Deleuze JF, How-Kit A. Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites. Aging (Albany NY) 2022; 14:7718-7733. [PMID: 36202132 PMCID: PMC9596211 DOI: 10.18632/aging.204316] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/23/2022] [Indexed: 11/25/2022]
Abstract
Aging is a progressive time-dependent biological process affecting differentially individuals, who can sometimes present exceptional longevity. Epigenetic alterations are one of the hallmarks of aging, which comprise the epigenetic drift and clock at DNA methylation level. In the present study, we estimated the DNA methylation-based age (DNAmage) using four epigenetic clocks based on a small number of CpGs in French centenarians and semi-supercentenarians (CSSC, n=214) as well as nonagenarians' and centenarians' offspring (NCO, n=143) compared to individuals from the French general population (CG, n=149). DNA methylation analysis of the nine CpGs included in the epigenetic clocks showed high correlation with chronological age (-0.66>R>0.54) and also the presence of an epigenetic drift for four CpGs that was only visible in CSSC. DNAmage analysis showed that CSSC and to a lesser extend NCO present a younger DNAmage than their chronological age (15-28.5 years for CSSC, 4.4-11.5 years for NCO and 4.2-8.2 years for CG), which were strongly significant in CSSC compared to CG (p-values<2.2e-16). These differences suggest that epigenetic aging and potentially biological aging are slowed in exceptionally long-lived individuals and that epigenetic clocks based on a small number of CpGs are sufficient to reveal alterations of the global epigenetic clock.
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Affiliation(s)
- Antoine Daunay
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France
| | - Lise M Hardy
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France.,Laboratory of Excellence GenMed, Paris, France
| | - Yosra Bouyacoub
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France.,Laboratory of Excellence GenMed, Paris, France
| | - Mourad Sahbatou
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France
| | - Mathilde Touvier
- Sorbonne Paris Nord University, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center Inserm U1153, Inrae U1125, Cnam, University of Paris (CRESS), Bobigny, France
| | - Hélène Blanché
- Laboratory of Excellence GenMed, Paris, France.,Centre de Ressources Biologiques, CEPH Biobank, Foundation Jean Dausset - CEPH, Paris, France
| | - Jean-François Deleuze
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France.,Laboratory of Excellence GenMed, Paris, France.,Centre de Ressources Biologiques, CEPH Biobank, Foundation Jean Dausset - CEPH, Paris, France.,Centre National de Recherche en Génomique Humaine, CEA, Institut François Jacob, Evry, France
| | - Alexandre How-Kit
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France
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Carratto TMT, Moraes VMS, Recalde TSF, Oliveira MLGD, Teixeira Mendes-Junior C. Applications of massively parallel sequencing in forensic genetics. Genet Mol Biol 2022; 45:e20220077. [PMID: 36121926 PMCID: PMC9514793 DOI: 10.1590/1678-4685-gmb-2022-0077] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/15/2022] [Indexed: 11/22/2022] Open
Abstract
Massively parallel sequencing, also referred to as next-generation sequencing, has positively changed DNA analysis, allowing further advances in genetics. Its capability of dealing with low quantity/damaged samples makes it an interesting instrument for forensics. The main advantage of MPS is the possibility of analyzing simultaneously thousands of genetic markers, generating high-resolution data. Its detailed sequence information allowed the discovery of variations in core forensic short tandem repeat loci, as well as the identification of previous unknown polymorphisms. Furthermore, different types of markers can be sequenced in a single run, enabling the emergence of DIP-STRs, SNP-STR haplotypes, and microhaplotypes, which can be very useful in mixture deconvolution cases. In addition, the multiplex analysis of different single nucleotide polymorphisms can provide valuable information about identity, biogeographic ancestry, paternity, or phenotype. DNA methylation patterns, mitochondrial DNA, mRNA, and microRNA profiling can also be analyzed for different purposes, such as age inference, maternal lineage analysis, body-fluid identification, and monozygotic twin discrimination. MPS technology also empowers the study of metagenomics, which analyzes genetic material from a microbial community to obtain information about individual identification, post-mortem interval estimation, geolocation inference, and substrate analysis. This review aims to discuss the main applications of MPS in forensic genetics.
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Affiliation(s)
- Thássia Mayra Telles Carratto
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
| | - Vitor Matheus Soares Moraes
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
| | | | | | - Celso Teixeira Mendes-Junior
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
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Freire-Aradas A, Girón-Santamaría L, Mosquera-Miguel A, Ambroa-Conde A, Phillips C, Casares de Cal M, Gómez-Tato A, Álvarez-Dios J, Pospiech E, Aliferi A, Syndercombe Court D, Branicki W, Lareu M. A common epigenetic clock from childhood to old age. Forensic Sci Int Genet 2022; 60:102743. [DOI: 10.1016/j.fsigen.2022.102743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 11/04/2022]
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Novel feature selection methods for construction of accurate epigenetic clocks. PLoS Comput Biol 2022; 18:e1009938. [PMID: 35984867 PMCID: PMC9432708 DOI: 10.1371/journal.pcbi.1009938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/31/2022] [Accepted: 07/11/2022] [Indexed: 11/22/2022] Open
Abstract
Epigenetic clocks allow us to accurately predict the age and future health of individuals based on the methylation status of specific CpG sites in the genome and are a powerful tool to measure the effectiveness of longevity interventions. There is a growing need for methods to efficiently construct epigenetic clocks. The most common approach is to create clocks using elastic net regression modelling of all measured CpG sites, without first identifying specific features or CpGs of interest. The addition of feature selection approaches provides the opportunity to optimise the identification of predictive CpG sites. Here, we apply novel feature selection methods and combinatorial approaches including newly adapted neural networks, genetic algorithms, and ‘chained’ combinations. Human whole blood methylation data of ~470,000 CpGs was used to develop clocks that predict age with R2 correlation scores of greater than 0.73, the most predictive of which uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks were modelled to build a clock with a R2 correlation score of 0.83. These two clocks are validated on two external datasets where they maintain excellent predictive accuracy. When compared with three published epigenetic clocks (Hannum, Horvath, Weidner) also applied to these validation datasets, our clocks outperformed all three models. We identified gene regulatory regions associated with selected CpGs as possible targets for future aging studies. Thus, our feature selection algorithms build accurate, generalizable clocks with a low number of CpG sites, providing important tools for the field. Epigenetic clocks accurately predict a person’s age by measuring the levels of a chemical mark (methylation) at specific sites of the DNA. More of these clocks are being built all the time, and there is a need for tools to best construct these clocks, and particularly to pick the specific DNA sites to include. We propose several novel machine-learning tools for the optimised selection of these DNA sites, known as feature selection approaches. We applied our approaches to a large human blood dataset to develop several clocks that accurately predict age using 35 or less DNA sites with more accuracy than previously published clocks when applied to other datasets for validation. Some of the DNA sites identified may be associated with interesting genes to explore further for their role in aging. These approaches should enable the building of more accurate, generalizable age prediction clocks from a low number of DNA sites.
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