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Kim HJ, Kim HJ, Jo C. A non-destructive predictive model for estimating the freshness/spoilage of packaged chicken meat using changes in drip metabolites. Int J Food Microbiol 2024; 419:110738. [PMID: 38772219 DOI: 10.1016/j.ijfoodmicro.2024.110738] [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: 09/06/2023] [Revised: 03/07/2024] [Accepted: 05/06/2024] [Indexed: 05/23/2024]
Abstract
This study investigates the possibility of utilizing drip as a non-destructive method for assessing the freshness and spoilage of chicken meat. The quality parameters [pH, volatile base nitrogen (VBN), and total aerobic bacterial counts (TAB)] of chicken meat were evaluated over a 13-day storage period in vacuum packaging at 4 °C. Simultaneously, the metabolites in the chicken meat and its drip were measured by nuclear magnetic resonance. Correlation (Pearson's and Spearman's rank) and pathway analyses were conducted to select the metabolites for model training. Binary logistic regression (model 1 and model 2) and multiple linear regression models (model 3-1 and model 3-2) were trained using selected metabolites, and their performance was evaluated using receiver operating characteristic (ROC) curves. As a result, the chicken meat was spoiled after 7 days of storage, exceeding 20 mg/100 g VBN and 5.7 log CFU/g TAB. The correlation analysis identified one organic acid, eight free amino acids, and five nucleic acids as highly correlated with chicken meat and its drip during storage. Pathway analysis revealed tyrosine and purine metabolism as metabolic pathways highly correlated with spoilage. Based on these findings, specific metabolites were selected for model training: ATP, glutamine, hypoxanthine, IMP, tyrosine, and tyramine. To predict the freshness and spoilage of chicken meat, model 1, trained using tyramine, ATP, tyrosine, and IMP from chicken meat, achieved a 99.9 % accuracy and had an ROC value of 0.884 when validated using drip metabolites. This model 1 was improved by training with tyramine and IMP from both chicken meat and its drip (model 2), which increased the ROC value for drip metabolites from 0.884 to 0.997. Finally, selected two metabolites (tyramine and IMP) can predict TAB and VBN quantitatively through models 3-1 and 3-2, respectively. Therefore, the model developed using metabolic changes in drip demonstrated the capability to non-destructively predict the freshness and spoilage of chicken meat at 4 °C. To make generic predictions, it is necessary to expand the model's applicability to various conditions, such as different temperatures, and validate its performance across multiple chicken batches.
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Affiliation(s)
- Hyun-Jun Kim
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, Research Institute of Agriculture and Life Science, Seoul National University, Seoul 08826, Republic of Korea
| | - Hye-Jin Kim
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, Research Institute of Agriculture and Life Science, Seoul National University, Seoul 08826, Republic of Korea
| | - Cheorun Jo
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, Research Institute of Agriculture and Life Science, Seoul National University, Seoul 08826, Republic of Korea; Institute of Green Bio Science and Technology, Seoul National University, Pyeongchang 25354, Republic of Korea; Department of Animal Product Technology, Faculty of Animal Husbandary, Universitas Padjadjaran, West Java 45363, Indonesia.
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2
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Ji Z, Xing Y, Li J, Feng X, Yang F, Zhu B, Yan J. Male-specific age prediction based on Y-chromosome DNA methylation with blood using pyrosequencing. Forensic Sci Int Genet 2024; 71:103050. [PMID: 38703560 DOI: 10.1016/j.fsigen.2024.103050] [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: 01/24/2024] [Revised: 04/14/2024] [Accepted: 04/19/2024] [Indexed: 05/06/2024]
Abstract
Age prediction is an important aspect of forensic science that offers valuable insight into identification. In recent years, extensive studies have been conducted on age prediction based on DNA methylation, and numerous studies have demonstrated that DNA methylation is a reliable biomarker for age prediction. However, almost all studies on age prediction based on DNA methylation have focused on age-related CpG sites in autosomes, which are concentrated on single-source DNA samples. Mixed samples, especially male-female mixed samples, are common in forensic casework. The application of Y-STRs and Y-SNPs can provide clues for the genetic typing of male individuals in male-female mixtures, but they cannot provide the age information of male individuals. Studies on Y-chromosome DNA methylation can address this issue. In this study, we identified five age-related CpG sites on the Y chromosome (Y-CpGs) and developed a male-specific age prediction model using pyrosequencing combined with a support vector machine algorithm. The mean absolute deviation of the model was 5.50 years in the training set and 6.74 years in the testing set. When we used a male blood sample to predict age, the deviation between the predicted and chronological age was 1.18 years. Then, we mixed the genomic DNA of the male and a female at ratios of 1:1, 1:5, 1:10, and 1:50, the range of deviation between the predicted and chronological age of the male in the mixture was 1.16-1.74 years. In addition, there was no significant difference between the methylation values of bloodstains and blood in the same sample, which indicates that our model is also suitable for bloodstain samples. Overall, our results show that age prediction using DNA methylation of the Y chromosome has potential applications in forensic science and can be of great help in predicting the age of males in male-female mixtures. Furthermore, this work lays the foundation for future research on age-related applications of Y-CpGs.
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Affiliation(s)
- Zhimin Ji
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China
| | - Yangfeng Xing
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China
| | - Junli Li
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China
| | - Xiaoxiao Feng
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China
| | - Fenglong Yang
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China.
| | - Bofeng Zhu
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China; Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, PR China.
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China.
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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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Karetnikov DI, Romanov SE, Baklaushev VP, Laktionov PP. Age Prediction Using DNA Methylation Heterogeneity Metrics. Int J Mol Sci 2024; 25:4967. [PMID: 38732187 PMCID: PMC11084170 DOI: 10.3390/ijms25094967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 04/27/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.
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Affiliation(s)
- Dmitry I. Karetnikov
- Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Stanislav E. Romanov
- Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Vladimir P. Baklaushev
- Federal Center for Brain and Neurotechnologies, Federal Medical and Biological Agency of Russia, 117513 Moscow, Russia
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
- Department of Medical Nanobiotechnology, Medical and Biological Faculty, Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, 117997 Moscow, Russia
| | - Petr P. Laktionov
- Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
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5
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So MH, Lee JE, Lee HY. Strategies to deal with genetic analyzer-specific DNA methylation measurements. Electrophoresis 2024; 45:906-915. [PMID: 38488745 DOI: 10.1002/elps.202300185] [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: 08/21/2023] [Revised: 02/25/2024] [Accepted: 03/02/2024] [Indexed: 05/23/2024]
Abstract
Targeted bisulfite sequencing using single-base extension (SBE) can be used to measure DNA methylation via capillary electrophoresis on genetic analyzers in forensic labs. Several accurate age prediction models have been reported using this method. However, using different genetic analyzers with different software settings can generate different methylation values, leading to significant errors in age prediction. To address this issue, the study proposes and compares four methods as follows: (1) adjusting methylation values using numerous actual body fluid DNA samples, (2) adjusting methylation values using control DNAs with varying methylation ratios, (3) constructing new age prediction models for each genetic analyzer type, and (4) constructing new age prediction models that could be applied to all types of genetic analyzers. To test the methods for adjusting values using actual body fluid DNA samples, previously reported adjusting equations were used for blood/saliva DNA age prediction markers (ELOVL2, FHL2, KLF14, MIR29B2CHG/C1orf132, and TRIM59). New equations were generated for semen DNA age prediction markers (TTC7B, LOC401324/cg12837463, and LOC729960/NOX4) by drawing polynomial regression lines between the results of the three types of genetic analyzers (3130, 3500, and SeqStudio). The same method was applied to obtain adjustment equations using 11 control DNA samples. To develop new age prediction models for each genetic analyzer type, linear regression analysis was conducted using DNA methylation data from 150 blood, 150 saliva, and 62 semen samples. For the genetic analyzer-independent models, control DNAs were used to formulate equations for calibrating the bias of the data from each genetic analyzer, and linear regression analysis was performed using calibrated body fluid DNA data. In the comparison results, the genetic analyzer-specific models showed the highest accuracy. However, genetic analyzer-independent models through bias adjustment also provided accurate age prediction results, suggesting its use as an alternative in situations with multiple constraints.
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Affiliation(s)
- Moon Hyun So
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Ji Eun Lee
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Hwan Young Lee
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Institute of Forensic and Anthropological Science, Seoul National University College of Medicine, Seoul, South Korea
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6
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Bettim CA, da Silva AV, Kahmann A, Dorn M, Alho CS, Avila E. MC1R and age heteroclassification of face phenotypes in the Rio Grande do Sul population. Int J Legal Med 2024; 138:859-872. [PMID: 38087053 DOI: 10.1007/s00414-023-03143-6] [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: 06/01/2023] [Accepted: 11/22/2023] [Indexed: 04/11/2024]
Abstract
BACKGROUND Forensic DNA phenotyping (FDP) consists of the use of methodologies for predicting externally visible characteristics (EVCs) from the genetic material of biological samples found in crime scenes and has proven to be a promising tool in aiding human identification in police activities. Currently, methods based on multiplex assays and statistical models of prediction of EVCs related to hair, skin, and iris pigmentation using panels of SNP and INDEL biomarkers have already been developed and validated by the forensic scientific community. As well as traces of pigmentation, an individual's perceived age (PA) can also be considered an EVC and its estimation in unknown individuals can be useful for the progress of investigations. Liu and colleagues (2016) were pioneers in evidencing that, in addition to lifestyle and environmental factors, the presence of SNP and INDEL variants in the MC1R gene - which encodes a transmembrane receptor responsible for regulating melanin production - seems to contribute to an individual's PA. The group highlighted the association between these MC1R gene polymorphisms and the PA in the European population, where carriers of risk haplotypes appeared to be up to 2 years older in comparison to their chronological age (CA). PURPOSE Understanding that genotype-phenotype relationships cannot be extrapolated between different population groups, this study aimed to test this hypothesis and verify the applicability of this variant panel in the Rio Grande do Sul admixed population. METHODS Based on genomic data from a sample of 261 volunteers representative of gaucho population and using a multiple linear regression (MLR) model, our group was able to verify a significant association among nine intronic variants in loci adjacent to MC1R (e.g., AFG3L1P, TUBB3, FANCA) and facial age appearance, whose PA was defined after age heteroclassification of standard frontal face images through 11 assessors. RESULTS Different from that observed in European populations, our results show that the presence of effect alleles (R) of the selected variants in our sample influenced both younger and older face phenotypes. The influence of each variant on PA is expressed as β values. CONCLUSIONS There are important molecular mechanisms behind the effects of MC1R locus on PA, and the genomic background of each population seems to be crucial to determine this influence.
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Affiliation(s)
- Cássio Augusto Bettim
- Structural Bioinformatics and Computational Biology Lab, Institute of Informatics, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
- National Science and Technology Institute for Forensic Science, Porto Alegre, RS, Brazil
| | - Alexsandro Vasconcellos da Silva
- National Science and Technology Institute for Forensic Science, Porto Alegre, RS, Brazil
- Technical Scientific and Identification Sections, Superintendency of Federal Police in Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Alessandro Kahmann
- National Science and Technology Institute for Forensic Science, Porto Alegre, RS, Brazil.
- National Science and Technology Institute for Children Cancer Biology and Pediatric Oncology, Porto Alegre, RS, Brazil.
- Interdisciplinary Department, Federal University of Rio Grande Do Sul, Tramandaí, RS, Brazil.
| | - Márcio Dorn
- Structural Bioinformatics and Computational Biology Lab, Institute of Informatics, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
- National Science and Technology Institute for Forensic Science, Porto Alegre, RS, Brazil
- National Science and Technology Institute for Children Cancer Biology and Pediatric Oncology, Porto Alegre, RS, Brazil
| | - Clarice Sampaio Alho
- National Science and Technology Institute for Forensic Science, Porto Alegre, RS, Brazil
- National Science and Technology Institute for Children Cancer Biology and Pediatric Oncology, Porto Alegre, RS, Brazil
| | - Eduardo Avila
- National Science and Technology Institute for Forensic Science, Porto Alegre, RS, Brazil
- Technical Scientific and Identification Sections, Superintendency of Federal Police in Rio Grande do Sul, Porto Alegre, RS, Brazil
- National Science and Technology Institute for Children Cancer Biology and Pediatric Oncology, Porto Alegre, RS, Brazil
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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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8
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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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Davydova E, Perenkov A, Vedunova M. Building Minimized Epigenetic Clock by iPlex MassARRAY Platform. Genes (Basel) 2024; 15:425. [PMID: 38674360 PMCID: PMC11049545 DOI: 10.3390/genes15040425] [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: 02/26/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Epigenetic clocks are valuable tools for estimating both chronological and biological age by assessing DNA methylation levels at specific CpG dinucleotides. While conventional epigenetic clocks rely on genome-wide methylation data, targeted approaches offer a more efficient alternative. In this study, we explored the feasibility of constructing a minimized epigenetic clock utilizing data acquired through the iPlex MassARRAY technology. The study enrolled a cohort of relatively healthy individuals, and their methylation levels of eight specific CpG dinucleotides in genes SLC12A5, LDB2, FIGN, ACSS3, FHL2, and EPHX3 were evaluated using the iPlex MassARRAY system and the Illumina EPIC array. The methylation level of five studied CpG sites demonstrated significant correlations with chronological age and an acceptable convergence of data obtained by the iPlex MassARRAY and Illumina EPIC array. At the same time, the methylation level of three CpG sites showed a weak relationship with age and exhibited a low concordance between the data obtained from the two technologies. The construction of the epigenetic clock involved the utilization of different machine-learning models, including linear models, deep neural networks (DNN), and gradient-boosted decision trees (GBDT). The results obtained from these models were compared with each other and with the outcomes generated by other well-established epigenetic clocks. In our study, the TabNet architecture (deep tabular data learning architecture) exhibited the best performance (best MAE = 5.99). Although our minimized epigenetic clock yielded slightly higher age prediction errors compared to other epigenetic clocks, it still represents a viable alternative to the genome-wide epigenotyping array.
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Affiliation(s)
- Ekaterina Davydova
- Institute of Biology and Biomedicine, Lobachevsky State University, 23 Gagarin Ave., Nizhny Novgorod 603022, Russia (M.V.)
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10
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Lee JE, Park SU, So MH, Lee HY. Age prediction using DNA methylation of Y-chromosomal CpGs in semen samples. Forensic Sci Int Genet 2024; 69:103007. [PMID: 38217952 DOI: 10.1016/j.fsigen.2024.103007] [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: 08/11/2023] [Revised: 12/29/2023] [Accepted: 01/03/2024] [Indexed: 01/15/2024]
Abstract
In cases of sexual assault, the evidence often exists as a mixture of female and male body fluids, and in many cases, contains a higher proportion of female body fluids than males. In these cases, Y-STR, rather than autosomal STRs, can provide useful information. It becomes very difficult to identify the true suspect if there is no match among known suspects or if a match exists for two or more suspects, e.g. two suspects from the same paternal lineage. However, age prediction using the DNA methylation of Y-chromosomal CpGs can help narrow the search for unknown suspects and discriminate between older and younger suspects. Therefore, the DNA methylation profiles of semen samples from 56 healthy Korean males were generated using Illumina's Infinium MethylationEPIC BeadChip Array. Among the ten identified age-associated CpG markers located in the Y-chromosome, nine were used to construct age prediction models. The identified markers were further investigated in the MPS analysis of 147 semen samples, and the multiplex assay was validated with the reliability, reproducibility and sensitivity tests. Several age prediction models were constructed using the MPS data with the multiple linear regression, stepwise linear regression, ridge linear regression, lasso regression, elastic net linear regression and support vector machine analyses, and all showed MAEs of 5 to 7 years in the test set samples. Six single-source female samples were also subjected to MPS analysis but showed very low coverage that could not affect the analysis of the mixed samples. Therefore, the age prediction models of the present study are expected to provide useful investigative leads, especially in mixed male and female samples from sexual assault cases.
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Affiliation(s)
- Ji Eun Lee
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, the Republic of Korea
| | - Sang Un Park
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, the Republic of Korea
| | - Moon Hyun So
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, the Republic of Korea
| | - Hwan Young Lee
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, the Republic of Korea; Institute of Forensic and Anthropological Science, Seoul National University College of Medicine, Seoul, the Republic of Korea.
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11
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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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12
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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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13
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Jung JY, So MH, Jeong KS, Kim SI, Kim EJ, Park JH, Kim E, Lee HY. Epigenetic age prediction using costal cartilage for the investigation of disaster victims and missing persons. J Forensic Sci 2024. [PMID: 38275209 DOI: 10.1111/1556-4029.15470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/04/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
The DNA intelligence tool, DNA methylation-based age prediction, can help identify disaster victims and suspects in criminal investigations. In this study, we developed a costal cartilage-based age prediction tool that uses massive parallel sequencing (MPS) of age-associated DNA methylation markers. Costal cartilage samples were obtained from 85 deceased Koreans, aged between 26 and 89 years. An MPS library was prepared using two rounds of multiplex polymerase chain reaction of nine genes (TMEM51, MIR29B2CHG, EDARADD, FHL2, TRIM59, ELOVL2, KLF14, ASPA, and PDE4C). The DNA methylation status of 45 CpG sites was determined and used to train an age prediction model via stepwise regression analysis. Nine CpGs in MIR29B2CHG, FHL2, TRIM59, ELOVL2, KLF14, and ASPA were selected for regression model construction. A leave-one-out cross-validation analysis revealed the high performance of the age prediction model, with a mean absolute error (MAE) and root mean square error of 4.97 and 6.43 years, respectively. Additionally, our model showed good performance with a MAE of 6.06 years in the analysis of data of 181 costal cartilage samples collected from Europeans. Our model effectively estimates the age of deceased individuals using costal cartilage samples; therefore, it can be a valuable forensic tool for disaster victim and missing person investigation.
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Affiliation(s)
- Ju Yeon Jung
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Forensic DNA Section, National Forensic Service Jeju Branch, Jeju, South Korea
| | - Moon Hyun So
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Kyu-Sik Jeong
- Forensic DNA Division, National Forensic Service, Wonju, South Korea
| | - Sang-In Kim
- Forensic DNA Division, National Forensic Service, Wonju, South Korea
| | - Eun Jin Kim
- Forensic DNA Division, National Forensic Service, Wonju, South Korea
| | - Ji Hwan Park
- Forensic DNA Division, National Forensic Service, Wonju, South Korea
| | - Eungsoo Kim
- Forensic DNA Division, National Forensic Service, Wonju, South Korea
| | - Hwan Young Lee
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Institute of Forensic and Anthropological Science, Seoul National University College of Medicine, Seoul, South Korea
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14
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Marcante B, Delicati A, Onofri M, Tozzo P, Caenazzo L. Estimation of Human Chronological Age from Buccal Swab Samples through a DNA Methylation Analysis Approach of a Five-Locus Multiple Regression Model. Int J Mol Sci 2024; 25:935. [PMID: 38256009 PMCID: PMC10815300 DOI: 10.3390/ijms25020935] [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: 12/20/2023] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
Recent advancements in forensic genetics have facilitated the extraction of additional characteristics from unidentified samples. This study delves into the predictive potential of a five-gene (ELOVL2, FHL2, KLF14, C1orf132, and TRIM59) methylation rate analysis for human age estimation using buccal swabs collected from 60 Italian volunteers. The methylation levels of specific CpG sites in the five genes were analyzed through bisulfite conversion, single-base extension, and capillary electrophoresis. A multivariate linear regression model was crafted on the training set, then the test set was employed to validate the predictive model. The multivariate predictive model revealed a mean absolute deviation of 3.49 years in the test set of our sample. While limitations include a modest sample size, the study provides valuable insights into the potential of buccal swab-based age prediction, aiding in criminal investigations where accurate age determination is crucial. Our results also highlight that it is necessary to investigate the effectiveness of predictive models specific to biological tissues and individual populations, since models already proven effective for other populations or different tissues did not show the same effectiveness in our study.
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Affiliation(s)
- Beatrice Marcante
- Legal Medicine Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (B.M.); (A.D.); (P.T.)
| | - Arianna Delicati
- Legal Medicine Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (B.M.); (A.D.); (P.T.)
| | - Martina Onofri
- Section of Legal Medicine, Department of Medicine and Surgery, Santa Maria Hospital, University of Perugia, 05100 Terni, Italy;
| | - Pamela Tozzo
- Legal Medicine Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (B.M.); (A.D.); (P.T.)
| | - Luciana Caenazzo
- Legal Medicine Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (B.M.); (A.D.); (P.T.)
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15
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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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16
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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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17
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Horvath S, Haghani A, Zoller JA, Lu AT, Ernst J, Pellegrini M, Jasinska AJ, Mattison JA, Salmon AB, Raj K, Horvath M, Paul KC, Ritz BR, Robeck TR, Spriggs M, Ehmke EE, Jenkins S, Li C, Nathanielsz PW. Pan-primate studies of age and sex. GeroScience 2023; 45:3187-3209. [PMID: 37493860 PMCID: PMC10643767 DOI: 10.1007/s11357-023-00878-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/16/2023] [Indexed: 07/27/2023] Open
Abstract
Age and sex have a profound effect on cytosine methylation levels in humans and many other species. Here we analyzed DNA methylation profiles of 2400 tissues derived from 37 primate species including 11 haplorhine species (baboons, marmosets, vervets, rhesus macaque, chimpanzees, gorillas, orangutan, humans) and 26 strepsirrhine species (suborders Lemuriformes and Lorisiformes). From these we present here, pan-primate epigenetic clocks which are highly accurate for all primates including humans (age correlation R = 0.98). We also carried out in-depth analysis of baboon DNA methylation profiles and generated five epigenetic clocks for baboons (Olive-yellow baboon hybrid), one of which, the pan-tissue epigenetic clock, was trained on seven tissue types (fetal cerebral cortex, adult cerebral cortex, cerebellum, adipose, heart, liver, and skeletal muscle) with ages ranging from late fetal life to 22.8 years of age. Using the primate data, we characterize the effect of age and sex on individual cytosines in highly conserved regions. We identify 11 sex-related CpGs on autosomes near genes (POU3F2, CDYL, MYCL, FBXL4, ZC3H10, ZXDC, RRAS, FAM217A, RBM39, GRIA2, UHRF2). Low overlap can be observed between age- and sex-related CpGs. Overall, this study advances our understanding of conserved age- and sex-related epigenetic changes in primates, and provides biomarkers of aging for all primates.
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Affiliation(s)
- Steve Horvath
- Altos Labs, San Diego, CA, USA.
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
| | | | - Joseph A Zoller
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Jason Ernst
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Matteo Pellegrini
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Anna J Jasinska
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and BiobehavioralSciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Julie A Mattison
- Translational Gerontology Branch, National Institute On Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Adam B Salmon
- The Sam and Ann Barshop Institute for Longevity and Aging Studies, and Department of Molecular Medicine, UT Health San Antonio, and the Geriatric Research Education and Clinical Center, South Texas Veterans Healthcare System, San Antonio, TX, USA
| | | | | | - Kimberly C Paul
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Beate R Ritz
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Epidemiology, UCLA Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Todd R Robeck
- Corporate Zoological Operations, SeaWorld Parks, Orlando, FL, USA
| | - Maria Spriggs
- Busch Gardens Tampa, SeaWorld Parks, Tampa, FL, 33612, USA
| | | | - Susan Jenkins
- Texas Pregnancy & Life-Course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources Department, Laramie, WY, USA
| | - Cun Li
- Texas Pregnancy & Life-Course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources Department, Laramie, WY, USA
| | - Peter W Nathanielsz
- Texas Pregnancy & Life-Course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources Department, Laramie, WY, USA
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18
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Mao X, Li Y, Zhong Y, Chen R, Wang K, Huang D, Luo X. Kruppel-like factor 14 ameliorated obesity and related metabolic disorders by promoting adipose tissue browning. Am J Physiol Endocrinol Metab 2023; 325:E744-E754. [PMID: 37938176 DOI: 10.1152/ajpendo.00226.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/03/2023] [Accepted: 11/04/2023] [Indexed: 11/09/2023]
Abstract
Obesity has been identified as a serious and debilitating disease that threatens human health, but the current treatment strategies still have some shortcomings. Exercise and dieting are difficult for many people to adhere to, and a series of surgical risks and pain brought about by volume reduction have made it difficult for the current weight loss effect to meet human expectations. In this study, we first found that mice with overexpression of the transcription factor Kruppel-like factor 14 (KLF14) in subcutaneous adipose tissue gained weight more slowly while consuming a high-fat diet than did control mice, and these mice also showed reduced insulin resistance and liver lipid deposition abnormalities. Mechanistically, the browning of white adipose tissue was promoted in adipose tissue with KLF14 overexpression; therefore, we preliminarily concluded that KLF14 can improve obesity by promoting the browning of white adipose tissue and energy consumption, thus ameliorating obesity and related metabolic disturbances. In summary, our results revealed that KLF14 may promote white adipose tissue browning, thus ameliorating high-fat diet-induced obesity and hepatic steatosis, as well as serum lipid levels and insulin resistance, thereby achieving a positive effect on metabolism.NEW & NOTEWORTHY Our study first explored the role of KLF14 in the development and progression of HFD-induced obesity in male mice. Its beneficial effect on adipose browning and metabolic disorders suggests that KLF14 may provide us a new therapeutic strategy for obesity and related metabolic complications. This health problem is of global concern and needs to be addressed.
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Affiliation(s)
- Xiaoxiang Mao
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Yuanxiang Li
- Department of Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Yi Zhong
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Ru Chen
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Kun Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Dandan Huang
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xi Luo
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
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19
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Banila C, Green D, Katsanos D, Viana J, Osmaston A, Menendez Vazquez A, Lynch M, Kaveh S. A noninvasive method for whole-genome skin methylome profiling. Br J Dermatol 2023; 189:750-759. [PMID: 37658851 DOI: 10.1093/bjd/ljad316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND Ageing, disease and malignant transformation of the skin are associated with changes in DNA methylation. So far, mostly invasive methodologies such as biopsies have been applied in collecting DNA methylation signatures. Tape stripping offers a noninvasive option for skin diagnostics. It enables the easy but robust capture of biologic material in large numbers of participants without the need for specialized medical personnel. OBJECTIVES To design and validate a methodology for noninvasive skin sample collection using tape stripping for subsequent DNA -methylation analysis. METHODS A total of 175 participants were recruited and provided tape-stripping samples from a sun-exposed area; 92 provided matched tape-stripping samples from a sun-protected area, and an additional 5 provided matched skin-shave biopsies from the same area. Using -enzymatic conversion and whole-genome Illumina sequencing, we generated genome-wide DNA methylation profiles that were used to evaluate the feasibility of noninvasive data acquisition, to compare with established sampling approaches and to investigate biomarker identification for age and ultraviolet (UV) exposure. RESULTS We found that tape-stripping samples showed strong concordance in their global DNA methylation landscapes to those of conventional invasive biopsies. Moreover, we showed sample reproducibility and consistent global methylation profiles in skin tape-stripping samples collected from different areas of the body. Using matched samples from sun-protected and sun-exposed areas of the body we were able to validate the capacity of our method to capture the effects of environmental changes and ageing in a cohort covering various ages, ethnicities and skin types. We found DNA methylation changes on the skin resulting from UV exposure and identified significant age-related hypermethylation of CpG islands, with a pronounced peak effect at 50-55 years of age, including methylation changes in well-described markers of ageing. CONCLUSIONS These data demonstrate the feasibility of using tape stripping combined with whole-genome sequencing as a noninvasive approach to measuring DNA methylation changes in the skin. In addition, they outline a viable experimental framework for the use of skin tape stripping, particularly when it is performed in large cohorts of patients to identify biomarkers of skin ageing, UV damage and, possibly, to track treatment response to therapeutic interventions.
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Affiliation(s)
| | - Daniel Green
- Mitra Bio, Translation and Innovation Hub, London, UK
| | | | - Joana Viana
- Mitra Bio, Translation and Innovation Hub, London, UK
| | - Alice Osmaston
- Centre for Infectious Disease Epidemiology, University College London, London, UK
| | | | - Magnus Lynch
- Centre for Gene Therapy and Regenerative Medicine, King's College London, London, UK
| | - Shakiba Kaveh
- Mitra Bio, Translation and Innovation Hub, London, UK
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20
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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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21
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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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22
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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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23
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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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24
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Kim HJ, Kim HJ, Kim HC, Lee D, Jung HY, Kang T, Jo C. Mathematical modeling for freshness/spoilage of chicken breast using chemometric analysis. Curr Res Food Sci 2023; 7:100590. [PMID: 37727874 PMCID: PMC10506101 DOI: 10.1016/j.crfs.2023.100590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 09/04/2023] [Accepted: 09/07/2023] [Indexed: 09/21/2023] Open
Abstract
Chicken meat spoilage is a significant concern for food safety and quality, and this study aims to predict the spoilage point of chicken breast meat through various attributes and metabolites. Chicken meat was stored in anaerobic packaging at 4 °C for 13 days, and various meat quality attributes (pH, drip loss, color, volatile basic nitrogen [VBN], total aerobic bacteria [TAB], and metabolites) were examined. First, the spoiled point (VBN >20 mg/100 g and/or TAB >7 log CFU/g) of the chicken breast meat was determined. Using univariate and multivariate analyses, twenty-four candidate metabolites were identified. A receiver operating characteristic (ROC) analysis was used to validate the obtained binary logistic regression model using nine metabolites (proline, methionine, glutamate, threonine, acetate, uridine 5'-monophosphate, hypoxanthine, glycine, and glutamine). The results showed a high area under the ROC curve value (0.992). Thus, this study confirmed the predictability of spoilage points in chicken breast meat through these nine metabolites.
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Affiliation(s)
- Hyun-Jun Kim
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hye-Jin Kim
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hyun Cheol Kim
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Republic of Korea
| | - Dongheon Lee
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hyun Young Jung
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Republic of Korea
| | - Taemin Kang
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Republic of Korea
| | - Cheorun Jo
- Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Republic of Korea
- Institute of Green BioScience and Technology, Seoul National University, Pyeongchang, 25354, Republic of Korea
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25
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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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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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Cui H, Xin Y, Cao F, Gan Z, Tian Y, Liu W, Shi P. The correlation between CpG island methylation of hTERT promoter and human age prediction. Leg Med (Tokyo) 2023; 63:102270. [PMID: 37207612 DOI: 10.1016/j.legalmed.2023.102270] [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: 02/22/2023] [Revised: 04/28/2023] [Accepted: 05/09/2023] [Indexed: 05/21/2023]
Abstract
DNA methylation is an epigenetic modification that occurs during the life cycle of individuals. Its degree is closely associated with the methylation status of CpG sites in its promoter region. Based on the previous screening that the hTERT methylation is both related to tumors and age, we suspected that the age inference based on hTERT methylation would be disturbed by the disease of the tested person. Herein, eight CpG sites in the hTERT promoter region were analyzed by real-time methylation-specific PCR, and we found that CpG2, CpG5, and CpG8 were closely related to the tumor (P < 0.05). The remaining five CpG sites had a large error in predicting age alone. Combining them to establish a model yielded better results, with an average age error of 4.35 years. This study provides a reliable and accurate detection method for the DNA methylation status of multiple CpG sites on the hTERT gene promoter, which can be used for the prediction of forensic age and assistant diagnosis of clinical diseases.
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Affiliation(s)
- Hanyue Cui
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Ye Xin
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Fangqi Cao
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China; Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology, Zhongshan North No 1 Road, Shanghai 200083, China
| | - Ziye Gan
- Ulink College of Shanghai, 559 Laiting South Road, Shanghai 201615, China
| | - Yuxiang Tian
- Department of Clinical Laboratory, Shanghai Xuhui District Dahua Hospital, Shanghai 200237, China
| | - Wenbin Liu
- Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology, Zhongshan North No 1 Road, Shanghai 200083, China.
| | - Ping Shi
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China.
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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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Smyth LJ, Cruise SM, Tang J, Young I, McGuinness B, Kee F, McKnight AJ. Differential methylation in CD44 and SEC23A is associated with time preference in older individuals. ECONOMICS AND HUMAN BIOLOGY 2023; 49:101233. [PMID: 36812724 DOI: 10.1016/j.ehb.2023.101233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/25/2022] [Accepted: 02/06/2023] [Indexed: 05/08/2023]
Abstract
Time preference is a measure used to ascertain the level of which individuals prefer smaller, immediate rewards over larger, delayed rewards. We explored how an individual's time preference associates with their epigenetic profile. Time preferences were ascertained by asking participants of the Northern Ireland COhort for the Longitudinal study of Ageing to make a series of choices between two hypothetical income scenarios. From these, eight 'time preference' categories were derived, ranging from "patient" to "impatient" on an ordinal scale. The Infinium High Density Methylation Assay, MethylationEPIC (Illumina) was used to evaluate the status of 862,927 CpGs. Time preference and DNA methylation data were obtained for 1648 individuals. Four analyses were conducted, assessing the methylation patterns at single site resolution between patient and impatient individuals using two adjustment models. In this discovery cohort analysis, two CpG sites were identified with significantly different levels of methylation (p < 9 × 10-8) between the individuals allocated to the patient group and the remaining population following adjustment for covariates; cg08845621 within CD44 and cg18127619 within SEC23A. Neither of these genes have previously been linked to time preference. Epigenetic modifications have not previously been linked to time preference using a population cohort but they may represent important biomarkers of accumulated, complex determinants of this trait. Further analysis is warranted of both the top-ranked results and of DNA methylation as an important link between measurable biomarkers and health behaviours.
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Affiliation(s)
- Laura J Smyth
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University Belfast, BT12 6BJ Northern Ireland, United Kingdom
| | - Sharon M Cruise
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University Belfast, BT12 6BJ Northern Ireland, United Kingdom
| | - Jianjun Tang
- School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China.
| | - Ian Young
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University Belfast, BT12 6BJ Northern Ireland, United Kingdom
| | - Bernadette McGuinness
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University Belfast, BT12 6BJ Northern Ireland, United Kingdom
| | - Frank Kee
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University Belfast, BT12 6BJ Northern Ireland, United Kingdom
| | - Amy Jayne McKnight
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University Belfast, BT12 6BJ Northern Ireland, United Kingdom
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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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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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Fokias K, Dierckx L, Van de Voorde W, Bekaert B. Age determination through DNA methylation patterns in fingernails and toenails. Forensic Sci Int Genet 2023; 64:102846. [PMID: 36867979 DOI: 10.1016/j.fsigen.2023.102846] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/05/2022] [Accepted: 02/09/2023] [Indexed: 02/18/2023]
Abstract
Over the past decade, age prediction based on DNA methylation has become a vastly investigated topic; many age prediction models have been developed based on different DNAm markers and using various tissues. However, the potential of using nails to this end has not yet been explored. Their inherent resistance to decay and ease of sampling would offer an advantage in cases where post-mortem degradation poses challenges concerning sample collection and DNA-extraction. In the current study, clippings from both fingernails and toenails were collected from 108 living test subjects (age range: 0-96 years). The methylation status of 15 CpGs located in 4 previously established age-related markers (ASPA, EDARADD, PDE4C, ELOVL2) was investigated through pyrosequencing of bisulphite converted DNA. Significant dissimilarities in methylation levels were observed between all four limbs, hence both limb-specific age prediction models and prediction models combining multiple sampling locations were developed. When applied to their respective test sets, these models yielded a mean absolute deviation between predicted and chronological age ranging from 5.48 to 9.36 years when using ordinary least squares regression. In addition, the assay was tested on methylation data derived from 5 nail samples collected from deceased individuals, demonstrating its feasibility for application in post-mortem cases. In conclusion, this study provides the first proof that chronological age can be assessed through DNA methylation patterns in nails.
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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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Gim JA. Integrative Approaches of DNA Methylation Patterns According to Age, Sex and Longitudinal Changes. Curr Genomics 2023; 23:385-399. [PMID: 37920553 PMCID: PMC10173416 DOI: 10.2174/1389202924666221207100513] [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: 07/22/2022] [Revised: 10/04/2022] [Accepted: 11/04/2022] [Indexed: 12/12/2022] Open
Abstract
Background In humans, age-related DNA methylation has been studied in blood, tissues, buccal swabs, and fibroblasts, and changes in DNA methylation patterns according to age and sex have been detected. To date, approximately 137,000 samples have been analyzed from 14,000 studies, and the information has been uploaded to the NCBI GEO database. Methods A correlation between age and methylation level and longitudinal changes in methylation levels was revealed in both sexes. Here, 20 public datasets derived from whole blood were analyzed using the Illumina BeadChip. Batch effects with respect to the time differences were correlated. The overall change in the pattern was provided as the inverse of the coefficient of variation (COV). Results Of the 20 datasets, nine were from a longitudinal study. All data had age and sex as common variables. Comprehensive details of age-, sex-, and longitudinal change-based DNA methylation levels in the whole blood sample were elucidated in this study. ELOVL2 and FHL2 showed the maximum correlation between age and DNA methylation. The methylation patterns of genes related to mental health differed according to age. Age-correlated genes have been associated with malformations (anteverted nostril, craniofacial abnormalities, and depressed nasal bridge) and drug addiction (drug habituation and smoking). Conclusion Based on 20 public DNA methylation datasets, methylation levels according to age and longitudinal changes by sex were identified and visualized using an integrated approach. The results highlight the molecular mechanisms underlying the association of sex and biological age with changes in DNA methylation, and the importance of optimal genomic information management.
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Affiliation(s)
- Jeong-An Gim
- Medical Science Research Center, College of Medicine, Korea University Guro Hospital, Seoul 08308, Republic of Korea
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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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Jiang L, Zhang K, Wei X, Li J, Wang S, Wang Z, Zhou Y, Zha L, Luo H, Song F. Developing a male-specific age predictive model based on Y-CpGs for forensic analysis. Forensic Sci Int 2023; 343:111566. [PMID: 36640536 DOI: 10.1016/j.forsciint.2023.111566] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/22/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023]
Abstract
In forensic work, predicting the age of the criminal suspect or victim could provide beneficial clues for investigation. Epigenetic age estimation based on age-correlated DNA methylation has been one of the most widely studied methods of age estimation. However, almost all available epigenetic age prediction models are based on autosomal CpGs, which are only applicable to single-source DNA samples. In this study, we screened the available methylation data sets to identify loci with potential to meet the objectives of this study and then established a male-specific age prediction model based on 2 SNaPshot systems that contain 13 Y-CpGs and the mean absolute deviation (MAD) values were 4-6 years. The multiplex methylation SNaPshot systems and age-predictive model have been validated for sensitivity (the DNA input could be as low as 0.5 ng) and male specificity. They are supposed to have feasibility in forensic practice. In addition, it demonstrated that the method was also applicable to bloodstains, which were commonly found at crime scenes. The results showed good performance (the training set: R2 = 0.9341, MAD = 4.65 years; the test set: R2 = 0.8952, MAD = 5.73 years) in case investigation for predicting male age. For mixtures, when the male to female DNA ratio is 1:1, 1:10, the deviation between the actual age and the predicted age obtained by the model was less than 8 years, which offers great hope for future prediction of the age of males in mixtures and will be a powerful tool for special cases, such as sexual assault. Furthermore, the work provides a basis for the application of Y-CpGs in forensic science.
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Affiliation(s)
- Lanrui Jiang
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Ke Zhang
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China; Public Security Bureau of Zhengzhou City, Zhengzhou, Henan Province 450003, China
| | - Xiaowen Wei
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Jiahang Li
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Shuangshuang Wang
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Zefei Wang
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Yuxiang Zhou
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Lagabaiyila Zha
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No172. Tongzipo Road, Changsha, Hunan Province 410013, China
| | - Haibo Luo
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China.
| | - Feng Song
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China.
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Song M, Bai H, Zhang P, Zhou X, Ying B. Promising applications of human-derived saliva biomarker testing in clinical diagnostics. Int J Oral Sci 2023; 15:2. [PMID: 36596771 PMCID: PMC9810734 DOI: 10.1038/s41368-022-00209-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/23/2022] [Accepted: 11/03/2022] [Indexed: 01/05/2023] Open
Abstract
Saliva testing is a vital method for clinical applications, for its noninvasive features, richness in substances, and the huge amount. Due to its direct anatomical connection with oral, digestive, and endocrine systems, clinical usage of saliva testing for these diseases is promising. Furthermore, for other diseases that seeming to have no correlations with saliva, such as neurodegenerative diseases and psychological diseases, researchers also reckon saliva informative. Tremendous papers are being produced in this field. Updated summaries of recent literature give newcomers a shortcut to have a grasp of this topic. Here, we focused on recent research about saliva biomarkers that are derived from humans, not from other organisms. The review mostly addresses the proceedings from 2016 to 2022, to shed light on the promising usage of saliva testing in clinical diagnostics. We recap the recent advances following the category of different types of biomarkers, such as intracellular DNA, RNA, proteins and intercellular exosomes, cell-free DNA, to give a comprehensive impression of saliva biomarker testing.
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Affiliation(s)
- Mengyuan Song
- grid.13291.380000 0001 0807 1581Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Bai
- grid.13291.380000 0001 0807 1581Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ping Zhang
- grid.13291.380000 0001 0807 1581State Key Laboratory of Oral Diseases & Human Saliva Laboratory & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Xuedong Zhou
- grid.13291.380000 0001 0807 1581State Key Laboratory of Oral Diseases & Human Saliva Laboratory & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
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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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40
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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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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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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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A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes. iScience 2022; 25:105304. [PMID: 36304118 PMCID: PMC9593711 DOI: 10.1016/j.isci.2022.105304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/11/2022] [Accepted: 10/02/2022] [Indexed: 11/23/2022] Open
Abstract
Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms. 130 CpGs have been used by two or more aging clocks applied to human buccal tissue Common CpG genes are linked to the adaptive immune system and telomere maintenance Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock Several compounds associated with common CpG genes regulate lifespan in animals
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47
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Habibe JJ, Clemente-Olivo MP, Scheithauer TPM, Rampanelli E, Herrema H, Vos M, Mieremet A, Nieuwdorp M, van Raalte DH, Eringa EC, de Vries CJM. Glucose-mediated insulin secretion is improved in FHL2-deficient mice and elevated FHL2 expression in humans is associated with type 2 diabetes. Diabetologia 2022; 65:1721-1733. [PMID: 35802167 PMCID: PMC9477948 DOI: 10.1007/s00125-022-05750-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/09/2022] [Indexed: 02/05/2023]
Abstract
AIMS/HYPOTHESIS The general population is ageing, involving an enhanced incidence of chronic diseases such as type 2 diabetes. With ageing, DNA methylation of FHL2 increases, as well as expression of the four and a half LIM domains 2 (FHL2) protein in human pancreatic islets. We hypothesised that FHL2 is actively involved in glucose metabolism. METHODS Publicly available microarray datasets from human pancreatic islets were analysed for FHL2 expression. In FHL2-deficient mice, we studied glucose clearance and insulin secretion. Gene expression analysis and glucose-stimulated insulin secretion (GSIS) were determined in isolated murine FHL2-deficient islets to evaluate insulin-secretory capacity. Moreover, knockdown and overexpression of FHL2 were accomplished in MIN6 cells to delineate the underlying mechanism of FHL2 function. RESULTS Transcriptomics of human pancreatic islets revealed that individuals with elevated levels of HbA1c displayed increased FHL2 expression, which correlated negatively with insulin secretion pathways. In line with this observation, FHL2-deficient mice cleared glucose more efficiently than wild-type littermates through increased plasma insulin levels. Insulin sensitivity was comparable between these genotypes. Interestingly, pancreatic islets isolated from FHL2-deficient mice secreted more insulin in GSIS assays than wild-type mouse islets even though insulin content and islet size was similar. To support this observation, we demonstrated increased expression of the transcription factor crucial in insulin secretion, MAF BZIP transcription factor A (MafA), higher expression of GLUT2 and reduced expression of the adverse factor c-Jun in FHL2-deficient islets. The underlying mechanism of FHL2 was further delineated in MIN6 cells. FHL2-knockdown led to enhanced activation of forkhead box protein O1 (FOXO1) and its downstream genes such as Mafa and Pdx1 (encoding pancreatic and duodenal homeobox 1), as well as increased glucose uptake. On the other hand, FHL2 overexpression in MIN6 cells blocked GSIS, increased the formation of reactive oxygen species and increased c-Jun activity. CONCLUSIONS/INTERPRETATION Our data demonstrate that FHL2 deficiency improves insulin secretion from beta cells and improves glucose tolerance in mice. Given that FHL2 expression in humans increases with age and that high expression levels of FHL2 are associated with beta cell dysfunction, we propose that enhanced FHL2 expression in elderly individuals contributes to glucose intolerance and the development of type 2 diabetes. DATA AVAILABILITY The human islet microarray datasets used are publicly available and can be found on https://www.ncbi.nlm.nih.gov/geo/ .
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Affiliation(s)
- Jayron J Habibe
- Department of Medical Biochemistry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Diabetes and Metabolism, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Gastroenterology, Endocrinology and Metabolism, University of Amsterdam, Amsterdam, the Netherlands
- Department of Physiology, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Maria P Clemente-Olivo
- Department of Medical Biochemistry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Diabetes and Metabolism, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Gastroenterology, Endocrinology and Metabolism, University of Amsterdam, Amsterdam, the Netherlands
| | - Torsten P M Scheithauer
- Department of Experimental Vascular Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Elena Rampanelli
- Department of Experimental Vascular Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Hilde Herrema
- Department of Experimental Vascular Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Mariska Vos
- Department of Medical Biochemistry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Diabetes and Metabolism, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Gastroenterology, Endocrinology and Metabolism, University of Amsterdam, Amsterdam, the Netherlands
| | - Arnout Mieremet
- Department of Medical Biochemistry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Diabetes and Metabolism, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Gastroenterology, Endocrinology and Metabolism, University of Amsterdam, Amsterdam, the Netherlands
| | - Max Nieuwdorp
- Department of Experimental Vascular Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Daniel H van Raalte
- Department of Internal Medicine, Diabetes Center, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Etto C Eringa
- Department of Physiology, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Physiology, Cardiovascular Institute Maastricht, Maastricht, the Netherlands
| | - Carlie J M de Vries
- Department of Medical Biochemistry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Cardiovascular Sciences, Diabetes and Metabolism, University of Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Gastroenterology, Endocrinology and Metabolism, University of Amsterdam, Amsterdam, the Netherlands.
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Ogata A, Kondo M, Yoshikawa M, Okano M, Tsutsumi T, Aboshi H. Dental age estimation based on DNA methylation using real-time methylation-specific PCR. Forensic Sci Int 2022; 340:111445. [DOI: 10.1016/j.forsciint.2022.111445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/17/2022] [Accepted: 08/28/2022] [Indexed: 11/28/2022]
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Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites. Int J Legal Med 2022; 136:1655-1665. [PMID: 35819508 DOI: 10.1007/s00414-022-02865-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/04/2022] [Indexed: 10/17/2022]
Abstract
Age-related CpG sites (AR-CpGs) are currently the most promising biomarkers for forensic age estimation. In our previous studies, we first validated the age correlation of seven reported AR-CpGs in blood samples of Chinese Han population. Subsequently, we screened some good age predictors from blood samples of Chinese Han population, and built pyrosequencing-based age prediction models. However, it is still important to select a set of high-performance AR-CpGs in a specific racial group and establish a simple and efficient method for accurate age estimation for forensic purpose. In this study, eight AR-CpGs, namely chr6: 11,044,628 (ELOVL2), cg06639320 (FHL2), chr1: 207,823,723 (C1orf132), cg19283806 (CCDC102B), cg14361627 (KLF14), cg17740900 (SYNE2), cg07553761 (TRIM59), and cg26947034, were selected based on our previous studies, and a multiplex methylation SNaPshot assay was developed to investigate DNA methylation levels at these AR-CpGs in 529 blood samples (aged 2-82 years) from Han Chinese population. All selected CpG sites showed strong age correlation with the correlation coefficient (r) from 0.8363 to 0.9251. Multiple linear regression (MLR) and support vector regression (SVR) age prediction models were simultaneously established to fit change characteristics of DNA methylation levels of eight AR-CpGs with the age in 374 donors' blood samples. The MLR model enabled age prediction with R2 = 0.923, mean absolute error (MAE) = 3.52, while the SVR model enabled age prediction with R2 = 0.935, MAE = 2.88. One hundred fifty-five independent samples were used as a validation set to test the two models' performance, and the prediction MAE for the validation set was 3.71 and 3.34 for the MLR and SVR models, respectively. For the MLR and SVR models, the correct prediction rate at ± 5 years reached a high level of 79.35% and 83.23%, respectively. In general, these statistical parameters indicated that the SVR model outperformed the MLR model in age prediction of the Han Chinese population. In addition, our method provides sufficient sensitivity in forensic applications and allows for 100% efficiency when examining bloodstains kept in room conditions for up to 43 days. These results indicate that our multiplex methylation SNaPshot assay is a reliable, effective, and accurate method for age prediction in blood samples from the Chinese Han population.
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50
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Ye Z, Jiang L, Zhao M, Liu J, Dai H, Hou Y, Wang Z. Epigenome-wide screening of CpG markers to develop a multiplex methylation SNaPshot assay for age prediction. Leg Med (Tokyo) 2022; 59:102115. [PMID: 35810521 DOI: 10.1016/j.legalmed.2022.102115] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/05/2022] [Accepted: 07/02/2022] [Indexed: 11/30/2022]
Abstract
Age prediction can provide important information about the contributors of biological evidence left at crime scenes. DNA methylation has been regarded as the most promising age-predictive biomarker. Measuring themethylation level at the genome-wide scaleis an important step to screen specific markers for forensic age prediction. In present study, we screened out five age-related CpG sites from the public EPIC BeadChip data and evaluated them in a training set (115 blood) by multiplex methylation SNaPshot assay. Through full subset regression, the five markers were narrowed down to three, namely cg10501210 (C1orf132), cg16867657 (ELOVL2), and cg13108341 (DNAH9), of which the last one was a newly discovered age-related CpG site. An age prediction model was built based on these three markers, explaining 86.8% of the variation of age with a mean absolute deviation (MAD) of 4.038 years. Then, the multiplex methylation SNaPshot assay was adjusted according to the age prediction model. Considering that bloodstains are one of the most common biological samples in practical cases, three validation sets composed of 30 blood, 30 fresh bloodstains and 30 aged bloodstains were used for evaluation of the age prediction model. The MAD of each set was estimated as 4.734, 4.490, and 5.431 years, respectively, suggesting that our age prediction model was applicable for age prediction for blood and bloodstains in Chinese Han population of 11-71 age. In general, this study describes a workflow of screening CpG markers from public chip data and presents a 3-CpG markers model for forensic age prediction.
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Affiliation(s)
- Ziwei Ye
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, Beijing 100088, China
| | - Lirong Jiang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Mengyao Zhao
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Hao Dai
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, Beijing 100088, China.
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