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Jain N, Li JL, Tong L, Jasmine F, Kibriya MG, Demanelis K, Oliva M, Chen LS, Pierce BL. DNA methylation correlates of chronological age in diverse human tissue types. Epigenetics Chromatin 2024; 17:25. [PMID: 39118140 PMCID: PMC11308253 DOI: 10.1186/s13072-024-00546-6] [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: 02/26/2024] [Accepted: 06/15/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND While the association of chronological age with DNA methylation (DNAm) in whole blood has been extensively studied, the tissue-specificity of age-related DNAm changes remains an active area of research. Studies investigating the association of age with DNAm in tissues such as brain, skin, immune cells, fat, and liver have identified tissue-specific and non-specific effects, thus, motivating additional studies of diverse human tissue and cell types. RESULTS Here, we performed an epigenome-wide association study, leveraging DNAm data (Illumina EPIC array) from 961 tissue samples representing 9 tissue types (breast, lung, colon, ovary, prostate, skeletal muscle, testis, whole blood, and kidney) from the Genotype-Tissue Expression (GTEx) project. We identified age-associated CpG sites (false discovery rate < 0.05) in 8 tissues (all except skeletal muscle, n = 47). This included 162,002 unique hypermethylated and 90,626 hypomethylated CpG sites across all tissue types, with 130,137 (80%) hypermethylated CpGs and 74,703 (82%) hypomethylated CpG sites observed in a single tissue type. While the majority of age-associated CpG sites appeared tissue-specific, the patterns of enrichment among genomic features, such as chromatin states and CpG islands, were similar across most tissues, suggesting common mechanisms underlying cellular aging. Consistent with previous findings, we observed that hypermethylated CpG sites are enriched in regions with repressed polycomb signatures and CpG islands, while hypomethylated CpG sites preferentially occurred in non-CpG islands and enhancers. To gain insights into the functional effects of age-related DNAm changes, we assessed the correlation between DNAm and local gene expression changes to identify age-related expression quantitative trait methylation (age-eQTMs). We identified several age-eQTMs present in multiple tissue-types, including in the CDKN2A, HENMT1, and VCWE regions. CONCLUSION Overall, our findings will aid future efforts to develop biomarkers of aging and understand mechanisms of aging in diverse human tissue types.
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Affiliation(s)
- Niyati Jain
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, 60637, USA
| | - James L Li
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
- Interdisciplinary Scientist Training Program, University of Chicago, Chicago, IL, 60637, USA
| | - Lin Tong
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
| | - Farzana Jasmine
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, 60637, USA
| | - Muhammad G Kibriya
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
| | - Kathryn Demanelis
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA, 15232, USA
| | - Meritxell Oliva
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
- Genomics Research Center, AbbVie, North Chicago, IL, 60064, USA
| | - Lin S Chen
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA.
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, 60637, USA.
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Rosko AE, Elsaid MI, Woyach J, Islam N, Lepola N, Urrutia J, Christian LM, Presley C, Mims A, Burd CE. Determining the relationship of p16 INK4a and additional molecular markers of aging with clinical frailty in hematologic malignancy. J Cancer Surviv 2024; 18:1168-1178. [PMID: 38678524 PMCID: PMC11324703 DOI: 10.1007/s11764-024-01591-6] [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: 10/03/2023] [Accepted: 04/04/2024] [Indexed: 05/01/2024]
Abstract
PURPOSE Older adults with hematologic malignancies (HM) have unique challenges due to age and fitness. The primary aim of this pilot study was to benchmark the ability of multiple biomarkers of aging (p16, epigenetic clocks, T cell gene expression profiles, and T cell receptor excision circles (TREC) to identify frailty as measured by a clinical impairment index (I2) in patients with HM. METHODS 70 patients newly diagnosed with HM had peripheral blood T lymphocytes (PBTL) analyzed for p16INK4a expression using the OSU_Senescence Nanostring CodeSet. PBTL epigenetic age was measured using 7 epigenetic clocks, and TREC were quantified by qRT-PCR. A composite clinical impairment index (I2) was generated by combining values from 11 geriatric metrics (Independent Activities of Daily Living (iADL), physical health score, Short Physical Performance Battery (SPPB), Body Mass Index (BMI), Eastern Cooperative Oncology Group (ECOG) performance status, self-reported KPS, Blessed Orientation Memory Concentration (BOMC), polypharmacy, Mental Health Inventory (MHI)-17, Medical Outcomes Study (MOS) subscales). Clinical frailty was defined as a score of 7 or greater on the I2. RESULTS Age-adjusted p16INK4a was similar in newly diagnosed patients and healthy controls (p > 0.1). PBTL p16INK4a levels correlated positively with the Hannum [r = 0.35, 95% CI (0.09-0.75); p adj. = 0.04] and PhenoAge [r = 0.37, 95% CI (0.11-0.59); p adj. = 0.04] epigenetic clocks. The discrimination ability of the I2 model was calculated using the area under the receiver operating characteristic curve (AUC). After adjusting for chronologic age and disease group, baseline p16INK4a [AUC = 0.76, 95% CI (0.56-0.98); p = 0.01], Hannum [AUC = 0.70, 95% CI (0.54-0.85); p = 0.01], PhenoAge [AUC = 0.71, 95% CI (0.55-0.86); p = 0.01], and DunedinPACE [AUC = 0.73, 95% CI (0.57-0.88); p = < 0.01] measures showed the greatest potential to identify clinical frailty using the I2. CONCLUSIONS Our pilot data suggest that multiple blood-based aging biomarkers have potential to identify frailty in older adults with HM. IMPLICATIONS FOR CANCER SURVIVORS We developed the I2 index to quantify impairments across geriatric domains and discovered that PBTL p16, Hannum, PhenoAge, and DunedinPACE are promising indicators of frailty in HM.
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Affiliation(s)
- Ashley E Rosko
- Division of Hematology, The Ohio State University, Columbus, OH, USA.
- James Comprehensive Cancer Center, 300 West 10th Ave, Columbus, Ohio, 43210, United States.
| | - Mohamed I Elsaid
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, The Ohio State University, Columbus, OH, USA
| | - Jennifer Woyach
- Division of Hematology, The Ohio State University, Columbus, OH, USA
| | - Nowshin Islam
- Division of Hematology, The Ohio State University, Columbus, OH, USA
| | - Noah Lepola
- Departments of Molecular Genetics, Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA
| | - Jazmin Urrutia
- Departments of Molecular Genetics, Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA
| | - Lisa M Christian
- Department of Psychiatry and Behavioral Health, Institute for Behavioral Medicine Research, The Ohio State University, Columbus, OH, USA
| | - Carolyn Presley
- Division of Medical Oncology, The Ohio State University, Columbus, OH, USA
| | - Alice Mims
- Division of Hematology, The Ohio State University, Columbus, OH, USA
| | - Christin E Burd
- Departments of Molecular Genetics, Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA
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Martínez-Magaña JJ, Hurtado-Soriano J, Rivero-Segura NA, Montalvo-Ortiz JL, Garcia-delaTorre P, Becerril-Rojas K, Gomez-Verjan JC. Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology. Arch Med Res 2024; 55:103033. [PMID: 38955096 DOI: 10.1016/j.arcmed.2024.103033] [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: 01/10/2024] [Revised: 05/10/2024] [Accepted: 06/17/2024] [Indexed: 07/04/2024]
Abstract
Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.
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Affiliation(s)
- José Jaime Martínez-Magaña
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Post-Traumatic Stress Disorder, Clinical Neuroscience Division, West Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | | | | | - Janitza L Montalvo-Ortiz
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Post-Traumatic Stress Disorder, Clinical Neuroscience Division, West Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - Paola Garcia-delaTorre
- Unidad de Investigación Epidemiológica y en Servicios de Salud, Área de Envejecimiento, Centro Médico Nacional, Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
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Roig-Genoves JV, García-Giménez JL, Mena-Molla S. A miRNA-based epigenetic molecular clock for biological skin-age prediction. Arch Dermatol Res 2024; 316:326. [PMID: 38822910 PMCID: PMC11144124 DOI: 10.1007/s00403-024-03129-3] [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: 03/25/2024] [Revised: 04/27/2024] [Accepted: 05/02/2024] [Indexed: 06/03/2024]
Abstract
Skin aging is one of the visible characteristics of the aging process in humans. In recent years, different biological clocks have been generated based on protein or epigenetic markers, but few have focused on biological age in the skin. Arrest the aging process or even being able to restore an organism from an older to a younger stage is one of the main challenges in the last 20 years in biomedical research. We have implemented several machine learning models, including regression and classification algorithms, in order to create an epigenetic molecular clock based on miRNA expression profiles of healthy subjects to predict biological age-related to skin. Our best models are capable of classifying skin samples according to age groups (18-28; 29-39; 40-50; 51-60 or 61-83 years old) with an accuracy of 80% or predict age with a mean absolute error of 10.89 years using the expression levels of 1856 unique miRNAs. Our results suggest that this kind of epigenetic clocks arises as a promising tool with several applications in the pharmaco-cosmetic industry.
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Affiliation(s)
| | - José Luis García-Giménez
- Consortium Center for Biomedical Network Research on Rare Diseases (CIBERER), Institute of Health Carlos III, Valencia, 46010, Spain
- INCLIVA Health Research Institute, INCLIVA, Valencia, 46010, Spain
- EpiDisease S.L (Spin-off from the CIBER-ISCIII), Parc Científic de la Universitat de Valencia, Paterna, 46980, Spain
- Department of Physiology, Faculty of Pharmacy, University of Valencia, Burjassot, 46100, Spain
| | - Salvador Mena-Molla
- INCLIVA Health Research Institute, INCLIVA, Valencia, 46010, Spain.
- EpiDisease S.L (Spin-off from the CIBER-ISCIII), Parc Científic de la Universitat de Valencia, Paterna, 46980, Spain.
- Department of Physiology, Faculty of Pharmacy, University of Valencia, Burjassot, 46100, Spain.
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5
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Zhao J, Li H, Qu J, Zong X, Liu Y, Kuang Z, Wang H. A multi-organization epigenetic age prediction based on a channel attention perceptron networks. Front Genet 2024; 15:1393856. [PMID: 38725481 PMCID: PMC11080615 DOI: 10.3389/fgene.2024.1393856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/09/2024] [Indexed: 05/12/2024] Open
Abstract
DNA methylation indicates the individual's aging, so-called Epigenetic clocks, which will improve the research and diagnosis of aging diseases by investigating the correlation between methylation loci and human aging. Although this discovery has inspired many researchers to develop traditional computational methods to quantify the correlation and predict the chronological age, the performance bottleneck delayed access to the practical application. Since artificial intelligence technology brought great opportunities in research, we proposed a perceptron model integrating a channel attention mechanism named PerSEClock. The model was trained on 24,516 CpG loci that can utilize the samples from all types of methylation identification platforms and tested on 15 independent datasets against seven methylation-based age prediction methods. PerSEClock demonstrated the ability to assign varying weights to different CpG loci. This feature allows the model to enhance the weight of age-related loci while reducing the weight of irrelevant loci. The method is free to use for academics at www.dnamclock.com/#/original.
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Affiliation(s)
- Jian Zhao
- School of Computer Science and Technology, Changchun University, Changchun, China
| | - Haixia Li
- School of Computer Science and Technology, Changchun University, Changchun, China
| | - Jing Qu
- School of Computer Science and Technology, Jilin University, Changchun, China
- School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China
| | - Xizeng Zong
- Clinical Research Centre, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Yuchen Liu
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Zhejun Kuang
- School of Computer Science and Technology, Changchun University, Changchun, China
| | - Han Wang
- School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China
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Meng D, Zhang S, Huang Y, Mao K, Han JDJ. Application of AI in biological age prediction. Curr Opin Struct Biol 2024; 85:102777. [PMID: 38310737 DOI: 10.1016/j.sbi.2024.102777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/12/2023] [Accepted: 01/15/2024] [Indexed: 02/06/2024]
Abstract
The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.
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Affiliation(s)
- Dawei Meng
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Shiqiang Zhang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Yuanfang Huang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, 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.
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de Lima Camillo LP. pyaging: a Python-based compendium of GPU-optimized aging clocks. Bioinformatics 2024; 40:btae200. [PMID: 38603598 PMCID: PMC11058068 DOI: 10.1093/bioinformatics/btae200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 03/28/2024] [Accepted: 04/09/2024] [Indexed: 04/13/2024] Open
Abstract
MOTIVATION Aging is intricately linked to diseases and mortality. It is reflected in molecular changes across various tissues which can be leveraged for the development of biomarkers of aging using machine learning models, known as aging clocks. Despite advancements in the field, a significant challenge remains: the lack of robust, Python-based software tools for integrating and comparing these diverse models. This gap highlights the need for comprehensive solutions that can handle the complexity and variety of data in aging research. RESULTS To address this gap, I introduce pyaging, a comprehensive open-source Python package designed to facilitate aging research. pyaging harmonizes dozens of aging clocks, covering a range of molecular data types such as DNA methylation, transcriptomics, histone mark ChIP-Seq, and ATAC-Seq. The package is not limited to traditional model types; it features a diverse array, from linear and principal component models to neural networks and automatic relevance determination models. Thanks to a PyTorch-based backend that enables GPU acceleration, pyaging is capable of rapid inference, even when dealing with large datasets and complex models. In addition, the package's support for multi-species analysis extends its utility across various organisms, including humans, various mammals, and Caenorhabditis elegans. AVAILABILITY AND IMPLEMENTATION pyaging is accessible on GitHub, at https://github.com/rsinghlab/pyaging, and the distribution is available on PyPi, at https://pypi.org/project/pyaging/. The software is also archived on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10335011.
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Georgievskaya A, Tlyachev T, Kiselev K, Hillebrand G, Chekanov K, Danko D, Golodyaev A, Majmudar G. Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females. Exp Dermatol 2024; 33:e15045. [PMID: 38509744 DOI: 10.1111/exd.15045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/11/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024]
Abstract
Predicting a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace. Often, convolutional neural network (CNN) models are built using images of the face as biometric data. However, hands hold telltale signs of a person's age. To determine the utility of using only hand images in predicting CA, we developed two deep CNNs based on 1) dorsal hand images (H) and 2) frontal face images (F). Subjects (n = 1454) were Indian women, 20-80 years, across three geographic cohorts (Mumbai, New Delhi and Bangalore) and having a broad variation in skin tones. Images were randomised: 70% of F and 70% of H were used to train CNNs. The remaining 30% of F and H were retained for validation. CNN validation showed mean absolute error for predicting CA using F and H of 4.1 and 4.7 years, respectively. In both cases correlations of predicted and actual age were statistically significant (r(F) = 0.93, r(H) = 0.90). The CNNs for F and H were validated for dark and light skin tones. Finally, by blurring or accentuating visible features on specific regions of the hand and face, we identified those features that contributed to the CNN models. For the face, areas of the inner eye corner and around the mouth were most important for age prediction. For the hands, knuckle texture was a key driver for age prediction. Collectively, for AI estimates of CA, CNNs based solely on hand images are a viable alternative and comparable to CNNs based on facial images.
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Affiliation(s)
| | | | | | - Greg Hillebrand
- James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, Ohio, USA
| | | | | | | | - Gopa Majmudar
- James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, Ohio, USA
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9
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Prosz A, Pipek O, Börcsök J, Palla G, Szallasi Z, Spisak S, Csabai I. Biologically informed deep learning for explainable epigenetic clocks. Sci Rep 2024; 14:1306. [PMID: 38225268 PMCID: PMC10789766 DOI: 10.1038/s41598-023-50495-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 12/20/2023] [Indexed: 01/17/2024] Open
Abstract
Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.
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Affiliation(s)
- Aurel Prosz
- Danish Cancer Institute, Copenhagen, Denmark
| | - Orsolya Pipek
- Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Judit Börcsök
- Danish Cancer Institute, Copenhagen, Denmark
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
| | - Gergely Palla
- Department of Biological Physics, ELTE Eötvös Loránd University, Budapest, Hungary
- Health Services Management Training Centre, Semmelweis University, Budapest, Hungary
| | | | - Sandor Spisak
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary.
| | - István Csabai
- Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
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Abudahab S, Slattum PW, Price ET, McClay JL. Epigenetic regulation of drug metabolism in aging: utilizing epigenetics to optimize geriatric pharmacotherapy. Pharmacogenomics 2024; 25:41-54. [PMID: 38126340 PMCID: PMC10794944 DOI: 10.2217/pgs-2023-0199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
We explore the relationship between epigenetic aging and drug metabolism. We review current evidence for changes in drug metabolism in normal aging, followed by a description of how epigenetic modifications associated with age can regulate the expression and functionality of genes. In particular, we focus on the role of epigenome-wide studies of human and mouse liver in understanding these age-related processes with respect to xenobiotic processing. We highlight genes encoding drug metabolizing enzymes and transporters revealed to be affected by epigenetic aging in these studies. We conclude that substantial evidence exists for epigenetic aging impacting drug metabolism and transport genes, but more work is needed. We further highlight the promise of pharmacoepigenetics applied to enhancing drug safety in older adults.
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Affiliation(s)
- Sara Abudahab
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Patricia W Slattum
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
- Virginia Center on Aging, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Elvin T Price
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Joseph L McClay
- Department of Pharmacotherapy & Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
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11
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Kalyakulina A, Yusipov I, Moskalev A, Franceschi C, Ivanchenko M. eXplainable Artificial Intelligence (XAI) in aging clock models. Ageing Res Rev 2024; 93:102144. [PMID: 38030090 DOI: 10.1016/j.arr.2023.102144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/07/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023]
Abstract
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.
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Affiliation(s)
- Alena Kalyakulina
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
| | - Igor Yusipov
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Alexey Moskalev
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Claudio Franceschi
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Mikhail Ivanchenko
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
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12
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Shi L, Hai B, Kuang Z, Wang H, Zhao J. ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method. Bioengineering (Basel) 2023; 11:34. [PMID: 38247911 PMCID: PMC10813502 DOI: 10.3390/bioengineering11010034] [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: 11/08/2023] [Revised: 12/13/2023] [Accepted: 12/26/2023] [Indexed: 01/23/2024] Open
Abstract
Aging is a significant contributing factor to degenerative diseases such as cancer. The extent of DNA methylation in human cells indicates the aging process and screening for age-related methylation sites can be used to construct epigenetic clocks. Thereby, it can be a new aging-detecting marker for clinical diagnosis and treatments. Predicting the biological age of human individuals is conducive to the study of physical aging problems. Although many researchers have developed epigenetic clock prediction methods based on traditional machine learning and even deep learning, higher prediction accuracy is still required to match the clinical applications. Here, we proposed an epigenetic clock prediction method based on a Resnet neuro networks model named ResnetAge. The model accepts 22,278 CpG sites as a sample input, supporting both the Illumina 27K and 450K identification frameworks. It was trained using 32 public datasets containing multiple tissues such as whole blood, saliva, and mouth. The Mean Absolute Error (MAE) of the training set is 1.29 years, and the Median Absolute Deviation (MAD) is 0.98 years. The Mean Absolute Error (MAE) of the validation set is 3.24 years, and the Median Absolute Deviation (MAD) is 2.3 years. Our method has higher accuracy in age prediction in comparison with other methylation-based age prediction methods.
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Affiliation(s)
- Lijuan Shi
- Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, China; (L.S.); (B.H.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130022, China
| | - Boquan Hai
- Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, China; (L.S.); (B.H.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130022, China
| | - Zhejun Kuang
- Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, China; (L.S.); (B.H.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130022, China
| | - Han Wang
- The Institution of Computational Biology of Northeast Normal University, Changchun 130000, China;
| | - Jian Zhao
- Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, China; (L.S.); (B.H.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130022, China
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Yu D, Li M, Linghu G, Hu Y, Hajdarovic KH, Wang A, Singh R, Webb AE. CellBiAge: Improved single-cell age classification using data binarization. Cell Rep 2023; 42:113500. [PMID: 38032797 PMCID: PMC10791072 DOI: 10.1016/j.celrep.2023.113500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 10/20/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods.
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Affiliation(s)
- Doudou Yu
- Molecular Biology, Cell Biology, and Biochemistry Graduate Program, Brown University, Providence, RI 02912, USA; Data Science Institute, Brown University, Providence, RI 02912, USA
| | - Manlin Li
- Data Science Institute, Brown University, Providence, RI 02912, USA
| | - Guanjie Linghu
- Data Science Institute, Brown University, Providence, RI 02912, USA
| | - Yihuan Hu
- Data Science Institute, Brown University, Providence, RI 02912, USA
| | | | - An Wang
- Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, RI 02912, USA; Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.
| | - Ashley E Webb
- Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI 02912, USA; Center on the Biology of Aging, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA; Center for Translational Neuroscience, Brown University, Providence, RI 02912, USA.
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14
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Pathare ADS, Loid M, Saare M, Gidlöf SB, Zamani Esteki M, Acharya G, Peters M, Salumets A. Endometrial receptivity in women of advanced age: an underrated factor in infertility. Hum Reprod Update 2023; 29:773-793. [PMID: 37468438 PMCID: PMC10628506 DOI: 10.1093/humupd/dmad019] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/24/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Modern lifestyle has led to an increase in the age at conception. Advanced age is one of the critical risk factors for female-related infertility. It is well known that maternal age positively correlates with the deterioration of oocyte quality and chromosomal abnormalities in oocytes and embryos. The effect of age on endometrial function may be an equally important factor influencing implantation rate, pregnancy rate, and overall female fertility. However, there are only a few published studies on this topic, suggesting that this area has been under-explored. Improving our knowledge of endometrial aging from the biological (cellular, molecular, histological) and clinical perspectives would broaden our understanding of the risks of age-related female infertility. OBJECTIVE AND RATIONALE The objective of this narrative review is to critically evaluate the existing literature on endometrial aging with a focus on synthesizing the evidence for the impact of endometrial aging on conception and pregnancy success. This would provide insights into existing gaps in the clinical application of research findings and promote the development of treatment options in this field. SEARCH METHODS The review was prepared using PubMed (Medline) until February 2023 with the keywords such as 'endometrial aging', 'receptivity', 'decidualization', 'hormone', 'senescence', 'cellular', 'molecular', 'methylation', 'biological age', 'epigenetic', 'oocyte recipient', 'oocyte donation', 'embryo transfer', and 'pregnancy rate'. Articles in a language other than English were excluded. OUTCOMES In the aging endometrium, alterations occur at the molecular, cellular, and histological levels suggesting that aging has a negative effect on endometrial biology and may impair endometrial receptivity. Additionally, advanced age influences cellular senescence, which plays an important role during the initial phase of implantation and is a major obstacle in the development of suitable senolytic agents for endometrial aging. Aging is also accountable for chronic conditions associated with inflammaging, which eventually can lead to increased pro-inflammation and tissue fibrosis. Furthermore, advanced age influences epigenetic regulation in the endometrium, thus altering the relation between its epigenetic and chronological age. The studies in oocyte donation cycles to determine the effect of age on endometrial receptivity with respect to the rates of implantation, clinical pregnancy, miscarriage, and live birth have revealed contradictory inferences indicating the need for future research on the mechanisms and corresponding causal effects of women's age on endometrial receptivity. WIDER IMPLICATIONS Increasing age can be accountable for female infertility and IVF failures. Based on the complied observations and synthesized conclusions in this review, advanced age has been shown to have a negative impact on endometrial functioning. This information can provide recommendations for future research focusing on molecular mechanisms of age-related cellular senescence, cellular composition, and transcriptomic changes in relation to endometrial aging. Additionally, further prospective research is needed to explore newly emerging therapeutic options, such as the senolytic agents that can target endometrial aging without affecting decidualization. Moreover, clinical trial protocols, focusing on oocyte donation cycles, would be beneficial in understanding the direct clinical implications of endometrial aging on pregnancy outcomes.
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Affiliation(s)
- Amruta D S Pathare
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Marina Loid
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
- Competence Centre on Health Technologies, Tartu, Estonia
| | - Merli Saare
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
- Competence Centre on Health Technologies, Tartu, Estonia
| | - Sebastian Brusell Gidlöf
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
- Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Masoud Zamani Esteki
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Department of Genetics and Cell Biology, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Ganesh Acharya
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
- Department of Clinical Medicine, Women’s Health and Perinatology Research Group, UiT The Arctic University of Norway, Tromsø, Norway
| | - Maire Peters
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
- Competence Centre on Health Technologies, Tartu, Estonia
| | - Andres Salumets
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
- Competence Centre on Health Technologies, Tartu, Estonia
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Klose D, Needhamsen M, Ringh MV, Hagemann-Jensen M, Jagodic M, Kular L. Smoking affects epigenetic ageing of lung bronchoalveolar lavage cells in Multiple Sclerosis. Mult Scler Relat Disord 2023; 79:104991. [PMID: 37708820 DOI: 10.1016/j.msard.2023.104991] [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: 04/28/2023] [Revised: 06/18/2023] [Accepted: 09/02/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND A compelling body of evidence implicates cigarette smoking and lung inflammation in Multiple Sclerosis (MS) susceptibility and progression. Previous studies have reported epigenetic age (DNAm age) acceleration in blood immune cells and in glial cells of people with MS (pwMS) compared to healthy controls (HC). OBJECTIVES We aimed to examine biological ageing in lung immune cells in the context of MS and smoking. METHODS We analyzed age acceleration residuals in lung bronchoalveolar lavage (BAL) cells, constituted of mainly alveolar macrophages, from 17 pwMS and 22 HC in relation to smoking using eight DNA methylation-based clocks, namely AltumAge, Horvath, GrimAge, PhenoAge, Zhang, SkinBlood, Hannum, Monocyte clock as well as two RNA-based clocks, which capture different aspects of biological ageing. RESULTS After adjustment for covariates, five epigenetic clocks showed significant differences between the groups. Four of them, Horvath (Padj = 0.028), GrimAge (Padj = 4.28 × 10-7), SkinBlood (Padj = 0.001) and Zhang (Padj = 0.02), uncovered the sole effect of smoking on ageing estimates, irrespective of the clinical group. The Horvath, SkinBlood and Zhang clocks showed a negative impact of smoking while GrimAge detected smoking-associated age acceleration in BAL cells. On the contrary, the AltumAge clock revealed differences between pwMS and HC and indicated that, in the absence of smoking, BAL cells of pwMS were epigenetically 5.4 years older compared to HC (Padj = 0.028). Smoking further affected epigenetic ageing in BAL cells of pwMS specifically as non-smoking pwMS exhibited a 10.2-year AltumAge acceleration compared to pwMS smokers (Padj = 0.0049). Of note, blood-derived monocytes did not show any MS-specific or smoking-related AltumAge differences. The difference between BAL cells of pwMS smokers and non-smokers was attributable to the differential methylation of 114 AltumAge-CpGs (Padj < 0.05) affecting genes involved in innate immune processes such as cytokine production, defense response and cell motility. These changes functionally translated into transcriptional differences in BAL cells between pwMS smokers and non-smokers. CONCLUSIONS BAL cells of pwMS display inflammation-related and smoking-dependent changes associated to epigenetic ageing captured by the AltumAge clock. Future studies examining potential confounders, such as the distribution of distinct BAL myeloid cell types in pwMS compared to control individuals in relation to smoking may clarify the varying performance and DNAm age estimations among epigenetic clocks.
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Affiliation(s)
- Dennis Klose
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Maria Needhamsen
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Mikael V Ringh
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | | | - Maja Jagodic
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Lara Kular
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden.
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Statsenko Y, Kuznetsov NV, Morozova D, Liaonchyk K, Simiyu GL, Smetanina D, Kashapov A, Meribout S, Gorkom KNV, Hamoudi R, Ismail F, Ansari SA, Emerald BS, Ljubisavljevic M. Reappraisal of the Concept of Accelerated Aging in Neurodegeneration and Beyond. Cells 2023; 12:2451. [PMID: 37887295 PMCID: PMC10605227 DOI: 10.3390/cells12202451] [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: 08/04/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Genetic and epigenetic changes, oxidative stress and inflammation influence the rate of aging, which diseases, lifestyle and environmental factors can further accelerate. In accelerated aging (AA), the biological age exceeds the chronological age. OBJECTIVE The objective of this study is to reappraise the AA concept critically, considering its weaknesses and limitations. METHODS We reviewed more than 300 recent articles dealing with the physiology of brain aging and neurodegeneration pathophysiology. RESULTS (1) Application of the AA concept to individual organs outside the brain is challenging as organs of different systems age at different rates. (2) There is a need to consider the deceleration of aging due to the potential use of the individual structure-functional reserves. The latter can be restored by pharmacological and/or cognitive therapy, environment, etc. (3) The AA concept lacks both standardised terminology and methodology. (4) Changes in specific molecular biomarkers (MBM) reflect aging-related processes; however, numerous MBM candidates should be validated to consolidate the AA theory. (5) The exact nature of many potential causal factors, biological outcomes and interactions between the former and the latter remain largely unclear. CONCLUSIONS Although AA is commonly recognised as a perspective theory, it still suffers from a number of gaps and limitations that assume the necessity for an updated AA concept.
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Affiliation(s)
- Yauhen Statsenko
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Big Data Analytic Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nik V. Kuznetsov
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Daria Morozova
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Katsiaryna Liaonchyk
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Gillian Lylian Simiyu
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Darya Smetanina
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Aidar Kashapov
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Sarah Meribout
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Klaus Neidl-Van Gorkom
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Rifat Hamoudi
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London NW3 2PS, UK
| | - Fatima Ismail
- Department of Pediatrics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
| | - Suraiya Anjum Ansari
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Biochemistry and Molecular Biology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Bright Starling Emerald
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Anatomy, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Milos Ljubisavljevic
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Physiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
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Hughes BK, Wallis R, Bishop CL. Yearning for machine learning: applications for the classification and characterisation of senescence. Cell Tissue Res 2023; 394:1-16. [PMID: 37016180 PMCID: PMC10558380 DOI: 10.1007/s00441-023-03768-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/05/2023] [Indexed: 04/06/2023]
Abstract
Senescence is a widely appreciated tumour suppressive mechanism, which acts as a barrier to cancer development by arresting cell cycle progression in response to harmful stimuli. However, senescent cell accumulation becomes deleterious in aging and contributes to a wide range of age-related pathologies. Furthermore, senescence has beneficial roles and is associated with a growing list of normal physiological processes including wound healing and embryonic development. Therefore, the biological role of senescent cells has become increasingly nuanced and complex. The emergence of sophisticated, next-generation profiling technologies, such as single-cell RNA sequencing, has accelerated our understanding of the heterogeneity of senescence, with distinct final cell states emerging within models as well as between cell types and tissues. In order to explore data sets of increasing size and complexity, the senescence field has begun to employ machine learning (ML) methodologies to probe these intricacies. Most notably, ML has been used to aid the classification of cells as senescent, as well as to characterise the final senescence phenotypes. Here, we provide a background to the principles of ML tasks, as well as some of the most commonly used methodologies from both traditional and deep ML. We focus on the application of these within the context of senescence research, by addressing the utility of ML for the analysis of data from different laboratory technologies (microscopy, transcriptomics, proteomics, methylomics), as well as the potential within senolytic drug discovery. Together, we aim to highlight both the progress and potential for the application of ML within senescence research.
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Affiliation(s)
- Bethany K Hughes
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Ryan Wallis
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Cleo L Bishop
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK.
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18
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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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19
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Urban A, Sidorenko D, Zagirova D, Kozlova E, Kalashnikov A, Pushkov S, Naumov V, Sarkisova V, Leung GHD, Leung HW, Pun FW, Ozerov IV, Aliper A, Ren F, Zhavoronkov A. Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery. Aging (Albany NY) 2023; 15:4649-4666. [PMID: 37315204 PMCID: PMC10292881 DOI: 10.18632/aging.204788] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/24/2023] [Indexed: 06/16/2023]
Abstract
Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.
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Affiliation(s)
- Anatoly Urban
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | | | - Diana Zagirova
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | | | | | - Stefan Pushkov
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | | | | | | | - Hoi Wing Leung
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | - Frank W. Pun
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | - Ivan V. Ozerov
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | - Alex Aliper
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
- Insilico Medicine, Masdar City, United Arab Emirates
| | - Feng Ren
- Insilico Medicine, Shanghai, China
| | - Alex Zhavoronkov
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
- Insilico Medicine, Masdar City, United Arab Emirates
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20
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Doherty T, Dempster E, Hannon E, Mill J, Poulton R, Corcoran D, Sugden K, Williams B, Caspi A, Moffitt TE, Delany SJ, Murphy TM. A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator. BMC Bioinformatics 2023; 24:178. [PMID: 37127563 PMCID: PMC10152624 DOI: 10.1186/s12859-023-05282-4] [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: 03/29/2022] [Accepted: 04/11/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons. RESULTS We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general-suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities. CONCLUSIONS The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.
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Affiliation(s)
- Trevor Doherty
- School of Biological, Health and Sports Sciences, Technological University Dublin, Dublin, Ireland.
- SFI Centre for Research Training in Machine Learning, Technological University Dublin, Dublin, Ireland.
| | - Emma Dempster
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Richie Poulton
- Department of Psychology, University of Otago, Dunedin, 9016, New Zealand
| | - David Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, 27708, USA
| | - Karen Sugden
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Ben Williams
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Avshalom Caspi
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Terrie E Moffitt
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Sarah Jane Delany
- School of Computer Science, Technological University Dublin, Dublin, Ireland
| | - Therese M Murphy
- School of Biological, Health and Sports Sciences, Technological University Dublin, Dublin, Ireland
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21
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Martínez-Magaña JJ, Krystal JH, Girgenti MJ, Núnez-Ríos DL, Nagamatsu ST, Andrade-Brito DE, Montalvo-Ortiz JL. Decoding the role of transcriptomic clocks in the human prefrontal cortex. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.19.23288765. [PMID: 37163025 PMCID: PMC10168432 DOI: 10.1101/2023.04.19.23288765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk.
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Affiliation(s)
- José J. Martínez-Magaña
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
| | - John H. Krystal
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
- Psychiatry Service, VA Connecticut Health Care System, West Haven, CT, USA
| | - Matthew J. Girgenti
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
| | - Diana L. Núnez-Ríos
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
| | - Sheila T. Nagamatsu
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
| | - Diego E. Andrade-Brito
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
| | | | - Janitza L. Montalvo-Ortiz
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
- Psychiatry Service, VA Connecticut Health Care System, West Haven, CT, USA
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22
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Silveira PP, Meaney MJ. Examining the biological mechanisms of human mental disorders resulting from gene-environment interdependence using novel functional genomic approaches. Neurobiol Dis 2023; 178:106008. [PMID: 36690304 DOI: 10.1016/j.nbd.2023.106008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/30/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023] Open
Abstract
We explore how functional genomics approaches that integrate datasets from human and non-human model systems can improve our understanding of the effect of gene-environment interplay on the risk for mental disorders. We start by briefly defining the G-E paradigm and its challenges and then discuss the different levels of regulation of gene expression and the corresponding data existing in humans (genome wide genotyping, transcriptomics, DNA methylation, chromatin modifications, chromosome conformational changes, non-coding RNAs, proteomics and metabolomics), discussing novel approaches to the application of these data in the study of the origins of mental health. Finally, we discuss the multilevel integration of diverse types of data. Advance in the use of functional genomics in the context of a G-E perspective improves the detection of vulnerabilities, informing the development of preventive and therapeutic interventions.
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Affiliation(s)
- Patrícia Pelufo Silveira
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
| | - Michael J Meaney
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore; Brain - Body Initiative, Agency for Science, Technology and Research (ASTAR), Singapore.
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23
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Bernabeu E, McCartney DL, Gadd DA, Hillary RF, Lu AT, Murphy L, Wrobel N, Campbell A, Harris SE, Liewald D, Hayward C, Sudlow C, Cox SR, Evans KL, Horvath S, McIntosh AM, Robinson MR, Vallejos CA, Marioni RE. Refining epigenetic prediction of chronological and biological age. Genome Med 2023; 15:12. [PMID: 36855161 PMCID: PMC9976489 DOI: 10.1186/s13073-023-01161-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/06/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. METHODS First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women's Health Initiative study). RESULTS Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10-60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. CONCLUSIONS The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.
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Affiliation(s)
- Elena Bernabeu
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Altos Labs, San Diego, USA
| | - Lee Murphy
- Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK
| | - Nicola Wrobel
- Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Sarah E Harris
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - David Liewald
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Cathie Sudlow
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- BHF Data Science Centre, Health Data Research UK, London, UK
- Edinburgh Medical School, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Altos Labs, San Diego, USA
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | | | - Catalina A Vallejos
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- The Alan Turing Institute, London, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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24
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Sugino RP, Ohira M, Mansai SP, Kamijo T. Comparative epigenomics by machine learning approach for neuroblastoma. BMC Genomics 2022; 23:852. [PMID: 36572864 PMCID: PMC9793522 DOI: 10.1186/s12864-022-09061-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 12/02/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Neuroblastoma (NB) is the second most common pediatric solid tumor. Because the number of genetic mutations found in tumors are small, even in some patients with unfavorable NB, epigenetic variation is expected to play an important role in NB progression. DNA methylation is a major epigenetic mechanism, and its relationship with NB prognosis has been a concern. One limitation with the analysis of variation in DNA methylation is the lack of a suitable analytical model. Therefore, in this study, we performed a random forest (RF) analysis of the DNA methylome data of NB from multiple databases. RESULTS RF is a popular machine learning model owing to its simplicity, intuitiveness, and computational cost. RF analysis identified novel intermediate-risk patient groups with characteristic DNA methylation patterns within the low-risk group. Feature selection analysis based on probe annotation revealed that enhancer-annotated regions had strong predictive power, particularly for MYCN-amplified NBs. We developed a gene-based analytical model to identify candidate genes related to disease progression, such as PRDM8 and FAM13A-AS1. RF analysis revealed sufficient predictive power compared to other machine learning models. CONCLUSIONS RF is a useful tool for DNA methylome analysis in cancer epigenetic studies, and has potential to identify a novel cancer-related genes.
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Affiliation(s)
- Ryuichi P. Sugino
- grid.416695.90000 0000 8855 274XResearch Institute for Clinical Oncology, Saitama Cancer Center, Ina, Saitama, 362-0806 Japan
| | - Miki Ohira
- grid.416695.90000 0000 8855 274XResearch Institute for Clinical Oncology, Saitama Cancer Center, Ina, Saitama, 362-0806 Japan
| | - Sayaka P. Mansai
- grid.416695.90000 0000 8855 274XResearch Institute for Clinical Oncology, Saitama Cancer Center, Ina, Saitama, 362-0806 Japan
| | - Takehiko Kamijo
- grid.416695.90000 0000 8855 274XResearch Institute for Clinical Oncology, Saitama Cancer Center, Ina, Saitama, 362-0806 Japan ,grid.263023.60000 0001 0703 3735Laboratory of Tumor Molecular Biology, Department of Graduate School of Science and Engineering, Saitama University, Kita-Urawa, Saitama, Japan
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25
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Deryabin PI, Borodkina AV. Epigenetic clocks provide clues to the mystery of uterine ageing. Hum Reprod Update 2022; 29:259-271. [PMID: 36515535 DOI: 10.1093/humupd/dmac042] [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: 08/12/2022] [Revised: 11/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Rising maternal ages and age-related fertility decline are a global challenge for modern reproductive medicine. Clinicians and researchers pay specific attention to ovarian ageing and hormonal insufficiency in this regard. However, uterine ageing is often left out of the picture, with the majority of reproductive clinicians being close to unanimous on the absence of age-related functional decline in the uterine tissues. Therefore, most existing techniques to treat an age-related decline in implantation rates are based primarily on hormonal supplementation and oocyte donation. Solving the issue of uterine ageing might lead to an adjustment to these methods. OBJECTIVE AND RATIONALE A focus on uterine ageing and the possibility of slowing it emerged with the development of the information theory of ageing, which identifies genomic instability and erosion of the epigenetic landscape as important drivers of age-related decline in the functionality of most cells and tissues. Age-related smoothing of this landscape and a decline in tissue function can be assessed by measuring the ticking of epigenetic clocks. Within this review, we explore whether the uterus experiences age-related alterations using this elegant approach. We analyse existing data on epigenetic clocks in the endometrium, highlight approaches to improve the accuracy of the clocks in this cycling tissue, speculate on the endometrial pathologies whose progression might be predicted by the altered speed of epigenetic clocks and discuss the possibilities of slowing down the ticking of these clocks. SEARCH METHODS Data for this review were identified by searches of Medline, PubMed and Google Scholar. References from relevant articles using the search terms 'ageing', 'maternal age', 'female reproduction', 'uterus', 'endometrium', 'implantation', 'decidualization', 'epigenetic clock', 'biological age', 'DNA methylation', 'fertility' and 'infertility' were selected. A total of 95 articles published in English between 1985 and 2022 were included, six of which describe the use of the epigenetic clock to evaluate uterine/endometrium ageing. OUTCOMES Application of the Horvath and DNAm PhenoAge epigenetic clocks demonstrated a poor correlation with chronological age in the endometrium. Several approaches were suggested to enhance the predictive power of epigenetic clocks for the endometrium. The first was to increase the number of samples in the training dataset, as for the Zang clock, or to use more sophisticated clock-building algorithms, as for the AltumAge clock. The second method is to adjust the clocks according to the dynamic nature of the endometrium. Using either approach revealed a strong correlation with chronological age in the endometrium, providing solid evidence for age-related functional decline in this tissue. Furthermore, age acceleration/deceleration, as estimated by epigenetic clocks, might be a promising tool to predict or to gain insights into the origin of various endometrial pathologies, including recurrent implantation failure, cancer and endometriosis. Finally, there are several strategies to slow down or even reverse epigenetic clocks that might be applied to reduce the risk of age-related uterine impairments. WIDER IMPLICATIONS The uterine factor should be considered, along with ovarian issues, to correct for the decline in female fertility with age. Epigenetic clocks can be tested to gain a deeper understanding of various endometrial disorders.
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Affiliation(s)
- Pavel I Deryabin
- Mechanisms of Cellular Senescence Group, Institute of Cytology of the Russian Academy of Sciences, Saint-Petersburg, Russia
| | - Aleksandra V Borodkina
- Mechanisms of Cellular Senescence Group, Institute of Cytology of the Russian Academy of Sciences, Saint-Petersburg, Russia
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26
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Turpin M, Salbert G. 5-methylcytosine turnover: Mechanisms and therapeutic implications in cancer. Front Mol Biosci 2022; 9:976862. [PMID: 36060265 PMCID: PMC9428128 DOI: 10.3389/fmolb.2022.976862] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/26/2022] [Indexed: 12/04/2022] Open
Abstract
DNA methylation at the fifth position of cytosine (5mC) is one of the most studied epigenetic mechanisms essential for the control of gene expression and for many other biological processes including genomic imprinting, X chromosome inactivation and genome stability. Over the last years, accumulating evidence suggest that DNA methylation is a highly dynamic mechanism driven by a balance between methylation by DNMTs and TET-mediated demethylation processes. However, one of the main challenges is to understand the dynamics underlying steady state DNA methylation levels. In this review article, we give an overview of the latest advances highlighting DNA methylation as a dynamic cycling process with a continuous turnover of cytosine modifications. We describe the cooperative actions of DNMT and TET enzymes which combine with many additional parameters including chromatin environment and protein partners to govern 5mC turnover. We also discuss how mathematical models can be used to address variable methylation levels during development and explain cell-type epigenetic heterogeneity locally but also at the genome scale. Finally, we review the therapeutic implications of these discoveries with the use of both epigenetic clocks as predictors and the development of epidrugs that target the DNA methylation/demethylation machinery. Together, these discoveries unveil with unprecedented detail how dynamic is DNA methylation during development, underlying the establishment of heterogeneous DNA methylation landscapes which could be altered in aging, diseases and cancer.
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Affiliation(s)
- Marion Turpin
- Sp@rte Team, UMR6290 CNRS, Institute of Genetics and Development of Rennes, Rennes, France
- University of Rennes 1, Rennes, France
| | - Gilles Salbert
- Sp@rte Team, UMR6290 CNRS, Institute of Genetics and Development of Rennes, Rennes, France
- University of Rennes 1, Rennes, France
- *Correspondence: Gilles Salbert,
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