scholarly journals Age-associated epigenetic change in chimpanzees and humans

2020 ◽  
Vol 375 (1811) ◽  
pp. 20190616
Author(s):  
Elaine E. Guevara ◽  
Richard R. Lawler ◽  
Nicky Staes ◽  
Cassandra M. White ◽  
Chet C. Sherwood ◽  
...  
Keyword(s):  
Genomic Sequence ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Epigenetic Change ◽  
Theme Issue ◽  
Absolute Deviation ◽  
Test Dataset ◽  
Genomic Distribution ◽  
Age Related ◽  
Genome Wide

Methylation levels have been shown to change with age at sites across the human genome. Change at some of these sites is so consistent across individuals that it can be used as an ‘epigenetic clock’ to predict an individual's chronological age to within a few years. Here, we examined how the pattern of epigenetic ageing in chimpanzees compares with humans. We profiled genome-wide blood methylation levels by microarray for 113 samples from 83 chimpanzees aged 1–58 years (26 chimpanzees were sampled at multiple ages during their lifespan). Many sites (greater than 65 000) showed significant change in methylation with age and around one-third (32%) of these overlap with sites showing significant age-related change in humans. At over 80% of sites showing age-related change in both species, chimpanzees displayed a significantly faster rate of age-related change in methylation than humans. We also built a chimpanzee-specific epigenetic clock that predicted age in our test dataset with a median absolute deviation from known age of only 2.4 years. However, our chimpanzee clock showed little overlap with previously constructed human clocks. Methylation at CpGs comprising our chimpanzee clock showed moderate heritability. Although the use of a human microarray for profiling chimpanzees biases our results towards regions with shared genomic sequence between the species, nevertheless, our results indicate that there is considerable conservation in epigenetic ageing between chimpanzees and humans, but also substantial divergence in both rate and genomic distribution of ageing-associated sites. This article is part of the theme issue ‘Evolution of the primate ageing process'.

scholarly journals Exploring Epigenetic Age in Response to Intensive Relaxing Training: A Pilot Study to Slow Down Biological Age

2019 ◽  
Vol 16 (17) ◽  
pp. 3074 ◽  
Author(s):  
Pavanello ◽  
Campisi ◽  
Tona ◽  
Lin ◽  
Iliceto
Keyword(s):  
Myocardial Infarction ◽  
Age Estimation ◽  
Healthy Subjects ◽  
Molecular Mechanisms ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Epigenetic Clock ◽  
Age Related ◽  
Epigenetic Age

DNA methylation (DNAm) is an emerging estimator of biological aging, i.e., the often-defined “epigenetic clock”, with a unique accuracy for chronological age estimation (DNAmAge). In this pilot longitudinal study, we examine the hypothesis that intensive relaxing training of 60 days in patients after myocardial infarction and in healthy subjects may influence leucocyte DNAmAge by turning back the epigenetic clock. Moreover, we compare DNAmAge with another mechanism of biological age, leucocyte telomere length (LTL) and telomerase. DNAmAge is reduced after training in healthy subjects (p = 0.053), but not in patients. LTL is preserved after intervention in healthy subjects, while it continues to decrease in patients (p = 0.051). The conventional negative correlation between LTL and chronological age becomes positive after training in both patients (p < 0.01) and healthy subjects (p < 0.05). In our subjects, DNAmAge is not associated with LTL. Our findings would suggest that intensive relaxing practices influence different aging molecular mechanisms, i.e., DNAmAge and LTL, with a rejuvenating effect. Our study reveals that DNAmAge may represent an accurate tool to measure the effectiveness of lifestyle-based interventions in the prevention of age-related diseases.

scholarly journals Epigenetic clock and methylation studies in the rhesus macaque

GeroScience ◽  
2021 ◽  
Author(s):  
Steve Horvath ◽  
Joseph A. Zoller ◽  
Amin Haghani ◽  
Anna J. Jasinska ◽  
Ken Raj ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Rhesus Macaque ◽  
Mammalian Species ◽  
Vervet Monkey ◽  
Chronological Age ◽  
Biological Research ◽  
Epigenetic Clock ◽  
Methylation Array ◽  
Tissue Samples ◽  
Age Related

AbstractMethylation levels at specific CpG positions in the genome have been used to develop accurate estimators of chronological age in humans, mice, and other species. Although epigenetic clocks are generally species-specific, the principles underpinning them appear to be conserved at least across the mammalian class. This is exemplified by the successful development of epigenetic clocks for mice and several other mammalian species. Here, we describe epigenetic clocks for the rhesus macaque (Macaca mulatta), the most widely used nonhuman primate in biological research. Using a custom methylation array (HorvathMammalMethylChip40), we profiled n = 281 tissue samples (blood, skin, adipose, kidney, liver, lung, muscle, and cerebral cortex). From these data, we generated five epigenetic clocks for macaques. These clocks differ with regard to applicability to different tissue types (pan-tissue, blood, skin), species (macaque only or both humans and macaques), and measure of age (chronological age versus relative age). Additionally, the age-based human-macaque clock exhibits a high age correlation (R = 0.89) with the vervet monkey (Chlorocebus sabaeus), another Old World species. Four CpGs within the KLF14 promoter were consistently altered with age in four tissues (adipose, blood, cerebral cortex, skin). Future studies will be needed to evaluate whether these epigenetic clocks predict age-related conditions in the rhesus macaque.

scholarly journals Functional genomics analysis identifies T and NK cell activation as a driver of epigenetic clock progression

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Thomas H. Jonkman ◽  
Koen F. Dekkers ◽  
Roderick C. Slieker ◽  
Crystal D. Grant ◽  
M. Arfan Ikram ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Cell Activation ◽  
Nk Cell ◽  
Cell Types ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Cpg Dinucleotides ◽  
Age Related ◽  
Gene Sets ◽  
Potential Biomarker

Abstract Background Epigenetic clocks use DNA methylation (DNAm) levels of specific sets of CpG dinucleotides to accurately predict individual chronological age. A popular application of these clocks is to explore whether the deviation of predicted age from chronological age is associated with disease phenotypes, where this deviation is interpreted as a potential biomarker of biological age. This wide application, however, contrasts with the limited insight in the processes that may drive the running of epigenetic clocks. Results We perform a functional genomics analysis on four epigenetic clocks, including Hannum’s blood predictor and Horvath’s multi-tissue predictor, using blood DNA methylome and transcriptome data from 3132 individuals. The four clocks result in similar predictions of individual chronological age, and their constituting CpGs are correlated in DNAm level and are enriched for similar histone modifications and chromatin states. Interestingly, DNAm levels of CpGs from the clocks are commonly associated with gene expression in trans. The gene sets involved are highly overlapping and enriched for T cell processes. Further analysis of the transcriptome and methylome of sorted blood cell types identifies differences in DNAm between naive and activated T and NK cells as a probable contributor to the clocks. Indeed, within the same donor, the four epigenetic clocks predict naive cells to be up to 40 years younger than activated cells. Conclusions The ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells, an established feature of immuno-senescence. This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes.

scholarly journals The effect of age on DNA methylation in whole blood among Bangladeshi men and women

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Rick J. Jansen ◽  
Lin Tong ◽  
Maria Argos ◽  
Farzana Jasmine ◽  
Muhammad Rakibuz-Zaman ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Whole Blood ◽  
Cpg Islands ◽  
Chronological Age ◽  
Cpg Sites ◽  
Age Related ◽  
Genome Wide ◽  
A Genome ◽  
Effect Of Age ◽  
Age Related Changes

Abstract Background It is well-known that methylation changes occur as humans age, however, understanding how age-related changes in DNA methylation vary by sex is lacking. In this study, we characterize the effect of age on DNA methylation in a sex-specific manner and determine if these effects vary by genomic context. We used the Illumina HumanMethylation 450 K array and DNA derived from whole blood for 400 adult participants (189 males and 211 females) from Bangladesh to identify age-associated CpG sites and regions and characterize the location of these age-associated sites with respect to CpG islands (vs. shore, shelf, or open sea) and gene regions (vs. intergenic). We conducted a genome-wide search for age-associated CpG sites (among 423,604 sites) using a reference-free approach to adjust for cell type composition (the R package RefFreeEWAS) and performed an independent replication analysis of age-associated CpGs. Results The number of age-associated CpGs (p < 5 x 10− 8) were 986 among men and 3479 among women of which 2027(63.8%) and 572 (64.1%) replicated (using Bonferroni adjusted p < 1.2 × 10− 5). For both sexes, age-associated CpG sites were more likely to be hyper-methylated with increasing age (compared to hypo-methylated) and were enriched in CpG islands and promoter regions compared with other locations and all CpGs on the array. Although we observed strong correlation between chronological age and previously-developed epigenetic age models (r ≈ 0.8), among our top (based on lowest p-value) age-associated CpG sites only 12 for males and 44 for females are included in these prediction models, and the median chronological age compared to predicted age was 44 vs. 51.7 in males and 45 vs. 52.1 in females. Conclusions Our results describe genome-wide features of age-related changes in DNA methylation. The observed associations between age and methylation were generally consistent for both sexes, although the associations tended to be stronger among women. Our population may have unique age-related methylation changes that are not captured in the established methylation-based age prediction model we used, which was developed to be non-tissue-specific.

scholarly journals Trends in DNA methylation with age replicate across diverse human populations

2016 ◽  
Author(s):  
Shyamalika Gopalan ◽  
Oana Carja ◽  
Maud Fagny ◽  
Etienne Patin ◽  
Justin W. Myrick ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Meta Analysis ◽  
Chronological Age ◽  
Human Populations ◽  
Strongly Correlated ◽  
Hunter Gatherers ◽  
Cpg Sites ◽  
Age Related ◽  
Genome Wide ◽  
Methylation Patterns

AbstractAging is associated with widespread changes in genome-wide patterns of DNA methylation. Thousands of CpG sites whose tissue-specific methylation levels are strongly correlated with chronological age have been previously identified. However, the majority of these studies have focused primarily on cosmopolitan populations living in the developed world; it is not known if age-related patterns of DNA methylation at these loci are similar across a broad range of human genetic and ecological diversity. We investigated genome-wide methylation patterns using saliva and whole blood derived DNA from two traditionally hunting and gathering African populations: the Baka of the western Central African rainforest and the ≠Khomani San of the South African Kalahari Desert. We identify hundreds of CpG sites whose methylation levels are significantly associated with age, thousands that are significant in a meta-analysis, and replicate trends previously reported in populations of non-African descent. We confirm that an age-associated site in the gene ELOVL2 shows a remarkably congruent relationship with aging in humans, despite extensive genetic and environmental variation across populations. We also demonstrate that genotype state at methylation quantitative trait loci (meQTLs) can affect methylation trends at some known age-associated CpG sites. Our study explores the relationship between CpG methylation and chronological age in populations of African hunter-gatherers, who rely on different diets across diverse ecologies. While many age-related CpG sites replicate across populations, we show that considering common genetic variation at meQTLs further improves our ability to detect previously identified age associations.

scholarly journals The aging DNA methylome reveals environment-by-aging interactions in a model teleost

2021 ◽  
Author(s):  
Emily M Bertucci ◽  
Marilyn W Mason ◽  
Olin E Rhodes ◽  
Benjamin B Parrott
Keyword(s):  
Dna Methylation ◽  
Environmental Factors ◽  
Environmental Drivers ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Ongoing Research ◽  
Dna Methylome ◽  
Age Related ◽  
Epigenetic Aging ◽  
Epigenetic Age

The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome (epigenetic aging) is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.

scholarly journals Development of Epigenetic Clocks for Key Ruminant Species

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 96
Author(s):  
Alex Caulton ◽  
Ken G. Dodds ◽  
Kathryn M. McRae ◽  
Christine Couldrey ◽  
Steve Horvath ◽  
...  
Keyword(s):  
Large Body ◽  
Ovis Aries ◽  
Farm Animal ◽  
Tissue Type ◽  
Capra Hircus ◽  
Chronological Age ◽  
Individual Species ◽  
Epigenetic Clock ◽  
Cpg Sites ◽  
Age Related

Robust biomarkers of chronological age have been developed in humans and model mammalian species such as rats and mice using DNA methylation data. The concept of these so-called “epigenetic clocks” has emerged from a large body of literature describing the relationship between genome-wide methylation levels and age. Epigenetic clocks exploit this phenomenon and use small panels of differentially methylated cytosine (CpG) sites to make robust predictions of chronological age, independent of tissue type. Here, we present highly accurate livestock epigenetic clocks for which we have used the custom mammalian methylation array “HorvathMammalMethyl40” to construct the first epigenetic clock for domesticated goat (Capra hircus), cattle (Bos taurus), Red (Cervus elaphus) and Wapiti deer (Cervus canadensis) and composite-breed sheep (Ovis aries). Additionally, we have constructed a ‘farm animal clock’ for all species included in the study, which will allow for robust predictions to be extended to various breeds/strains. The farm animal clock shows similarly high accuracy to the individual species’ clocks (r > 0.97), utilizing only 217 CpG sites to estimate age (relative to the maximum lifespan of the species) with a single mathematical model. We hypothesise that the applications of this livestock clock could extend well beyond the scope of chronological age estimates. Many independent studies have demonstrated that a deviation between true age and clock derived molecular age is indicative of past and/or present health (including stress) status. There is, therefore, untapped potential to utilize livestock clocks in breeding programs as a predictor for age-related production traits.

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

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