Methylation clocks for age estimation in toothed whales and dolphins
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ABSTRACT:
PROVIDER: PRJNA725202 | ENA |
REPOSITORIES: ENA
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Project description:An Infinium microarray platform (GPL28271, HorvathMammalMethylChip40) was used to generate DNA methylation data from several tissues (skin, blood) in toothed whales and dolphins. Tissues: skin and blood.
2021-04-27 | GSE173330 | GEO
Project description:DNA methylation clocks as a predictor for ageing and age estimation in naked mole-rats, Heterocephalus glaber
| PRJNA573950 | ENA
Project description:The naked mole-rat, Heterocephalus glaber (NMR), the longest-lived rodent with a maximum possible lifespan exceeding 33 years, is emerging as an important non-model organism for the study of longevity and healthspan. As such it is of significance and interest in the study of biomarkers for ageing in mammals. Recent breakthroughs in this field have indicated that ‘epigenetic clocks’ based on the temporal accumulation of DNA methylation at specific genomic sites can enable accurate age estimates for tissues across the lifespan of an individual. Here, we validate the hypothesis of an epigenetic clock in NMRs, and create a method for predicting the age of naked mole-rats based on changes in methylation of targeted CpG sites in regions known as ageing-associated differentially methylated positions (aDMPs). In the discovery phase, we performed a targeted analysis of 51 different CpGs in 24 different NMR livers spanning an age range from 39 weeks to 1,144 weeks. Of these 51 different sites, 23 were found to be significantly associated with age (p < 0.05). We then built a predictor of age using the 23 sites that showed an association with age. To test the accuracy of this model, we predicted age in an additional test set of 19 different livers spanning an age range 43 to 1,196 weeks. Our model was able to successfully predict age in the test with a root mean squared error (RMSE) of 166.11 weeks. We also profiled a 20 skin samples with the same age range and found a striking correlation between the predicted age of these samples versus their actual age (R=0.93). However, this correlation when compared to the liver samples showed a lower predicted age than actual age, suggesting that skin tissue ages slower than the liver in NMRs. Finally, we have produced freely available software tool that will take in raw sequencing data and produce an age prediction for new NMR samples. Our model will enable the prediction of age in wild caught naked mole-rats and captive animals of unknown age, and will be invaluable for further mechanistic studies of mammalian ageing.
2020-04-07 | GSE137957 | GEO
Project description:Age is a key demographic in conservation biology where individual age classes show diffuse differences in terms of important population dynamics metrics such as morbidity and mortality. Furthermore, several traits including reproductive potential show clear senescence with aging. Thus, the ability to estimate the ages for the individuals of a population as part of age class assignment is critical in understanding both the current population structure as well as in modelling and predicting the future survival of species. This study explored the utility of age-related changes in methylation for six candidate genes, EDARADD, ELOVL2, FHL2, GRIA2, ITGA2B, and PENK, to create an age estimation model in captive cheetah. Gene orthologues between humans and cheetah were retrieved from NCBI containing a hundred CpG’s. Target regions were assayed for differential methylation and fragmentation patterns in fifty samples using mass array technology for a total of seventy-seven CpG clusters. Correlation analyses between CpG methylation and chronological age identified six CpG’s with an age relationship, of which four were hypomethylated and two were hypermethylated. Regression models, fitted for different combinations of CpG’s, indicated that age models using four and six CpG’s were most accurate, with the six CpG model having superior correlation and predictive power (R2 = 0.70, Mean Absolute Error = 25 months). This model was more accurate than previous attempts using methylation sensitive Polymerase Chain Reaction and performed similarly to models created using a candidate gene approach in several other mammal species, making methylation a promising tool of age estimation in cheetah.
2024-01-14 | GSE252541 | GEO
Project description:Epigenetic clocks are a common group of tools used to measure biological aging – the progressive deterioration of cells, tissues and organs. Epigenetic clocks have been trained almost exclusively using blood-based tissues but there is growing interest in estimating epigenetic age using less-invasive oral-based tissues (i.e., buccal or saliva) in both research and commercial settings. However, differentiated cell types across body tissues exhibit unique DNA methylation landscapes and age-related alterations to the DNA methylome. Applying epigenetic clocks derived from blood-based tissues to estimate epigenetic age of oral-based tissues may introduce biases. We tested the within-person comparability of common epigenetic clocks across four tissue types: buccal epithelial, saliva, dry blood spots, and peripheral blood mononuclear cells. We tested 163 distinct tissue samples from 47 individuals aged 19-70 years. Overall, there were significant within-person differences in epigenetic clock estimates from oral-based versus blood-based tissues, with average differences of almost 30 years observed in some age clocks. In addition, most epigenetic clock estimates of blood-based tissues exhibited low correlation with estimates from oral-based tissues despite controlling for cellular proportions and other technical factors. Notably, the Skin and Blood clock exhibited the lowest age acceleration values of any clock across all tissue types, indicating its unique ability to accurately estimate chronological age in both oral- and blood-based tissues. Our findings indicate that application of blood-derived epigenetic clocks in oral-based tissues may not yield comparable estimates of epigenetic age, highlighting the need for careful consideration of tissue type when estimating epigenetic age. NOTE: The full study included children and adult samples, however, the current data only includes the adult samples (sample sizes and age range have been adjusted to reflect the adult data only).
2025-02-07 | GSE280465 | GEO
Project description:We assessed DNA methylation in whole blood samples from an Australian population across different age groups. Our study focused on evaluating the accuracy of DNA methylation age estimation using the Horvath 2018 and PhenoAge clocks. Additionally, we examined previously reported functional measures of aging in peripheral blood mononuclear cells (PBMCs). Our findings indicated that DNA methylation age effectively predicted donor age, while only a limited number of tested cell functions showed correlations with donor age.
2023-09-15 | GSE235717 | GEO
Project description:DNA methylation is a reaction that results in the formation of 5-methylcytosine when a methyl group is added to the cytosine’s C5 position. As organisms age, DNA methylation patterns change in a reproducible fashion. This phenomenon has established DNA methylation as a valuable biomarker in aging studies. Epigenetic clocks based on weighted combinations of methylation sites have been developed to accurately predict the age of an individual from their methylome. However, many epigenetic clocks, particularly those that utilize penalized regression, model the changes in methylation linearly with age. Moreover, these models, which use methylation levels as features, are not robust to missing data and do not account for the count-based nature of bisulfite sequence data. Additionally, the models are generally not interpretable. To overcome these challenges, we present BayesAge, an extension of the previously developed scAge approach that was developed for the analysis of single cell DNA methylation datasets. BayesAge utilizes maximum likelihood estimation (MLE) to infer ages, models count data using binomial distributions, and uses LOWESS smoothing to capture the non-linear dynamics between methylation and age. Our approach is designed for use with bulk bisulfite sequencing datasets. BayesAge outperforms scAge in several respects. Specifically, BayesAge’s age residuals are not age associated, thus providing a less biased representation of epigenetic age variation across populations. Moreover, BayesAge enables the estimation of error bounds on age inference and, when run on downsampled data, its coefficient of determination between predicted and actual ages surpasses both scAge and penalized regression.
2024-03-20 | GSE261769 | GEO
Project description:Sex differences in lifespan are widespread across animal taxa, but their causes remain unresolved. Alterations to the epigenome are hypothesized to contribute to vertebrate aging, and DNA methylation-based aging clocks allow for quantitative estimation of biological aging trajectories. Here, we investigate the influence of age, sex, and their interaction on genome-wide DNA methylation patterns in the brown anole (Anolis sagrei), a lizard with pronounced female-biased survival and longevity. We develop a series of age predictor models and find that, contrary to our predictions, rates of epigenetic aging were not slower in female lizards. However, methylation states at loci acquiring age-associated changes appear to be more “youthful” in young females, suggesting that female DNA methylomes are preemptively fortified in early life in opposition to the direction of age-related drift. Collectively, our findings provide insights into epigenetic aging in reptiles and suggest that early-life epigenetic profiles may be more informative than rates of change over time for predicting sex biases in longevity.
2025-02-03 | GSE285624 | GEO
Project description:The development of a precise blood or skin tissue DNA Epigenetic Aging Clock for Odontocete (OEAC) would solve current age estimation inaccuracies for wild odontocetes. Therefore, we determined genome-wide DNA methylation profiles using a custom array (HorvathMammalMethyl40) across skin and blood samples (n = 446) from known age animals representing nine odontocete species within 4 phylogenetic families to identify age associated CG dinucleotides (CpGs). The top CpGs were used to create a cross-validated OEAC clock which was highly correlated for individuals (r = 0.94) and for unique species (median r = 0.93). Finally, we applied the OEAC for estimating the age and sex of 22 wild Norwegian killer whales. DNA methylation patterns of age associated CpGs are highly conserved across odontocetes. These similarities allowed us to develop an odontocete epigenetic aging clock (OEAC) which can be used for species conservation efforts by provide a mechanism for estimating the age of free ranging odontocetes from either blood or skin samples.
| S-EPMC8167141 | biostudies-literature
Project description:Transcriptomics analysis of fats of toothed whales
| PRJDB11720 | ENA