Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1312265 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Chronological aging correlates with epigenetic modifications at specific loci, calibrated to species lifespan. Such ‘epigenetic clocks’ appear conserved among mammals, but whether they are cell-autonomous and restricted by maximal organismal lifespan remains unknown. We used a multi-lifetime murine model of repeat vaccination and memory T cell transplantation to test whether epigenetic aging tracks with cellular replication, and if such clocks continue ‘counting’ beyond species lifespan. We found that memory T cell epigenetic clocks tick independently of host age and continue through four lifetimes. Instead of recording chronological time, T cells recorded proliferative experience through modification of cell cycle regulatory genes. Applying this epigenetic profile across a range of human T cell contexts, we found that naïve T cells appeared ‘young’ regardless of organism age, while in pediatric patients, T-cell acute lymphoblastic leukemia (T-ALL) appeared to have epigenetically aged for up to 200 years. Thus, T cell epigenetic clocks measure replicative history and can continue to accumulate well-beyond organismal lifespan.
2024-05-06 | GSE263941 | GEO
Project description:Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.
2023-06-30 | GSE182598 | GEO
Project description:Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.
2023-07-03 | PXD028281 | Pride
Project description:Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.
2023-07-03 | PXD028295 | Pride
Project description:Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. We found that human naive CD8+ T cells, which decrease during aging, exhibit an epigenetic age 15–20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created a new clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock showed a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming
2023-12-27 | GSE252045 | GEO
Project description:The lifespans of proteins can range from moments to years within mammalian tissues. Protein lifespan is relevant to organismal aging, as long-lived proteins can accrue damage over time. It is unclear how protein lifetime is shaped by tissue context, where both cell division and proteolytic degradation contribute to protein turnover. Here, we develop turnover and replication analysis by 15N isotope labeling (TRAIL) for parallel quantification of protein and cell lifetimes. We deploy TRAIL over 32 days in 4 mouse tissues to quantify cell proliferation with high precision and no toxicity and determine that protein lifespan varies independently of cell lifespan. Variation in protein lifetime is non-random: multiprotein complexes such as the ribosome have consistent lifetimes across tissues, while mitochondria, peroxisomes, and lipid droplets have variable lifetimes across tissues. To model the effects of aging on tissue homeostasis, we apply TRAIL to progeroid mice and uncover fat-specific alterations in cell lifetime and proteome composition, as well as a broad decrease in protein turnover flux. These data indicate that environmental factors influence protein turnover in vivo and provide a framework to understand proteome aging in tissue context.
2023-03-21 | PXD033649 | Pride
Project description:Proteins are the cornerstone of human life, yet the proteomic landscape of aging across human tissues remains largely unexplored. Here, we present a comprehensive proteomic analysis of 520 samples from 15 human tissues, spanning a 50-year lifespan. Our study compiles a proteomic encyclopedia of multiple human organs, revealing an age-related decoupling between proteomic and transcriptomic profiles, and a loss of proteostasis characterized by the accumulation of amyloid proteins. We identified both shared and organ-specific proteomic signatures of aging, expanding the catalog of biomarkers for organ senescence. By pinpointing protein determinants of organ aging, we developed multi-organ proteomic aging clocks, uncovered disease risk factors, and potential therapeutic targets. Our dynamic analysis of proteomic temporal patterns indicates that organs experience a precipitous aging shift around age 50, with vasculature, which extensively interacts with other organs, showing early and notably aggressive senescence. Furthermore, we established a plasma proteomic signature of human aging, traced their organ origins, and identified key proteins driving endothelial senescence. Collectively, our work provides an unprecedented dynamic proteomic atlas of human aging, laying a foundation for decoding, assessing, and intervening in the aging process with proteins at the core.
2024-12-06 | PXD058638 |
Project description:Human DNA-methylation data have been used to develop highly accurate biomarkers of aging ("epigenetic clocks"). Recent studies demonstrate that similar epigenetic clocks for mice (Mus Musculus) can be slowed by gold standard anti-aging interventions such as calorie restriction and growth hormone receptor knock-outs. Using DNA methylation data from previous publications with data collected in house for a total 1189 samples spanning 193,651 CpG sites, we developed 4 novel epigenetic clocks by choosing different regression models (elastic net- versus ridge regression) and by considering different sets of CpGs (all CpGs vs highly conserved CpGs). We demonstrate that accurate age estimators can be built on the basis of highly conserved CpGs. However, the most accurate clock results from applying elastic net regression to all CpGs. While the anti-aging effect of calorie restriction could be detected with all types of epigenetic clocks, only ridge regression based clocks replicated the finding of slow epigenetic aging effects in dwarf mice. Overall, this study demonstrates that there are trade-offs when it comes to epigenetic clocks in mice. Highly accurate clocks might not be optimal for detecting the beneficial effects of anti-aging interventions.
2018-11-29 | GSE120136 | GEO
Project description:Human DNA-methylation data have been used to develop highly accurate biomarkers of aging ("epigenetic clocks"). Recent studies demonstrate that similar epigenetic clocks for mice (Mus Musculus) can be slowed by gold standard anti-aging interventions such as calorie restriction and growth hormone receptor knock-outs. Using DNA methylation data from previous publications with data collected in house for a total 1189 samples spanning 193,651 CpG sites, we developed 4 novel epigenetic clocks by choosing different regression models (elastic net- versus ridge regression) and by considering different sets of CpGs (all CpGs vs highly conserved CpGs). We demonstrate that accurate age estimators can be built on the basis of highly conserved CpGs. However, the most accurate clock results from applying elastic net regression to all CpGs. While the anti-aging effect of calorie restriction could be detected with all types of epigenetic clocks, only ridge regression based clocks replicated the finding of slow epigenetic aging effects in dwarf mice. Overall, this study demonstrates that there are trade-offs when it comes to epigenetic clocks in mice. Highly accurate clocks might not be optimal for detecting the beneficial effects of anti-aging interventions.
2018-11-29 | GSE120132 | GEO