AGE

 promoter methylation is associate social factor in dogs

Yuan 2011 longevity between mouse and human Framingham study. 

George Williams, evolutionary theory of aging. antagnistic pleotropic genes. 

Hongjie

Lu Wang, UT San Antonio

Alex UW Seattle

=>single cell aging: 

worm

https://c.elegans.aging.atlas.research.calicolabs.com/

fly 

=> Hevolution

GPT for few-shot genomics analysis across species

=> women in Japan and immigrant in US have different breast-cancer risks, suggesting environmental factors on age-dependent risks, city of hope presentation. 

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Karen Guerrero Vazquez

Authors: Karen Guerrero Vazquez, Pilib Ó Broin, Katarzyna Goljanek-Whysall

Selection of miRNA candidates for the treatment of sarcopenia using network-based analysis and differential expression scoring. Karen Guerrero Vazquez 1, Pilib Ó Broin 1, Katarzyna Goljanek-Whysall 2. 1 School of Mathematical & Statistical Sciences, National University of Ireland Galway, 2 School of Medicine, National University of Ireland Galway. Sarcopenia is a natural consequence of aging and leads to progressive muscle wasting. Currently, there is no cure for this condition, and target identification and validation remain pressing challenges. Many potential therapeutic targets have failed in clinical trials or shown poor association with the disease. This project aims to address these challenges by creating a model of microRNA:target interactions for more efficient in silico selection of potential therapeutic targets for sarcopenia. We conducted a novel network-based analysis of microRNA involvement in aging using RNAseq and microarray data from five studies, with a total of 246 samples of skeletal muscle from healthy participants with ages ranging from 19 to 85 years old. We analyzed young, middle age and older adults and determined interactions between genes coming from co-expression, colocalization, genetic interaction, physical interaction, predicted and shared protein domain calculated with GeneMania. Next, we predicted microRNAs that are putative regulators of shortlisted genes using target prediction tools, such as TargetScan, mirDB and mirTarbase. Finally, we add tissue expression from Diana-miTed and miRNATissue Atlas2. After identifying the network of gene:gene and microRNA:gene interactions, the relevance of each node was calculated using multiple scoring metrics and measures of centrality in order to identify the most likely key regulatory microRNAs and their targets during aging. Our model of microRNA-target interactions is specifically tailored to the context of muscle aging and offers a more comprehensive and accurate representation of the complex regulatory mechanisms involved in muscle aging than existing tools, as it integrates multiple layers of biological information. By identifying a few tens of microRNAs and genes with potential therapeutic power, our model offers a valuable and efficient approach to target identification and validation for the treatment of sarcopenia. This abstract has emanated from research supported in part by a research grant from Science Foundation Ireland under Grant number [18/CRT/6214]

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Explaining the asynchrony of aging through cell population dynamicsMing Yang1, Benjamin R. Harrison1, Daniel E.L. Promislow1,21 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195 USA 2 Department of Biology, University of Washington, Seattle, WA 98195 USA Different tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cell lifespan explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis, and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.This work was supported by NIH R01 AG063371 (to D. Promislow and S. Pletcher) and Norn Group Impetus Grant (to D. Promislow).

https://www.biorxiv.org/content/10.1101/2023.05.31.543091v1