Abstract
Great amounts of omics data are generated in aging research, but their diverse and partly complementary nature requires integrative analysis approaches for investigating aging processes and connections to age-related diseases. To establish a broader picture of the genetic and epigenetic landscape of human aging we performed a large-scale meta-analysis of 6600 human genes by combining 35 datasets that cover aging hallmarks, longevity, changes in DNA methylation and gene expression, and different age-related diseases. To identify biological relationships between aging-associated genes we incorporated them into a protein interaction network and characterized their network neighborhoods. In particular, we computed a comprehensive landscape of more than 1000 human aging clusters, network regions where genes are highly connected and where gene products commonly participate in similar processes. In addition to clusters that capture known aging processes such as nutrient-sensing and mTOR signaling, we present a number of clusters with a putative functional role in linking different aging processes as promising candidates for follow-up studies. To enable their detailed exploration, all datasets and aging clusters are made freely available via an interactive website (https://gemex.eurac.edu/bioinf/age/).
Highlights
Age is an important risk factor for a number of diseases (Cutler and Mattson 2006; Niccoli and Partridge 2012)
One area that has been intensely studied in recent years are aging-related changes in DNA methylation, an epigenetic process associated with controlling gene expression (Jones et al 2015)
The meta-analysis presented here unifies 35 datasets related to a wide range of aging aspects, grouped into the four categories DNA methylation changes (ME), gene expression changes (EX), age-related diseases (ARD), and curated aging data (AGE)
Summary
Age is an important risk factor for a number of diseases (Cutler and Mattson 2006; Niccoli and Partridge 2012). A common foundation of the complex aging-related changes on a cellular and molecular level has been proposed via the nine hallmarks of aging (Lopez-Otın et al 2013), but while key mechanisms and processes have been identified, their molecular foundations remain largely uncharacterized (Kenyon 2010). One area that has been intensely studied in recent years are aging-related changes in DNA methylation, an epigenetic process associated with controlling gene expression (Jones et al 2015). Two meta analyses reported 11 and 41 high-confidence CpG markers, respectively, that were found in at least four different studies and identified common biological processes and cellular pathways of differentially methylated genes (Steegenga et al 2014; Jones et al 2015)
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