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We observed that the target proteins of the FDA-approved drug rapamycin (a known inhibitor of mTOR) were conserved across all four species. Further, we explored our results’ translational potential by assessing the protein interactions with all the reported drugs and compounds that have been experimentally verified to promote longevity in the three-comparator species. This analysis also revealed mTOR signaling as shared biological processes across four different human tissue-specific PPINs for liver, heart, skeletal muscle, and adipose tissue. We investigated conserved modules in various tissue-specific PPINs of the longevity-associated genes based upon their protein expression. To corroborate our findings, we repeated our analysis in 43 different human tissues. Our study implies that the interaction of proteins involved in the mechanistic target of rapamycin (mTOR) signaling pathway is somewhat limited to each species or that a “rewiring” of specific networks has taken place over time. We observed three important signaling pathways: FoxO signaling, mTOR signaling, and autophagy to be common and highly enriched in all four species ( p -value ≤ 0.001).

We characterized experimentally determined protein-protein interaction networks (PPIN) involving each species’ genes from 9 known protein databases and studied the enriched biological pathways among the individually constructed PPINs.

Many of these genes were initially identified in non-human species, and we investigated their orthologs in three non-human species for further analysis. We gathered 15 independent sources of information about genes potentially involved in human longevity and lifespan ( N = 5836) and subjected them to various integrated analyses. These efforts have generated an enormous amount of high-throughput data covering different levels in the physiologic hierarchy, e.g., genetic, epigenetic, transcriptomic, proteomic, and metabolomic, etc. Intensive research efforts have been undertaken to slow human aging and therefore potentially delay the onset of age-related diseases. All clusters can be visualized and interactively explored at our web resource. Drug targets are depicted as octagonal nodes. Genes in the AGE category have a solid red border, ARD genes yellow fill color, EX genes blue text color, and ME genes a violet dotted border genes reported in an AGE and ME set have a red dotted border. Genes without an aging association are depicted as rectangles with grey fill color, no border, and black text labels. The four boxes provide visualizations of selected aging clusters, where nodes represent genes and edges represent physical protein interactions, protein co-complex relationships, or functional associations.

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Each cluster is filled with two circles: the inner circle represents the percentage of aging genes in the cluster, ranging from a small black slice (one gene) to full black (all genes in the cluster), the outer circle describes the categories represented by those aging genes, with red for curated aging (AGE), yellow for age-related disease (ARD), blue for gene expression (EX), and violet for DNA methylation (ME). Node size and edge width represent the cluster size and the number of shared genes, respectively. The aging clusters are depicted as nodes in the main network, edges indicate genes that are shared between the connected clusters. Landscape of all 1079 human aging clusters in the combined interaction network. The online version of this article (10.1007/s1052-5) contains supplementary material, which is available to authorized users. To enable their detailed exploration, all datasets and aging clusters are made freely available via an interactive website (). 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. 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.

To identify biological relationships between aging-associated genes we incorporated them into a protein interaction network and characterized their network neighborhoods. 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. 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.