PLoS Computational BiologySystems BiologyPeer-reviewed journal article
Cross-species functional modules link proteostasis to human normal aging
Andrea KomljenovićiD, Hao LiiD, Vincenzo SorrentinoiD, Zoltán KutalikiD, Johan H. AuwerxiD, Marc Robinson‐RechaviiD
Abstract
The evolutionarily conserved nature of the few well-known anti-aging interventions that affect lifespan, such as caloric restriction, suggests that aging-related research in model organisms is directly relevant to human aging. Since human lifespan is a complex trait, a systems-level approach will contribute to a more comprehensive understanding of the underlying aging landscape. Here, we integrate evolutionary and functional information of normal aging across human and model organisms at three levels: gene-level, process-level, and network-level. We identify evolutionarily conserved modules of normal aging across diverse taxa, and importantly, we show that proteostasis involvement is conserved in healthy aging. Additionally, we find that mechanisms related to protein quality control network are enriched in 22 age-related genome-wide association studies (GWAS) and are associated to caloric restriction. These results demonstrate that a systems-level approach, combined with evolutionary conservation, allows the detection of candidate aging genes and pathways relevant to human normal aging.\n\nHighlightsO_LINormal aging is evolutionarily conserved at the module level.\nC_LIO_LICore pathways in healthy aging are related to mechanisms of protein quality network\nC_LIO_LIThe evolutionarily conserved pathways of healthy aging react to caloric restriction.\nC_LIO_LIOur integrative approach identifies evolutionarily conserved functional modules and showed enrichment in several age-related GWAS studies.\nC_LI
- organism: human
- organism: mouse
- organism: rat
- assay: proteomics
- research focus: gene regulatory network inference
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 15
- FWCI
- 0.73
- Field percentile
- top 37%
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