Literature DB >> 27179790

Systems-level analysis of human aging genes shed new light on mechanisms of aging.

Quanwei Zhang1, Ruben Nogales-Cadenas2, Jhih-Rong Lin2, Wen Zhang2, Ying Cai2, Jan Vijg2,3, Zhengdong D Zhang1.   

Abstract

Although studies over the last decades have firmly connected a number of genes and molecular pathways to aging, the aging process as a whole still remains poorly understood. To gain novel insights into the mechanisms underlying aging, instead of considering aging genes individually, we studied their characteristics at the systems level in the context of biological networks. We calculated a comprehensive set of network characteristics for human aging-related genes from the GenAge database. By comparing them with other functional groups of genes, we identified a robust group of aging-specific network characteristics. To find the structural basis and the molecular mechanisms underlying this aging-related network specificity, we also analyzed protein domain interactions and gene expression patterns across different tissues. Our study revealed that aging genes not only tend to be network hubs, playing important roles in communication among different functional modules or pathways, but also are more likely to physically interact and be co-expressed with essential genes. The high expression of aging genes across a large number of tissue types also points to a high level of connectivity among aging genes. Unexpectedly, contrary to the depletion of interactions among hub genes in biological networks, we observed close interactions among aging hubs, which renders the aging subnetworks vulnerable to random attacks and thus may contribute to the aging process. Comparison across species reveals the evolution process of the aging subnetwork. As the organisms become more complex, the complexity of its aging mechanisms increases and their aging hub genes are more functionally connected.
© The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Year:  2016        PMID: 27179790      PMCID: PMC6390412          DOI: 10.1093/hmg/ddw145

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


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