Analysis of aging-related protein interactome and cross-network module comparisons across tissues provide new insights into aging. (June 2021)

• Record Type: Journal Article
• Authors: Randhawa, Vinay; Kumar, Manoj
• Is Part Of: Computational biology and chemistry. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: AE Aging-related essential -- ANE Aging-related non-essential -- BC Betweenness Centrality -- BP Biological Process -- CC Closeness Centrality -- GO Gene Ontology -- k Degree; Aging protein interactome -- Network variability -- Cross-network comparison -- Network correlation; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107506
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Aging-related proteins are functionally related to each other to strongly regulate or influence the aging process. Aging-related essential proteins influence the aging process at a comparatively higher expression. Aging affects organs in strikingly different ways by differentially regulating various regulatory interactions. Cross-network comparisons identified two conserved aging-related modules across various human tissues. Abstract: Delaying the human aging process and thus eliminating the risk factors for age-related diseases is one of the prime objectives. While various aging-associated genes and proteins have been characterized, which provide a significant understanding of the human aging process, a significant success in regulating aging is not achieved yet. Understanding how aging proteins interact with each other and also with other proteins could provide important insights into the underlying mechanisms governing the aging process. Therefore, in this work, information of gene expression was included to the static aging-related protein interactome to understand the network-based relationships among aging-related essential (AE) proteins, aging-related non-essential (ANE) proteins, and housekeeping-proteins that could regulate or influence aging. Comprehensive analyses provided various systems-level insights into the regulatory characteristics of aging; for example, (i) network-based correlation analysis predicted functional relationships among AE proteins and ANE proteins; (ii) network variability analysis predicted aging to affect different tissues in strikingly different ways by differentially regulating various regulatory interactions; (iii) cross-network comparisons identified two aging-related modules to be significantly conserved across most of the tissues. Overall, the findings obtained during this study could be helpful for researchers to delay, prevent, or even reverse various aspects of the aging.