Systematic analysis of the gerontome reveals links between aging and age-related diseases

• Published in: Human molecular genetics Vol. 25; no. 21; pp. ddw307 - 4818
• Main Authors: Fernandes, Maria, Wan, Cen, Tacutu, Robi, Barardo, Diogo, Rajput, Ashish, Wang, Jingwei, Thoppil, Harikrishnan, Thornton, Daniel, Yang, Chenhao, Freitas, Alex, de Magalhães, João Pedro
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.11.2016
• Subjects: Age Factors; Aging - genetics; Animals; Caenorhabditis elegans; Databases, Nucleic Acid; Drosophila; Evolution, Molecular; Genome, Human; Humans; Longevity - genetics; Mice; Saccharomyces cerevisiae; Sequence Analysis, DNA - methods
• ISSN: 0964-6906; 1460-2083; 1460-2083
• DOI: 10.1093/hmg/ddw307

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases.