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

    Fernandes, M. and Wan, Cen and Tacutu, R. and Barardo, D. and Rajput, A. and Wang, J. and Thoppil, H. and Thornton, D. and Yang, C. and Freitas, A. and de Magalhaes, J.P. (2016) Systematic analysis of the gerontome reveals links between aging and age-related diseases. Human Molecular Genetics 25 (21), pp. 4804-4818. ISSN 0964-6906.

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    Abstract

    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.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Bioinformatics, Bloomsbury Centre for (Closed)
    Depositing User: Cen Wan
    Date Deposited: 31 Jan 2020 09:21
    Last Modified: 11 Jul 2020 11:55
    URI: https://eprints.bbk.ac.uk/id/eprint/30711

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