Systematic analysis of the gerontome reveals links between aging and age-related diseases.
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.
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.|LaSIGE - Large-Scale Informatics Systems Laboratory, Faculty of Sciences, University of Lisbon, Portugal
Maria
M
Fernandes
School of Computing, University of Kent, Canterbury, UK.|Department of Computer Science, University College London, London, UK
Cen
C
Wan
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Robi
R
Tacutu
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Diogo
D
Barardo
Research Group for Computational Systems Biology, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany.
Ashish
A
Rajput
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Jingwei
J
Wang
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Harikrishnan
H
Thoppil
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Daniel
D
Thornton
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Chenhao
C
Yang
School of Computing, University of Kent, Canterbury, UK.
Alex
A
Freitas
Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
João Pedro
JP
de Magalhães
D000367:Age Factors; D000375:Aging / Q000235:genetics*; D000818:Animals; D017173:Caenorhabditis elegans; D030561:Databases, Nucleic Acid; D004330:Drosophila; D019143:Evolution, Molecular; D015894:Genome, Human; D006801:Humans; D008136:Longevity / Q000235:genetics*; D051379:Mice; D012441:Saccharomyces cerevisiae; D017422:Sequence Analysis, DNA / Q000379:methods