Fernandes, Maria, Wan, Cen, Tacutu, Robi, Barardo, Diogo, Rajput, Ashish, Wang, Jingwei, Thoppil, Harikrishnan, Thornton, Daniel, Yang, Chenhao, Freitas, Alex A., and others. (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. (doi:10.1093/hmg/ddw307) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:60842)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: https://doi.org/10.1093/hmg/ddw307 |
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.
Item Type: | Article |
---|---|
DOI/Identification number: | 10.1093/hmg/ddw307 |
Uncontrolled keywords: | data mining, machine learning, ageing, bioinformatics, age-related diseases |
Subjects: | Q Science > Q Science (General) > Q335 Artificial intelligence |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Alex Freitas |
Date Deposited: | 10 Mar 2017 15:50 UTC |
Last Modified: | 05 Nov 2024 10:54 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/60842 (The current URI for this page, for reference purposes) |
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):