Abstract
BackgroundIdentification of genes that modulate longevity is a major focus of aging-related research and an area of intense public interest. In addition to facilitating an improved understanding of the basic mechanisms of aging, such genes represent potential targets for therapeutic intervention in multiple age-associated diseases, including cancer, heart disease, diabetes, and neurodegenerative disorders. To date, however, targeted efforts at identifying longevity-associated genes have been limited by a lack of predictive power, and useful algorithms for candidate gene-identification have also been lacking.Methodology/Principal FindingsWe have utilized a shortest-path network analysis to identify novel genes that modulate longevity in Saccharomyces cerevisiae. Based on a set of previously reported genes associated with increased life span, we applied a shortest-path network algorithm to a pre-existing protein–protein interaction dataset in order to construct a shortest-path longevity network. To validate this network, the replicative aging potential of 88 single-gene deletion strains corresponding to predicted components of the shortest-path longevity network was determined. Here we report that the single-gene deletion strains identified by our shortest-path longevity analysis are significantly enriched for mutations conferring either increased or decreased replicative life span, relative to a randomly selected set of 564 single-gene deletion strains or to the current data set available for the entire haploid deletion collection. Further, we report the identification of previously unknown longevity genes, several of which function in a conserved longevity pathway believed to mediate life span extension in response to dietary restriction.Conclusions/SignificanceThis work demonstrates that shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity and represents the first application of network analysis of aging to be extensively validated in a biological system. The novel longevity genes identified in this study are likely to yield further insight into the molecular mechanisms of aging and age-associated disease.
Highlights
Network-based approaches are one useful method for representing complex biological systems [1]
We are encouraged by the success of our initial shortest-path longevity network (SPLN) analysis at predicting novel longevity-associated genes (LAGs); we recognize that some features of our experimental design may have limited the predictive power of the SPLN
The set of previously reported LAGs used to derive the SPLN was generated based on replicative life span (RLS) data from studies performed by multiple laboratories using a variety of yeast isolates of diverse genetic composition (Table S1)
Summary
Network-based approaches are one useful method for representing complex biological systems [1]. As the technical acumen for characterizing complex interactions has advanced, so has the feasibility of building large scale networks via sophisticated computational tools that facilitate elucidation of such systems [2]. Xue et al have characterized protein–protein interaction network modules associated with aging in humans and flies and performed limited biological validation of their network in Caenorhabditis elegans [9]. Several potential longevity-associated genes (LAGs) were predicted in C. elegans, based on interactions with previously described LAGs [10]. To date targeted efforts at identifying longevity-associated genes have been limited by a lack of predictive power, and useful algorithms for candidate gene-identification have been lacking
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