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      Shortest-Path Network Analysis Is a Useful Approach toward Identifying Genetic Determinants of Longevity

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          Identification 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 Findings

          We 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.


          This 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.

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          Most cited references 33

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          Transcriptional regulatory networks in Saccharomyces cerevisiae.

          We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
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            Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis.

            The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.
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              Lethality and centrality in protein networks.


                Author and article information

                Role: Editor
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                25 November 2008
                : 3
                : 11
                [1 ]Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [2 ]Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [3 ]Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
                [4 ]Department of Pathology, University of Washington, Seattle, Washington, United States of America
                Centre for Genomic Regulation, Spain
                Author notes

                Conceived and designed the experiments: JRM TMW DB MK. Performed the experiments: JRM LAF MT MK. Analyzed the data: JRM DB BKK MK. Contributed reagents/materials/analysis tools: TMW BKK MK. Wrote the paper: MK.

                Managbanag et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Pages: 9
                Research Article
                Genetics and Genomics
                Computational Biology/Systems Biology
                Genetics and Genomics/Functional Genomics



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