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Prediction of C. elegans Longevity Genes by Human and Worm Longevity Networks

Overview of attention for article published in PLOS ONE, October 2012
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Title
Prediction of C. elegans Longevity Genes by Human and Worm Longevity Networks
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0048282
Pubmed ID
Authors

Robi Tacutu, David E. Shore, Arie Budovsky, João Pedro de Magalhães, Gary Ruvkun, Vadim E. Fraifeld, Sean P. Curran

Abstract

Intricate and interconnected pathways modulate longevity, but screens to identify the components of these pathways have not been saturating. Because biological processes are often executed by protein complexes and fine-tuned by regulatory factors, the first-order protein-protein interactors of known longevity genes are likely to participate in the regulation of longevity. Data-rich maps of protein interactions have been established for many cardinal organisms such as yeast, worms, and humans. We propose that these interaction maps could be mined for the identification of new putative regulators of longevity. For this purpose, we have constructed longevity networks in both humans and worms. We reasoned that the essential first-order interactors of known longevity-associated genes in these networks are more likely to have longevity phenotypes than randomly chosen genes. We have used C. elegans to determine whether post-developmental inactivation of these essential genes modulates lifespan. Our results suggest that the worm and human longevity networks are functionally relevant and possess a high predictive power for identifying new longevity regulators.

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The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 3%
Netherlands 1 <1%
Canada 1 <1%
Belgium 1 <1%
Japan 1 <1%
Unknown 107 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 30%
Student > Ph. D. Student 29 25%
Student > Bachelor 11 10%
Student > Master 10 9%
Professor > Associate Professor 7 6%
Other 16 14%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 44%
Biochemistry, Genetics and Molecular Biology 26 23%
Computer Science 7 6%
Neuroscience 4 4%
Medicine and Dentistry 4 4%
Other 12 11%
Unknown 11 10%