A network biology approach to aging in yeast

The Howard Hughes Medical Institute, Bioinformatics Program, Center for Advanced Biotechnology and Department of Biomedical Engineering. Boston University, 44 Cummington Street, Boston, MA 02215, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 02/2009; 106(4):1145-50. DOI: 10.1073/pnas.0812551106
Source: PubMed


In this study, a reverse-engineering strategy was used to infer and analyze the structure and function of an aging and glucose repressed gene regulatory network in the budding yeast Saccharomyces cerevisiae. The method uses transcriptional perturbations to model the functional interactions between genes as a system of first-order ordinary differential equations. The resulting network model correctly identified the known interactions of key regulators in a 10-gene network from the Snf1 signaling pathway, which is required for expression of glucose-repressed genes upon calorie restriction. The majority of interactions predicted by the network model were confirmed using promoter-reporter gene fusions in gene-deletion mutants and chromatin immunoprecipitation experiments, revealing a more complex network architecture than previously appreciated. The reverse-engineered network model also predicted an unexpected role for transcriptional regulation of the SNF1 gene by hexose kinase enzyme/transcriptional repressor Hxk2, Mediator subunit Med8, and transcriptional repressor Mig1. These interactions were validated experimentally and used to design new experiments demonstrating Snf1 and its transcriptional regulators Hxk2 and Mig1 as modulators of chronological lifespan. This work demonstrates the value of using network inference methods to identify and characterize the regulators of complex phenotypes, such as aging.

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    • "The methods presented here are just a selection of the available methods for graph inference. Several other methods such as Boolean networks (Shmulevich et al., 2002) or differential equation systems (Chen et al., 1999; Lorenz et al., 2009) are commonly used for modelling biological networks. "
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    • "With current high quality tools for mining of published data sets such as WormMart [110] and YeastMine [111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relationships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115] [116] [117] [118]. Recently , the novel computational method of network identification by regression (NIR) [119] has been used to identify new lifespan effects, by using transcriptional perturbations to build a model of functional interactions [118]. Although we have focused here on the most widely studied model organisms , others such as the previously mentioned S. pombe, P. anserina, and N. crassa, as well as the rapidly developing vertebrate model system N. furzeri [120] [121], will greatly contribute to this cross-species leverage in systems-level investigations of aging. "
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