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: 9.81). 02/2009; 106(4):1145-50. DOI: 10.1073/pnas.0812551106
Source: PubMed

ABSTRACT 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.

1 Follower
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Whole-genome studies involving a phenotype of interest are increasingly prevalent, in part due to a dramatic increase in speed at which many high throughput technologies can be performed coupled to simultaneous decreases in cost. This type of genome-scale methodology has been applied to the phenotype of lifespan, as well as to whole-transcriptome changes during the aging process or in mutants affecting aging. The value of high throughput discovery-based science in this field is clearly evident, but will it yield a true systems-level understanding of the aging process? Here we review some of this work to date, focusing on recent findings and the unanswered puzzles to which they point. In this context, we also discuss recent technological advances and some of the likely future directions that they portend.
    Current Genomics 11/2012; 13(7):500-7. DOI:10.2174/138920212803251454 · 2.87 Impact Factor
  • Source
    • "Still, considering the possibility that multicellular organisms evolved from unicellular populations that gradually acquired skills of complex intercellular communication (Bassler and Losick, 2006) leading to task allocation through differentiation (Wahl, 2002), it is intriguing to ask what we can learn from one process while studying the other. This underlines the importance of a recent interdisciplinary effort by (Lorenz et al., 2009) to map the complex regulatory interactions among a handful of genes affecting longevity in response to calorie restriction in baker's yeast (Saccharomyces cerevisiae). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Aging affects all known organisms and has been studied extensively. Yet, the underlying mechanisms are insufficiently understood, possibly due to the multiscale complexity involved in this process: the aging of multicellular organisms depends on the aging of their cells, which depends on molecular events occurring in each cell. However, the aging of unicellular populations seeded in new niches and the aging of metazoans are surprisingly similar, indicating that the multiscale aspects of aging may have been conserved since the beginnings of cellular life on Earth. This underlines the importance of aging research in unicellular organisms such as a recent study by Lorenz et al., [(2009) Proc. Natl. Acad. Sci. U.S.A. 106, 1145-1150]. In their paper, the authors combine computational network identification with extensive experimentation and literature mining to discover and validate numerous regulatory interactions among ten genes involved in the cellular response to glucose starvation. Since low levels of glucose (calorie restriction) have been known to extend the longevity of various eukaryotes, the authors test the effect of Snf1 kinase overexpression on chronological aging and discover that this key regulator of glucose repression and two of its newly discovered synergistic repressors significantly affect the chronological lifespan of baker's yeast.
    06/2010; 4(3-4):94-9. DOI:10.2976/1.3366829
  • Source
    • "Although the plasma membrane glucose sensors Snf3 has not been linked to life span regulation, deletion of HXK2, the first glycolytic enzyme that phosphorylates glucose and functions as glucose-responsive transcriptional repressor , extends yeast RLS (Lin et al. 2000; 2002; Kaeberlein et al. 2005; Lamming et al. 2005). Similarly, mutants with deficiencies in the Ras/Cyr1/cAMP/PKA signaling pathway and those lacking both HXK2 and MIG1, a transcription factor involved in glucose repression, have extended chronological life span (Longo 1997; Fabrizio et al. 2001; Lorenz et al. 2009). The partially-conserved Ras/cAMP/PKA and Tor/Sch9 signaling pathways integrate the nutrient and other environmental cues to regulate cell growth and division in yeast (Jorgensen et al. 2004; Martin et al. 2004; Santangelo 2006). "
    Calorie Restriction, Aging and Longevity, 01/2010: chapter 6, Part 1: pages 97-109; Springer Science.
Show more


Available from