4.7 Article

Inference of transcription modification in long-live yeast strains from their expression profiles

Journal

BMC GENOMICS
Volume 8, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/1471-2164-8-219

Keywords

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Funding

  1. NHGRI NIH HHS [P50 HG002790] Funding Source: Medline
  2. NIGMS NIH HHS [R01 GM075308, R01 GM75308-01] Funding Source: Medline

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Background: Three kinases: Sch9, PKA and TOR, are suggested to be involved in both the replicative and chronological ageing in yeast. They function in pathways whose down-regulation leads to life span extension. Several stress response proteins, including two transcription factors Msn2 and Msn4, mediate the longevity extension phenotype associated with decreased activity of either Sch9, PKA, or TOR. However, the mechanisms of longevity, especially the underlying transcription program have not been fully understood. Results: We measured the gene expression profiles in wild type yeast and three long-lived mutants: sch9 Delta, ras2 Delta, and tor1 Delta. To elucidate the transcription program that may account for the longevity extension, we identified the transcription factors that are systematically and significantly associated with the expression differentiation in these mutants with respect to wild type by integrating microarray expression data with motif and ChIP-chip data, respectively. Our analysis suggests that three stress response transcription factors, Msn2, Msn4 and Gis1, are activated in all the three mutants. We also identify some other transcription factors such as Fhl1 and Hsf1, which may also be involved in the transcriptional modification in the long-lived mutants. Conclusion: Combining microarray expression data with other data sources such as motif and ChIP-chip data provides biological insights into the transcription modification that leads to life span extension. In the chronologically long-lived mutant: sch9 Delta, ras2 Delta, and tor1 Delta, several common stress response transcription factors are activated compared with the wild type according to our systematic transcription inference.

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