Identifying Stress Transcription Factors Using Gene Expression and TF-Gene Association Data

Abstract

Unicellular organisms such as yeasts have evolved to survive environmental stresses by rapidly reorganizing the genomic expression program to meet the challenges of harsh environments. The complex adaptation mechanisms to stress remain to be elucidated. In this study, we developed Stress Transcription Factor Identifi cation Algorithm (STFIA), which integrates gene expression and TF-gene association data to identify the stress transcription factors (TFs) of six kinds of stresses. We identifi ed some general stress TFs that are in response to various stresses, and some specifi c stress TFs that are in response to one specifi c stress. The biological signifi cance of our fi ndings is validated by the literature. We found that a small number of TFs may be suffi cient to control a wide variety of expression patterns in yeast under different stresses. Two implications can be inferred from this observation. First, the adaptation mechanisms to different stresses may have a bowtie structure. Second, there may exist extensive regulatory cross-talk among different stress responses. In conclusion, this study proposes a network of the regulators of stress responses and their mechanism of action.

Systematic identification of yeast cell cycle transcription factors using multiple data sources

“…[ 2 ]. The hypergeometric distribution is used to test the statistical significance [ 46 , 47 ]. The procedure for checking whether TF j is a cell cycle TF is as follows.…”

Cross-stress resistance in Saccharomyces cerevisiae yeast—new insight into an old phenomenon

“…Their activity, coordinated with other specific factors, leads to a characteristic overlapping of their effects. Overlapping of the effects of the stress response at the regulator level is also sometimes referred to as regulatory cross-talk (Wu and Chen 2009 ).…”

Identifying gene regulatory modules of heat shock response in yeast

“…For example, let R ={TF u , TF v , TF w } be a TF set, G = {gene k | c k , u = c k , v = c k , w = 1} be the set of genes that are regulated by all the TFs in R , S be the set of heat-inducible genes in the yeast genome identified in Step 3, T = G ∩ S be the set of heat-inducible genes that are regulated by all the TFs in R , and Y be the set of all genes in the yeast genome. Then the p -value for rejecting the null hypothesis (H 0 : R is not a heat-responsive TF set) is calculated as [ 37 ]:…”