Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes

Abstract

We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper-and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (ϳ 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.

A genome-wide cis-regulatory element discovery method based on promoter sequences and gene co-expression networks

“…It is important to note that the reference microarray gene expression dataset [38] used for modeling here is different from the gene expression dataset [36] used for constructing the co-expression networks and cis -regulatory network. From Figure 2, it can be seen that our main model has the best result based on both the correlation and the root mean square error (RMSE).…”

“…To construct a gene co-expression network, we used gene expression microarray data from [36], which contains the yeast gene expression data in response to a variety of environmental changes. This data set contains 173 arrays, and as in most previous studies, we selected the top 3000 genes with the highest variances [36].…”

“…This data set contains 173 arrays, and as in most previous studies, we selected the top 3000 genes with the highest variances [36]. After quantile normalizing the expression data, we constructed a co-expression network using the method described in [37].…”

A neural network algorithm for semi-supervised node label learning from unbalanced data

“…-Expr data contains 250 gene expression measures of 4523 genes relative to two experiments described in (Spellman et al, 1998) and (Gasch et al, 2000).…”

Transcript level coordination of carbon pathways during silicon starvation‐induced lipid accumulation in the diatom Thalassiosira pseudonana

“…Silicon is required for cell wall synthesis and growth in most diatoms, and silicon starvation also induces growth arrest and the formation of lipid droplets (Shifrin & Chisholm, 1981; Roessler, 1988; Traller & Hildebrand, 2013). In contrast to nitrogen starvation (Gasch et al ., 2000; Hockin et al ., 2012; Bender et al ., 2014), silicon starvation has little effect on overall metabolic activities, making it a unique approach to distinguish between cell cycle arrest and secondary effects that arise during nutrient limitation (Darley & Volcani, 1969; Claquin et al ., 2002). …”