Deletion of a subgroup of ribosome-related genes minimizes hypoxia-induced changes and confers hypoxia tolerance.

Hypoxia is a widely occurring condition experienced by diverse organisms under numerous physiological and disease conditions. To probe the molecular mechanisms underlying hypoxia responses and tolerance, we performed a genome-wide screen to identify mutants with enhanced hypoxia tolerance in the model eukaryote, the yeast Saccharomyces cerevisiae. Yeast provides an excellent model for genomic and proteomic studies of hypoxia. We identified five genes whose deletion significantly enhanced hypoxia tolerance. They are RAI1, NSR1, BUD21, RPL20A, and RSM22, all of which encode functions involved in ribosome biogenesis. Further analysis of the deletion mutants showed that they minimized hypoxia-induced changes in polyribosome profiles and protein synthesis. Strikingly, proteomic analysis by using the iTRAQ profiling technology showed that a substantially fewer number of proteins were changed in response to hypoxia in the deletion mutants, compared with the parent strain. Computational analysis of the iTRAQ data indicated that the activities of a group of regulators were regulated by hypoxia in the wild-type parent cells, but such regulation appeared to be diminished in the deletion strains. These results show that the deletion of one of the genes involved in ribosome biogenesis leads to the reversal of hypoxia-induced changes in gene expression and related regulators. They suggest that modifying ribosomal function is an effective mechanism to minimize hypoxia-induced specific protein changes and to confer hypoxia tolerance. These results may have broad implications in understanding hypoxia responses and tolerance in diverse eukaryotes ranging from yeast to humans.

A predictive model of the oxygen and heme regulatory network in yeast.

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.

Regulation of the HAP1 gene involves positive actions of histone deacetylases.

The yeast transcriptional regulator Hap1 promotes both transcriptional activation and repression. Previous studies have shown that Hap1 binds to the promoter of its own gene and represses its transcription. In this report, we identified the DNA site that allows Hap1-binding with high affinity. This Hap1-binding site contains only one CGG triplet and is distinct from the typical Hap1-binding upstream activation sequences (UASs) mediating transcriptional activation. Furthermore, at the HAP1 promoter, Ssa is bound to DNA with Hap1, whereas Hsp90 is not bound. Intriguingly, we found that histone deacetylases, including Rpd3, Hda1, Sin3 and Hos1, are not required for the repression of the HAP1 gene by Hap1. Rather, they are required for transcriptional activation of the HAP1 promoter, and this requirement is dependent on the HAP1 basal promoter. These results reveal a complex mechanism of transcriptional regulation at the HAP1 promoter, involving multiple DNA elements and regulatory proteins.