The Schizosaccharomyces pombe Hsp104 Disaggregase Is Unable to Propagate the [PSI+] Prion

The molecular chaperone Hsp104 is a crucial factor in the acquisition of thermotolerance in yeast. Under stress conditions, the disaggregase activity of Hsp104 facilitates the reactivation of misfolded proteins. Hsp104 is also involved in the propagation of fungal prions. For instance, the well-characterized [PSI+] prion of Saccharomyces cerevisiae does not propagate in Δhsp104 cells or in cells overexpressing Hsp104. In this study, we characterized the functional homolog of Hsp104 from Schizosaccharomyces pombe (Sp_Hsp104). As its S. cerevisiae counterpart, Sp_hsp104+ is heat-inducible and required for thermotolerance in S. pombe. Sp_Hsp104 displays low disaggregase activity and cannot propagate the [PSI+] prion in S. cerevisiae. When overexpressed in S. cerevisiae, Sp_Hsp104 confers thermotolerance to Δhsp104 cells and reactivates heat-aggregated proteins. However, overexpression of Sp_Hsp104 does not propagate nor eliminate [PSI+]. Strikingly, [PSI+] was cured by overexpression of a chimeric chaperone bearing the C-terminal domain (CTD) of the S. cerevisiae Hsp104 protein. Our study demonstrates that the ability to untangle aggregated proteins is conserved between the S. pombe and S. cerevisiae Hsp104 homologs, and points to a role of the CTD in the propagation of the S. cerevisiae [PSI+] prion

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