Hsp90 Orchestrates Transcriptional Regulation by Hsf1 and Cell Wall Remodelling by MAPK Signalling during Thermal Adaptation in a Pathogenic Yeast
Bibliographic record
Abstract
Thermal adaptation is essential in all organisms. In yeasts, the heat shock response is commanded by the heat shock transcription factor Hsf1. Here we have integrated unbiased genetic screens with directed molecular dissection to demonstrate that multiple signalling cascades contribute to thermal adaptation in the pathogenic yeast Candida albicans. We show that the molecular chaperone heat shock protein 90 (Hsp90) interacts with and down-regulates Hsf1 thereby modulating short term thermal adaptation. In the longer term, thermal adaptation depends on key MAP kinase signalling pathways that are associated with cell wall remodelling: the Hog1, Mkc1 and Cek1 pathways. We demonstrate that these pathways are differentially activated and display cross talk during heat shock. As a result ambient temperature significantly affects the resistance of C. albicans cells to cell wall stresses (Calcofluor White and Congo Red), but not osmotic stress (NaCl). We also show that the inactivation of MAP kinase signalling disrupts this cross talk between thermal and cell wall adaptation. Critically, Hsp90 coordinates this cross talk. Genetic and pharmacological inhibition of Hsp90 disrupts the Hsf1-Hsp90 regulatory circuit thereby disturbing HSP gene regulation and reducing the resistance of C. albicans to proteotoxic stresses. Hsp90 depletion also affects cell wall biogenesis by impairing the activation of its client proteins Mkc1 and Hog1, as well as Cek1, which we implicate as a new Hsp90 client in this study. Therefore Hsp90 modulates the short term Hsf1-mediated activation of the classic heat shock response, coordinating this response with long term thermal adaptation via Mkc1- Hog1- and Cek1-mediated cell wall remodelling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".