Functional Divergence of Hsp90 Genetic Interactions in Biofilm and Planktonic Cellular States
Bibliographic record
Abstract
Candida albicans is among the most prevalent opportunistic fungal pathogens. Its capacity to cause life-threatening bloodstream infections is associated with the ability to form biofilms, which are intrinsically drug resistant reservoirs for dispersal. A key regulator of biofilm drug resistance and dispersal is the molecular chaperone Hsp90, which stabilizes many signal transducers. We previously identified 226 C. albicans Hsp90 genetic interactors under planktonic conditions, of which 56 are involved in transcriptional regulation. Six of these transcriptional regulators have previously been implicated in biofilm formation, suggesting that Hsp90 genetic interactions identified in planktonic conditions may have functional significance in biofilms. Here, we explored the relationship between Hsp90 and five of these transcription factor genetic interactors: BCR1, MIG1, TEC1, TUP1, and UPC2. We deleted each transcription factor gene in an Hsp90 conditional expression strain, and assessed biofilm formation and morphogenesis. Strikingly, depletion of Hsp90 conferred no additional biofilm defect in the mutants. An interaction was observed in which deletion of BCR1 enhanced filamentation upon reduction of Hsp90 levels. Further, although Hsp90 modulates expression of TEC1, TUP1, and UPC2 in planktonic conditions, it has no impact in biofilms. Lastly, we probed for physical interactions between Hsp90 and Tup1, whose WD40 domain suggests that it might interact with Hsp90 directly. Hsp90 and Tup1 formed a stable complex, independent of temperature or developmental state. Our results illuminate a physical interaction between Hsp90 and a key transcriptional regulator of filamentation and biofilm formation, and suggest that Hsp90 has distinct genetic interactions in planktonic and biofilm cellular states.
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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".