Chaperone Networks in Fungal Pathogens of Humans
Bibliographic record
Abstract
The heat shock proteins (HSPs) function as chaperones to facilitate proper folding and modification of proteins and are of particular importance when organisms are subjected to unfavourable conditions. The human fungal pathogens are subjected to such conditions within the context of infection as they are exposed to human body temperature as well as the host immune response. Herein, the roles of the major classes of HSPs are briefly reviewed and their known contributions in human fungal pathogens are described with a focus on Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus. The Hsp90s and Hsp70s in human fungal pathogens broadly contribute to thermotolerance, morphological changes required for virulence, and tolerance to antifungal drugs. There are also examples of J domain co-chaperones and small HSPs influencing the elaboration of virulence factors in human fungal pathogens. However, there are diverse members in these groups of chaperones and there is still much to be uncovered about their contributions to pathogenesis. These HSPs do not act in isolation, but rather they form a network with one another. Interactions between chaperones define their specific roles and enhance their protein folding capabilities. Recent efforts to characterize these HSP networks in human fungal pathogens have revealed that there are unique interactions relevant to these pathogens, particularly under stress conditions. The chaperone networks in the fungal pathogens are also emerging as key coordinators of pathogenesis and antifungal drug tolerance, suggesting that their disruption is a promising strategy for the development of antifungal therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".