Regulatory Circuitry Governing Fungal Development, Drug Resistance, and Disease
Bibliographic record
Abstract
Pathogenic fungi have become a leading cause of human mortality due to the increasing frequency of fungal infections in immunocompromised populations and the limited armamentarium of clinically useful antifungal drugs. Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus are the leading causes of opportunistic fungal infections. In these diverse pathogenic fungi, complex signal transduction cascades are critical for sensing environmental changes and mediating appropriate cellular responses. For C. albicans, several environmental cues regulate a morphogenetic switch from yeast to filamentous growth, a reversible transition important for virulence. Many of the signaling cascades regulating morphogenesis are also required for cells to adapt and survive the cellular stresses imposed by antifungal drugs. Many of these signaling networks are conserved in C. neoformans and A. fumigatus, which undergo distinct morphogenetic programs during specific phases of their life cycles. Furthermore, the key mechanisms of fungal drug resistance, including alterations of the drug target, overexpression of drug efflux transporters, and alteration of cellular stress responses, are conserved between these species. This review focuses on the circuitry regulating fungal morphogenesis and drug resistance and the impact of these pathways on virulence. Although the three human-pathogenic fungi highlighted in this review are those most frequently encountered in the clinic, they represent a minute fraction of fungal diversity. Exploration of the conservation and divergence of core signal transduction pathways across C. albicans, C. neoformans, and A. fumigatus provides a foundation for the study of a broader diversity of pathogenic fungi and a platform for the development of new therapeutic strategies for fungal disease.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".