Post-Translational Modifications Drive Success and Failure of Fungal–Host Interactions
Bibliographic record
Abstract
Post-translational modifications (PTMs) change the structure and function of proteins and regulate a diverse array of biological processes. Fungal pathogens rely on PTMs to modulate protein production and activity during infection, manipulate the host response, and ultimately, promote fungal survival. Given the high mortality rates of fungal infections on a global scale, along with the emergence of antifungal-resistant species, identifying new treatment options is critical. In this review, we focus on the role of PTMs (e.g., phosphorylation, acetylation, ubiquitination, glycosylation, and methylation) among the highly prevalent and medically relevant fungal pathogens, Candida spp., Aspergillus spp., and Cryptococcus spp. We explore the role of PTMs in fungal stress response and host adaptation, the use of PTMs to manipulate host cells and the immune system upon fungal invasion, and the importance of PTMs in conferring antifungal resistance. We also provide a critical view on the current knowledgebase, pose questions key to our understanding of the intricate roles of PTMs within fungal pathogens, and provide research opportunities to uncover new therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".