Proteome and secretome profiling of zinc availability in Cryptococcus neoformans identifies Wos2 as a subtle influencer of fungal virulence determinants
Bibliographic record
Abstract
BACKGROUND: Fungal infections impact over 25% of the global population. For the opportunistic fungal pathogen, Cryptococcus neoformans, infection leads to cryptococcosis. In the presence of the host, disease is enabled by elaboration of sophisticated virulence determinants, including polysaccharide capsule, melanin, thermotolerance, and extracellular enzymes. Conversely, the host protects itself from fungal invasion by regulating and sequestering transition metals (e.g., iron, zinc, copper) important for microbial growth and survival. RESULTS: Here, we explore the intricate relationship between zinc availability and fungal virulence via mass spectrometry-based quantitative proteomics. We observe a core proteome along with a distinct zinc-regulated protein-level signature demonstrating a shift away from transport and ion binding under zinc-replete conditions towards transcription and metal acquisition under zinc-limited conditions. In addition, we revealed a novel connection among zinc availability, thermotolerance, as well as capsule and melanin production through the detection of a Wos2 ortholog in the secretome under replete conditions. CONCLUSIONS: Overall, we provide new biological insight into cellular remodeling at the protein level of C. neoformans under regulated zinc conditions and uncover a novel connection between zinc homeostasis and fungal virulence determinants.
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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".