Combatting fungal infections through the discovery and elucidation of novel anti‐virulence strategies
Bibliographic record
Abstract
Fungal pathogens are critically important threats to global health with over 300 million people affected by serious fungal diseases worldwide. Fungal pathogens have evolved sophisticated strategies, including the secretion of virulence factors to interfere with host cell functions and to perturb immune responses. Our ‘infectome’ analysis identifies previously undescribed proteins involved in fungal virulence and host immune response, representing an opportunity to elucidate molecular mechanisms of host‐pathogen interplay during disease. Using state‐of‐the‐art mass spectrometry‐based proteomics we profile the total proteome and secretome of Cryptococcus neoformans wild‐type (H99) under in vitro growth conditions. We also define the infectome of C. neoformans and BALB/c macrophages in single runs using high resolution mass spectrometry on a Quadrupole Orbitrap instrument. The in vitro and infectome datasets were integrated using Perseus and candidate fungal proteins of interest were prioritized based on predicted secretory roles, novelty, and abundance profiles. Deletion strains were constructed by double‐joint PCR and characterized by phenotypic screening and cell death assays. Virulence‐associated candidates will be evaluated in a murine infection model and further characterized by immunofluorescence and interactome analyses. Our preliminary results identify interactions between the host and pathogen critical for disease. Comprehensive profiling of infection from both host and pathogen perspectives unveils new anti‐virulence strategies to combat fungal infection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".