Mass Spectrometry-Based Proteomics of Fungal Pathogenesis, Host–Fungal Interactions, and Antifungal Development
Bibliographic record
Abstract
The prevalence of fungal diseases is increasing on a global scale, ranging from acute to systemic infections caused by commensal or pathogenic microorganisms, often associated with the immune status of the host. Morbidity and mortality rates remain high and our ability to treat fungal infections is challenged by a limited arsenal of antifungal agents and the emergence of drug resistant pathogens. There is a high demand for new approaches to elucidate the fungal mechanisms of pathogenesis and the interplay between host and pathogen to discover novel treatment options. Moreover, the need for improved drug efficacy and reduced host toxicity requires the identification and characterization of antifungal biological targets and molecular mechanisms of action. Mass spectrometry (MS)-based proteomics is a rapidly advancing field capable of addressing these priorities by providing comprehensive information on the dynamics of cellular processes, modifications, and interactions. In this Review, we focus on applications of MS-based proteomics in a diverse array of fungal pathogens and host systems to define and distinguish the molecular details of fungal pathogenesis and host-fungal interactions. We also explore the emerging role of MS-based proteomics in the discovery and development of novel antifungal therapies and provide insight into the future of MS-based proteomics in fungal biology.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".