Auranofin is active against Histoplasma capsulatum and reduces the expression of virulence-related genes
Bibliographic record
Abstract
BACKGROUND: Auranofin is an approved anti-rheumatic drug that has a broad-range inhibitory action against several microorganisms, including human pathogenic fungi. The auranofin activity against Histoplasma capsulatum, the dimorphic fungus that causes histoplasmosis, has not been properly addressed. Since there are few therapeutic options for this life-threatening systemic mycosis, this study evaluated the effects of auranofin on H. capsulatum growth and expression of virulence factors. METHODOLOGY/PRINCIPAL FINDINGS: Minimal inhibitory and fungicidal concentrations (MIC and MFC, respectively) of auranofin against 15 H. capsulatum strains with distinct genetic backgrounds were determined using the yeast form of the fungus and a microdilution protocol. Auranofin activity was also assessed on a macrophage model of infection and on a Tenebrio molitor invertebrate animal model. Expression of virulence-related genes was compared between auranofin treated and untreated H. capsulatum yeast cells using a quantitative PCR assay. Auranofin affected the growth of different strains of H. capsulatum, with MIC and MFC values ranging from 1.25 to 5.0 μM and from 2.5 to >10 μM, respectively. Auranofin was able to kill intracellular H. capsulatum yeast cells and conferred protection against the fungus in the experimental animal model of infection. Moreover, the expression of catalase A, HSP70, superoxide dismutase, thioredoxin reductase, serine proteinase, cytochrome C peroxidase, histone 2B, formamidase, metallopeptidase, Y20 and YPS3 proteins were reduced after six hours of auranofin treatment. CONCLUSIONS/SIGNIFICANCE: Auranofin is fungicidal against H. capsulatum and reduces the expression of several virulence-related genes, which makes this anti-rheumatic drug a good candidate for new medicines against histoplasmosis.
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 | 0.000 | 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".