Antifungals influence the immune-related transcriptomic landscape of human monocytes after Aspergillus fumigatus infection
Bibliographic record
Abstract
The heterogeneity of clinical responses to antifungals in aspergillosis is partially understood. We hypothesized that besides direct antifungal effects, these discrepancies may be related to different immunomodulatory profiles. Human THP-1 monocytes were coincubated in vitro with Aspergillus fumigatus and variable concentrations of voriconazole (0.5, 1 and 2 mg/l), caspofungin (1 and 2 mg/l), amphotericin B deoxycholate (0.25, 0.5 and 1 mg/l) and liposomal amphotericin B (1, 2 and 3 mg/l). After 6 h of coincubation, total cellular RNA was extracted, converted into cDNA, and transcription of 84 genes involved in antifungal immunity was measured through RT-qPCR. The presence of A. fumigatus was the main driver of the global immune-related transcriptomic response. After Aspergillus infection, thirty genes were upregulated, while 19 genes were downregulated. Discrepancies across antifungals were also evident; voriconazole-containing conditions showed similar reaction to natural infection, while the use of liposomal Amphotericin B significantly decreased the inflammatory response. Chemokines (notably CCL20 and CXCL2) and pro-inflammatory cytokines (IL1A, IL1B, IL23, G-CSF) exhibited the most pronounced differences across antifungals. Pattern recognition receptors and adaptor protein transcription were minimally affected. Protein-protein-interaction network analysis showed that IL23A played a dominant role in upregulated genes. Pathway enrichment analysis indicated that cytokine-cytokine receptor integration, TNF signaling pathways and Toll-like receptor pathways were highly involved. This exploratory study confirms the heterogeneous immunomodulatory role of antifungals. Overall, voriconazole appears to maintain an early pro-inflammatory response seen in natural infection. Assessment of immunomodulatory response with clinical response may provide a better rationale for differences observed across antifungals.
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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".