Extracellular vesicles produced during fungal infection in humans are immunologically active
Bibliographic record
Abstract
Abstract Of the known 1.5 million fungal species, Candida spp., Cryptococcus spp., and Paracoccidioides spp. are the main pathogenic species causing serious diseases with almost two million annual deaths. The diagnosis and treatment of fungal infections are challenging since of the limited access to diagnostic tests and the emergence of antifungal resistance. Extracellular vesicles (EVs) promote the interactions of fungal cells with other organisms and play an important role in the pathogen–host relationship. Owing to the complexity of fungal EVs and the lack of clinical studies on their roles in human infections, we studied the EVs from the serum and urine samples of patients with fungal infections caused by Candida albicans , Cryptococcus neoformans , and Paracoccidioides brasiliensis and determined their roles. Steroids, sphingolipids, and fatty acids were identified as the main secondary metabolites via mass spectrometry analysis. We asked whether these metabolites in EVs could play roles in modulating the host immune response. Our findings revealed the polarization of the proinflammatory profile in murine and human macrophages, with the increased production of cytokines, such as the tumor necrosis factor-α, interferon-γ, and interleukin-6, and an increased expression of the inducible nitric oxide synthase gene, a M1 response marker. Therefore, circulating EVs from patients with fungal infections are likely involved in the disease pathophysiology. Our findings provide insights into the roles of EVs in fungal infections in clinical samples and in vitro, suggesting possible targets for systemic mycoses therapy. Significance Statement Fungal infections cause approximately 1.6 million deaths annually. Due to therapeutic and diagnostic limitations, it is mandatory to understand and develop new immunological interventions. Despite several in vitro studies on the production of extracellular vesicles (EVs) from fungal pathogens, this study is a pioneer in the identification and characterization of EVs in the course of fungal infection in humans. Our group demonstrated the presence of EVs in clinical samples from patients diagnosed with candidiasis, cryptococcosis, and paracoccidioidomycosis, as well as the EVs interaction produced by host and fungal pathogen with the immune system, resulting in relationships that may be beneficial for the progression or elimination of fungal disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".