Unravelling the proteomic signature of extracellular vesicles released by drug-resistant Leishmania infantum parasites
Bibliographic record
Abstract
Leishmaniasis constitutes the 9th largest disease burden among all infectious diseases. Control of this disease is based on a short list of chemotherapeutic agents headed by pentavalent antimonials, followed by miltefosine and amphotericin B; drugs that are far from ideal due to host toxicity, elevated cost, limited access, and high rates of drug resistance. Knowing that the composition of extracellular vesicles (EVs) can vary according to the state of their parental cell, we hypothesized that EVs released by drug-resistant Leishmania infantum parasites could contain unique and differently enriched proteins depending on the drug-resistance mechanisms involved in the survival of their parental cell line. To assess this possibility, we studied EV production, size, morphology, and protein content of three well-characterized drug-resistant L. infantum cell lines and a wild-type strain. Our results are the first to demonstrate that drug-resistance mechanisms can induce changes in the morphology, size, and distribution of L. infantum EVs. In addition, we identified L. infantum's core EV proteome. This proteome is highly conserved among strains, with the exception of a handful of proteins that are enriched differently depending on the drug responsible for induction of antimicrobial resistance. Furthermore, we obtained the first snapshot of proteins enriched in EVs released by antimony-, miltefosine- and amphotericin-resistant parasites. These include several virulence factors, transcription factors, as well as proteins encoded by drug-resistance genes. This detailed study of L. infantum EVs sheds new light on the potential roles of EVs in Leishmania biology, particularly with respect to the parasite's survival in stressful conditions. This work outlines a crucial first step towards the discovery of EV-based profiles capable of predicting response to antileishmanial agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".