Global Lipidomics Reveals the Lipid Composition Heterogeneity of Extracellular Vesicles from Drug-Resistant Leishmania
Bibliographic record
Abstract
Background: The rise of drug-resistant Leishmania strains presents a significant challenge in the treatment of Leishmaniasis, a neglected tropical disease. Extracellular vesicles (EVs) produced by these parasites have gained attention for their role in drug resistance and host–pathogen interactions. Methods: This study developed and applied a novel lipidomics workflow to explore the lipid profiles of EVs from three types of drug-resistant Leishmania infatum strains compared to a wild-type strain. EVs were isolated through ultracentrifugation, and their lipid content was extracted using a modified Matyash protocol. LC-MS analysis was performed, and data processing in MS-DIAL enabled lipid identification and quantification. Statistical analysis in MetaboAnalyst revealed strain-specific lipid alterations, highlighting potential links between lipid composition and drug resistance mechanisms. Results: Our results show distinct alterations in lipid composition associated with drug resistance. Specifically, drug-resistant strains exhibited reduced levels of phosphatidylcholine (PC) and phosphatidylglycerol (PG), particularly in the amphotericin B-resistant strain LiAmB1000.1. Sterol and glycerolipid species, including cholesteryl ester (CE) and triacylglycerol (TG) were also found to be diminished in LiAmB1000.1. These changes suggest significant lipid remodeling under drug pressure, potentially altering the biophysical properties of EV membranes and their capacity for molecule transfer. Furthermore, the lipidomic profiles of EVs from the other resistant strains, LiSb2000.1 and LiMF200.5, also displayed unique alterations, underscoring strain-specific adaptations to different drug resistance mechanisms. Conclusions: These significant alterations in lipid composition suggest potential lipid-based mechanisms underlying drug resistance in Leishmania, providing new avenues for therapeutic intervention.
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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".