An exosome-based secretion pathway is responsible for protein export from <i>Leishmania</i> and communication with macrophages
Bibliographic record
Abstract
Specialized secretion systems are used by numerous bacterial pathogens to export virulence factors into host target cells. Leishmania and other eukaryotic intracellular pathogens also deliver effector proteins into host cells; however, the mechanisms involved have remained elusive. In this report, we identify exosome-based secretion as a general mechanism for protein secretion by Leishmania, and show that exosomes are involved in the delivery of proteins into host target cells. Comparative quantitative proteomics unambiguously identified 329 proteins in Leishmania exosomes, accounting for >52% of global protein secretion from these organisms. Our findings demonstrate that infection-like stressors (37 degrees C +/- pH 5.5) upregulated exosome release more than twofold and also modified exosome protein composition. Leishmania exosomes and exosomal proteins were detected in the cytosolic compartment of infected macrophages and incubation of macrophages with exosomes selectively induced secretion of IL-8, but not TNF-alpha. We thus provide evidence for an apparently broad-based mechanism of protein export by Leishmania. Moreover, we describe a mechanism for the direct delivery of Leishmania molecules into macrophages. These findings suggest that, like mammalian exosomes, Leishmania exosomes function in long-range communication and immune modulation.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".