Leishmania Exosomes Modulate Innate and Adaptive Immune Responses through Effects on Monocytes and Dendritic Cells
Bibliographic record
Abstract
We investigated the properties of leishmania exosomes with respect to influencing innate and adaptive immune responses. Exosomes from Leishmania donovani modulated human monocyte cytokine responses to IFN-γ in a bimodal fashion by promoting IL-10 production and inhibiting that of TNF-α. Moreover, these vesicles were inhibitory with respect to cytokine responses (IL-12p70, TNF-α, and IL-10) by human monocyte-derived dendritic cells. Exosomes from wild-type (WT) L. donovani failed to prime monocyte-derived dendritic cells to drive the differentiation of naive CD4 T cells into IFN-γ-producing Th1 cells. In contrast, vesicles from heat shock protein (HSP)100(-/-) L. donovani showed a gain-of-function and proinflammatory phenotype and promoted the differentiation of naive CD4 lymphocytes into Th1 cells. Proteomic analysis showed that exosomes from WT and HSP100(-/-) leishmania had distinct protein cargo, suggesting that packaging of proteins into exosomes is dependent in part on HSP100. Treatment of C57BL/6 mice with WT L. donovani exosomes prior to challenge with WT organisms exacerbated infection and promoted IL-10 production in the spleen. In contrast, HSP100(-/-) exosomes promoted spleen cell production of IFN-γ and did not adversely affect hepatic parasite burdens. Furthermore, the proparasitic properties of WT exosomes were not species specific because BALB/c mice exposed to Leishmania major exosomes showed increased Th2 polarization and exacerbation of disease in response to infection with L. major. These findings demonstrate that leishmania exosomes are predominantly immunosuppressive. Moreover, to our knowledge, this is the first evidence to suggest that changes in the protein cargo of exosomes may influence the impact of these vesicles on myeloid cell function.
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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".