RNA interference reveals a role for TLR2 and TLR3 in the recognition of <i>Leishmania donovani</i> promastigotes by interferon–γ‐primed macrophages
Bibliographic record
Abstract
Leishmania donovani promastigotes evade the induction of a proinflammatory response during their invasion of naive macrophages. However, their entry into IFN-gamma-primed macrophages is accompanied by the secretion of nitric oxide (NO) and proinflammatory cytokines. In the present study, we addressed the hypothesis that priming with IFN-gamma induces the expression of a receptor that enables mouse macrophages to recognize L. donovani promastigotes. We observed that in IFN-gamma-primed macrophages, L. donovani promastigotes stimulated Interleukin-1 receptor-associated kinase-1 (IRAK-1) activity. We next showed that Toll-like receptor (TLR)3 is barely detectable in naive macrophages but is expressed in IFN-gamma-treated macrophages. Silencing of TLR3, TLR2, IRAK-1 and myeloid differentiation factor 88 (MyD88) expression by RNA interference revealed that both TLR are involved in the secretion of NO and TNF-alpha induced by L. donovani promastigotes. Using L. donovani mutants, we showed that TLR2-mediated responses are dependent on Galbeta1,4Manalpha-PO(4)-containing phosphoglycans, whereas TLR3-mediated responses are independent of these glycoconjugates. Furthermore, our data indicate a participation of TLR2 and TLR3 in the phagocytosis of L. donovani promastigotes and a role for TLR3 in the leishmanicidal activity of the IFN-gamma-primed macrophages. Collectively, our data are consistent with a model where recognition of L. donovani promastigotes depends on the macrophage activation status and requires the expression of TLR3.
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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 | 0.001 |
| 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".