Alteration of macrophage signalling and functions by the protozoan parasite «Leishmania»
Bibliographic record
Abstract
Parasites of the genus Leishmania are able to secure their survival and propagation within their host by altering key signalling pathways involved in the ability of macrophages (MØs) to directly kill pathogens or to activate cells of the adaptive immune system. One important step in this immune evasion process is the Leishmania-induced activation of host protein tyrosine phosphatase SHP-1. SHP-1 has been shown to directly inactivate JAK2 and Erk1/2, and to play a role in the negative regulation of several transcription factors involved in MØ activation such as: NF-B, STAT-1α, and AP-1. These signalling alterations contribute to the inactivation of critical MØ functions such as the production of IFN-γ-induced nitric oxide (NO), a free radical associated with parasite killing and clearance. In addition to interfering with IFN-γ receptor signalling, Leishmania is able to alter several LPS-mediated responses (e.g. IL-12, TNF-α, NO production) through mechanisms not yet fully understood. A main goal of this study was to better understand the mechanisms used by the parasite to block Toll-like receptor (TLR)-mediated functions. Experiments performed revealed a pivotal role for SHP-1 in the inhibition of TLR-induced MØ activation through binding to and inactivating IL-1 receptor-associated kinase 1 (IRAK-1). We identified the binding site as an evolutionarily conserved ITIM-like motif, which we named kinase tyrosine-based inhibitory motif (KTIM). Further experiments and sequence analysis revealed that several cytosolic kinases other than IRAK-1 possess potential KTIMs, suggesting it could represent a regulatory mechanism widely used by kinases. The final experimental section aimed to explore the differential ability of the two different stages of Leishmania, promastigotes and amastigotes, to alter MØ signalling and function. In conclusion, this work uncovers a new mechanism whereby Leishmania is able
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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".