Regulation of Toll‐like receptor‐2 expression by the Gal‐lectin of <i>Entamoeba histolytica</i>
Bibliographic record
Abstract
The Gal/GalNAc lectin (Gal-lectin) of Entamoeba histolytica is a surface molecule involved in parasite adherence to host cells and is the most promising subunit vaccine candidate against amoebiasis. As macrophages are the major effector cells in host defense against amoebas, we studied the molecular mechanisms by which Gal-lectin activates macrophage. Microarray analysis showed that Gal-lectin up-regulated mRNAs of several cytokines and receptor genes involved in proinflammatory responses. The mechanism whereby the Gal-lectin regulates Toll-like receptor 2 (TLR-2) expression in macrophages was studied. Native Gal-lectin increased TLR-2 mRNA expression in a dose- and time-dependent fashion; peak response occurred with 1 microg/ml after 2 h stimulation. By immunoflourescence, enhanced surface expression of TLR-2 was observed after 12 h. With the use of nonoverlapping anti-Gal-lectin monoclonal antibodies that map to the carbohydrate recognition domain, amino acid 596-1082 was identified as the TLR-2 stimulating region. The Gal-lectin increased TLR-2 gene transcription, and the half-life of the mRNA transcripts was 1.4 h. Inhibition of nuclear factor (NF)-kappaB suppressed TLR-2 mRNA induction by the Gal-lectin. Moreover, cells pretreated with an inhibitor of p38 kinase (SB 208530) inhibited Gal-lectin induced TLR-2 mRNA expression by 40%. We conclude that the Gal-lectin activates NF-kappaB and MAP kinase-signaling pathways in macrophages culminating in the induction of several genes including TLR-2 and hypothesize that this could have a significant impact on macrophage activation and contribute to amoebic pathogenesis.
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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".