Angiopoietin-1 inhibits toll-like receptor 4 signalling in cultured endothelial cells: role of miR-146b-5p
Bibliographic record
Abstract
AIMS: Bacterial lipopolysaccharides (LPS) induce innate immune inflammatory responses in endothelial cells by activating toll-like receptor 4 (TLR4) signalling. Here, we investigate the effects of angiopoietin-1 (Ang-1) on LPS-induced TLR4 signalling and the role of the miR-146 family of micro RNAs in the effects of Ang-1 on TRL4 signalling. METHODS AND RESULTS: Leucocyte adhesion to human umbilical vein endothelial cells (HUVECs) was detected using fluorescence microscopy. Adhesion molecule, pro-inflammatory cytokine, miR-146a, and miR-146b-5p expressions in HUVECs were quantified using real-time PCR. TLR4 signalling protein levels were measured using immunoblotting. Exposure of HUVECs to LPS for 4-6 h induces robust inflammatory responses, including enhanced leucocyte adhesion, up-regulation of adhesion molecule expression (VCAM1, ICAM1, E-SELECTIN), enhanced cytokine production (TNFα, IL1β, IL6, and IL8), and increased NFκB luciferase reporter activity. Addition of Ang-1 to the culture medium for 24 h prior to LPS exposure significantly attenuates these responses. Prolonged Ang-1 exposure significantly decreases IRAK1 and TRAF6 protein levels but has no effect on TLR4, MYD88, IRAK4, or TAK1 expressions. Ang-1 triggers significant up-regulation of miR-146b-5p levels but has no effect on miR-146a or miR-146b-3p expressions. Transfection of HUVECs with a miR-146b-5p mimic significantly attenuates LPS-induced inflammatory responses and IRAK1 and TRAF6 expressions. In HUVECs transfected with a miR-146b-5p inhibitor, Ang-1 has no effect on LPS-induced inflammatory responses or IRAK1 and TRAF6 expressions. CONCLUSION: Ang-1 disrupts TLR4 signalling, resulting in inhibition of LPS-induced inflammatory responses in endothelial cells. This inhibition occurs through selective targeting of IRAK1 and TRAF6 proteins by miR-146b-5p.
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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".