Hypoxia Diminishes Toll-Like Receptor 4 Expression Through Reactive Oxygen Species Generated by Mitochondria in Endothelial Cells
Bibliographic record
Abstract
Hypoxia and inflammation often occur simultaneously due to prevention of adequate gas exchange. Understanding the influence of hypoxia on the inflammatory response is important because hypoxia directly regulates expression of many genes, including those regulating inflammation, and plays a role in modulating the resolution of an inflammatory response. LPS is a major mediator of cellular injury and inflammation that induces its effects through Toll-like receptor 4 (TLR4). The aim of this study was to evaluate the effect of hypoxia on TLR4 expression. Hypoxia decreased TLR4 expression on cultured endothelial cells. Furthermore, LPS-induced ICAM-1 up-regulation was decreased by hypoxia. Because reactive oxygen species (ROS) generated from mitochondria are one of the signaling molecules induced by hypoxia, the role of ROS in hypoxia-induced TLR4 down-regulation was evaluated. Our data showed that hypoxia increased ROS generation and that hypoxia-induced TLR4 down-regulation was inhibited by myxothiazol, a mitochondrial site III electron transport inhibitor. Hypoxia also inhibited AP-1 translocation. Since the TLR4 promoter has a binding site for AP-1, hypoxia-induced TLR4 down-regulation may be due to an ROS-mediated decrease in AP-1-binding activity. We conclude that hypoxia decreases TLR4 expression in endothelial cells and that this change is mediated by mitochondrial ROS leading to attenuation of AP-1 transcriptional activity.
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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".