Hypoxic gene activation by lipopolysaccharide in macrophages: implication of hypoxia-inducible factor 1α
Bibliographic record
Abstract
Hypoxia-inducible factor 1 (HIF-1) regulates many genes induced by low oxygen conditions. The expression of important hypoxic genes such as glucose transporter 1 and vascular endothelial growth factor are increased in macrophages during wound healing and in the presence of the endotoxin, lipopolysaccharide (LPS). Recent studies have demonstrated that nonhypoxic stimuli can also activate HIF-1 in a cell-specific manner. Here, we demonstrate that in macrophages, LPS can control the activation of hypoxia-regulated genes through the HIF-1 pathway. We show that in these cells, protein expression levels of HIF-1alpha are strongly increased to levels comparable to hypoxic induction. HIF-1alpha mRNA levels are markedly increased following LPS stimulation, suggesting a transcriptional induction. In functional studies, the LPS-induced HIF-1 complex could specifically bind to the HIF-1 DNA-binding motif. Additionally, when cells were transfected with an HIF-1-specific reporter construct, LPS could strongly activate the expression of the reporter to levels that surpassed those observed after hypoxic induction. This induction was blocked by the cotransfection of a dominant-negative form of HIF-1alpha. These results indicate that the HIF-1 complex is involved in macrophage gene activation following LPS exposure and identify a novel pathway that could play a determinant role during inflammation and wound healing.
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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".