MEK2 Negatively Regulates Lipopolysaccharide-Mediated IL-1β Production through HIF-1α Expression
Bibliographic record
Abstract
Abstract LPS-activated macrophages require metabolic reprogramming and glucose uptake mediated by hypoxia-inducible factor (HIF)–1 α and glucose transporter 1 (Glut1) expression for proinflammatory cytokine production, especially IL-1β. This process is tightly regulated through activation of MAPK kinases, including the MEK/ERK pathway as well as several transcription factors including HIF-1α. Although MAPK kinase (MEK) 2 deficiency had no significant effect on NO, TNF-α, or IL-12 production in response to LPS challenge, MEK2-deficient murine bone marrow–derived macrophages (BMDMs) exhibited lower IL-10 production. Importantly, MEK2-deficient BMDMs exhibited a preserved ERK1/2 phosphorylation, higher HIF-1α and Glut1 levels, and substantially increased IL-1β as well as IL-6 production in response to LPS stimulation. Knockdown of HIF-1α expression via short interference RNA decreased the level of HIF-1α expression in MEK2-deficient BMDMs and decreased IL-1β production in response to LPS treatment. Furthermore, we performed gain of function experiments by overexpressing MEK2 protein in RAW264.7 cells. LPS stimulation of MEK2 overexpressed in RAW264.7 cells led to a marked decreased IL-1β production. Finally, we investigated the role of Mek1 and Mek2 double and triple mutation on ERK phosphorylation, HIF-1α expression, and IL-1β production. We found that MEK2 is the major kinase, which inversely proportionally regulates HIF-1α and IL-1β expression independent of ERK activation. Our findings demonstrate a novel regulatory function for MEK2 in response to TLR4 activation in IL-1β production through modulating HIF-1α expression.
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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) | 0.002 | 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".