The NFE2L2 (NRF2) transcription factor controls genes involved in the oxidative stress response and inflammation in myometrial cells
Bibliographic record
Abstract
The myometrium is the smooth muscle layer of the uterus, which mediates uterine contractions during labor. We treated PHM1-31 myometrial cells with the proinflammatory cytokine interleukin-1 beta (IL1B) and measured a significant increase in reactive oxygen species (ROS). We found that IL1B induces NFE2L2 (NRF2) transcription factor levels. We further showed that siRNA mediated knockdown of NFE2L2 results in a significant increase in ROS. Downregulation of NFE2L2 leads to a decrease of heme oxygenase-1 (HMOX1) and aldo-keto reductase family 1 member B (AKR1B) at the transcript and protein level both in the absence and presence of IL1B. NFE2L2 knockdown also results in reduced ferritin heavy chain 1 (FTH1) mRNA expression, but only upon IL1B exposure, while FTH1 protein is downregulated both under basal and IL1B treatment conditions. We confirmed that NFE2L2 directly binds to the regulatory regions of these targets. Previous reports have linked HMOX1 and FTH1 to the oxidative stress response, and AKR1B1 to prostaglandin synthesis. Our data demonstrate that NFE2L2 functions as a key regulator of inflammatory and oxidative stress signaling through the regulation of HMOX1, FTH1, and AKR1B1 expression in myometrial cells. While HMOX1 and FTH1 have established roles in oxidative stress responses, our findings identify AKR1B1 as a novel target of NFE2L2 in myometrial cells, suggesting a role for the transcription factor in prostaglandin metabolism. Thus, NFE2L2 links inflammation and the oxidative stress response to critical pathways that control myometrial cell function and parturition, highlighting their potential as therapeutic targets for treating infection-induced preterm labor.
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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 | 0.001 |
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".