NFE2L3 regulates inflammation and oxidative stress-related genes in the colon
Bibliographic record
Abstract
The molecular mechanisms leading to inflammatory bowel disease (IBD) are only partially understood. We investigated the role of the transcription factor NFE2L3 in a mouse model of colitis by inducing inflammation using dextran sodium sulfate (DSS). We confirmed the presence of inflammation by histological analysis and elevated levels of the inflammation marker lipocalin-2 (LCN2) in the stool. We found that Lcn2 transcript levels are significantly less elevated in Nfe2l3 −/− mice than wild type mice. We further showed a reduction of Nfe2l3 mRNA, in wildtype mice upon DSS treatment. We cross referenced ENCODE ChIP data of NFE2L3 binding partners MAFF and MAFK with known IBD and DSS effectors and identified Stat1 , Hmox1 , and Slc7a11 as potential NFE2L3 targets. These proteins are induced during colitis to suppress the immune response, reduce oxidative stress, and trigger ferroptosis, respectively. We analyzed the candidate targets and observed an increase in their protein expression upon DSS treatment in wild type but not in Nfe2l3 −/− mice. Furthermore, in the absence of DSS, we observed an increase in the basal levels of pSTAT1 and SLC7A11 proteins in Nfe2l3 −/− mice. These data suggest that the NFE2L3 transcription factor primes the microenvironment towards a pro-inflammatory ready state during inflammatory bowel disease (IBD).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".