All-Trans Retinoic Acid Induces TGF-β2 in Intestinal Epithelial Cells via RhoA- and p38α MAPK-Mediated Activation of the Transcription Factor ATF2
Bibliographic record
Abstract
OBJECTIVE: We have shown previously that preterm infants are at risk of necrotizing enterocolitis (NEC), an inflammatory bowel necrosis typically seen in infants born prior to 32 weeks' gestation, because of the developmental deficiency of transforming growth factor (TGF)-β2 in the intestine. The present study was designed to investigate all-trans retinoic acid (atRA) as an inducer of TGF-β2 in intestinal epithelial cells (IECs) and to elucidate the involved signaling mechanisms. METHODS: AtRA effects on intestinal epithelium were investigated using IEC6 cells. TGF-β2 expression was measured using reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) and Western blots. Signaling pathways were investigated using Western blots, transiently-transfected/transduced cells, kinase arrays, chromatin immunoprecipitation, and selective small molecule inhibitors. RESULTS: AtRA-treatment of IEC6 cells selectively increased TGF-β2 mRNA and protein expression in a time- and dose-dependent fashion, and increased the activity of the TGF-β2 promoter. AtRA effects were mediated via RhoA GTPase, Rho-associated, coiled-coil-containing protein kinase 1 (ROCK1), p38α MAPK, and activating transcription factor (ATF)-2. AtRA increased phospho-ATF2 binding to the TGF-β2 promoter and increased histone H2B acetylation in the TGF-β2 nucleosome, which is typically associated with transcriptional activation. CONCLUSIONS: AtRA induces TGF-β2 expression in IECs via RhoA- and p38α MAPK-mediated activation of the transcription factor ATF2. Further studies are needed to investigate the role of atRA as a protective/therapeutic agent in gut mucosal inflammation.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".