Dual Effect of Butyrate on IL–1 <i>β</i> – Mediated Intestinal Epithelial Cell Inflammatory Response
Bibliographic record
Abstract
Butyrate (NaBu), a product of intestinal microbial metabolism, has been proposed as an anti-inflammatory agent for treating inflammatory bowel diseases. However, the molecular mechanisms implicated in the modulation of intestinal epithelial cell inflammatory response to NaBu remain unknown. Here, microarray analysis performed on nontransformed human crypt intestinal epithelial cells (HIEC) shows that NaBu regulated specifically the short-term IL-1beta -dependent induction of different inflammatory genes. While NaBu significantly increased the IL-1beta -induction of genes like SAA2, C3, and IL-1alpha , other inflammatory genes like CXCL5, CXCL11, and IL-1beta were decreased. Induction of various genes such as CXCL8, CCL20, and IL-6 was unaffected by NaBu. We show that, compared to genes that are upregulated or downregulated by NaBu, genes that are unaffected by NaBu were induced more rapidly after IL-1beta treatment and contained a higher concentration of transcription factor binding sites in their promoter region. In addition, transient treatment with IL-1beta was sufficient for subsequent induction of NaBu-upregulated and NaBu-unaffected classes of genes, while a continuous presence of IL-1beta was required for NaBu-downregulated gene expression. In conclusion, our results suggest that fundamental differences predispose inflammatory genes to specific regulation by NaBu in intestinal epithelial cells, thereby allowing precise control of 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) | 0.001 | 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 | 0.001 |
| 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".