Butyric acid inhibits a Toll-like receptor 2 agonist-mediated increase of Toll-like receptor 3-induced chemokine production by HT-29 intestinal epithelial cells (IRC10P.413)
Bibliographic record
Abstract
Abstract The intestinal epithelium interacts with varied microbial components and metabolites, integrating multiple signals that influence immunity. We examined the time and dose-dependent effects of a Toll-Like Receptor (TLR) 2 agonist lipoprotein Pam3Cys (P3C) and the bacterial fermentation metabolite butyric acid (BA) (10mM and 25mM) on TLR3 agonist (poly(I:C)) (pIC)-induced chemokine production by HT-29 intestinal epithelial cells (IEC). Pre-treatment of HT-29 IEC with P3C enhanced (p(IC))-induced IL-8 production. However, pre-treatment of HT-29 IEC with P3C and 25mM BA together inhibited p(I:C)-induced IL-8 production, suggesting BA blocked the enhancing effect of this TLR2 agonist on TLR3-induced chemokine production. This inhibitory effect was not observed when IEC were pre-treated with a lower concentration of BA (10mM). Co-incubation of IEC with P3C and p(I:C) did not enhance TLR3 agonist-induced IL-8 production, while co-incubation with 10mM or 25mM BA acid significantly reduced p(I:C)-induced IL-8 and CXCL-10 production by HT-29 IEC. In addition, a p38 blocker (SB203580) significantly inhibited p(I:C)-induced IL-8 production by HT-29 IEC, while co-incubation with SB203580 and 10mM BA showed no additive inhibition. These findings suggest that while TLR2 agonists can upregulate TLR3-induced chemokine production, butyric acid inhibits this process, and the effects of TLR2 agonists and BA are dependent on timing of contact with IEC relative to TLR3 agonist stimulation.
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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 | 0.001 |
| 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".