Effects of the <i>In Vivo</i> Supply of Butyrate on Histone Acetylation of Cecum in Piglets
Bibliographic record
Abstract
BACKGROUND: In vitro, butyrate inhibits histone deacetylase and down-regulates expression of cyclin D1. We hypothesized that an increased entry rate of butyrate into the cecal lumen would have similar effects in vivo. METHODS: We used frozen cecal tissue and data from previous studies, one showing that lactulose supplementation caused an increased rate of cecal synthesis of butyrate and decreased cecal cell proliferation and density of clostridia and the other showing that cecal cell proliferation was increased by an exogenous cecal butyrate infusion at a comparable rate. The ratio of acetylated to total histones (AH ratio) and cyclin D1 mRNA expression were measured in cecal tissue. RESULTS: Lactulose supplementation caused a 189% increase in the AH ratio (p = .004), which inversely correlated with cecal cell proliferation (r = -0.782; p = .008). With cecal butyrate infusion, we observed a significant decrease in histone acetylation (p = .02), which also inversely correlated with cecal cell proliferation (r = -0.797; p = .002). Cyclin D1 expression was increased 6.5-fold by lactulose feeding (p = .02) but decreased 50% with cecal butyrate infusion (p = .004). CONCLUSIONS: The effects on histone acetylation of increased "endogenous" butyrate production produced by lactulose feeding, but not exogenous cecal infusion of butyrate, mirror those in vitro. Thus, bacterial production and exogenous infusion of butyrate have opposite effects on histone acetylation and cyclin D1 expression, suggesting that the composition of bacterial flora may play a role in butyrate's in vivo effects on the cell cycle.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".