Dietary sulforaphane modulates hepatic anti-oxidative genes via REV-ERBα and histone modifications in pigs
Bibliographic record
Abstract
Sulforaphane (SFN), a naturally occurring isothiocyanate found in cruciferous vegetables, is known for its anti-inflammatory and antioxidant effects in the body. However, whether its dietary addition impact porcine liver health, and if so, by which mechanims remains unclear. In this study, the diet of growing pigs was supplemented with 1 g kg −1 SFN and was found to improve growth performance and hepatocellular proliferation. Further analyses revealed that SFN decreased hepatic and serum malondialdehyde levels, while increasing glutathione peroxidase (GSH-PX) activity in the liver. Transcriptomic and proteomic studies demonstrated that SFN down-regulated multiple pathways, including oxidative phosphorylation, inflammatory responses, IL-6-JAK-STAT3 signaling, and TNFα signaling via NFκB. Meanwhile, it upregulated NRF2/GPX4/HO-1 expression and reduced IL-6 and TNFα expression. Mechanistic studies identified potential NR1D1 and NRF2 binding elements in the promoters of the GPX4 and HO-1 genes in the liver. Furthermore, Metabolomic profiling revealed a decline in serum β-hydroxybutyrate levels after the administration of SFN, while further analysis confirmed that SFN enhanced a type of epigenetic modification in the liver, lysine β-hydroxybutyrylation (Kbhb). These results highlight SFN protective roles against liver inflammation and oxidative damage and propose a novel mechanism involving NRF2 and NR1D1 synergy, with SFN's promotion of hepatic Kbhb necessitating further exploration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".