An abundant ginger compound furanodienone alleviates gut inflammation via the xenobiotic nuclear receptor PXR in mice
Bibliographic record
Abstract
The literature documenting the value of drug-like molecules found in natural products is vast. Although many dietary and herbal remedies have been found to be effective for treating intestinal inflammation, the identification of their active components has lagged behind. In this study, we find that a major ginger component, furanodienone (FDN), is a selective pregnane X receptor (PXR) ligand with agonistic transcriptional outcomes. We show that FDN binds within a sub-pocket of the PXR ligand binding domain (LBD), with subsequent alterations in LBD structure. Using male mice, we show that orally provided FDN has potent PXR-dependant anti-inflammatory outcomes that are colon-specific. Increased affinity and target gene activation in the presence of synergistically acting agonists indicates further opportunities for augmenting FDN activity, efficacy and safety. Collectively, these results support the translational potential of FDN as a therapeutic agent for the treatment and prevention of colonic diseases. Here the authors discover that furanodienone (FDN), a small molecule derived from ginger, exhibits PXR agonist activity selective for the intestine and show that when FDN is administered orally in pharmacological doses ameliorates induced colitis in male mice.
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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 | 0.001 | 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 | 0.000 |
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".