Bile acid signaling through FXR induces intracellular adhesion molecule-1 expression in mouse liver and human hepatocytes
Bibliographic record
Abstract
Previous studies have demonstrated a dramatic induction of inflammatory gene expression in livers from mice fed a high-fat, high-cholesterol diet containing cholate after 3-5 wk. To determine the contribution of cholate in mediating these inductions, C57BL/6 mice were fed a chow diet supplemented with increasing concentrations of cholic acid (CA) for 5 days. A dose-dependent induction in the hepatic levels of TNF-alpha, VCAM-1, ICAM-1, and SAA-2 mRNA were observed. As positive controls, a dose-dependent repression of cholesterol 7alpha-hydroxylase and a dose-dependent induction of small heterodimer partner (SHP) expression were also observed, suggesting that farnesoid X receptor (FXR) was activated. In addition, ICAM-1 and SHP mRNA levels were also induced in primary human hepatocytes when treated with chenodeoxycholic acid or GW4064, a FXR-selective agonist. The involvement of FXR in CA-induced inflammatory gene expression was further investigated in the human hepatic cell line HepG2. Both ICAM-1 and SHP expression were induced in a dose- and time-dependent manner by treatment with the FXR-selective agonist GW4064. Moreover, the induction of ICAM-1 by GW4064 was inhibited by the FXR antagonist guggulsterone or with transfection of FXR siRNA. Finally, the activity of FXR was mapped to a retinoic acid response element (RARE) site containing an imbedded farnesoid X response element (FXRE) on the human ICAM-1 promoter and FXR and retinoid X receptor were demonstrated to bind to this site. Finally, FXR-mediated activation of ICAM-1 could be further enhanced by TNF-alpha cotreatment in hepatocytes, suggesting a potential cooperation between cytokine and bile acid-signaling pathways during hepatic inflammatory events.
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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 | 0.000 |
| 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".