A novel model to study mechanisms of cholestasis in human cholangiocytes reveals a role for the SIPR2 pathway
Bibliographic record
Abstract
BACKGROUND: Ductular reactivity is central to the pathophysiology of cholangiopathies. Mechanisms underlying the reactive phenotype activation by exogenous inflammatory mediators and bile acids are poorly understood. METHODS: Using human extrahepatic cholangiocyte organoids (ECOs) we developed an injury model emulating the cholestatic microenvironment with exposure to inflammatory mediators and various pathogenic bile acids. Moreover, we explored roles for the bile acid activated Sphingosine-1-phosphate receptor 2 (S1PR2) and potential beneficial effects of therapeutic bile acids UDCA and norUDCA. RESULTS: Synergistic exposure to bile acids (taurocholic acid, glycocholic acid, glycochenodeoxycholic acid) and TNF-α for 24 hours induced a reactive state as measured by ECO diameter, proliferation, lactate dehydrogenase activity and reactive phenotype markers. While NorUDCA and UDCA treatments given 8 hours after injury induction both suppressed reactive phenotype activation and most injury parameters, proliferation was improved by NorUDCA only. Extrahepatic cholangiocyte organoid stimulation with S1PR2 agonist sphingosine-1-phosphate reproduced the cholangiocyte reactive state and upregulated S1PR2 downstream mediators; these effects were suppressed by S1PR2 antagonist JET-013 (JET), downstream mediator extracellular signal-regulated kinase 1/2 inhibitor, and by norUDCA or UDCA treatments. JET also partially suppressed reactive phenotype after bile acid injury. CONCLUSIONS: We developed a novel model to study the reactive cholangiocyte state in response to pathological stimuli in cholestasis and demonstrated a contributory role of S1PR2 signaling in both injury and NorUDCA/UDCA treatments. This model is a valuable tool to further explore the pathophysiology of human cholangiopathies.
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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 | 0.001 |
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".