Chemogenomic Screening in a Patient‐Derived 3D Fatty Liver Disease Model Reveals the CHRM1‐TRPM8 Axis as a Novel Module for Targeted Intervention
Bibliographic record
Abstract
Abstract Metabolic dysfunction‐associated steatohepatitis (MASH) is a leading cause of chronic liver disease with few therapeutic options. To narrow the translational gap in the development of pharmacological MASH treatments, a 3D liver model from primary human hepatocytes and non‐parenchymal cells derived from patients with histologically confirmed MASH was established. The model closely mirrors disease‐relevant endpoints, such as steatosis, inflammation and fibrosis, and multi‐omics analyses show excellent alignment with biopsy data from 306 MASH patients and 77 controls. By combining high‐content imaging with scalable biochemical assays and chemogenomic screening, multiple novel targets with anti‐steatotic, anti‐inflammatory, and anti‐fibrotic effects are identified. Among these, activation of the muscarinic M 1 receptor (CHRM1) and inhibition of the TRPM8 cation channel result in strong anti‐fibrotic effects, which are confirmed using orthogonal genetic assays. Strikingly, using biosensors based on bioluminescence resonance energy transfer, a functional interaction along a novel MASH signaling axis in which CHRM1 inhibits TRPM8 via G q/11 and phospholipase C‐mediated depletion of phosphatidylinositol 4,5‐bisphosphate can be demonstrated. Combined, this study presents the first patient‐derived 3D MASH model, identifies a novel signaling module with anti‐fibrotic effects, and highlights the potential of organotypic culture systems for phenotype‐based chemogenomic drug target identification at scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".