Comprehensive analysis of extracellular matrix remodelling via cyclophilin inhibition in human models of alcohol‐related liver fibrosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Chronic liver disease and hepatic fibrosis constitute a threat to global health. Clinical translation of preclinical research has been limited, highlighting an urgent need for novel treatments. Cyclophilin inhibitors have shown beneficial effects in liver disease; however, the underlying mechanism of action and the effect across different aetiologies remain elusive. Here, we investigate the impact of a pan-cyclophilin inhibitor (rencofilstat, RCF) in human models of fibrosis and alcohol-related liver disease. EXPERIMENTAL APPROACH: RCF was tested in human precision-cut liver slices (PCLS) and primary human hepatic stellate cells (HSCs). Fibrosis and cell activation were assessed using transcriptomic and protein analysis. A comprehensive characterisation of changes in extracellular matrix (ECM) biochemical and structural composition was performed in PCLS and HSC-derived matrix using proteomics, imaging and bioinformatic tools to study ECM alignment. PCLS stiffness upon treatment was assessed by atomic force microscopy. KEY RESULTS: Transcriptomic and proteomic analyses of PCLS revealed a dramatic impact of RCF on ECM organisation and remodelling. Biochemical composition and fibre alignment analysis of the ECM obtained from HSCs showed a reduction in the amount of ECM core proteins and associated enzymes by RCF, reshaping the architecture of matrix fibres without affecting the HSC activation. The disordered matrix detected in RCF-treated HSC cultures reflected a less-stiff ECM, which was confirmed in the PCLS. CONCLUSIONS AND IMPLICATIONS: This work provides evidence for a novel mechanism linking cyclophilins and ECM remodelling in advanced 3D models of liver disease, with potential applications in therapeutic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".