Acyl‐CoA Thioesterase 1 Contributes to Transition of Steatosis to Metabolic‐Associated Steatohepatitis
Bibliographic record
Abstract
Background. Metabolic dysfunction‐associated steatohepatitis (MASH) has become the leading cause of chronic liver disease, but there has been no approved pharmacotherapy to date. Methods. We used a network analysis approach to delineate protein‐protein interactions that contribute to the transition from steatosis to MASH, in order to identify and target this transition as a potential pharmacotherapeutic strategy. Acyl‐CoA thioesterase 1 (ACOT1) was identified as a critical node in the protein‐protein interaction (PPI) network of the transition from steatosis to MASH in patient samples. ACOT1 overexpression and silencing effects were tested in vivo on C57BL/6 mice exposed to high‐fat diet (HFD) and inoculated with an adenoviral system to modulate ACOT1 expression. Transcriptomic and untargeted lipidomic profiles were performed on the mouse livers. Results. ACOT1 expression was 3‐fold higher in MASH as compared to steatosis. In patient samples, ACOT1 was significantly correlated with the severity of MASH as reflected by the nonalcoholic fatty liver disease score. Experimental validation showed that downregulation of ACOT1 resulted in decreased lipid accumulation and prevention of MASH in vivo. Conversely, upregulation of ACOT1 via an adenoviral vector resulted in development of MASH, whereas control mice only developed steatosis. Lipidomic analysis revealed glycerophospholipids to be especially abundant in MASH accelerated by ACOT1 upregulation. Conclusion. These results suggest that ACOT1 contributes to the transition from steatosis to MASH through modulation of glycerophospholipid accumulation and its potential as a novel therapeutic target in MASH. This trial is registered with NCT02148471 .
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 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) | 0.001 | 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) | 0.001 | 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".