A gut microbial metabolite of linoleic acid ameliorates liver fibrosis by inhibiting TGF-β signaling in hepatic stellate cells
Bibliographic record
Abstract
Abstract The antidiabetic drug pioglitazone ameliorates insulin resistance by activating the transcription factor PPARγ. In addition to its blood glucose–lowering action, pioglitazone exerts pleiotropic effects including amelioration of nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH). The mechanism by which pioglitazone achieves this latter effect has remained unclear, however. We here show that pioglitazone administration increases the amount of linoleic acid (LA) metabolites in adipose tissue of KK-Ay mice. These metabolites are produced by lactic acid bacteria in the gut, and pioglitazone also increased the fraction of Lactobacillus in the gut microbiota. Administration of the LA metabolite HYA (10-hydroxy-cis-12-octadecenoic acid) to C57BL/6J mice fed a high-fat diet improved liver histology including steatosis, inflammatory cell infiltration, and fibrosis. Gene ontology analysis of RNA-sequencing data for the liver revealed that the top category for genes downregulated by HYA treatment was related to extracellular matrix, and the expression of individual genes related to fibrosis was confirmed to be attenuated by HYA treatment. Mechanistically, HYA suppressed TGF-β–induced Smad3 phosphorylation and fibrosis-related gene expression in human hepatic stellate cells (LX-2). Our results implicate LA metabolites in the mechanism by which pioglitazone ameliorates liver fibrosis, and they suggest that HYA is a potential therapeutic for NAFLD/NASH.
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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 | 0.000 |
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".