Elevated miR-34a induced by lipotoxicity and inflammation mediates pathophysiological communication between hepatocytes and hepatic stellate cells in liver fibrosis
Bibliographic record
Abstract
Increased mortality in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) imposes an urgent need to elucidate the pathogenesis of MASLD so that novel therapeutic strategies may be identified. Here, we delineate the mechanism of microRNA-34a-5p (miR-34a) in the progressive liver injury of MASLD and liver fibrosis. Specifically, liver tissue from patients with obesity-associated hepatic steatosis, metabolic dysfunction-associated steatohepatitis (MASH), and fibrosis, as well as liver tissues from a human MASLD-like mouse model, were utilized for this study. We found that lipotoxicity resulting from obesity or saturated free fatty acid treatment induced miR-34a expression in human liver tissue or mouse hepatocytes, which was accompanied by dysregulation of lipoprotein metabolism, activation of inflammation, and ballooning degeneration of hepatocytes. Moreover, increased cellular miR-34a induced by treatment with saturated fat, palmitic acid, or transfection of miR-34a mimic was released from injured hepatocytes into the conditional cell culture media, which mediated pathological communications between hepatocytes and hepatic stellate cells, activated pro-fibrogenic signaling in hepatic stellate cells, and induced extracellular matrix remodeling. These phenotypes were recapitulated in a human MASLD-like mouse model in which MASLD and liver fibrosis were induced via streptozotocin treatment and high-fat feeding. Elevated expression of miR-34a was found in mouse liver tissues, which conveyed the progressive liver injury from steatosis to MASH and liver fibrosis. Our findings demonstrate that elevated miR-34a induced by lipotoxicity and metabolic inflammation are key driving factors in the progressive liver injury from simple steatosis to MASH and liver fibrosis.
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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".