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.
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) | 0.000 | 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".