MicroRNA-134 deactivates hepatic stellate cells by targeting TGF-β activated kinase 1-binding protein 1
Bibliographic record
Abstract
Aberrant expression of microRNAs is associated with liver fibrogenesis. We previously found that microRNA-134 (miR-134) expression was reduced in fibrosis-based hepatocarcinogenesis induced by diethylinitrosamine. Herein we investigate the role and mechanisms of miR-134 in hepatic fibrosis. Our data show that miR-134 expression is reduced in rat hepatic fibrogenesis induced by carbontetrachloride, bile duct ligation, and dimethylnitrosamine, as well as in activated hepatic stellate cells (HSCs). Moreover, miR-134 inhibited HSC proliferation, and decreased the expression of smooth muscle actin and collagen I in HSCs, whereas the miR-134 inhibitor increased HSC activation. MiR-134 also negatively regulated transforming growth factor-β-activated kinase 1-binding protein 1 (TAB1) expression in both human and rat HSCs by directly binding to its 3' untranslated region. Importantly, TAB1 expression was significantly elevated during liver fibrogenesis and HSC activation. Knockdown of TAB1 inhibited the proliferation and fibrogenic behavior of HSCs, and significantly reduced the effect of the miR-134 inhibitor on HSC proliferation. Collectively, these data suggest that miR-134 inhibits the activation of HSCs via directly targeting TAB1, and the restoration of miR-134 or targeting TAB1 is of clinical significance in the treatment of 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 | 0.000 |
| 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".