Compounds that modulate AMPK activity and hepatic steatosis impact the biosynthesis of microRNAs required to maintain lipid homeostasis in hepatocytes
Bibliographic record
Abstract
BACKGROUND: While the impact of metformin in hepatocytes leads to fatty acid (FA) oxidation and decreased lipogenesis, hepatic microRNAs (miRNAs) have been associated with fat overload and impaired metabolism, contributing to the pathogenesis of non-alcoholic fatty liver disease (NAFLD). METHODS: We investigated the expression of hundreds of miRNAs in primary hepatocytes challenged by compounds modulating steatosis, palmitic acid and compound C (as inducers), and metformin (as an inhibitor). Then, additional hepatocyte and rodent models were evaluated, together with transient mimic miRNAs transfection, lipid droplet staining, thin-layer chromatography, quantitative lipidomes, and mitochondrial activity, while human samples outlined the translational significance of this work. FINDINGS: Our results show that treatments triggering fat accumulation and AMPK disruption may compromise the biosynthesis of hepatic miRNAs, while the knockdown of the miRNA-processing enzyme DICER in human hepatocytes exhibited increased lipid deposition. In this context, the ectopic recovery of miR-30b and miR-30c led to significant changes in genes related to FA metabolism, consistent reduction of ceramides, higher mitochondrial activity, and enabled β-oxidation, redirecting FA metabolism from energy storage to expenditure. INTERPRETATION: Current findings unravel the biosynthesis of hepatic miR-30b and miR-30c in tackling inadequate FA accumulation, offering a potential avenue for the treatment of NAFLD. FUNDING: Instituto de Salud Carlos III (ISCIII), Govern de la Generalitat (PERIS2016), Associació Catalana de Diabetis (ACD), Sociedad Española de Diabetes (SED), Fondo Europeo de Desarrollo Regional (FEDER), Xunta de Galicia, Ministerio de Economía y Competitividad (MINECO), "La Caixa" Foundation, and CIBER de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN).
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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) | 0.002 | 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".