MicroRNA-26b protects against MASH development in mice and can be efficiently targeted with lipid nanoparticles
Bibliographic record
Abstract
Abstract Background & Aims The prevalence of metabolic dysfunction-associated steatohepatitis (MASH) is increasing, urging more research into the underlying mechanisms. MicroRNA-26b (miR-26b) might play a role in several MASH-related pathways. Therefore, we aimed to determine the role of miR-26b in MASH and its therapeutic potential using miR-26b mimic-loaded lipid nanoparticles (LNPs). Methods Apoe -/- Mir26b -/- , Apoe -/- LysM cre Mir26b fl/fl mice, and respective controls were fed a western-type diet to induce MASH. Plasma and liver samples were characterized regarding lipid metabolism, hepatic inflammation, and fibrosis. Additionally, miR-26b mimic-loaded LNPs were injected in Apoe -/- Mir26b -/- mice to rescue the phenotype and key results were validated in human precision-cut liver slices. Finally, kinase profiling was used to elucidate underlying mechanisms. Results Apoe -/- Mir26b -/- mice showed increased hepatic lipid levels, coinciding with increased expression of scavenger receptor a and platelet glycoprotein 4. Similar effects were found in mice lacking myeloid-specific miR-26b . Additionally, hepatic TNF and IL-6 levels and amount of infiltrated macrophages were increased in Apoe -/- Mir26b -/- mice. Moreover, Tgfb expression was increased by the miR-26b deficiency, leading to more hepatic fibrosis. A murine treatment model with miR-26b mimic-loaded LNPs reduced hepatic lipids, rescuing the observed phenotype. Kinase profiling identified increased inflammatory signaling upon miR-26b deficiency, which was rescued by LNP treatment. Finally, miR-26b mimic-loaded LNPs also reduced inflammation in human precision-cut liver slices. Conclusions Overall, our study demonstrates that the detrimental effects of miR-26b deficiency in MASH can be rescued by LNP treatment. This novel discovery leads to more insight into MASH development, opening doors to potential new treatment options using LNP technology. Graphical abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".