<i>MicroRNA-26b−/−</i> augments atherosclerosis, while mimic-loaded nanoparticles reduce atherogenesis
Bibliographic record
Abstract
AIMS: Increasing evidence has shown that microRNAs (miRs) play a fundamental role in atherosclerosis, but the exact role of various miRs remains elusive. Preliminary data showed that, with a five-fold increase, miR-26b was highly expressed in human atherosclerotic plaques compared to healthy vessels. Therefore, we aimed to determine its cell-specific effects on atherosclerosis development and its therapeutic potential. METHODS AND RESULTS: We examined the role of miR-26b in atherosclerosis by using whole-body Apoe-/-Mir26b-/- and myeloid cell-specific miR-26b-deficient (LysM-Cre) mice on a Western-type diet (WTD). Atherosclerotic plaque size and phenotype, as well as the phenotype and function of bone marrow-derived macrophages (BMDMs) from Apoe-/-Mir26b-/- mice, were investigated. Lipid nanoparticles (LNPs) served as vehicles for miR-26b mimics to restore miR-26b levels in miR-26b-deficient BMDMs in vitro and in mice in vivo. Apoe-/-Mir26b-/- mice have a striking 2.8-fold increase in atherosclerotic lesion size in the aortic arch after 12-week WTD, compared to control Apoe-/-, while lesions in the aortic root were unaffected. Consistent with a more advanced plaque phenotype, collagen, smooth muscle cell, and necrotic core content were all significantly increased in plaques from Apoe-/-Mir26b-/- mice, whilst the relative macrophage content was significantly reduced. This phenotype could also be observed in Apoe-/-Mir26b-/- mice after 4-week WTD. Intriguingly, relative plaque size in the arches of Apoe-/-LysmCre+Mir26bfl/fl mice was increased by 2.5-fold, suggesting a role for myeloid-specific miR-26b in atherosclerosis development. Further highlighting its myeloid-specific effects, Apoe-/-Mir26b-/- BMDMs showed an increase in pro-inflammatory cytokine secretion, which could be rescued by LNPs containing miR-26b mimics. MiR-26b pull-down analysis revealed AnnexinA2 as one of the novel targets playing a key role in these effects, which could be validated in BMDMs in vitro. Furthermore, in vivo treatment of Apoe-/-Mir26b-/- mice as well as ex vivo treatment of human plaques with miR-26b-mimic-loaded LNPs demonstrated their therapeutic potential and human relevance, respectively. CONCLUSION: Overall, our results clearly demonstrate an atheroprotective role of miR-26b by attenuating lesion formation, mainly by suppressing inflammation and stimulating collagen breakdown. Furthermore, the therapeutic potential of miR-26b mimic-loaded LNPs could be proven, opening up new avenues for miRNA-based treatment options in the future.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".