Gut Microbiota Metabolism of Anthocyanin Promotes Reverse Cholesterol Transport in Mice Via Repressing miRNA-10b
Bibliographic record
Abstract
RATIONALE: We and others have demonstrated that anthocyanins have antiatherogenic capability. Because intact anthocyanins are absorbed very poorly, the low level of circulating parent anthocyanins may not fully account for their beneficial effect. We found recently that protocatechuic acid (PCA), a metabolite of cyanidin-3 to 0-β-glucoside (Cy-3-G), has a remarkable antiatherogenic effect. OBJECTIVE: To investigate whether mouse gut microbiota metabolizes Cy-3-G into PCA and to determine whether and how PCA contributes to the antiatherogenic potency of its precursor, Cy-3-G. METHODS AND RESULTS: PCA was determined as a gut microbiota metabolite of Cy-3-G in ApoE(-/-) mice, verified by the utilization of antibiotics to eliminate gut microbiota and further microbiota acquisition. PCA but not Cy-3-G at physiologically reachable concentrations promoted cholesterol efflux from macrophages and macrophage ABCA1 and ABCG1 expression. By conducting a miRNA microarray screening, we revealed that expression of miRNA-10b in macrophages can be reduced by PCA. Functional analyses demonstrated that miRNA-10b directly represses ABCA1 and ABCG1 and negatively regulates cholesterol efflux from murine- and human-derived macrophages. Further in vitro and ex vivo analyses verified that PCA accelerates macrophage cholesterol efflux, correlating with the regulation of miRNA-10b-ABCA1/ABCG1 cascade, whereas Cy-3-G consumption promoted macrophage RCT and regressed atherosclerotic lesion in a gut microbiotaendependent manner. CONCLUSIONS: PCA, as the gut microbiota metabolite of Cy-3-G, exerts the antiatherogenic effect partially through this newly defined miRNA-10b-ABCA1/ABCG1-cholesterol efflux signaling cascade. Thus, gut microbiota is a potential novel target for atherosclerosis prevention and treatment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".