Cranberry Proanthocyanidin and Its Microbial Metabolite 3,4‐Dihydroxyphenylacetic Acid, but Not 3‐(4‐Hydroxyphenyl)‐Propionic Acid, Partially Reverse Pro‐Inflammatory microRNA Responses in Human Intestinal Epithelial Cells
Bibliographic record
Abstract
SCOPE: The molecular basis underlying the anti-inflammatory and anticarcinogenic properties of cranberries is incompletely understood. The effects of a cranberry proanthocyanidin-rich extract (PAC) and two of its gut microbial metabolites, 3,4-dihydroxyphenylacetic acid (DHPAA) and 3-(4-hydroxyphenyl)-propionic acid (HPPA), on intestinal epithelial cells microRNA (miRNA) expression and their downstream pathways at homeostasis and in inflammatory conditions, are investigated. METHODS AND RESULTS: The expression of 799 miRNAs is quantitatively assessed in differentiated Caco-2BBe1 cells pre-treated with PAC, DHPAA, or HPPA and stimulated with interleukin (IL)-1β or not. PAC, DHPAA, and HPPA generate subsets of shared and distinct miRNA responses. At homeostasis, miRNAs affected by the metabolites, but not PAC, targeted genes enriched in kinase, Wnt, and growth factor signaling, cell growth and proliferation, apoptosis, and specific cancer pathways. In an inflammatory environment, PAC and DHPAA, but not HPPA, reverses the expression of 16 and two IL-1β-induced miRNAs, respectively, regulating inflammatory and cancer pathways. CONCLUSION: miRNA modulation is a novel mechanism for PAC bioactivity in the gut. The gut microbiota may be necessary to unlock these effects at homeostasis and partially in inflammation.
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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".