Data on microRNA expression, predicted gene targets and pathway analysis in response to different concentrations of a cranberry proanthocyanidin-rich extract and its metabolite 3-(4-hydroxyphenyl)-propionic acid in intestinal Caco-2BBe1 cells
Bibliographic record
Abstract
Cranberry-derived proanthocyanidin (PAC) is processed by the gut microbiota to produce 3-(4-hydroxyphenyl)-propionic acid (HPPA), among other metabolites. These data are in support of the article entitled, “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,” published in Molecular Nutrition and Food Research [1]. Here we describe data generated by nCounter® Human v3 miRNA Expression Panel of RNA obtained from Caco-2BBe1 cells exposed to two different concentrations of cranberry extract rich in PAC (50 μg/ml or 100 μg/ml) or 3-(4-hydroxyphenyl)-propionic acid (5 μg/ml or 10 μg/ml) for 24 hours, then stimulated with 1 ng/ml of IL-1ß or not (mock) for three hours. The raw data are publicly available at the NCBI GEO database GSE237078. This work also includes descriptive methodological procedures, treatment-responsive microRNA (miRNA) expression profiles in Caco-2BBe1 cells, and in silico mRNA gene target and pathway enrichment analyses of significantly differentially expressed miRNAs (q < 0.001). Cranberry and its components have recognized health benefits, particularly in relation to combatting inflammation and pathogenic bacterial adhesion. These data will be valuable as a reference to study the response of intestinal cells to other polyphenol-rich food sources, analyze gut microbial responses to cranberry and its metabolites in different cell lines and mammalian hosts to elucidate individualized effects, and to delineate the role of the gut microbiota in facilitating the benefits of cranberry. Moreover, these data will aid in expanding our knowledge on the mechanisms underlying the benefits of cranberry and its components.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".