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Record W3029748316 · doi:10.1093/cdn/nzaa045_063

Cranberry-Derived Proanthocyanidin and Its Gut Microbial Metabolites Affect the Intestinal miRNome in a Distinct Manner In Vitro

2020· article· en· W3029748316 on OpenAlexaff
Zoe Lofft, Amel Taïbi, Elena M. Comelli

Bibliographic record

VenueCurrent Developments in Nutrition · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrebioticmicroRNAPolyphenolBiologyGut floraCaco-2ProanthocyanidinKEGGFold changeViability assayBiochemistryChemistryGene expressionCellGeneTranscriptomeAntioxidant

Abstract

fetched live from OpenAlex

Polyphenols are emerging as novel prebiotic compounds. Cranberries are a rich source of polyphenols, such as proanthocyanidin (PAC), which has known benefits including anti-cancer properties. In the colon, PACs are catabolized by the gut microbiota into 3,4-dihydroxyphenylacetic acid (DHPAA) and 3-(4-hydroxyphenyl) propionic acid (HPPA), which may mediate prebiotic effects. Mechanisms are unknown but may involve host microRNA (miRNA). The objective of this study was to investigate the effects of nutritionally relevant doses of cranberry PAC, DHPAA, and HPPA on the human intestinal miRNome. Differentiated Caco-2BBe1 colonic epithelial cells, a morphologically homogenous subclone of Caco-2 cells, were treated with cranberry extract containing 94% PAC (Ocean Spray Cranberries, 50 μg/ml), DHPAA, HPPA (5 μg/ml) or control vehicle (Dulbecco's Modified Eagle Medium) for 24 hours. Experiments were repeated 3 times. Cell viability was assessed by fluorescence microscopy. Cell RNA was extracted and used for miRNA profiling via NanoString Technology. Data were processed and normalized in nSolverTM 4.0, statistics and hierarchical clustering were done with R and ClustVis. Gene targets were predicted with miRNet and pathway enrichment analysis was done in PathDIP. The treatments had no effect on cell viability. Of the 829 miRNAs assessed, 248 were expressed. Five miRNAs were differentially expressed among groups (ANOVA, P < 0.01; FDR < 5%). Unsupervised hierarchical clustering with these miRNAs revealed perfect separation based on treatment. These miRNAs were found to target 686 genes, enriched in 116 KEGG pathways including “miRNAs in cancer” and “pathways in cancer”. Treatment-specific responses included increased miR-2116-5p, miR-6721-5p, and miR-1290 for PAC, DHPAA, and HPPA, respectively. Pathways in glucagon signaling and central carbon metabolism in cancer were enriched in response to DHPPA only. Cranberry PAC and polyphenol metabolites at concentrations representing dietary intakes elicit different miRNA signatures in colonic cells, providing a novel mechanism to explain their effects on intestinal health. Cranberry-mediated miRNA modulation may represent a potential strategy for preventing chronic disease. NSERC, Ocean Spray Cranberries, Inc., NSERC Graduate Scholarship.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.270
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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