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Record W4391873427 · doi:10.1093/jcag/gwad061.186

A186 INFLAMMATION MODIFIES DOSE-DEPENDENT RESPONSES OF INTESTINAL ANTI-TUMOUR MICRORNAS TO CRANBERRY PROANTHOCYANIDIN AND ITS MICROBIAL METABOLITE 3-(4-HYDROXYPHENYL)-PROPIONIC ACID

2024· article· en· W4391873427 on OpenAlexaffabout
Zoe Dimoff, Zoe Lofft, Fei Liang, Sheng Chen, Inke Paetau‐Robinson, Christina Khoo, Amel Taïbi, Elena M. Comelli

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProanthocyanidinMetaboliteChemistryInflammationPharmacologymicroRNAFood scienceAntioxidantBiochemistryMedicineInternal medicinePolyphenol

Abstract

fetched live from OpenAlex

Abstract Background Cranberries are a rich source of proanthocyanidins (PAC), a type of polyphenol with anti-cancer properties. We previously found that PAC, along with its microbially-derived metabolite 3-(4-hydroxyphenyl)-propionic acid (HPPA), trigger unique regulatory responses of microRNAs (miRNAs) in intestinal epithelial cells including the upregulation of miR-146a-5p. Both miR-146a-5p and miR363-3p are anti-inflammatory and downregulate anti-tumorigenic signalling pathways such as the interleukin (IL)-17 and wingless-related integration site (Wnt) respectively. miR-146a-5p also attenuates IL-17-promoting cytokines levels (e.g., IL-6). Aims To determine if miR-146a-5p and miR-363-3p respond to different physiologically relevant concentrations of PAC and HPPA in inflammatory and non-inflammatory conditions. Methods Fully differentiated Caco-2BBe1 colonic epithelial cells were treated with two doses of PAC-enriched cranberry extract (50μg/ml, 100μg/ml), HPPA (5μg/ml, 10μg/ml), or with Dulbecco’s Modified Eagle Medium (DMEM; control) for 24 hours, followed by IL-1β (1ng/ml) or mock stimulation for three hours. Human IL-6 homogeneous time-resolved fluorescence (HTRF) kits were used to quantify IL-6 in cell supernatant. RNA was extracted and used for miRNA profiling using Nanostring technology. Statistical analysis was performed in R version 4.2.1 with the R-packages NanostringDiff, NanostringNorm, and Pheatmap. Results At homeostasis, 42 and 2 (miR-146a and miR363-3p) miRNAs, respectively, uniquely responded to increasing PAC or HPPA concentrations. In the inflammatory state, no miRNAs responded to increasing concentrations of PAC and HPPA. However, the expression of miR-363-3p increased (qampersand:003C0.001) in response to 50μg/ml of PAC + IL-1β but decreased in response to 5μg/ml of HPPA + IL-1β , and the expression of miR-146a-5p increased (qampersand:003C0.001) in response to 5μg/ml of HPPA + IL-1β, and 50μg/ml PAC + IL-1β. Predicted miRNA gene-pathway analysis revealed that miR-146a-5p, miR-363-3p, and the other 42 miRNAs commonly target pathways involved in the tumorigenesis of colorectal cancer such as mitogen-activated protein kinases (MAPK), hedgehog, and Wnt pathways. Though, in inflamed cells, only HPPA (5 or 10 μg/ml) attenuated IL-6 secretion (pampersand:003C0.05), which may be driven by increased expression of miR-146a-5p. Conclusions These findings suggest that cranberries proanthocyanidins and their metabolites affect miRNAs involved in cancer related pathways in different manners and concentrations, which may depend on the inflammatory status. The gut microbiota may be partially responsible for unlocking these effects. Funding Agencies Ocean Spray Cranberries, Inc. and the Natural Sciences of Engineering Research Council of Canada (NSERC)

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.232
Teacher spread0.223 · 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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