Absorption and Metabolism of Phenolics from Digests of Polyphenol-Rich Potato Extracts Using the Caco-2/HepG2 Co-Culture System
Bibliographic record
Abstract
The bioactivity of dietary polyphenols depends upon gastrointestinal and hepatic metabolism of secondary microbial phenolic metabolites generated via colonic microbiota-mediated biotransformation. A polyphenol-rich potato extract (PRPE) containing chlorogenic, caffeic, and ferulic acids and rutin was digested in a dynamic multi-reactor gastrointestinal simulator of the human intestinal microbial ecosystem (GI model). Simulated digestion showed extensive degradation of the parent compounds and the generation of microbial phenolic metabolites. To characterize the transport and metabolism of microbial phenolic metabolites following digestion, a co-culture of intestinal Caco-2 and hepatic HepG2 cells was exposed to the PRPE-derived digests obtained from the colonic vessels. Following a 2 h incubation of the digesta with the Caco-2/HepG2 co-cultures, approximately 10-15% of ferulic, dihydrocaffeic, and dihydroferulic acids and 3-5% of 3-hydroxybenzoic, 3-hydroxyphenylpropionic, and coumaric acids were observed in the basolateral side, whereas 3-hydroxyphenylacetic acid, phenylpropanoic acid, and cinnamic acid were not detected. Subsequent HepG2 cellular metabolism led to major increases in ferulic, dihydrocaffeic, 3-hydroxyphenylpropionic, and coumaric acids ranging from 160-370%. These findings highlight the importance of hepatic metabolism towards the generation of secondary metabolites of polyphenols despite low selective Caco-2 cellular uptake of microbial phenolic metabolites.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".