Transport of a tripeptide, Gly‐Pro‐Hyp, across the porcine intestinal brush‐border membrane
Bibliographic record
Abstract
The transcellular transport of oligopeptides across intestinal epithelial cells has attracted considerable interest in investigations into how biologically active peptides express diverse physiological functions in the body. It has been postulated that the tripeptide, Gly-Pro-Hyp, which is frequently found in collagen sequences, exhibits bioactivity. However, the mechanism of uptake of dietary di- and tripeptides by intestinal epithelial cells is not well understood. In this study, we used porcine brush-border membrane (BBM) vesicles to assess Gly-Pro-Hyp uptake, because these vesicles can structurally and functionally mimic in vivo conditions of human intestinal apical membranes. The present study demonstrated the time-dependent degradation of this tripeptide into the free-form Gly and a dipeptide, Pro-Hyp, on the apical side of the BBM vesicles. In parallel with the hydrolysis of the tripeptide, the dipeptide Pro-Hyp was identified in the BBM intravesicular space environment. We found that the transcellular transport of Pro-Hyp across the BBM was inhibited by the addition of a competitive substrate (Gly-Pro) for peptide transporter (PEPT1) and was pH-dependent. These results indicate that Gly-Pro-Hyp can be partially hydrolyzed by the brush-border membrane-bound aminopeptidase N to remove Gly, and that the resulting Pro-Hyp is, in part, transported into the small intestinal epithelial cells via the H+-coupled PEPT1. Gly-Pro-Hyp cannot cross the epithelial apical membrane in an intact form, and Pro-Hyp is highly resistant to hydrolysis by intestinal mucosal apical proteases.
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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) | 0.000 | 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 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".