Identification of novel umami peptides with anti-inflammatory and antioxidant effects in spent hen protein hydrolysate
Bibliographic record
Abstract
Spent hens, a major byproduct of the laying hen industry, were used to prepare umami peptides with anti-inflammatory and antioxidant activities. Among nine hydrolysates, the Protex 6 L-Thermoase product (SPH-6 L-T, 2 mg/mL) showed the strongest umami intensity, comparable to 10 mM monosodium glutamate. In HEK293 cells expressing the type I taste receptor TAS1R1, SPH-6 L-T, at 2 mg/mL, significantly increased intracellular Ca 2+ mobilization; in tumor necrosis factor α (TNFα)-stimulated endothelial cells, SPH-6 L-T, at 500 μg/mL, significantly reduced cyclooxygenase-2 (COX2) ( p = 0.0058) and reactive oxygen species (ROS) ( p = 0.0031). Peptidomics identified 1318 di−/tripeptides and 170 oligopeptides. Four peptides (AR, AQ, SKEGGKVT, ELEEEIEAE), all derived from myosin heavy chain, showed strong umami taste (0.2 mg/mL) and favorable docking to the TAS1R1 Venus flytrap domain. Two oligopeptides (5 mM) induced sustained Ca 2+ responses (> 5 min), and all four reduced ROS ( p < 0.0001); SKEGGKVT (50 μM) also significantly inhibited COX2 expression ( p = 0.018). These findings highlight spent hens as a good source of multifunctional umami peptides. • Spent hen hydrolysate (SPH-6 L-T), produced using Protex 6 L and Thermoase, exhibits both umami taste and bioactive properties. • SPH-6 L-T activates TAS1R1- and CaSR-mediated Ca 2+ mobilization. • Four peptides (AR, AQ, SKEGGKVT, and ELEEEIEAE) identified through peptidomics and molecular docking, demonstrated umami taste and antioxidant activity. • All four peptides triggered TAS1R1-mediated Ca 2+ mobilization.
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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) | 0.001 | 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".