Enzyme-specific casein hydrolysates enhance calcium absorption and bone mineralization: Mechanistic insights from osteoblast activation and peptide profiling
Bibliographic record
Abstract
Calcium bioavailability and bone mineralization are critical for skeletal health; however, conventional calcium supplements often face limitations in absorption efficiency. This study investigates how enzyme-specific hydrolysis of CN generates bioactive peptides with distinct capacities to promote calcium absorption and bone formation. Papain-derived CN hydrolysate significantly outperformed calcium chloride in restoring bone health in osteoporotic mice, elevating serum osteocalcin levels by 1.8-fold and reducing tartrate-resistant acid phosphatase levels by 41% compared with inorganic calcium. Mechanistically, papain hydrolysates upregulated the expression of TRPV5 and TRPV6 calcium transporters in intestinal cells, thereby facilitating intestinal calcium uptake. Peptidomic profiling revealed enzyme-dependent cleavage patterns: papain preferentially targets glutamate- and lysine-rich sites (e.g., ES, EK, QS), yielding peptides such as QPKTKVIPYVRYL and RELEELNVPGEIVE, which synergistically enhance calcium chelation and osteogenic signaling. Notably, micro-computed tomography analysis confirmed that papain hydrolysates restored trabecular bone density and microarchitecture in murine femurs, outperforming inorganic calcium supplementation. These findings establish a structure-activity framework for designing enzyme-tailored CN peptides to address calcium deficiency disorders, offering a transformative strategy for the development of functional nutraceuticals.
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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".