Pea protein-derived tripeptide LRW shows osteoblastic activity on MC3T3-E1 cells<i>via</i>the activation of the Akt/Runx2 pathway
Bibliographic record
Abstract
Osteoporosis is a bone disease affecting more than 2 million people comprising 1 in 3 women and 1 in 5 men in Canada. One possible approach to prevent this disease is to stimulate the activity of osteoblasts (bone-forming cells) using food protein-derived bioactive peptides. In our previous study, an ACE inhibitory tripeptide LRW (Leu-Arg-Trp) was identified from pea protein. This work aims to investigate the effect of tripeptide LRW on promoting osteoblastic activity. The tripeptide LRW treatment (50 μM) in MC3T3-E1 cells increased cell proliferation (4-fold increase) as indicated by BrdU incorporation assay. Moreover, we found that tripeptide LRW stimulated osteoblastic differentiation by increasing the levels of type 1 collagen (COL1A2; 3-fold increase), alkaline phosphatase (ALP; 4-fold increase), and runt-related transcription factor 2 (Runx2; 2-fold increase) and the activation of the protein kinase B (Akt) signaling pathway. Furthermore, tripeptide LRW increased matrix mineralization as evidenced by Alizarin-S red staining and nodule formation, osteoprotegerin levels (OPG; 2-fold increase), and wound healing based on cell migration assay. Overall, pea protein-derived bioactive peptide LRW can positively modulate the activity of osteoblasts probably via the Akt/Runx2 pathway, indicating its potential use for the prevention of osteoporosis.
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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".