Tripeptide IRW Protects MC3T3-E1 Cells against Ang II Stress in an AT2R Dependent Manner
Bibliographic record
Abstract
Multiple strategies including the use of bioactive peptides and other nutraceuticals are being adopted to maintain bone health. This study provides an improved and deeper understanding of the pharmacological effects that a bioactive peptide IRW (Ile-Arg-Trp) extends on bone health. Our results showed that IRW treatment protects osteoblasts against Ang II induced decline in cell proliferation and restores protein levels of collagen type I alpha 2 chain (COL1A2) and alkaline phosphatase (ALP) levels in MC3T3-E1 cells (p < 0.05). Apart from augmentation of these mineralization factors, the angiotensin II (Ang II) induced apoptotic stress in osteoblasts was mitigated by IRW as well. At the molecular level, IRW abolished the cytochrome-c release via modulation of pro-and anti-apoptotic genes in MC3T3-E1 cells (p < 0.05). Interestingly, IRW also increased cellular levels of cytoprotective local RAAS factors such as MasR, Ang (1−7), ACE2, and AT2R, and lowered the levels of Ang II effector receptor (AT1R). Further, our results indicated a lower content of inflammation and osteoclastogenesis biomarkers such as cyclooxygenase 2 (COX2), nuclear factor kappa B (NF-κB), and receptor activator of nuclear factor kappa-B ligand (RANKL) following IRW treatment in MC3T3-E1 cells (p < 0.05). The use of an antagonist-guided cell study indicated that IRW contributed to the process of cytoprotection and proliferation of osteoblasts via Runt-related transcription factor 2 (RUNX2) in face of Ang II stress in an AT2R dependent manner. The key findings of our study showed that IRW could potentially have a therapeutic role in the treatment and/or prevention of bone disorders.
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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) | 0.001 | 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 | 0.001 |
| 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".