Peroxynitrite‐induced changes in 72kDa matrix metalloproteinase‐2 activity are further regulated by its phosphorylation status
Bibliographic record
Abstract
Matrix metalloproteinase-2 (MMP-2) is a key protease in several oxidative stress related pathologies. 72kDa MMP-2 activity is modulated by peroxynitrite (ONOO−), or phosphorylation, however, the effect of these together has not been tested. The activity of human recombinant 72kDa MMP-2 following various in vitro treatments was measured by gelatin zymography. ONOO− treatment (15min, 37°C) resulted in concentration-dependent changes in MMP-2 activity, with 0.3–10μM increasing and 100μM attenuating its activity. Dephosphorylation of MMP-2 with alkaline phosphatase (20min, 37°C) significantly increased its activity, either with or without ONOO−. Glutathione (GSH, 30μM) without or with 100μM ONOO−, decreased MMP-2 activity. Treatment of MMP-2 with 0.3μM ONOO− in the presence of 30μM glutathione increased its activity and even moreso its dephosphorylated form. GSH did not significantly affect MMP-2 activity with 1–10μM ONOO−. With 100μM ONOO−, dephosphorylation partially protected MMP-2 from the attenuation of its activity. These results suggest that ONOO− activation (at low μM) and inactivation (at high μM) of 72kDa MMP-2, in the presence or absence of glutathione, is further modulated by its phosphorylation status. This may be relevant to pathophysiological conditions associated with increased oxidative stress and intracellular activation of MMP-2. Grant support from the CIHR.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".