N-Terminomic Identification of Intracellular MMP-2 Substrates in Cardiac Tissue
Bibliographic record
Abstract
Proteases are enzymes that induce irreversible post-translational modifications by hydrolyzing amide bonds in proteins. One of these proteases is matrix metalloproteinase-2 (MMP-2), which has been shown to modulate extracellular matrix remodeling and intracellular proteolysis during myocardial injury. However, the substrates of MMP-2 in heart tissue are limited, and lesser known are the cleavage sites. Here, we used degradomics to investigate the substrates of intracellular MMP-2 in rat ventricular extracts. First, we designed a novel, constitutively active MMP-2 fusion protein (MMP-2-Fc) that we expressed and purified from mammalian cells. Using this protease, we proteolyzed ventricular extracts and used subtiligase-mediated N-terminomic labeling which identified 95 putative MMP-2-Fc proteolytic cleavage sites using mass spectrometry. The intracellular MMP-2 cleavage sites identified in heart tissue extracts were enriched for proteins primarily involved in metabolism, as well as the breakdown of fatty acids and amino acids. We further characterized the cleavage of three of these MMP-2-Fc substrates based on the gene ontology analysis. We first characterized the cleavage of sarco/endoplasmic reticulum calcium ATPase (SERCA2a), a known MMP-2 substrate in myocardial injury. We then characterized the cleavage of malate dehydrogenase (MDHM) and phosphoglycerate kinase 1 (PGK1), representing new cardiac tissue substrates. Our findings provide insights into the intracellular substrates of MMP-2 in cardiac cells, suggesting that MMP-2 activation plays a role in cardiac metabolism.
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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 | 0.001 |
| 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".