Kallikrein-Related Peptidase 14 Activates Zymogens of Membrane Type Matrix Metalloproteinases (MT-MMPs)—A CleavEx Based Analysis
Bibliographic record
Abstract
Kallikrein-related peptidases (KLKs) and matrix metalloproteinases (MMPs) are secretory proteinases known to proteolytically process components of the extracellular matrix, modulating the pericellular environment in physiology and in pathologies. The interconnection between these families remains elusive. To assess the cross-activation of these families, we developed a peptide, fusion protein-based exposition system (Cleavage of exposed amino acid sequences, CleavEx) aiming at investigating the potential of KLK14 to recognize and hydrolyze proMMP sequences. Initial assessment identified ten MMP activation domain sequences which were validated by Edman degradation. The analysis revealed that membrane-type MMPs (MT-MMPs) are targeted by KLK14 for activation. Correspondingly, proMMP14-17 were investigated in vitro and found to be effectively processed by KLK14. Again, the expected neo-N-termini of the activated MT-MMPs was confirmed by Edman degradation. The effectiveness of proMMP activation was analyzed by gelatin zymography, confirming the release of fully active, mature MT-MMPs upon KLK14 treatment. Lastly, MMP14 was shown to be processed on the cell surface by KLK14 using murine fibroblasts overexpressing human MMP14. Herein, we propose KLK14-mediated selective activation of cell-membrane located MT-MMPs as an additional layer of their regulation. As both, KLKs and MT-MMPs, are implicated in cancer, their cross-activation may constitute an important factor in tumor progression and metastasis.
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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".