Extracellular matrix and proteolysis: mechanisms driving irreversible changes and shaping cell behavior
Bibliographic record
Abstract
The extracellular matrix (ECM) provides structural support and dynamic signaling cues, governing cellular behavior and tissue integrity. ECM remodeling, critically regulated by irreversible proteolysis, profoundly impacts development, homeostasis, and disease. This review examines the major families of ECM-degrading proteases-matrix metalloproteinases (MMPs), serine proteases, a disintegrin and metalloproteinases (ADAMs), metalloproteinase with thrombospondin motifs (ADAMTSs), and cysteine proteases-emphasizing their shared regulatory mechanisms and proteolytic activity in reshaping the tissue microenvironment. These proteases exhibit functional redundancy, particularly in the generation of matrikines, growth factors, and cytokines from common ECM substrates, all contributing to ECM softening. These overlaps in substrates and the resulting bioactive molecules amplify proteolysis within the tissue. The generated matrikines, growth factors, and cytokines further drive ECM remodeling through feedback loops, influencing the expression and activation of proteolytic enzymes. Despite these shared mechanisms, protease families demonstrate cell-specific functional specialization shaped by transcriptional programs, microenvironmental signals, and subcellular targeting, ensuring precise spatiotemporal proteolysis during processes such as development, wound healing, and immune responses. Dysregulation of this intricate proteolytic network contributes to chronic pathologies and cancer. Thus, understanding and targeting these processes is crucial for therapeutic intervention and the improved regulation of biological functions. Collectively, these insights reveal how irreversible ECM proteolysis orchestrates complex, context-dependent biological responses in both health and disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".