Matrix Metalloproteinase 10 Degradomics in Keratinocytes and Epidermal Tissue Identifies Bioactive Substrates With Pleiotropic Functions*
Bibliographic record
Abstract
Matrix metalloproteinases (MMPs) are important players in skin homeostasis, wound repair, and in the pathogenesis of skin cancer. It is now well established that most of their functions are related to processing of bioactive proteins rather than components of the extracellular matrix (ECM). MMP10 is highly expressed in keratinocytes at the wound edge and at the invasive front of tumors, but hardly any non-ECM substrates have been identified and its function in tissue repair and carcinogenesis is unclear. To better understand the role of MMP10 in the epidermis, we employed multiplexed iTRAQ-based Terminal Amine Isotopic Labeling of Substrates (TAILS) and monitored MMP10-dependent proteolysis over time in secretomes from keratinocytes. Time-resolved abundance clustering of neo-N termini classified MMP10-dependent cleavage events by efficiency and refined the MMP10 cleavage site specificity by revealing a so far unknown preference for glutamate in the P1 position. Moreover, we identified and validated the integrin alpha 6 subunit, cysteine-rich angiogenic inducer 61 and dermokine as novel direct MMP10 substrates and provide evidence for MMP10-dependent but indirect processing of phosphatidylethanolamine-binding protein 1. Finally, we sampled the epidermal proteome and degradome in unprecedented depth and confirmed MMP10-dependent processing of dermokine in vivo by TAILS analysis of epidermis from transgenic mice that overexpress a constitutively active mutant of MMP10 in basal keratinocytes. The newly identified substrates are involved in cell adhesion, migration, proliferation, and/or differentiation, indicating a contribution of MMP10 to local modulation of these processes during wound healing and cancer development. Data are available via ProteomeXchange with identifier PXD002474.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".