Novel Biological Substrates of Human Kallikrein 7 Identified through Degradomics
Bibliographic record
Abstract
Kallikrein-related peptidases (KLKs) are a group of serine proteases widely expressed in various tissues and involved in a wide range of physiological and pathological processes. Although our understanding of the pathophysiological roles of most KLKs has blossomed in recent years, identification of the direct endogenous substrates of human KLKs remains an unmet objective. In this study we employed a degradomics approach to systemically investigate the endogenous substrates of KLK7 in an effort to understand the molecular pathways underlying KLK7 action in skin. We identified several previously known as well as novel protein substrates. Our most promising candidates were further validated with the use of targeted quantitative proteomics (selected reaction monitoring methods) and in vitro recombinant protein digestion assays. Our study revealed midkine, CYR61, and tenascin-C as endogenous substrates for KLK7. Interestingly, some of these substrates (e.g. midkine) were prone to proteolytic cleavage only by KLK7 (and not by other skin-associated KLKs), whereas others (e.g. CYR61 and tenascin-C) could be digested by several KLKs. Furthermore, using melanoma cell line, we show that KLK7-mediated cleavage of midkine results in an overall reduction in the pro-proliferative and pro-migratory effect of midkine. An inverse relation between KLK7 and midkine is also observed in human melanoma tissues. In summary, our degradomics approach revealed three novel endogenous substrates for KLK7, which may shed more light on the pathobiological roles of KLK7 in human skin. Similar substrate screening approaches could be applied for the discovery of biological substrates of other protease.
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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) | 0.000 | 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 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".