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Record W1565356234 · doi:10.1074/jbc.m115.643551

Novel Biological Substrates of Human Kallikrein 7 Identified through Degradomics

2015· article· en· W1565356234 on OpenAlexaff
Yijing Yu, Ioannis Prassas, Apostolos Dimitromanolakis, Eleftherios P. Diamandis

Bibliographic record

VenueJournal of Biological Chemistry · 2015
Typearticle
Languageen
FieldMedicine
TopicCoagulation, Bradykinin, Polyphosphates, and Angioedema
Canadian institutionsUniversity Health NetworkUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMidkineKallikreinProteasesBiologyProteomicsSerine proteaseProteaseBiochemistryMolecular biologyChemistryEnzymeGrowth factorGene

Abstract

fetched live from OpenAlex

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. 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.335
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2015
Admission routes1
Has abstractyes

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