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Record W2620493457 · doi:10.1042/bcj20170174

Biochemical and functional characterization of the human tissue kallikrein 9

2017· article· en· W2620493457 on OpenAlexaff
Panagiota S. Filippou, Sofia Farkona, Davor Brinc, Yijing Yu, Ioannis Prassas, Eleftherios P. Diamandis

Bibliographic record

VenueBiochemical Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicCoagulation, Bradykinin, Polyphosphates, and Angioedema
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsKallikreinBiochemistryChemistryEnzyme

Abstract

fetched live from OpenAlex

Human tissue kallikrein 9 (KLK9) is a member of the kallikrein-related family of proteases. Despite its known expression profile, much less is known about the functional roles of this protease and its implications in normal physiology and disease. We present here the first data on the biochemical characterization of KLK9, investigate parameters that affect its enzymatic activity (such as inhibitors) and provide preliminary insights into its putative substrates. We show that mature KLK9 is a glycosylated chymotrypsin-like enzyme with strong preference for tyrosine over phenylalanine at the P1 cleavage position. The enzyme activity is enhanced by Mg2+ and Ca2+, but is reversibly attenuated by Zn2+. KLK9 is inhibited in vitro by many naturally occurring or synthetic protease inhibitors. Using a combination of degradomic and substrate specificity assays, we identified candidate KLK9 substrates in two different epithelial cell lines [the non-tumorigenic human keratinocyte cells (HaCaT) and the tumorigenic tongue squamous carcinoma cells (SCC9)]. Two potential KLK9 substrates [KLK10 and midkine (MDK)] were subjected to further validation. Taken together, our data delineate some functional and biochemical properties of KLK9 for future elucidation of the role of this enzyme in health and disease.

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 score0.419
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.025
GPT teacher head0.282
Teacher spread0.257 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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