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Proteinases, Proteinase‐Activated Receptors (PARs) and Transient Receptor Potential (TRP) Ion Channels: Driving Tumorigenesis in the Bladder Cancer Microenvironment

2016· article· en· W4389027172 on OpenAlexaffabout
Stacy Gibson, Koichiro Mihara, Mahmoud El‐Daly, Mahmoud Saifedine, Morley D. Hollenberg, M. Eric Hyndman

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldNeuroscience
TopicIon Channels and Receptors
Canadian institutionsInstitute of Infection and ImmunityProstate Cancer CanadaUniversity of Calgary
Fundersnot available
KeywordsTransient receptor potential channelTRPV4ReceptorCell biologyChemistryTRPM8CarcinogenesisSignal transductionSecretionTRPC1Cancer cellBiologyCancer researchBiochemistryCancer

Abstract

fetched live from OpenAlex

Overview and hypotheses We propose that bladder cancer initiation and progression involves proteinases that become elevated in the tumour microenvironment and that signal in part by cleaving and activating proteinase‐activated receptors (PARs). Because proteinases activate PARs through the cleavage of the N‐terminal cell surface domain of this subgroup of the G protein coupled receptor family, this mechanism would release the cleaved peptide along with the activating enzyme into the urine. We hypothesise that bladder cancer cells respond to PAR activation that can also involve signaling by transient receptor potential cation channels (TRPV4/TRPM8) along with the secretion of PAR‐regulating proteinases. Aims To test this hypothesis, our aims were therefore to: (1) explore the expression and function of proteinase activated receptors (PARs 1, 2 & 4) within bladder cancer‐derived cell lines, (2) determine the expression and function of transient receptor potential (TRP) channels known to be affected by the activation of PARs (TRPV4, TRPM8) and (3) analyze the ability of bladder cancer cell lines to secrete PAR‐regulating proteinases into their supernatant. Methods The functionality of PARs (1, 2 and 4), TRPV4 and TRPM8 in bladder cancer cell lines (T24, HTB‐9) was evaluated by monitoring calcium signaling (JPET 288:358, 1999) stimulated by increasing concentrations of target‐selective agonists for (1) The PARs: PAR1 (TFLLR‐NH 2 ), PAR2 (2fLIGRLO‐NH 2 ) and PAR4 (AYPGKF‐NH 2 ), (2) TRPV4 (GSK101) and (3) TRPM8 (Icilin). The relative abundance of T24 and HTB‐9 PAR and TRP (V4, M8) channel mRNA was evaluated using semi quantitative PCR, normalized to an actin signal. Production of PAR‐regulating proteinases by T24 and HTB‐9 cells was evaluated by a new novel PAR cleavage assay that monitors the release of an N‐terminal luciferase tag from cell surface‐expressed PARs 1 and 2. Results Bladder tumour‐derived HTB‐9 & T24 cells possess (assessed through PCR) functional (assessed through calcium signaling) PARs 1 & 2 but with a differential sensitivity towards PAR2 (HTB‐9 > T24) vs PAR1 activation (HTB‐9 = T24). PAR4 is not expressed. These cell lines also showed the expression of functional TRPV4 channels, but not TRPM8. Both cell lines secrete PAR‐regulating proteinases that cleave PARs 1 and 2. Conclusion Functional PARs 1 & 2 and TRPV4 channels which are present in bladder cancer cells may drive tumour progression. Further, tumour cell PAR‐regulating proteinase secretion can act via autocrine and paracrine mechanisms to enhance tumorigenesis. This mechanism may represent a therapeutic target for bladder cancer. Further, the secretion into the urine of the upregulated PAR‐regulating proteinases along with PAR N‐terminal peptide fragments may serve as a biomarker for bladder cancer progression. Support or Funding Information AIHS CRIO Grant, Prostate Cancer Canada Discovery Grant, Motorcycle Ride for Dad and CIHR

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.231
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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