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MP49-04 MATRIX METALLOPROTEINASE-9 CONTROLS SECRETION OF NERVE GROWTH FACTOR FROM BLADDER CELLS IN VITRO

2022· article· en· W4225245896 on OpenAlexaboutno aff
Aya Hajj, Aalya Hamouda, Stephanie Sirmakesyan, Philippe Cammisotto, Lysanne Campeau

Bibliographic record

VenueThe Journal of Urology · 2022
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsNerve growth factorMedicineCollagenaseMatrix metalloproteinaseIn vitroOveractive bladderSecretionEndocrinologyInternal medicineReceptorPathologyEnzymeBiologyBiochemistry

Abstract

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You have accessJournal of UrologyCME1 May 2022MP49-04 MATRIX METALLOPROTEINASE-9 CONTROLS SECRETION OF NERVE GROWTH FACTOR FROM BLADDER CELLS IN VITRO Aya Hajj, Aalya Hamouda, Stephanie Sirmakesyan, Philippe Cammisotto, and Lysanne Campeau Aya HajjAya Hajj More articles by this author , Aalya HamoudaAalya Hamouda More articles by this author , Stephanie SirmakesyanStephanie Sirmakesyan More articles by this author , Philippe CammisottoPhilippe Cammisotto More articles by this author , and Lysanne CampeauLysanne Campeau More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002624.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urine of female patients with overactive bladder syndrome (OAB) is characterized by low levels of the neurotrophin NGF with stable levels of its precursor proNGF. The resulting imbalance between NGF and proNGF may originate from an elevated activity of the proteolytic enzyme matrix metalloproteinase-9 (MMP-9) observed in the same samples. On the other hand, an inhibitor of the proinflammatory p75NTR receptor, THX-B, restored NGF levels to normal in a murine model of diabetic voiding dysfunction (Mossa et al 2020). The aim of the present work is to: 1) determine if NGF and MMP-9 are synthesized by cells of the bladder; 2) assess the role of MMP-9 in the synthesis and release of NGF by the same cells; and 3) examine how NGF secretion and MMP-9 might be affected by THX-B. METHODS: Rat bladders were digested by collagenase type IV to generate primary cultures of urothelial and smooth muscle cells. The expression of NGF, proNGF and MMP-9 were assessed by RT-qPCR and by immunoblotting. Cellular localisation of proteins was obtained by immunohistochemistry. Knock-down of MMP-9 was achieved using Crispr-Cas9. Levels of NGF and proNGF were measured by ELISA and enzyme activities by specific enzymatic kits. RESULTS: Urothelial and smooth muscle cells were found to contain and release significant amounts of NGF, proNGF and MMP-9 as revealed by RT-qPCR, immunoblotting and microscopy. The knock-down of MMP-9 using Crispr-Cas9 resulted in the disappearance of MMP-9 mRNA, proteins and enzymatic activity, in both cell types. In the MMP-9 knock-down cells, levels of secreted NGF were multiplied by a factor 4 to 9 while proNGF concentrations were not affected. On the other hand, incubation of urothelial cells with the p75 antagonist THX-B (5 µg/mL) for 24 hours increased NGF secretion and concomitantly decrease the activity of MMP-9. MMP-7, the enzyme converting proNGF to NGF, displayed an increased activity as well. On the other hand, THX-B had no effect on smooth muscle cells. Levels of mRNA were not affected by THX-B in both cell types. Pathways associated to p75NTR, namely erk, jnk, p38MAPK and cyclic AMP were also unchanged by treatment with THX-B. CONCLUSIONS: Bladder cells express, synthesize and release NGF, proNGF and MMP-9. The latter is central in the control of NGF secretion. THX-B targets urothelial cell to enhance NGF secretion by downregulation of MMP-9 and increased in MMP-7 activities, which could explain the improvement of diabetic voiding dysfunction in vivo. Source of Funding: Canadian Urology Association © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e854 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Aya Hajj More articles by this author Aalya Hamouda More articles by this author Stephanie Sirmakesyan More articles by this author Philippe Cammisotto More articles by this author Lysanne Campeau More articles by this author Expand All Advertisement PDF downloadLoading ...

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 score0.021
Threshold uncertainty score0.069

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0210.005

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 head0.011
GPT teacher head0.243
Teacher spread0.233 · 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
Published2022
Admission routes1
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