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Record W4377564519 · doi:10.1096/fj.202300222r

<scp>TRPV4</scp> regulates <i>β</i> 1 integrin‐mediated cell–matrix adhesions and collagen remodeling

2023· article· en· W4377564519 on OpenAlexafffund
Chenfan Ji, Yongqiang Wang, Qin Wang, Andrew Wang, Aiman Ali, Christopher A. McCulloch

Bibliographic record

VenueThe FASEB Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsIntegrinCollagen receptorCell biologyExtracellular matrixDownregulation and upregulationChemistryFocal adhesionTRPV4Matrix (chemical analysis)ReceptorSignal transductionBiologyBiochemistryTransient receptor potential channel

Abstract

fetched live from OpenAlex

-permeable plasma membrane channel that associates with focal adhesions, influences collagen remodeling, and is associated with fibrotic processes through undefined mechanisms. While TRPV4 is known to be activated by mechanical forces transmitted through collagen adhesion receptors containing the β1 integrin, it is not understood whether TRPV4 affects matrix remodeling by altering β1 integrin expression and function. We tested the hypothesis that TRPV4 regulates collagen remodeling through its impact on the β1 integrin in cell-matrix adhesions. In cultured fibroblasts derived from mouse gingival connective tissues, which exhibit very rapid collagen turnover, we found that higher TRPV4 expression is associated with reduced β1 integrin abundance and adhesion to collagen, reduced focal adhesion size and total adhesion area, and reduced alignment and compaction of extracellular fibrillar collagen. The reduction of β1 integrin expression mediated by TRPV4 is associated with the upregulation of miRNAs that target β1 integrin mRNA. Our data suggest a novel mechanism by which TRPV4 modulates collagen remodeling through post-transcriptional downregulation of β1 integrin expression and function.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.115
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.029
GPT teacher head0.299
Teacher spread0.270 · 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

Citations17
Published2023
Admission routes2
Has abstractyes

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