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Record W2996269264 · doi:10.1242/jcs.231134

Tissue transglutaminase 2 regulates tumor cell tensional homeostasis by increasing contractility

2019· article· en· W2996269264 on OpenAlexafffund
François Bordeleau, Wenjun Wang, Alysha Simmons, Marc A. Antonyak, Richard A. Cerione, Cynthia A. Reinhart‐King

Bibliographic record

VenueJournal of Cell Science · 2019
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsUniversité Laval
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteCancer Research SocietyNational Institutes of HealthNational Science Foundation
KeywordsContractilityBiologyTissue transglutaminaseCell biologyHomeostasisIntracellularRegulatorCellFocal adhesionReceptorMotilityEndocrinologySignal transductionBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Abnormal tensional cellular homeostasis is now considered a hallmark of cancer. Despite this, the origin of this abnormality remains unclear. In this work, we investigated the role of tissue transglutaminase 2 (TG2, also known as TGM2), a protein associated with poor prognosis and increased metastatic potential, and its relationship to the EGF receptor in the regulation of the mechanical state of tumor cells. Remarkably, we observed a TG2-mediated modulation of focal adhesion composition as well as stiffness-induced FAK activation, which was linked with a distinctive increase in cell contractility, in experiments using both pharmacological and shRNA-based approaches. Additionally, the increased contractility could be reproduced in non-malignant cells upon TG2 expression. Moreover, the increased cell contractility mediated by TG2 was largely due to the loss of EGFR-mediated inhibition of cell contractility. These findings establish intracellular TG2 as a regulator of cellular tensional homeostasis and suggest the existence of signaling switches that control the contribution of growth factor receptors in determining the mechanical state of a cell.

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)0.001
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 score0.071
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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