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Record W3126805840 · doi:10.1242/jcs.254359

Vimentin tunes cell migration on collagen by controlling β1 integrin activation and clustering

2021· article· en· W3126805840 on OpenAlexafffund
Zofia Ostrowska‐Podhorodecka, Isabel Ding, Wilson Lee, Jelena Tanic, Sevil Abbasi, Pamma D. Arora, Richard Liu, Alison E. Patteson, Paul A. Janmey, Christopher A. McCulloch

Bibliographic record

VenueJournal of Cell Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsVimentinPaxillinIntegrinFocal adhesionCell biologyCell migrationBiologyIntermediate Filament ProteinCDC42Intermediate filamentSignal transducing adaptor proteinCell adhesionCytoskeletonPhosphorylationActinCellImmunologyBiochemistryImmunohistochemistry

Abstract

fetched live from OpenAlex

Vimentin is a structural protein that is required for mesenchymal cell migration and directly interacts with actin, β1 integrin and paxillin. We examined how these interactions enable vimentin to regulate cell migration on collagen. In fibroblasts, depletion of vimentin increased talin-dependent activation of β1 integrin by more than 2-fold. Loss of vimentin was associated with reduction of β1 integrin clustering by 50% and inhibition of paxillin recruitment to focal adhesions by more than 60%, which was restored by vimentin expression. This reduction of paxillin was associated with 65% lower Cdc42 activation, a 60% reduction of cell extension formation and a greater than 35% decrease in cell migration on collagen. The activation of PAK1, a downstream effector of Cdc42, was required for vimentin phosphorylation and filament maturation. We propose that vimentin tunes cell migration through collagen by acting as an adaptor protein for focal adhesion proteins, thereby regulating β1 integrin activation, resulting in well-organized, mature integrin clusters.This article has an associated First Person interview with the first author of the paper.

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 score0.022
Threshold uncertainty score0.198

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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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

Citations54
Published2021
Admission routes2
Has abstractyes

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