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Record W2468455782 · doi:10.1242/jcs.191379

LAR protein tyrosine phosphatase regulates focal adhesions through CDK1

2016· article· en· W2468455782 on OpenAlexaff
Adil R. Sarhan, Trushar R. Patel, Alana R. Cowell, Michael G. Tomlinson, Carina Hellberg, John K. Heath, Debbie L. Cunningham, Neil A. Hotchin

Bibliographic record

VenueJournal of Cell Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsUniversity of Lethbridge
FundersEuropean CommissionHigher Committee for Education Development in IraqBritish Heart Foundation
KeywordsBiologyProtein tyrosine phosphataseFocal adhesionTyrosineCell biologyPhosphataseBiochemistryPhosphorylation

Abstract

fetched live from OpenAlex

Focal adhesions are complex multi-molecular structures that link the actin cytoskeleton to the extracellular matrix through integrin adhesion receptors and play a key role in regulation of many cellular functions. LAR (also known as PTPRF) is a receptor protein tyrosine phosphatase that regulates PDGF signalling and localises to focal adhesions. We have observed that loss of LAR phosphatase activity in mouse embryonic fibroblasts results in reduced numbers of focal adhesions and decreased adhesion to fibronectin. To understand how LAR regulates cell adhesion we used phosphoproteomic data, comparing global phosphorylation events in wild-type and LAR phosphatase-deficient cells, to analyse differential kinase activity. Kinase prediction analysis of LAR-regulated phosphosites identified a node of cytoskeleton- and adhesion-related proteins centred on cyclin-dependent kinase-1 (CDK1). We found that loss of LAR activity resulted in reduced activity of CDK1, and that CDK1 activity was required for LAR-mediated focal adhesion complex formation. We also established that LAR regulates CDK1 activity through c-Abl and Akt family proteins. In summary, we have identified a new role for a receptor protein tyrosine phosphatase in regulating CDK1 activity and hence cell adhesion to the extracellular matrix.

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)0.001
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 score0.025
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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

Citations29
Published2016
Admission routes1
Has abstractyes

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