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Record W3043205384 · doi:10.1083/jcb.201910041

PTEN dephosphorylates Abi1 to promote epithelial morphogenesis

2020· article· en· W3043205384 on OpenAlexaff
Yanmei Qi, Jie Liu, Joshua Chao, Peter A. Greer, Shaohua Li

Bibliographic record

VenueThe Journal of Cell Biology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsQueen's University
FundersNational Cancer Institute
KeywordsPTENCell biologyTensinPhosphataseBiologyActin cytoskeletonChemistryPI3K/AKT/mTOR pathwayCytoskeletonSignal transductionPhosphorylationBiochemistryCell

Abstract

fetched live from OpenAlex

The tumor suppressor PTEN is essential for early development. Its lipid phosphatase activity converts PIP3 to PIP2 and antagonizes the PI3K-Akt pathway. In this study, we demonstrate that PTEN's protein phosphatase activity is required for epiblast epithelial differentiation and polarization. This is accomplished by reconstitution of PTEN-null embryoid bodies with PTEN mutants that lack only PTEN's lipid phosphatase activity or both PTEN's lipid and protein phosphatase activities. Phosphotyrosine antibody immunoprecipitation and mass spectrometry were used to identify Abi1, a core component of the WASP-family verprolin homologous protein (WAVE) regulatory complex (WRC), as a new PTEN substrate. We demonstrate that PTEN dephosphorylation of Abi1 at Y213 and S216 results in Abi1 degradation through the calpain pathway. This leads to down-regulation of the WRC and reorganization of the actin cytoskeleton. The latter is critical to the transformation of nonpolar pluripotent stem cells into the polarized epiblast epithelium. Our findings establish a link between PTEN and WAVE-Arp2/3-regulated actin cytoskeletal dynamics in epithelial morphogenesis.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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".

Quick stats

Citations19
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Cell BiologySame topicPI3K/AKT/mTOR signaling in cancerFrench-language works237,207