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Record W2913645827 · doi:10.1073/pnas.1811923116

ERK2 regulates epithelial-to-mesenchymal plasticity through DOCK10-dependent Rac1/FoxO1 activation

2019· article· en· W2913645827 on OpenAlexaff
Sejeong Shin, Gwen R. Buel, Michal J. Nagiec, Min-Joon Han, Philippe P. Roux, John Blenis, Sang-Oh Yoon

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institute of General Medical SciencesUniversity of Illinois at Urbana-ChampaignNational Institutes of Health
KeywordsRAC1Epithelial–mesenchymal transitionCDC42MAPK/ERK pathwayTranscription factorCell biologyCancer researchBiologySignal transductionFOXO1MediatorDownregulation and upregulationGeneGenetics

Abstract

fetched live from OpenAlex

ERK is a key coordinator of the epithelial-to-mesenchymal transition (EMT) in that a variety of EMT-inducing factors activate signaling pathways that converge on ERK to regulate EMT transcription programs. However, the mechanisms by which ERK controls the EMT program are not well understood. Through an analysis of the global changes of gene expression mediated by ERK2, we identified the transcription factor FoxO1 as a potential mediator of ERK2-induced EMT, and thus we investigated the mechanism by which ERK2 regulates FoxO1. Additionally, our analysis revealed that ERK2 induced the expression of Dock10, a Rac1/Cdc42 GEF, during EMT. We demonstrate that the activation of the Rac1/JNK signaling axis downstream of Dock10 leads to an increase in FoxO1 expression and EMT. Taken together, our study uncovers mechanisms by which epithelial cells acquire less proliferative but more migratory mesenchymal properties and reveals potential therapeutic targets for cancers evolving into a metastatic disease state.

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 score0.076
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.318
Teacher spread0.278 · 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

Citations97
Published2019
Admission routes1
Has abstractyes

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