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Record W2057923468 · doi:10.1074/jbc.m606225200

Regulation of MAPK-activated Protein Kinase 5 Activity and Subcellular Localization by the Atypical MAPK ERK4/MAPK4

2006· article· en· W2057923468 on OpenAlexaff
Espen Åberg, Maria Perander, Bjarne Johansen, Catherine Julien, Sylvain Meloche, Stephen M. Keyse, Ole Morten Seternes

Bibliographic record

VenueJournal of Biological Chemistry · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsInstitute for Research in Immunology and Cancer
Fundersnot available
KeywordsProtein kinase AMAPK/ERK pathwayKinaseHeLaPhosphorylationCell biologyp38 mitogen-activated protein kinasesCytoplasmChemistryBiologyBiochemistryCell

Abstract

fetched live from OpenAlex

MAPK-activated protein kinase 5 (MK5) was recently identified as a physiological substrate of the atypical MAPK ERK3. Complex formation between ERK3 and MK5 results in phosphorylation and activation of MK5, concomitant stabilization of ERK3, and the nuclear exclusion of both proteins. However, ablation of ERK3 in HeLa cells using small interfering RNA or in fibroblasts derived from ERK3 null mice reduces the activity of endogenous MK5 by only 50%, suggesting additional mechanisms of MK5 regulation. Here we identify the ERK3-related kinase ERK4 as a bona fide interaction partner of MK5. Binding of ERK4 to MK5 is accompanied by phosphorylation and activation of MK5. Furthermore, complex formation also results in the relocalization of MK5 from nucleus to cytoplasm. However unlike ERK3, ERK4 is a stable protein, and its half-life is not modified by the presence or absence of MK5. Finally, although knock-down of ERK4 protein in HeLa cells reduces endogenous MK5 activity by approximately 50%, a combination of small interfering RNAs targeting both ERK4 and ERK3 causes a further reduction in the MK5 activity by more than 80%. We conclude that MK5 activation is dependent on both ERK3 and ERK4 in these cells and that these atypical MAPKs are both physiological regulators of MK5 activity.

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 score0.013
Threshold uncertainty score0.340

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations87
Published2006
Admission routes1
Has abstractyes

Explore more

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