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Record W2055053609 · doi:10.1083/jcb.200506030

Regulation of ubiquitin ligase dynamics by the nucleolus

2005· article· en· W2055053609 on OpenAlexafffund
Karim Mekhail, Mireille Khacho, Amanda Carrigan, Robert R.J. Hache, Lakshman Gunaratnam, Stephen Lee

Bibliographic record

VenueThe Journal of Cell Biology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of Ottawa
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsBiologyUbiquitin ligaseNucleolusUbiquitinCell biologyCullinMdm2Ubiquitin-Protein LigasesFluorescence recovery after photobleachingBiochemistryNucleusGene

Abstract

fetched live from OpenAlex

Cellular pathways relay information through dynamic protein interactions. We have assessed the kinetic properties of the murine double minute protein (MDM2) and von Hippel-Lindau (VHL) ubiquitin ligases in living cells under physiological conditions that alter the stability of their respective p53 and hypoxia-inducible factor substrates. Photobleaching experiments reveal that MDM2 and VHL are highly mobile proteins in settings where their substrates are efficiently degraded. The nucleolar architecture converts MDM2 and VHL to a static state in response to regulatory cues that are associated with substrate stability. After signal termination, the nucleolus is able to rapidly release these proteins from static detention, thereby restoring their high mobility profiles. A protein surface region of VHL's beta-sheet domain was identified as a discrete [H+]-responsive nucleolar detention signal that targets the VHL/Cullin-2 ubiquitin ligase complex to nucleoli in response to physiological fluctuations in environmental pH. Data shown here provide the first evidence that cells have evolved a mechanism to regulate molecular networks by reversibly switching proteins between a mobile and static 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)0.000
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 score0.037
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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
Published2005
Admission routes2
Has abstractyes

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