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Record W2142907545 · doi:10.1128/mcb.01450-08

The RasGAP Proteins Ira2 and Neurofibromin Are Negatively Regulated by Gpb1 in Yeast and ETEA in Humans

2010· article· en· W2142907545 on OpenAlexaff
Vernon T. Phan, Vivianne W. Ding, Fenglei Li, Robert J. Chalkley, Alma L. Burlingame, Frank McCormick

Bibliographic record

VenueMolecular and Cellular Biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsIONICS Mass Spectrometry (Canada)
FundersNational Center for Research ResourcesU.S. Public Health Service
KeywordsNeurofibromin 1BiologyGTPase-activating proteinSaccharomyces cerevisiaeCell biologyMutantUbiquitinSignal transducing adaptor proteinUltrabithoraxYeastSignal transductionGeneticsG proteinGene

Abstract

fetched live from OpenAlex

The neurofibromatosis type 1 (NF1) gene encodes the GTPase-activating protein (GAP) neurofibromin, which negatively regulates Ras activity. The yeast Saccharomyces cerevisiae has two neurofibromin homologs, Ira1 and Ira2. To understand how these proteins are regulated, we utilized an unbiased proteomics approach to identify Ira2 and neurofibromin binding partners. We demonstrate that the Gpb1/Krh2 protein binds and negatively regulates Ira2 by promoting its ubiquitin-dependent proteolysis. We extended our findings to show that in mammalian cells, the ETEA/UBXD8 protein directly interacts with and negatively regulates neurofibromin. ETEA contains both UBA and UBX domains. Overexpression of ETEA downregulates neurofibromin in human cells. Purified ETEA, but not a mutant of ETEA that lacks the UBX domain, ubiquitinates the neurofibromin GAP-related domain in vitro. Silencing of ETEA expression increases neurofibromin levels and downregulates Ras activity. These findings provide evidence for conserved ubiquitination pathways regulating the RasGAP proteins Ira2 (in yeast) and neurofibromin (in humans).

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 score0.020
Threshold uncertainty score0.624

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 head0.005
GPT teacher head0.213
Teacher spread0.209 · 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

Citations59
Published2010
Admission routes1
Has abstractyes

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