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Record W4324346847 · doi:10.1261/rna.079555.122

Phosphorylation controls the oligomeric state of She2 and mRNA localization in yeast

2023· article· en· W4324346847 on OpenAlexafffund
Nastaran Farajzadeh, Karen Shahbabian, Yani Bouaziz, Emmanuelle Querido, Pascal Chartrand

Bibliographic record

VenueRNA · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsUniversité de Montréal
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaUniversité de MontréalInstitut de Recherche Clinique De Montréal
KeywordsBiologyMessenger RNACell biologyPhosphorylationRNA-binding proteinRNAMolecular biologyGeneticsGene

Abstract

fetched live from OpenAlex

Messenger RNA (mRNA) localization is an important mechanism controlling local protein synthesis. In budding yeast, asymmetric localization of transcripts such as ASH1 mRNA to the bud tip depends on the She2 RNA-binding protein. She2 assembles as a tetramer to bind RNA, but the regulation of this process as part of the mRNA locasome is still unclear. Here, we performed a phosphoproteomic analysis of She2 in vivo and identified new phosphosites, several of which are located at the dimerization or tetramerization interfaces of She2. Remarkably, phosphomimetic mutations at these residues disrupt the capacity of She2 to promote Ash1 asymmetric accumulation. A detailed analysis of one of these residues, T109, shows that a T109D mutation inhibits She2 oligomerization and its interaction with She3 and the importin-α Srp1. She2 proteins harboring the T109D mutation also display reduced expression. More importantly, this phosphomimetic mutation strongly impairs the capacity of She2 to bind RNA and disrupts ASH1 mRNA localization. These results demonstrate that the control of She2 oligomerization by phosphorylation constitutes an important regulatory step in the mRNA localization pathway.

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 score0.105
Threshold uncertainty score0.123

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 head0.260
Teacher spread0.250 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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