MétaCan
Menu
Back to cohort
Record W2010047992 · doi:10.1016/j.celrep.2013.11.039

Staufen2 Regulates Neuronal Target RNAs

2013· article· en· W2010047992 on OpenAlexafffund
Jacki Heraud-Farlow, Tejaswini Sharangdhar, Xiao Li, Philipp Pfeifer, Stefanie Tauber, Denise Orozco, Alexandra Hörmann, Sabine Thomas, Ashley Farlow, Dieter Edbauer, Howard D. Lipshitz, Quaid Morris, Martin Bilban, Michael Doyle, Michael Kiebler

Bibliographic record

VenueCell Reports · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Toronto
FundersEuropean Social FundCanadian Institutes of Health ResearchAustrian Science Fund
KeywordsBiologyTranslation (biology)RNA-binding proteinMessenger RNARNACell biologySynaptic plasticityUntranslated regionThree prime untranslated regionRegulatormicroRNAGeneGeneticsReceptor

Abstract

fetched live from OpenAlex

RNA-binding proteins play crucial roles in directing RNA translation to neuronal synapses. Staufen2 (Stau2) has been implicated in both dendritic RNA localization and synaptic plasticity in mammalian neurons. Here, we report the identification of functionally relevant Stau2 target mRNAs in neurons. The majority of Stau2-copurifying mRNAs expressed in the hippocampus are present in neuronal processes, further implicating Stau2 in dendritic mRNA regulation. Stau2 targets are enriched for secondary structures similar to those identified in the 3' UTRs of Drosophila Staufen targets. Next, we show that Stau2 regulates steady-state levels of many neuronal RNAs and that its targets are predominantly downregulated in Stau2-deficient neurons. Detailed analysis confirms that Stau2 stabilizes the expression of one synaptic signaling component, the regulator of G protein signaling 4 (Rgs4) mRNA, via its 3' UTR. This study defines the global impact of Stau2 on mRNAs in neurons, revealing a role in stabilization of the levels of synaptic targets.

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 score0.057
Threshold uncertainty score0.348

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations93
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueCell ReportsSame topicRNA Research and SplicingFrench-language works237,207