MétaCan
Menu
Back to cohort
Record W3026681831 · doi:10.1016/j.celrep.2020.107660

NF45 and NF90 Regulate Mitotic Gene Expression by Competing with Staufen-Mediated mRNA Decay

2020· article· en· W3026681831 on OpenAlexafffund
Sami Nourreddine, Geneviève Lavoie, Justine S. Paradis, Khaled Ben El Kadhi, Antoine Méant, Léo Aubert, Benoı̂t Grondin, Patrick Gendron, Benoı̂t Chabot, Michel Bouvier, Sébastien Carreno, Philippe P. Roux

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversité de SherbrookeUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéFondation du Grand défi Pierre Lavoie
KeywordsMitosisBiologyRNA-binding proteinCell biologyTranscriptomeMessenger RNAGene expressionGeneRegulation of gene expressionCell cycleGenetics

Abstract

fetched live from OpenAlex

In human cells, the expression of ∼1,000 genes is modulated throughout the cell cycle. Although some of these genes are controlled by specific transcriptional programs, very little is known about their post-transcriptional regulation. Here, we analyze the expression signature associated with all 687 RNA-binding proteins (RBPs) and identify 39 that significantly correlate with cell cycle mRNAs. We find that NF45 and NF90 play essential roles in mitosis, and transcriptome analysis reveals that they are necessary for the expression of a subset of mitotic mRNAs. Using proteomics, we identify protein clusters associated with the NF45-NF90 complex, including components of Staufen-mediated mRNA decay (SMD). We show that depletion of SMD components increases the binding of mitotic mRNAs to the NF45-NF90 complex and rescues cells from mitotic defects. Together, our results indicate that the NF45-NF90 complex plays essential roles in mitosis by competing with the SMD machinery for a common set of mRNAs.

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 score0.012
Threshold uncertainty score0.441

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 head0.218
Teacher spread0.211 · 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

Citations55
Published2020
Admission routes2
Has abstractyes

Explore more

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