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Record W2804980253 · doi:10.1016/j.celrep.2018.04.039

Mechanism of Nonsense-Mediated mRNA Decay Stimulation by Splicing Factor SRSF1

2018· article· en· W2804980253 on OpenAlexfundno aff
Isabel Aznarez, Tomoki Nomakuchi, Jaclyn Tetenbaum-Novatt, Mohammad Alinoor Rahman, Oliver I. Fregoso, Holly A. Rees, Adrian R. Krainer

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsNonsense-mediated decayRNA splicingSplicing factorMechanism (biology)Messenger RNAStimulationCell biologyNonsenseChemistryBiologyComputational biologyGeneticsRNAPhysicsNeuroscienceGeneQuantum mechanics

Abstract

fetched live from OpenAlex

The splicing factor SRSF1 promotes nonsense-mediated mRNA decay (NMD), a quality control mechanism that degrades mRNAs with premature termination codons (PTCs). Here we show that transcript-bound SRSF1 increases the binding of NMD factor UPF1 to mRNAs while in, or associated with, the nucleus, bypassing UPF2 recruitment and promoting NMD. SRSF1 promotes NMD when positioned downstream of a PTC, which resembles the mode of action of exon junction complex (EJC) and NMD factors. Moreover, splicing and/or EJC deposition increase the effect of SRSF1 on NMD. Lastly, SRSF1 enhances NMD of PTC-containing endogenous transcripts that result from various events. Our findings reveal an alternative mechanism for UPF1 recruitment, uncovering an additional connection between splicing and NMD. SRSF1's role in the mRNA's journey from splicing to decay has broad implications for gene expression regulation and genetic diseases.

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 score0.004
Threshold uncertainty score0.414

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 head0.012
GPT teacher head0.264
Teacher spread0.253 · 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

Citations91
Published2018
Admission routes1
Has abstractyes

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