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Record W2081308694 · doi:10.1074/jbc.m308897200

Reprogramming Alternative Pre-messenger RNA Splicing through the Use of Protein-binding Antisense Oligonucleotides

2003· article· en· W2081308694 on OpenAlexafffund
Jonathan Villemaire, I. Dion, Sherif Abou Elela, Benoı̂t Chabot

Bibliographic record

VenueJournal of Biological Chemistry · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversité de Sherbrooke
FundersNational Cancer InstituteGenome Canada
KeywordsOligonucleotideReprogrammingRNA splicingMessenger RNARNA-binding proteinAlternative splicingRNAPost-transcriptional modificationAntisense RNAComputational biologyCell biologyChemistryBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Alternative pre-messenger RNA splicing is a major contributor to proteomic diversity in higher eukaryotes and represents a key step in the control of protein function in a large variety of biological systems. As a means of artificially altering splice site choice, we have investigated the impact of positioning proteins in the vicinity of 5' splice sites. We find that a recombinant GST-MS2 protein interferes with 5' splice site use, most efficiently when it binds upstream of that site. To broaden the use of proteins as steric inhibitors of splicing, we have tested the activity of antisense oligonucleotides carrying binding sites for the heterogeneous nuclear ribonucleoprotein A1/A2 proteins. In a HeLa cell extract, tailed oligonucleotides complementary to exonic sequences elicit strong shifts in 5' splice site selection. In four different human cell lines, an interfering oligonucleotide carrying A1/A2 binding sites also shifted the alternative splicing of the Bcl-x pre-mRNA more efficiently than oligonucleotides acting through duplex formation only. The use of protein-binding oligonucleotides that interfere with U1 small nuclear ribonucleoprotein binding therefore represents a novel and powerful approach to control splice site selection in cells.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.010
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.056
GPT teacher head0.307
Teacher spread0.252 · 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

Citations73
Published2003
Admission routes2
Has abstractyes

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