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Record W2041922277 · doi:10.1083/jcb.200407085

The RNA-binding protein SUP-12 controls muscle-specific splicing of the ADF/cofilin pre-mRNA in <i>C. elegans </i>

2004· article· en· W2041922277 on OpenAlexaff
Akwasi Anyanful, Kanako Ono, Robert Johnsen, Hinh Ly, Victor L. Jensen, David L. Baillie, Shoichiro Ono

Bibliographic record

VenueThe Journal of Cell Biology · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsSimon Fraser University
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsBiologyAlternative splicingRNA splicingCofilinCaenorhabditis elegansGene isoformRNA-binding proteinIntronCell biologyMessenger RNAGeneMolecular biologyRNAGeneticsActin cytoskeletonCytoskeletonCell

Abstract

fetched live from OpenAlex

Tissue-specific alternative pre-mRNA splicing is essential for increasing diversity of functionally different gene products. In Caenorhabditis elegans, UNC-60A and UNC-60B, nonmuscle and muscle isoforms of actin depolymerizing factor (ADF)/cofilin, are expressed by alternative splicing of unc-60 and regulate distinct actin-dependent developmental processes. We report that SUP-12, a member of a new family of RNA recognition motif (RRM) proteins, including SEB-4, regulates muscle-specific splicing of unc-60. In sup-12 mutants, expression of UNC-60B is decreased, whereas UNC-60A is up-regulated in muscle. sup-12 mutations strongly suppress muscle defects in unc-60B mutants by allowing expression of UNC-60A in muscle that can substitute for UNC-60B, thus unmasking their functional redundancy. SUP-12 is expressed in muscle and localized to the nuclei in a speckled pattern. The RRM domain of SUP-12 binds to several sites of the unc-60 pre-mRNA including the UG repeats near the 3'-splice site in the first intron. Our results suggest that SUP-12 is a novel tissue-specific splicing factor and regulates functional redundancy among ADF/cofilin isoforms.

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)0.002
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 score0.006
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.244
Teacher spread0.234 · 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

Citations53
Published2004
Admission routes1
Has abstractyes

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