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Record W2225873253 · doi:10.1371/journal.pgen.1005768

Prp4 Kinase Grants the License to Splice: Control of Weak Splice Sites during Spliceosome Activation

2016· article· en· W2225873253 on OpenAlexaff
Daniela Eckert, Nicole Andrée, Aleh Razanau, Susanne Zock‐Emmenthal, Martin Lützelberger, Susann Plath, Henning Schmidt, Ángel Guerra-Moreno, Luca Cozzuto, José Ayté, Norbert F. Käufer

Bibliographic record

VenuePLoS Genetics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Manitoba
FundersMinisterio de Ciencia e InnovaciónDeutscher Akademischer AustauschdienstUniversitat Pompeu Fabra
KeywordsBiologySpliceosomeIntronRNA splicingGeneticsSplicing factorAlternative splicingSplice site mutationSR proteinCell biologyGeneRNAExon

Abstract

fetched live from OpenAlex

The genome of the fission yeast Schizosaccharomyces pombe encodes 17 kinases that are essential for cell growth. These include the cell-cycle regulator Cdc2, as well as several kinases that coordinate cell growth, polarity, and morphogenesis during the cell cycle. In this study, we further characterized another of these essential kinases, Prp4, and showed that the splicing of many introns is dependent on Prp4 kinase activity. For detailed characterization, we chose the genes res1 and ppk8, each of which contains one intron of typical size and position. Splicing of the res1 intron was dependent on Prp4 kinase activity, whereas splicing of the ppk8 intron was not. Extensive mutational analyses of the 5' splice site of both genes revealed that proper transient interaction with the 5' end of snRNA U1 governs the dependence of splicing on Prp4 kinase activity. Proper transient interaction between the branch sequence and snRNA U2 was also important. Therefore, the Prp4 kinase is required for recognition and efficient splicing of introns displaying weak exon1/5' splice sites and weak branch sequences.

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 score0.009
Threshold uncertainty score0.479

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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

Citations33
Published2016
Admission routes1
Has abstractyes

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