MétaCan
Menu
Back to cohort
Record W2083537672 · doi:10.1128/mcb.01574-07

Different Mechanisms for Pseudouridine Formation in Yeast 5S and 5.8S rRNAs

2008· article· en· W2083537672 on OpenAlexaff
Wayne A. Decatur, Murray N. Schnare

Bibliographic record

VenueMolecular and Cellular Biology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsDalhousie University
FundersNational Institute of General Medical SciencesU.S. Public Health Service
KeywordsSmall nucleolar RNAPseudouridineBiologyRibosomal RNARibonucleoproteinGuide RNA23S ribosomal RNATransfer RNARNARibosomeGeneticsSaccharomyces cerevisiaeNon-coding RNAYeastGeneGenome

Abstract

fetched live from OpenAlex

The selection of sites for pseudouridylation in eukaryotic cytoplasmic rRNA occurs by the base pairing of the rRNA with specific guide sequences within the RNA components of box H/ACA small nucleolar ribonucleoproteins (snoRNPs). Forty-four of the 46 pseudouridines (Psis) in the cytoplasmic rRNA of Saccharomyces cerevisiae have been assigned to guide snoRNAs. Here, we examine the mechanism of Psi formation in 5S and 5.8S rRNA in which the unassigned Psis occur. We show that while the formation of the Psi in 5.8S rRNA is associated with snoRNP activity, the pseudouridylation of 5S rRNA is not. The position of the Psi in 5.8S rRNA is guided by snoRNA snR43 by using conserved sequence elements that also function to guide pseudouridylation elsewhere in the large-subunit rRNA; an internal stem-loop that is not part of typical yeast snoRNAs also is conserved in snR43. The multisubstrate synthase Pus7 catalyzes the formation of the Psi in 5S rRNA at a site that conforms to the 7-nucleotide consensus sequence present in other substrates of Pus7. The different mechanisms involved in 5S and 5.8S rRNA pseudouridylation, as well as the multiple specificities of the individual trans factors concerned, suggest possible roles in linking ribosome production to other processes, such as splicing and tRNA synthesis.

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 score0.171
Threshold uncertainty score0.459

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 head0.226
Teacher spread0.214 · 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

Citations31
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueMolecular and Cellular BiologySame topicRNA modifications and cancerFrench-language works237,207