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Record W2011813312 · doi:10.1016/j.celrep.2015.01.030

Mitochondrial RNA Granules Are Centers for Posttranscriptional RNA Processing and Ribosome Biogenesis

2015· article· en· W2011813312 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCell Reports · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health Research
KeywordsRNABiologyCell biologyRNA editingRibosomeeIF4AMessenger RNARibosome biogenesisNon-coding RNAMolecular biologyBiochemistryGene

Abstract

fetched live from OpenAlex

Cytoplasmic RNA granules play a central role in mRNA metabolism, but the importance of mitochondrial RNA granules remains relatively unexplored. We characterized their proteome and found that they contain a large toolbox of proteins dedicated to RNA metabolism. Investigation of four uncharacterized putative RNA-binding proteins-two RNA helicases, DHX30 and DDX28, and two proteins of the Fas-activated serine-threonine kinase (FASTKD) family, FASTKD2 and FASTKD5-demonstrated that both helicases and FASTKD2 are required for mitochondrial ribosome biogenesis. RNA-sequencing (RNA-seq) analysis showed that DDX28 and FASTKD2 bound the 16S rRNA. FASTKD5 is required for maturing precursor mRNAs that are not flanked by tRNAs and that therefore cannot be processed by the canonical mRNA maturation pathway. Silencing FASTKD5 rendered mature COX I mRNA almost undetectable, which severely reduced the synthesis of COX I, resulting in a complex IV assembly defect. These data demonstrate that mitochondrial RNA granules are centers for posttranscriptional RNA processing and the biogenesis of mitochondrial ribosomes.

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.

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 score0.027
Threshold uncertainty score0.428

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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