Mitochondrial RNA Granules Are Centers for Posttranscriptional RNA Processing and Ribosome Biogenesis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".