FASTKD5 processes mitochondrial pre-mRNAs at non-canonical cleavage sites
Bibliographic record
Abstract
Abstract The regulation of mammalian mitochondrial gene expression is largely post-transcriptional and the first step in translating the 13 polypeptides encoded in mtDNA is endonucleolytic cleavage of the primary polycistronic transcripts. As the rRNAs and most of the mRNAs in mtDNA are flanked by tRNAs, the release of the mature RNAs occurs mostly by excision of the tRNAs. Processing the non-canonical mRNAs, not flanked by tRNAs, requires FASTKD5, but the molecular mechanism remains unknown. To investigate this, we created and characterized a knockout cell line to use as an assay system. The absence of FASTKD5 resulted in a severe combined OXPHOS assembly defect due to the inability to translate mRNAs with unprocessed 5’-UTRs. Analysis of RNA processing of FASTKD5 variants allowed us to map amino acid residues essential for function. Remarkably, this map was RNA substrate-specific, arguing against a one size fits all model. A reconstituted in vitro system with purified FASTKD5 protein and synthetic RNA substrates showed that FASTKD5 on its own was able to cleave client substrates correctly, but not non-specific RNA sequences. These results establish FASTKD5 as the missing piece of the biochemical machinery required to completely process the primary mitochondrial transcript.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".