Trans-splicing and RNA editing of LSU rRNA in <i>Diplonema</i> mitochondria
Bibliographic record
Abstract
Mitochondrial ribosomal RNAs (rRNAs) often display reduced size and deviant secondary structure, and sometimes are fragmented, as are their corresponding genes. Here we report a mitochondrial large subunit rRNA (mt-LSU rRNA) with unprecedented features. In the protist Diplonema, the rnl gene is split into two pieces (modules 1 and 2, 534- and 352-nt long) that are encoded by distinct mitochondrial chromosomes, yet the rRNA is continuous. To reconstruct the post-transcriptional maturation pathway of this rRNA, we have catalogued transcript intermediates by deep RNA sequencing and RT-PCR. Gene modules are transcribed separately. Subsequently, transcripts are end-processed, the module-1 transcript is polyuridylated and the module-2 transcript is polyadenylated. The two modules are joined via trans-splicing that retains at the junction ∼ 26 uridines, resulting in an extent of insertion RNA editing not observed before in any system. The A-tail of trans-spliced molecules is shorter than that of mono-module 2, and completely absent from mitoribosome-associated mt-LSU rRNA. We also characterize putative antisense transcripts. Antisense-mono-modules corroborate bi-directional transcription of chromosomes. Antisense-mt-LSU rRNA, if functional, has the potential of guiding concomitantly trans-splicing and editing of this rRNA. Together, these findings open a window on the investigation of complex regulatory networks that orchestrate multiple and biochemically diverse post-transcriptional events.
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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) | 0.000 | 0.000 |
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.
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".