Chimeric mitochondrial RNA transcripts predict mitochondrial genome deletion mutations in mitochondrial genetic diseases and aging
Bibliographic record
Abstract
Although it is well understood that mitochondrial DNA (mtDNA) deletion mutations cause incurable diseases and contribute to aging, little is known about the transcriptional products that arise from these DNA structural variants. We hypothesized that mitochondrial genomes containing deletion mutations express chimeric mitochondrial RNAs. To test this, we analyzed human and rat RNA sequencing data to identify, quantitate, and characterize chimeric mitochondrial RNAs. We observe increased chimeric mitochondrial RNA frequency in samples from patients with mitochondrial genetic diseases and in samples from aged humans. The spectrum of chimeric mitochondrial transcripts reflects the known pattern of mtDNA deletion mutations. To test the hypothesis that mtDNA deletions induce chimeric RNA transcripts, we treated 18 month old and 34 month old rats with guanidinopropionic acid to induce high levels of skeletal muscle mtDNA deletion mutations. With mtDNA deletion induction, we demonstrate that the chimeric mitochondrial transcript frequency also increases and correlates strongly with an orthogonal DNA-based mutation assay performed on identical samples. Further, we show that the frequency of chimeric mitochondrial transcripts predicts expression of both nuclear and mitochondrial genes central to mitochondrial function, demonstrating the utility of these events as metrics of age-induced metabolic change. Mapping and quantitation of chimeric mitochondrial RNAs provide an accessible, orthogonal approach to DNA-based mutation assays, offer a potential method for identifying mitochondrial pathology in widely accessible data sets, and open a new area of study in mitochondrial genetics and transcriptomics.
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 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 | 0.001 |
| 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) | 0.001 | 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".