Mitochondrial DNA exhibits resistance to induced point and deletion mutations
Bibliographic record
Abstract
The accumulation of somatic mitochondrial DNA (mtDNA) mutations contributes to the pathogenesis of human disease. Currently, mitochondrial mutations are largely considered results of inaccurate processing of its heavily damaged genome. However, mainly from a lack of methods to monitor mtDNA mutations with sufficient sensitivity and accuracy, a link between mtDNA damage and mutation has not been established. To test the hypothesis that mtDNA-damaging agents induce mtDNA mutations, we exposed MutaTMMouse mice to benzo[a]pyrene (B[a]P) or N-ethyl-N-nitrosourea (ENU), daily for 28 consecutive days, and quantified mtDNA point and deletion mutations in bone marrow and liver using our newly developed Digital Random Mutation Capture (dRMC) and Digital Deletion Detection (3D) assays. Surprisingly, our results demonstrate mutagen treatment did not increase mitochondrial point or deletion mutation frequencies, despite evidence both compounds increase nuclear DNA mutations and demonstrated B[a]P adduct formation in mtDNA. These findings contradict models of mtDNA mutagenesis that assert the elevated rate of mtDNA mutation stems from damage sensitivity and abridged repair capacity. Rather, our results demonstrate induced mtDNA damage does not readily convert into mutation. These findings suggest robust mitochondrial damage responses repress induced mutations after mutagen exposure.
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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 | 0.000 |
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".