Advanced age increases frequencies of de novo mitochondrial mutations in macaque oocytes and somatic tissues
Bibliographic record
Abstract
Mutations in mitochondrial DNA (mtDNA) contribute to multiple diseases. However, how new mtDNA mutations arise and accumulate with age remains understudied because of the high error rates of current sequencing technologies. Duplex sequencing reduces error rates by several orders of magnitude via independently tagging and analyzing each of the two template DNA strands. Here, using duplex sequencing, we obtained high-quality mtDNA sequences for somatic tissues (liver and skeletal muscle) and single oocytes of 30 unrelated rhesus macaques, from 1 to 23 y of age. Sequencing single oocytes minimized effects of natural selection on germline mutations. In total, we identified 17,637 tissue-specific de novo mutations. Their frequency increased ∼3.5-fold in liver and ∼2.8-fold in muscle over the ∼20 y assessed. Mutation frequency in oocytes increased ∼2.5-fold until the age of 9 y, but did not increase after that, suggesting that oocytes of older animals maintain the quality of their mtDNA. We found the light-strand origin of replication (OriL) to be a hotspot for mutation accumulation with aging in liver. Indeed, the 33-nucleotide-long OriL harbored 12 variant hotspots, 10 of which likely disrupt its hairpin structure and affect replication efficiency. Moreover, in somatic tissues, protein-coding variants were subject to positive selection (potentially mitigating toxic effects of mitochondrial activity), the strength of which increased with the number of macaques harboring variants. Our work illuminates the origins and accumulation of somatic and germline mtDNA mutations with aging in primates and has implications for delayed reproduction in modern human societies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".