Rewriting nuclear epigenetic scripts in mitochondrial diseases as a strategy for heteroplasmy control
Bibliographic record
Abstract
Mitochondrial diseases, caused by mutations in nuclear or mitochondrial DNA (mtDNA), have limited treatment options. For mtDNA mutations, reducing the mutant-to-wild-type mtDNA ratio (heteroplasmy shift) is a promising strategy, though it currently faces challenges. Previous research showed that severe mitochondrial dysfunction triggers an adaptive nuclear epigenetic response, through changes in DNA methylation, absent or less important for subtle mitochondrial impairment. Therefore, we hypothesized that targeting nuclear DNA methylation could impair cells with high-mutant mtDNA load while sparing those with lower levels, reducing overall heteroplasmy. Using cybrid models harboring two disease-causing mtDNA mutations-m.13513 G > A and m.8344 A > G-at varying heteroplasmies, we discovered that both the mutation type and load distinctly shape the nuclear DNA methylome. We found this methylation pattern critical for the survival of high-heteroplasmy cells but not for low-heteroplasmy ones. Treatment with FDA-approved DNA methylation inhibitors selectively impacted high-heteroplasmy cybrids and reduced heteroplasmy. These findings were validated in cultured cells and xenografts. Our findings highlight nuclear DNA methylation as a key regulator of heteroplasmic cell survival and a potential therapeutic target for mitochondrial diseases.
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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 | 0.001 |
| 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".