DNA methylation status of nuclear-encoded mitochondrial genes underlies the tissue-dependent mitochondrial functions
Bibliographic record
Abstract
BACKGROUND: Mitochondria are semi-autonomous, semi-self-replicating organelles harboring their own DNA (mitochondrial DNA, mtDNA), and their dysregulation is involved in the development of various diseases. While mtDNA does not generally undergo epigenetic modifications, almost all mitochondrial proteins are encoded by nuclear DNA. However, the epigenetic regulation of nuclear-encoded mitochondrial genes (nuclear mt genes) has not been comprehensively analyzed. RESULTS: We analyzed the DNA methylation status of 899 nuclear mt genes in the liver, brain, and heart tissues of mouse, and identified 636 nuclear mt genes carrying tissue-dependent and differentially methylated regions (T-DMRs). These nuclear mt genes are involved in various mitochondrial functions and they also include genes related to human diseases. T-DMRs regulate the expression of nuclear mt genes. Nuclear mt genes with tissue-specific hypomethylated T-DMRs were characterized by enrichment of the target genes of specific transcription factors such as FOXA2 in the liver, and CEBPA and STAT1 in the brain. CONCLUSIONS: A substantial proportion of nuclear mt genes contained T-DMRs, and the DNA methylation status of numerous T-DMRs should underlie tissue-dependent mitochondrial functions.
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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) | 0.001 | 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".