Progressive increase in mtDNA 3243A>G heteroplasmy causes abrupt transcriptional reprogramming
Bibliographic record
Abstract
Variation in the intracellular percentage of normal and mutant mitochondrial DNAs (mtDNA) (heteroplasmy) can be associated with phenotypic heterogeneity in mtDNA diseases. Individuals that inherit the common disease-causing mtDNA tRNA(Leu(UUR)) 3243A>G mutation and harbor ∼10-30% 3243G mutant mtDNAs manifest diabetes and occasionally autism; individuals with ∼50-90% mutant mtDNAs manifest encephalomyopathies; and individuals with ∼90-100% mutant mtDNAs face perinatal lethality. To determine the basis of these abrupt phenotypic changes, we generated somatic cell cybrids harboring increasing levels of the 3243G mutant and analyzed the associated cellular phenotypes and nuclear DNA (nDNA) and mtDNA transcriptional profiles by RNA sequencing. Small increases in mutant mtDNAs caused relatively modest defects in oxidative capacity but resulted in sharp transitions in cellular phenotype and gene expression. Cybrids harboring 20-30% 3243G mtDNAs had reduced mtDNA mRNA levels, rounded mitochondria, and small cell size. Cybrids with 50-90% 3243G mtDNAs manifest induction of glycolytic genes, mitochondrial elongation, increased mtDNA mRNA levels, and alterations in expression of signal transduction, epigenomic regulatory, and neurodegenerative disease-associated genes. Finally, cybrids with 100% 3243G experienced reduced mtDNA transcripts, rounded mitochondria, and concomitant changes in nuclear gene expression. Thus, striking phase changes occurred in nDNA and mtDNA gene expression in response to the modest changes of the mtDNA 3243G mutant levels. Hence, a major factor in the phenotypic variation in heteroplasmic mtDNA mutations is the limited number of states that the nucleus can acquire in response to progressive changes in mitochondrial retrograde signaling.
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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".