Subcellular origin of mitochondrial DNA deletions in human skeletal muscle
Bibliographic record
Abstract
OBJECTIVE: In patients with mitochondrial DNA (mtDNA) maintenance disorders and with aging, mtDNA deletions sporadically form and clonally expand within individual muscle fibers, causing respiratory chain deficiency. This study aimed to identify the sub-cellular origin and potential mechanisms underlying this process. METHODS: Serial skeletal muscle cryosections from patients with multiple mtDNA deletions were subjected to subcellular immunofluorescent, histochemical, and genetic analysis. RESULTS: We report respiratory chain-deficient perinuclear foci containing mtDNA deletions, which show local elevations of both mitochondrial mass and mtDNA copy number. These subcellular foci of respiratory chain deficiency are associated with a local increase in mitochondrial biogenesis and unfolded protein response signaling pathways. We also find that the commonly reported segmental pattern of mitochondrial deficiency is consistent with the three-dimensional organization of the human skeletal muscle mitochondrial network. INTERPRETATION: We propose that mtDNA deletions first exceed the biochemical threshold causing biochemical deficiency in focal regions adjacent to the myonuclei, and induce mitochondrial biogenesis before spreading across the muscle fiber. These subcellular resolution data provide new insights into the possible origin of mitochondrial respiratory chain deficiency in mitochondrial myopathy. Ann Neurol 2018;84:289-301.
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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".