Bone marrow‐derived mitochondrial DNA has limited capacity for inter‐tissue transfer in vivo
Bibliographic record
Abstract
Studies have shown that mitochondrial DNA (mtDNA) can be exchanged between tissues; however, the mechanism(s) behind this phenomenon remain unclear. Exosomes and other extracellular vesicles (EVs) including microvesicles (MV) have been shown to contain mtDNA. EVs can be derived from a number of tissues; however, the source and relative proportion of EVs containing mtDNA remains unknown. We sampled whole blood and the EV fractions (exosome-enriched, MV-enriched, and apoptotic body-enriched) as well as several tissues (epithelial-cheek and urine sediment), connective (fibroblasts), and skeletal muscle in two subjects who received allogenic bone marrow transplants. Next generation sequencing of the mtDNA confirmed that all EV fractions contained mtDNA and most was derived from the donor, confirming that most of the EV fractions in the serum are bone marrow/blood cell-derived. Even after exposure to the donor mtDNA in EV fractions (and potentially free in the plasma) for years, there was little to no transfer of the donor mtDNA to the host mtDNA fraction in epithelial, connective, or skeletal muscle tissues. These data call into question the potential therapeutic use of bone marrow transplant or EV-based delivery systems for mtDNA-based disorders and establish bone marrow as the primary source of most of the mtDNA enriched EVs in serum.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".