Mitochondrial DNA segregation in hematopoietic lineages does not depend on MHC presentation of mitochondrially encoded peptides
Bibliographic record
Abstract
Mutations in mitochondrial DNA (mtDNA) are associated with a broad spectrum of clinical disorders. The segregation pattern of pathogenic mtDNAs is an important determinant of both the onset and the severity of the disease phenotype, but the mechanisms controlling mtDNA segregation remain poorly understood. To investigate this, we previously generated heteroplasmic mice containing two different mtDNA haplotypes and showed that BALB/c mtDNA was invariably selected over NZB mtDNA in blood and spleen. Here, we have characterized this process in hematopoietic tissues and tested whether it involves the presentation of mtDNA-encoded peptides by MHC class Ib molecules. Selection against NZB mtDNA was widespread across different hematopoietic cell lineages and proportional to heteroplasmy levels. Backcrossing heteroplasmic mice with CAST/Ei, a strain in which the MHC class Ib molecule H2-M3 is silent, completely abolished selection against NZB mtDNA in the spleen. To test whether this effect depended on an intact immune system, we generated heteroplasmic mice missing functional copies of Tap1, beta2m or Rag1 to impair presentation or recognition of mtDNA-encoded peptides. The kinetics of selection against NZB mtDNA were unaltered in these mice compared with their wild-type littermates. We conclude that mtDNA selection in hematopoietic tissues is not based on an immune mechanism, but likely involves metabolic 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".