Real-Time PCR Quantification of Heteroplasmy in a Mouse Model with Mitochondrial DNA of C57BL/6 and NZB/BINJ Strains
Bibliographic record
Abstract
Mouse models are widely employed to study mitochondrial inheritance, which have implications to several human diseases caused by mutations in the mitochondrial genome (mtDNA). These mouse models take advantage of polymorphisms between the mtDNA of the NZB/BINJ and the mtDNA of common inbred laboratory (i.e., C57BL/6) strains to generate mice with two mtDNA haplotypes (heteroplasmy). Based on PCR followed by restriction fragment length polymorphism (PCR-RFLP), these studies determine the level of heteroplasmy across generations and in different cell types aiming to understand the mechanisms underlying mitochondrial inheritance. However, PCR-RFLP is a time-consuming method of low sensitivity and accuracy that dependents on the use of restriction enzyme digestions. A more robust method to measure heteroplasmy has been provided by the use of real-time quantitative PCR (qPCR) based on allelic refractory mutation detection system (ARMS-qPCR). Herein, we report an ARMS-qPCR assay for quantification of heteroplasmy using heteroplasmic mice with mtDNA of NZB/BINJ and C57BL/6 origin. Heteroplasmy and mtDNA copy number were estimated in germline and somatic tissues, providing evidence of the reliability of the approach. Furthermore, it enabled single-step quantification of heteroplasmy, with sensitivity to detect as low as 0.1% of either NZB/BINJ or C57BL/6 mtDNA. These findings are relevant as the ARMS-qPCR assay reported here is fully compatible with similar heteroplasmic mouse models used to study mitochondrial inheritance in mammals.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".