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The resilience of the mitochondrial genome (mtDNA) to a high mutational pressure depends, in part, on negative purifying selection in the germline. A paradigm in the field has been that such selection, at least in part, takes place in primordial germ cells (PGCs). Specifically, Floros et al. (Nature Cell Biology 20: 144–51) reported an increase in the synonymity of mtDNA mutations (a sign of purifying selection) between early-stage and late-stage PGCs. We re-analyzed Floros’ et al. data and determined that their mutational dataset was significantly contaminated with single nucleotide variants (SNVs) derived from a nuclear sequence of mtDNA origin (NUMT) located on chromosome 5. Contamination was caused by co-amplification of the NUMT sequence by cross-specific PCR primers. Importantly, when we removed NUMT-derived SNVs, the evidence of purifying selection was abolished. In addition to bulk PGCs, Floros et al. reported the analysis of single-cell late-stage PGCs, which were amplified with different sets of PCR primers that cannot amplify the NUMT sequence. Accordingly, there were no NUMT-derived SNVs among single PGC mutations. Interestingly, single PGC mutations show a decrease of synonymity with increased intracellular mutant fraction. More specifically, nonsynonymous mutations show faster intracellular genetic drift towards higher mutant fraction than synonymous ones. This pattern is incompatible with predominantly negative selection. This suggests that germline selection of mtDNA mutations is a complex phenomenon and that the part of this process that takes place in PGCs may be predominantly positive. However counterintuitive, positive germline selection of detrimental mtDNA mutations has been reported previously and potentially may be evolutionarily advantageous. 

Abstract Background Third-generation sequencing offers some advantages over next-generation sequencing predecessors, but with the caveat of harboring a much higher error rate. Clustering-related sequences is an essential task in modern biology. To accurately cluster sequences rich in errors, error type and frequency need to be accounted for. Levenshtein distance is a well-established mathematical algorithm for measuring the edit distance between words and can specifically weight insertions, deletions and substitutions. However, there are drawbacks to using Levenshtein distance in a biological context and hence has rarely been used for this purpose. We present novel modifications to the Levenshtein distance algorithm to optimize it for clustering error-rich biological sequencing data. Results We successfully introduced a bidirectional frameshift allowance with end-user determined accommodation caps combined with weighted error discrimination. Furthermore, our modifications dramatically improved the computational speed of Levenstein distance. For simulated ONT MinION and PacBio Sequel datasets, the average clustering sensitivity for 3GOLD was 41.45% (S.D. 10.39) higher than Sequence-Levenstein distance, 52.14% (S.D. 9.43) higher than Levenshtein distance, 55.93% (S.D. 8.67) higher than Starcode, 42.68% (S.D. 8.09) higher than CD-HIT-EST and 61.49% (S.D. 7.81) higher than DNACLUST. For biological ONT MinION data, 3GOLD clustering sensitivity was 27.99% higher than Sequence-Levenstein distance, 52.76% higher than Levenshtein distance, 56.39% higher than Starcode, 48% higher than CD-HIT-EST and 70.4% higher than DNACLUST. Conclusion Our modifications to Levenshtein distance have improved its speed and accuracy compared to the classic Levenshtein distance, Sequence-Levenshtein distance and other commonly used clustering approaches on simulated and biological third-generation sequenced datasets. Our clustering approach is appropriate for datasets of unknown cluster centroids, such as those generated with unique molecular identifiers as well as known centroids such as barcoded datasets. A strength of our approach is high accuracy in resolving small clusters and mitigating the number of singletons. 

The mtDNA ‘mutator’ mouse, also called the ‘POLG’ mouse, is a well-characterized model frequently used for studies of progeroid aging. Harboring a mutation in the proofreading domain of the mitochondrial polymerase, polymerase-γ ( Polg ), POLG mice acquire mtDNA mutations at an accelerated rate. This results in premature mitochondrial dysfunction and a systemic aging phenotype. Previous work has demonstrated that the progeroid phenotype in POLG is attenuated following endurance exercise, the only reported intervention to extend health span and lifespan of these mice. Herein, oocyte quality was evaluated in sedentary and exercised POLG mice. In mice homozygous for the Polg mutation, litter size is dramatically reduced as compared to heterozygous Polg mice. Following ovarian hyper-stimulation, oocytes were retrieved until 9 months of age in exercised and sedentary groups, with no oocytes ovulated thereafter. Although ovulated oocyte numbers were not impacted by exercise, we did find a modest improvement in both the ovarian follicle reserve and in oocyte quality based on meiotic spindle assembly, chromosomal segregation and mitochondrial distribution at 7 months of age in exercised POLG mice as compared to sedentary counterparts. Of note, analysis of mtDNA mutational load revealed no differences between exercised and sedentary groups. Collectively, these data indicate that exercise differentially influences somatic tissues of the POLG mouse as compared to oocytes, highlighting important mechanistic differences between mitochondrial regulatory mechanisms in the soma and the germline. 

Abstract Mitochondria are well-characterized regarding their function in both energy production and regulation of cell death; however, the heterogeneity that exists within mitochondrial populations is poorly understood. Typically analyzed as pooled samples comprised of millions of individual mitochondria, there is little information regarding potentially different functionality across subpopulations of mitochondria. Herein we present a new methodology to analyze mitochondria as individual components of a complex and heterogeneous network, using a nanoscale and multi–parametric flow cytometry-based platform. We validate the platform using multiple downstream assays, including electron microscopy, ATP generation, quantitative mass-spectrometry proteomic profiling, and mtDNA analysis at the level of single organelles. These strategies allow robust analysis and isolation of mitochondrial subpopulations to more broadly elucidate the underlying complexities of mitochondria as these organelles function collectively within a cell.