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Abstract In addition to critical roles in bioenergetics, mitochondria are key contributors to the regulation of many other functions in cells, ranging from steroidogenesis to apoptosis. Numerous studies further demonstrate that cell type‐specific differences exist in mitochondria, with cells of a given lineage tailoring their endogenous mitochondrial population to suit specific functional needs. These findings, coupled with studies of the therapeutic potential of mitochondrial transplantation, provide a strong impetus to better understand how mitochondria can influence cell function or fate. Here an inducible mitochondrial depletion modelis used to study how cells lacking endogenous mitochondria respond, on a global protein expression level, to transplantation with lineage‐mismatched (LM) mitochondria. It is shown that LM mitochondrial transplantation does not alter the proteomic profile in nonmitochondria–depleted recipient cells; however, enforced depletion of endogenous mitochondria results in dramatic changes in the proteomic landscape, which returns to the predepletion state following internalization of LM mitochondria. These data, derived from a cell system that can be rendered free of influence by endogenous mitochondria, indicate that transplantation of mitochondria—even from a source that differs significantly from the recipient cell population, effectively restores a normal proteomic landscape to cells lacking their own mitochondria. 

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. 

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. 

Mitochondria are at the intersection of aging and fertility, with research efforts centered largely on the role that these specialized organelles play in the relatively rapid decline in oocyte quality that occurs as females approach reproductive senescence. In addition to various roles in oocyte maturation, fertilization, and embryogenesis, mitochondria are critical to granulosa cell function. Herein, we provide a review of the literature pertaining to the role of mitochondria in granulosa cell function, with emphasis on how mitochondrial aging in granulosa cells may impact reproduction in female mammals. 

The generation of functional gametes, both eggs and sperm, from murine pluripotent stem cell (PSC) sources, has set the stage for the eventual use of this emerging technology in other species. With the field enthusiastically embracing this eventuality, in particular for animal conservation efforts, there are a number of key factors to consider regarding the applicability of these methods across species, particularly with regard to the generation of eggs. To date, published studies point to the need for fetal somatic tissue and primitive granulosa cells to serve as a niche for the growth and maturation of oocytes generated from PSCs. In practice, the need for such tissue represents a major limitation when attempting to apply this to species in which access to fetal ovaries is limited or unethical. To circumvent this, we and others have derived methods to generate ovarian granulosa cells from PSCs, albeit with low yield. Herein we present an update on the status of generating early stage granulosa cells from PSCs, and provide evidence for improvements based on a stepwise, 2-dimensional protocol for the directed differentiation of human PSCs.