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Heteroplasmic mitochondrial DNA (mtDNA) mutations are a major cause of inherited disease and contribute to common late-onset human disorders. The late onset and clinical progression of mtDNA-associated disease is thought to be due to changing heteroplasmy levels, but it is not known how and when this occurs. Performing high-throughput single-cell genotyping in two mouse models of human mtDNA disease, we saw unanticipated cell-to-cell differences in mtDNA heteroplasmy levels that emerged prenatally and progressively increased throughout life. Proliferating spleen cells and nondividing brain cells had a similar single-cell heteroplasmy variance, implicating mtDNA or organelle turnover as the major force determining cell heteroplasmy levels. The two different mtDNA mutations segregated at different rates with no evidence of selection, consistent with different rates of random genetic drift in vivo, leading to the accumulation of cells with a very high mutation burden at different rates. This provides an explanation for differences in severity seen in human diseases caused by similar mtDNA mutations.

Mitochondrial activity differs markedly between organs, but it is not known how and when this arises. Here we show that cell lineage-specific expression profiles involving essential mitochondrial genes emerge at an early stage in mouse development, including tissue-specific isoforms present before organ formation. However, the nuclear transcriptional signatures were not independent of organelle function. Genetically disrupting intra-mitochondrial protein synthesis with two different mtDNA mutations induced cell lineage-specific compensatory responses, including molecular pathways not previously implicated in organellar maintenance. We saw downregulation of genes whose expression is known to exacerbate the effects of exogenous mitochondrial toxins, indicating a transcriptional adaptation to mitochondrial dysfunction during embryonic development. The compensatory pathways were both tissue and mutation specific and under the control of transcription factors which promote organelle resilience. These are likely to contribute to the tissue specificity which characterizes human mitochondrial diseases and are potential targets for organ-directed treatments., Competing Interests: Declaration of interests The m.5024C>T mouse is available to lisence for commercial use from Max Plank Innovation. J.B.S. is a co-inventor on this license. All other authors declare no relevant conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Mammalian mitochondrial DNA (mtDNA) is inherited uniparentally through the female germline without undergoing recombination. This poses a major problem as deleterious mtDNA mutations must be eliminated to avoid a mutational meltdown over generations. At least two mechanisms that can decrease the mutation load during maternal transmission are operational: a stochastic bottleneck for mtDNA transmission from mother to child, and a directed purifying selection against transmission of deleterious mtDNA mutations. However, the molecular mechanisms controlling these processes remain unknown. In this study, we systematically tested whether decreased autophagy contributes to purifying selection by crossing the C5024T mouse model harbouring a single pathogenic heteroplasmic mutation in the tRNAAla gene of the mtDNA with different autophagy-deficient mouse models, including knockouts of Parkin, Bcl2l13, Ulk1, and Ulk2. Our study reveals a statistically robust effect of knockout of Bcl2l13 on the selection process, and weaker evidence for the effect of Ulk1 and potentially Ulk2, while no statistically significant impact is seen for knockout of Parkin. This points at distinctive roles of these players in germline purifying selection. Overall, our approach provides a framework for investigating the roles of other important factors involved in the enigmatic process of purifying selection and guides further investigations for the role of BCL2L13 in the elimination of non-synonymous mutations in protein-coding genes., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: N.G.L. is inventor of the C5024T mutant mouse licensed to the pharmaceutical industry by the Max Planck Society. N.G.L. is a scientific founder and holds stock in Pretzel Therapeutics Inc. The other authors have no competing interests., (Copyright: © 2023 Kremer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Motivation: Even within well studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information., Results: We introduce the concept of signed distance correlation as a measure of dependency between two variables, and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods such as Pearson correlation and mutual information. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson correlation or mutual information., Supplementary Information: Supplementary Information and code are available at Bioinformatics and https://github.com/javier-pardodiaz/sdcorGCN online., (© The Author(s) (2021). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Summary: Gene co-expression networks can be constructed in multiple different ways, both in the use of different measures of co-expression, and in the thresholds applied to the calculated co-expression values, from any given dataset. It is often not clear which co-expression network construction method should be preferred. COGENT provides a set of tools designed to aid the choice of network construction method without the need for any external validation data., Availability and Implementation: https://github.com/lbozhilova/COGENT., Supplementary Information: Supplementary information is available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Background: Protein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on these scores, so that only interactions of sufficiently high quality are included. These networks are used to identify biologically relevant motifs or nodes using metrics such as degree or betweenness centrality. This type of analysis can be sensitive to the choice of threshold. If a node metric is to be useful for extracting biological signal, it should induce similar node rankings across PINs obtained at different reasonable confidence score thresholds., Results: We propose three measures-rank continuity, identifiability, and instability-to evaluate how robust a node metric is to changes in the score threshold. We apply our measures to twenty-five metrics and identify four as the most robust: the number of edges in the step-1 ego network, as well as the leave-one-out differences in average redundancy, average number of edges in the step-1 ego network, and natural connectivity. Our measures show good agreement across PINs from different species and data sources. Analysis of synthetically generated scored networks shows that robustness results are context-specific, and depend both on network topology and on how scores are placed across network edges., Conclusion: Due to the uncertainty associated with protein interaction detection, and therefore network structure, for PIN analysis to be reproducible, it should yield similar results across different confidence score thresholds. We demonstrate that while certain node metrics are robust with respect to threshold choice, this is not always the case. Promisingly, our results suggest that there are some metrics that are robust across networks constructed from different databases, and different scoring procedures.