Stochastic Models for Evolving Cellular Populations of Mitochondria: Disease, Development, and Ageing

Hoitzing, Hanne; Johnston, Iain G.; Jones, Nick

doi:10.1007/978-3-319-62627-7_13

Cited by 10 publications

(15 citation statements)

References 108 publications

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“…Several previous studies have considered the dynamics of mixed mtDNA populations with functional differences between types ( 49 ). However, these studies do not typically consider expression of respiratory machinery ( 50–53 ), although it can be pictured as a general mitochondrial quality state ( 37 , 54 ).…”

Section: Resultsmentioning

confidence: 99%

MtDNA sequence features associated with ‘selfish genomes’ predict tissue-specific segregation and reversion

Røyrvik

Johnston

2020

Nucleic Acids Research

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Mitochondrial DNA (mtDNA) encodes cellular machinery vital for cell and organism survival. Mutations, genetic manipulation, and gene therapies may produce cells where different types of mtDNA coexist in admixed populations. In these admixtures, one mtDNA type is often observed to proliferate over another, with different types dominating in different tissues. This ‘segregation bias’ is a long-standing biological mystery that may pose challenges to modern mtDNA disease therapies, leading to substantial recent attention in biological and medical circles. Here, we show how an mtDNA sequence’s balance between replication and transcription, corresponding to molecular ‘selfishness’, in conjunction with cellular selection, can potentially modulate segregation bias. We combine a new replication-transcription-selection (RTS) model with a meta-analysis of existing data to show that this simple theory predicts complex tissue-specific patterns of segregation in mouse experiments, and reversion in human stem cells. We propose the stability of G-quadruplexes in the mtDNA control region, influencing the balance between transcription and replication primer formation, as a potential molecular mechanism governing this balance. Linking mtDNA sequence features, through this molecular mechanism, to cellular population dynamics, we use sequence data to obtain and verify the sequence-specific predictions from this hypothesis on segregation behaviour in mouse and human mtDNA.

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Section: Resultsmentioning

confidence: 99%

MtDNA sequence features associated with ‘selfish genomes’ predict tissue-specific segregation and reversion

Røyrvik

Johnston

2020

Nucleic Acids Research

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“…When combined with a selective apoptotic threshold, whereby cells above a heteroplasmic threshold undergo cell death, mean heteroplasmy can be reduced. The mechanisms and timings of the mitochondrial bottleneck are debated (Jenuth et al, 1996; Cao et al, 2007; Cree et al, 2008; Wai et al, 2008); however, stochastic modeling has shown that several proposals are compatible with the induction of heteroplasmy variance through a combination of random mtDNA partitioning at division and passive turnover of mtDNA (Johnston et al, 2015; Johnston and Jones, 2016; Hoitzing et al, 2017b).…”

Section: Genetic Sources Of Mitochondrial Heterogeneitymentioning

confidence: 99%

Mitochondrial Heterogeneity

2019

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Cell-to-cell heterogeneity drives a range of (patho)physiologically important phenomena, such as cell fate and chemotherapeutic resistance. The role of metabolism, and particularly of mitochondria, is increasingly being recognized as an important explanatory factor in cell-to-cell heterogeneity. Most eukaryotic cells possess a population of mitochondria, in the sense that mitochondrial DNA (mtDNA) is held in multiple copies per cell, where the sequence of each molecule can vary. Hence, intra-cellular mitochondrial heterogeneity is possible, which can induce inter-cellular mitochondrial heterogeneity, and may drive aspects of cellular noise. In this review, we discuss sources of mitochondrial heterogeneity (variations between mitochondria in the same cell, and mitochondrial variations between supposedly identical cells) from both genetic and non-genetic perspectives, and mitochondrial genotype-phenotype links. We discuss the apparent homeostasis of mtDNA copy number, the observation of pervasive intra-cellular mtDNA mutation (which is termed “microheteroplasmy”), and developments in the understanding of inter-cellular mtDNA mutation (“macroheteroplasmy”). We point to the relationship between mitochondrial supercomplexes, cristal structure, pH, and cardiolipin as a potential amplifier of the mitochondrial genotype-phenotype link. We also discuss mitochondrial membrane potential and networks as sources of mitochondrial heterogeneity, and their influence upon the mitochondrial genome. Finally, we revisit the idea of mitochondrial complementation as a means of dampening mitochondrial genotype-phenotype links in light of recent experimental developments. The diverse sources of mitochondrial heterogeneity, as well as their increasingly recognized role in contributing to cellular heterogeneity, highlights the need for future single-cell mitochondrial measurements in the context of cellular noise studies.

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“…To account for the full set of processes that an individual mtDNA molecule may undergo, several stochastic modeling approaches have been developed (reviewed in Hoitzing et al, 2017). These approaches model every individual mtDNA molecule in a cell and subjects them to the physical processes that we may expect to occur during development.…”

Section: The “Genetic Bottleneck” As a Set Of Physical Processesmentioning

confidence: 99%

Varied Mechanisms and Models for the Varying Mitochondrial Bottleneck

Johnston

2019

Front. Cell Dev. Biol.

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Mitochondrial DNA (mtDNA) molecules exist in populations within cells, and may carry mutations. Different cells within an organism, and organisms within a family, may have different proportions of mutant mtDNA in these cellular populations. This diversity is often thought of as arising from a “genetic bottleneck.” This article surveys approaches to characterize and model the generation of this genetic diversity, aiming to provide an introduction to the range of concepts involved, and to highlight some recent advances in understanding. In particular, differences between the statistical “genetic bottleneck” (mutant proportion spread) and the physical mtDNA bottleneck and other cellular processes are highlighted. Particular attention is paid to the quantitative analysis of the “genetic bottleneck,” estimation of its magnitude from observed data, and inference of its underlying mechanisms. Evidence that the “genetic bottleneck” (mutant proportion spread) varies with age, between individuals and species, and across mtDNA sequences, is described. The interpretation issues that arise from sampling errors, selection, and different quantitative definitions are also discussed.

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Stochastic Models for Evolving Cellular Populations of Mitochondria: Disease, Development, and Ageing

Cited by 10 publications

References 108 publications

MtDNA sequence features associated with ‘selfish genomes’ predict tissue-specific segregation and reversion

MtDNA sequence features associated with ‘selfish genomes’ predict tissue-specific segregation and reversion

Mitochondrial Heterogeneity

Varied Mechanisms and Models for the Varying Mitochondrial Bottleneck

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