Mitochondrial Network State Scales mtDNA Genetic Dynamics

Abstract

Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may also be associated with healthy aging. mtDNA is stochastically replicated and degraded, and exists within organelles which undergo dynamic fusion and fission. The role of the resulting mitochondrial networks in the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood. Heteroplasmy variance is particularly important since it modulates the number of pathological cells in a tissue. Here, we provide the first wide-reaching theoretical framework which bridges mitochondrial network and genetic states. We show that, under a range of conditions, the (genetic) rate of increase in heteroplasmy variance and de novo mutation are proportionally modulated by the (physical) fraction of unfused mitochondria, independently of the absolute fission-fusion rate. In the context of selective fusion, we show that intermediate fusion:fission ratios are optimal for the clearance of mtDNA mutants. Our findings imply that modulating network state, mitophagy rate, and copy number to slow down heteroplasmy dynamics when mean heteroplasmy is low could have therapeutic advantages for mitochondrial disease and healthy aging. KEYWORDS mitochondrial DNA; mitochondrial networks; heteroplasmy variance; cellular noise M ITOCHONDRIAL DNA (mtDNA) encodes elements of the respiratory system vital for cellular function. Mutation of mtDNA is one of several leading hypotheses for the cause of normal aging (López-Otín et al. 2013; Kauppila et al. 2017), as well as underlying a number of heritable mtDNArelated diseases (Schon et al. 2012). Cells typically contain hundreds, or thousands, of copies of mtDNA per cell: each molecule encodes crucial components of the electron transport chain, which generates energy for the cell in the form of ATP. Consequently, the mitochondrial phenotype of a single cell is determined, in part, by its fluctuating population of mtDNA molecules (Wallace and Chalkia 2013; Stewart and Chinnery 2015; Aryaman et al. 2019; Johnston 2019). The broad biomedical implications of mtDNA mutation, combined with the countable nature of mtDNAs and the stochastic nature of their dynamics, offer the opportunity for mathematical understanding to provide important insights into human health and disease (Aryaman et al. 2019). An important observation in mitochondrial physiology is the threshold effect, whereby cells may often tolerate relatively high levels of mtDNA mutation until the fraction of mutated mtDNAs (termed heteroplasmy) exceeds a certain critical value where a pathological phenotype occurs (Rossignol et al. 2003; Picard et al. 2014; Stewart and Chinnery 2015; Aryaman et al. 2017). Fluctuations within individual cells mean that the fraction of mutant mtDNAs per cell is not constant within a tissue (Figure 1A), but follows a probability distribution which changes with time

Varied Mechanisms and Models for the Varying Mitochondrial Bottleneck

“…Ongoing research has provided evidence that the “genetic bottleneck” (mutant proportion spread) varies with age, species, individual, and genetic features. Intriguingly, the coupling of physical and genetic behavior of mitochondria (Equation 11; Tam et al, 2013; Aryaman et al, 2019) suggests that heterogeneity in mitochondrial dynamics may induce heterogeneity in mutant proportion.…”

“…This would be interpreted as a “bottleneck size” around 9.6. In followup theoretical developments (Aryaman et al, 2019), the factor f in Equation (11) has been shown to be the fraction of unfused mitochondria, that is, mitochondria containing mtDNAs subject to mitophagy (Youle and Narendra, 2011; Diot et al, 2016). Mitochondrial quality control, linked to fission-fusion dynamics, contributes to the turnover of mitochondria in the cell (Twig et al, 2008) and provides one way that mitochondrial dynamics may influence both mean and variance dynamics of mtDNA populations (Hoitzing et al, 2015; Johnston, 2018; Latorre-Pellicer et al, 2019).…”

“…Higher rates of quality control related turnover can result in higher cell-to-cell mutant proportion variance (Johnston et al, 2015) [and, if mitochondria associated with one mtDNA type are preferentially degraded, this selective pressure will also influence mean mutant proportions (Twig et al, 2008; Hoitzing et al, 2015)]. Equation (11) provides a coupling between the physical fission-fusion dynamics of mitochondria and the time behavior of mtDNA mutant proportion spread (Hoitzing et al, 2015; Johnston, 2018; Aryaman et al, 2019).…”

Visualizing, quantifying, and manipulating mitochondrial DNA in vivo

“…Mitochondrial DNA (mtDNA) encodes a variety of proteins, peptides, transfer RNAs, and ribosomal RNAs that support the functions of mitochondria and is thereby central to numerous physiological and pathophysiological processes, including development, disease, and aging ( 1 ). Both the number of mtDNAs in a cell (mtDNA copy number) and the sequences of mtDNAs are important phenotypic determinants ( 2 ). Mutations of mtDNA can cause a spectrum of mitochondrial diseases where the clinical expression depends on the specific mutation and the degree of heteroplasmy (the proportion of mutated mtDNA in a single cell) between WT and mutant mtDNA ( 3 , 4 ).…”

“…Nucleoids contain complexes of mtDNA with proteins and other factors that comprise the machinery required for regulated transcription ( 8 – 10 ). The abundance and sequence of mtDNA can affect mitochondrial function, whereas the mitochondrial network, which is regulated by dynamic fission and fusion events ( 11 ), can impact the turnover and copy number of mtDNA ( 2 , 12 ).…”