Abstract
The dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases. Cellular populations of mitochondria are under homeostatic control, but the details of the control mechanisms involved remain elusive. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. This formalism yields a wealth of biological results, including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant, that heteroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations.
Highlights
Most human cells contain 100-10,000 copies of mitochondrial DNA which are situated inside the mitochondria
We identify optimal strategies for the cell to control against mitochondrial DNA (mtDNA) damage and preserve energy production and use this theory to explore the action of recently developed mitochondrial gene therapies, which reduce the fraction of mutant mtDNA molecules inside cells
We show how treatment efficiency may depend on pre-treatment distributions of mutant and wildtype mtDNA molecules: treatments are less effective for tissues consisting
Summary
Most human cells contain 100-10,000 copies of mitochondrial DNA (mtDNA) which are situated inside the mitochondria. The existence of thresholds at which mutant loads begin to have an effect has profound implications for our understanding of disease onset, drawing attention to the variance dynamics of the mutant fraction in cellular populations. As this variance increases more cells can be above threshold, and show pathology, even if average mutant load is unchanged. The particular ‘effective cost’ that cellular control of mitochondria acts to minimise remains poorly understood: for example, both decreases [15] and increases [15, 16] in wildtype copy numbers have been observed for different mutations as the mutant load increases. Experimental tracking of mtDNA populations over time is challenging, necessitating predictive mathematical modelling to provide a quantitative understanding
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