Mitochondrial network structure controls cell-to-cell mtDNA variability generated by cell divisions

Abstract

Mitochondria are highly dynamic organelles, containing vital populations of mitochondrial DNA (mtDNA) distributed throughout the cell. Mitochondria form diverse physical structures in different cells, from cell-wide reticulated networks to fragmented individual organelles. These physical structures are known to influence the genetic makeup of mtDNA populations between cell divisions, but their influence on the inheritance of mtDNA at divisions remains less understood. Here, we use statistical and computational models of mtDNA content inside and outside the reticulated network to quantify how mitochondrial network structure can control the variances of inherited mtDNA copy number and mutant load. We assess the use of moment-based approximations to describe heteroplasmy variance and identify several cases where such an approach has shortcomings. We show that biased inclusion of one mtDNA type in the network can substantially increase heteroplasmy variance (acting as a genetic bottleneck), and controlled distribution of network mass and mtDNA through the cell can conversely reduce heteroplasmy variance below a binomial inheritance picture. Network structure also allows the generation of heteroplasmy variance while controlling copy number inheritance to sub-binomial levels, reconciling several observations from the experimental literature. Overall, different network structures and mtDNA arrangements within them can control the variances of key variables to suit a palette of different inheritance priorities.

Avoiding misleading estimates using mtDNA heteroplasmy statistics to study bottleneck size and selection

“…2022 ). These approaches have merit (and shortcomings ( Glastad and Johnston 2023 )) when attempting to build a bottom-up theory from microscopic dynamics; the Kimura model is convenient for top-down, data-driven analysis. For this reason, it is a valuable approximation of use in heteroplasmy analysis—providing the estimator used is appropriate for the statistical task at hand.…”

Evolution and maintenance of mtDNA gene content across eukaryotes

“…MtDNA sequence features partly determine segregation behaviour (Otten et al, 2018;Wilson et al, 2016). The physical distribution of mtDNA molecules in the mitochondrial population, which may be reticulated, fragmented, or a combination, shapes the segregation contribution of each of these processes (Aryaman et al, 2019;Edwards et al, 2021;Glastad & Johnston, 2023;Jajoo et al, 2016) -the physical behaviour of mitochondria shapes the genetic segregation of mtDNA.…”

Mitochondrial networks through the lens of mathematics

“…Shirihai et al have conducted studies on the relationship between mitochondrial dynamics and quality control, as well as how those concepts might be related to network structure [96,97]. Johnston et al have published many papers pertaining to mtDNA population dynamics; some of their more recent publications examine possible benefits of mitochondrial network structure from the perspectives of both cellular physiology and mitochondrial mutational burdens [38,[98][99][100][101]. Chialvo et al combine the theoretical framework from Meyer-Hermann's group and progressive coarse-graining of mitochondrial images to study mitochondrial network structures through the lenses of criticality and percolation theory [49,50].…”