Exchange on dynamic encounter networks allows plant mitochondria to collect complete sets of mitochondrial DNA products despite their incomplete genomes

Giannakis, Konstantinos; Chustecki, Joanna M.; Johnston, Iain G.

doi:10.1017/qpb.2022.15

Cited by 8 publications

(4 citation statements)

References 60 publications

(93 reference statements)

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“…To address how mitochondrial structure affects protein spread on a static network, recent modeling work showed that mitochondrial network snapshots that contain loops allow faster diffusive search compared to networks without loops [9, 10]. For spread on dynamic mitochondrial networks, a two-dimensional lattice model showed stronger dependence of effective diffusivity on connectivity for slower dynamics [31], a two-dimensional spatial model with mitochondrial motion along filaments suggested the rate of mitochondrial fusion impacts mitochondrial health [21], an aspatial model indicated more frequent fusion suppresses protein concentration fluctuations between mitochondria [37], and a model of heavily-fragmented mitochondria suggested mitochondrial DNA gene products efficiently spread (using a contact measure of spread) for parameters describing living plant cells [46]. These studies of dynamic networks considered spread through the mitochondrial network in a specific limit or as a secondary focus, and we are unaware of other work that focuses on how mitochondrial dynamics affect protein spread through the mitochondrial networks.…”

Section: Introductionmentioning

confidence: 99%

Mitochondrial network branching enables rapid protein spread with slower mitochondrial dynamics

Chuphal,

Brown

2024

Preprint

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Mitochondrial network structure is controlled by the dynamical processes of fusion and fission, which merge and split mitochondrial tubes into structures including branches and loops. To investigate the impact of mitochondrial network dynamics and structure on the spread of proteins and other molecules through mitochondrial networks, we used stochastic simulations of two distinct quantitative models that each included mitochondrial fusion and fission, and particle diffusion via the network. Better-connected mitochondrial networks and networks with faster dynamics exhibit more rapid particle spread on the network, with little further improvement once a network has become well-connected. As fragmented networks gradually become better connected, particle spread either steadily improves until the networks become well-connected for slow-diffusing particles or plateaus for fast-diffusing particles. We compared model mitochondrial networks with both end-to-end and end-to-side fusion, which form branches, to non-branching model networks that lack end-to-side fusion. To achieve the optimum (most rapid) spread that occurs on well-connected branching networks, non-branching networks require much faster fusion and fission dynamics. Thus the process of end-to-side fusion, which creates branches in mitochondrial networks, enables rapid spread of particles on the network with relatively slow fusion and fission dynamics. This modeling of protein spread on mitochondrial networks builds towards mechanistic understanding of how mitochondrial structure and dynamics regulate mitochondrial function.

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Section: Introductionmentioning

confidence: 99%

Mitochondrial network branching enables rapid protein spread with slower mitochondrial dynamics

Chuphal,

Brown

2024

Preprint

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“…Models for mtDNA segregation have typically regarded the cellular mtDNA population as well-mixed [36], but the mitochondria containing the population have varied morphologies and dynamics in different cell types, with important consequent and emergent properties [39][40][41][42]. Mitochondria can form cell-wide networks in some cell types, undergoing fission and fusion [43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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

Glastad

Johnston

2023

PLoS Comput Biol

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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.

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“…2008 ) and that the location and dynamics of organelles influence their function and capacity for exchange and interaction ( Chustecki et al. 2021 , Giannakis et al. 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Stromule Geometry Allows Optimal Spatial Regulation of Organelle Interactions in the Quasi-2D Cytoplasm

Erickson,

Prautsch,

Reynvoet

et al. 2023

Plant and Cell Physiology

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In plant cells, plastids form elongated extensions called stromules, the regulation and purposes of which remain unclear. Here, we quantitatively explore how different stromule structures serve to enhance the ability of a plastid to interact with other organelles: increasing the effective space for interaction and biomolecular exchange between organelles. Interestingly, electron microscopy and confocal imaging showed that the cytoplasm in Arabidopsis thaliana and Nicotiana benthamiana epidermal cells is extremely thin (around 100 nm in regions without organelles), meaning that inter-organelle interactions effectively take place in 2D. We combine these imaging modalities with mathematical modelling and new in planta experiments to demonstrate how different stromule varieties (single or multiple, linear or branching) could be employed to optimise different aspects of inter-organelle interaction capacity in this 2D space. We found that stromule formation and branching provide a proportionally higher benefit to interaction capacity in 2D than in 3D. Additionally, this benefit depends on optimal plastid spacing. We hypothesize that cells can promote the formation of different stromule architectures in the quasi-2D cytoplasm to optimise their interaction interface to meet specific requirements. These results provide new insight into the mechanisms underlying the transition from low to high stromule numbers, the consequences for interaction with smaller organelles, how plastid access and plastid to nucleus signaling are balanced, as well as the impact of plastid density on organelle interaction.

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Exchange on dynamic encounter networks allows plant mitochondria to collect complete sets of mitochondrial DNA products despite their incomplete genomes

Cited by 8 publications

References 60 publications

Mitochondrial network branching enables rapid protein spread with slower mitochondrial dynamics

Mitochondrial network branching enables rapid protein spread with slower mitochondrial dynamics

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

Stromule Geometry Allows Optimal Spatial Regulation of Organelle Interactions in the Quasi-2D Cytoplasm

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