Mathematical modeling of the role of mitochondrial fusion and fission in mitochondrial DNA maintenance - PubMed (original) (raw)
Mathematical modeling of the role of mitochondrial fusion and fission in mitochondrial DNA maintenance
Zhi Yang Tam et al. PLoS One. 2013.
Abstract
Accumulation of mitochondrial DNA (mtDNA) mutations has been implicated in a wide range of human pathologies, including neurodegenerative diseases, sarcopenia, and the aging process itself. In cells, mtDNA molecules are constantly turned over (i.e. replicated and degraded) and are also exchanged among mitochondria during the fusion and fission of these organelles. While the expansion of a mutant mtDNA population is believed to occur by random segregation of these molecules during turnover, the role of mitochondrial fusion-fission in this context is currently not well understood. In this study, an in silico modeling approach is taken to investigate the effects of mitochondrial fusion and fission dynamics on mutant mtDNA accumulation. Here we report model simulations suggesting that when mitochondrial fusion-fission rate is low, the slow mtDNA mixing can lead to an uneven distribution of mutant mtDNA among mitochondria in between two mitochondrial autophagic events leading to more stochasticity in the outcomes from a single random autophagic event. Consequently, slower mitochondrial fusion-fission results in higher variability in the mtDNA mutation burden among cells in a tissue over time, and mtDNA mutations have a higher propensity to clonally expand due to the increased stochasticity. When these mutations affect cellular energetics, nuclear retrograde signalling can upregulate mtDNA replication, which is expected to slow clonal expansion of these mutant mtDNA. However, our simulations suggest that the protective ability of retrograde signalling depends on the efficiency of fusion-fission process. Our results thus shed light on the interplay between mitochondrial fusion-fission and mtDNA turnover and may explain the mechanism underlying the experimentally observed increase in the accumulation of mtDNA mutations when either mitochondrial fusion or fission is inhibited.
Conflict of interest statement
Competing Interests: The authors have declared that no competing interests exist.
Figures
Figure 1. Two dimensional representation of the cell and the retrograde signaling function.
(A) A two dimensional representation of the cell partitioned into 16 compartments. (B) The retrograde signaling is modeled according to the COX activity response to a decrease in the wild-type mtDNA fractions in mouse cybrid cells carrying a mixture of wild-type and mutant mtDNA with a pathogenic 4696 bp deletion mutation. The parameters were obtained using least square estimation to the data.
Figure 2. Mitochondrial fusion-fission model.
(A) During a mitochondrial fusion, the nucleoid information (W and M) of the precursor mitochondria is retained and a fission site is created (bold line). During fission of a previously fused mitochondrion, a fission site is randomly chosen from the possible sites in the mitochondrion selected for fission. The redistribution of nucleoid contents between the two daughter mitochondria is determined randomly according to a Binomial distribution, while the particular nucleoids to be transferred are randomly taken from a Hypergeometric distribution. During fission of a primary mitochondrion, i.e. mitochondrion without any fission site, nucleoids are randomly distributed between two daughter mitochondria. (B) Steady state distribution of mitochondrial size as a function of mitochondrial size. In the figure inset, the fission propensity is shown as a function of mitochondrial size (number of nucleoids). (C) Mitochondrial fusion-fission and nucleoids mixing rate. Mitochondrial heterogeneity in each cell is represented by the mean coefficient of variation (COV) of RMmito. The mean COV of RM mito is scaled such that the steady state value is −100%. In this case, the mixing time τ is defined as the time for the scaled COV of RM mito to reach −63.2%. A faster decrease in the mean COV of RM mito indicates a faster mixing and hence is indicated by a smaller mixing time constant τ. Simulations were performed without mtDNA turnover.
Figure 3. Pseudo-code of the stochastic simulation algorithm implementation of the present model.
Figure 4. Simulations of mitochondrial fusion-fission without mtDNA turnover.
The mean COV of RM mito depends on the number and size distribution of mitochondria, and is therefore scaled such that the steady state value is −100%. In this case, the mixing time τ is defined as the time for the scaled COV of RM mito to reach −63.2%. (A) Lower mixing time τ is observed when the number of mitochondria becomes larger. (B) Inhibiting mitochondrial fusion or fission alone slows mixing. Simulations were performed by setting the parameter afusion or 
500 times lower than that the values reported in Table 1.
Figure 5. Simulations of mitochondrial fusion-fission with neutral mutations.
The simulations were done in triplicate and the error bars show the standard deviation. (A) The COV of RM cell increases at a slower rate with decreasing mixing time constant (i.e. faster fusion-fission). (B) The inhibition of fusion or fission and slower fusion-fission lead to an increase in the rate at which cells reach 80% mutation level. Simulations were performed with an initial RM cell of 10% mutation load in the presence of mtDNA turnover.
Figure 6. Simulations of mitochondrial fusion-fission with deleterious mutations.
The simulations were done in triplicate and the error bars show the standard deviation. (A) Inhibiting fusion or fission separately and slowing down fusion-fission quicken the accumulation of total mutation burden. (B) Similarly, the rate at which cells reach 80% mutation level increases with slower fusion-fission mixing of nucleoids. However, for the same mixing time constant, retrograde signalling reduces the rate at which cell undergo clonal expansion. Simulations were performed with an initial RM cell of 10%.
Figure 7. Amplification of clonal expansion by retrograde signalling when mitochondrial fusion-fission is slow.
The simulations were done in triplicate using τ = 120 days and an initial RM cell of 10%. The error bars show the standard deviation.
References
- Ryan MT, Hoogenraad NJ (2007) Mitochondrial-nuclear communications. Annu Rev Biochem 76: 701–722. - PubMed
- Birky CW (1994) Relaxed and Stringent Genomes - Why Cytoplasmic Genes Dont Obey Mendels Laws. J Heredity 85: 355–365.
- Kraytsberg Y, Nekhaeva E, Bodyak NB, Khrapko K (2003) Mutation and intracellular clonal expansion of mitochondrial genomes: two synergistic components of the aging process? Mech Age Dev124: 49–53. - PubMed
- Chinnery PF, Howell N, Lightowlers RN, Turnbull DM (1997) Molecular pathology of MELAS and MERRF - The relationship between mutation load and clinical phenotypes. Brain 120: 1713–1721. - PubMed
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