Figure 1.
Mitochondrial ROS vicious cycle.
A putative positive feedback mechanism between mtDNA and ROS is based on the hypothesis that ROS-induced damaged mtDNA produce defective components of the ETC, thereby increasing electron leakage in the OXPHOS process and ROS production. The vicious cycle is expected to give an exponential expansion of mtDNA mutations over time, which eventually causes the loss of mitochondrial function in generating ATP.
Figure 2.
(A) The in silico mouse model simulates the point mutation load of mtDNA in cells of a tissue such as heart and liver during development and postnatal stages. (B) Stochastic drift of point mutations in cells results as a consequence of mtDNA maintenance processes. Three sources of randomness are captured: (I) a random selection of a mitochondrion with ten mtDNA molecules from a well-mixed population, (II) a random replication or degradation of a mitochondrion, and (III) random occurrences of de novo mtDNA point mutation during replication.
Table 1.
Basic model parameters of the in silico stochastic mouse model.
Figure 3.
Stochastic determinants of age-dependent dynamics in the observed mtDNA point mutation frequency.
Heart tissue simulations provided the distribution of mutation frequency among 1,100 in-silico wild type mice. (A, B) The percentiles and probability distribution functions of the mutation frequency arising from the intrinsic stochasticity of cellular processes alone. The dotted line indicates the evolution of the average mutation frequency of 1,100 mice, which grows linearly with time. (C, D) The percentiles and probability distribution functions of the mutation frequency in the RMC assay of in silico wild-type mouse population. The apparent variability arises from the combined effect of intrinsic stochasticity and the (hypergeometric) sampling variability in the RMC protocol (details in the main text).
Figure 4.
Stochastic evolution of mtDNA states.
(A) represents the stochastic evolution of the wild-type mtDNA, while (B) illustrates the stochastic changes in the mutant mtDNA population. Red and blue curves indicate the outcomes of two independent realizations.
Figure 5.
Average mtDNA point mutation frequencies in WT and POLG mutator mice.
The variances in the in silico mouse data represent the intrinsic stochasticity only (without the RMC sampling variability).
Figure 6.
Mitochondrial DNA point mutation during mouse development.
Expansion of mtDNA point mutations during heart tissue development from in silico wild-type (A), POLG+/mut (B) and POLGmut/mut mice (C) population (n = 1,100). (A) The square symbols show examples of point mutation trajectory from two different mice, one of which suffers from a rare point mutation early in the development, resulting in the amplification of the mutation frequency in subsequent cell divisions. (B) Like in the wild-type cohort, de-novo point mutations generated in the POLG+/mut mice during the early cell divisions can accumulate very quickly, resulting in a high mutation load in the cells at birth. (C) Since the error rate of mtDNA replication in POLGmut/mut is much higher than the wild-type mtDNA replication, a significant proportion of the population (>75%) harbors mtDNA mutations at an early stage of development (before the 10th cell division). As a consequence, the resulting mutation load in the tissue is significantly higher than that in the wild-type tissues at birth.
Figure 7.
Average mtDNA point mutation accumulation in wild type and POLG mutator mouse models.
Wild-type mice have a low mutation burden at birth, but they accumulate relatively more mutations during their life. On the other hand, the POLG mice harbor a significant mutation load at birth due to error-prone mtDNA replications during development. In the post-mitotic stage however, the relative accumulation (in comparison to mutations at the birth) is significantly lower due to the slow turnover of mtDNA.