Computational modeling analysis of mitochondrial superoxide production under varying substrate conditions and upon inhibition of different segments of the electron transport chain.

A computational mechanistic model of superoxide (O2•-) formation in the mitochondrial electron transport chain (ETC) was developed to facilitate the quantitative analysis of factors controlling mitochondrial O2•- production and assist in the interpretation of experimental studies. The model takes into account all individual electron transfer reactions in Complexes I and III. The model accounts for multiple, often seemingly contradictory observations on the effects of ΔΨ and ΔpH, and for the effects of multiple substrate and inhibitor conditions, including differential effects of Complex III inhibitors antimycin A, myxothiazol and stigmatellin. Simulation results confirm that, in addition to O2•- formation in Complex III and at the flavin site of Complex I, the quinone binding site of Complex I is an additional superoxide generating site that accounts for experimental observations on O2•- production during reverse electron transfer. However, our simulation results predict that, when cytochrome c oxidase is inhibited during oxidation of succinate, ROS production at this site is eliminated and almost all superoxide in Complex I is generated by reduced FMN, even when the redox pressure for reverse electron transfer from succinate is strong. In addition, the model indicates that conflicting literature data on the kinetics of electron transfer in Complex III involving the iron-sulfur protein-cytochrome bL complex can be resolved in favor of a dissociation of the protein only after electron transfer to cytochrome bH. The model predictions can be helpful in understanding factors driving mitochondrial superoxide formation in intact cells and tissues

Closure of VDAC causes oxidative stress and accelerates the Ca2+-induced mitochondrial permeability transition in rat liver mitochondria

The electron transport chain of mitochondria is a major source of reactive oxygen species (ROS), which play a critical role in augmenting the Ca2+-induced mitochondrial permeability transition (MPT). Mitochondrial release of superoxide anions (O2•-) from the intermembrane space (IMS) to the cytosol is mediated by voltage dependent anion channels (VDAC) in the outer membrane. Here, we examined whether closure of VDAC increases intramitochondrial oxidative stress by blocking efflux of O2•- from the IMS and sensitizing to the Ca2+-induced MPT. Treatment of isolated rat liver mitochondria with 5 µM G3139, an 18-mer phosphorothioate blocker of VDAC, accelerated onset of the MPT by 6.8 ± 1.4 min within a range of 100–250 µM Ca2+. G3139-mediated acceleration of the MPT was reversed by 20 µM butylated hydroxytoluene, a water soluble antioxidant. Pre-treatment of mitochondria with G3139 also increased accumulation of O2•- in mitochondria, as monitored by dihydroethidium fluorescence, and permeabilization of the mitochondrial outer membrane with digitonin reversed the effect of G3139 on O2•- accumulation. Mathematical modeling of generation and turnover of O2•- within the IMS indicated that closure of VDAC produces a 1.55-fold increase in the steady-state level of mitochondrial O2•-. In conclusion, closure of VDAC appears to impede the efflux of superoxide anions from the IMS, resulting in an increased steady-state level of O2•-˜, which causes an internal oxidative stress and sensitizes mitochondria toward the Ca2+-induced MPT

Correction: A mathematical model of multisite phosphorylation of tau protein.

[This corrects the article DOI: 10.1371/journal.pone.0192519.]

A mathematical model of multisite phosphorylation of tau protein

<div><p>Abnormal tau metabolism followed by formation of tau deposits causes a number of neurodegenerative diseases called tauopathies including Alzheimer’s disease. Hyperphosphorylation of tau protein precedes tau aggregation and is a topic of interest for the development of pharmacological interventions to prevent pathology progression at early stages. The development of a mathematical model of multisite phosphorylation of tau would be helpful for searching for the targets of pharmacological interventions and candidates for biomarkers of pathology progression. In the present study, we for the first time developed a model of multisite phosphorylation of tau protein and elucidated the relative contribution of kinases to phosphorylation of distinct sites. The model describes phosphorylation of tau or PKA-prephosphorylated tau by GSK3β and CDK5 and dephosphorylation by PP2A, accurately reproducing the data for short-term kinetics of tau (de)phosphorylation. Our results suggest that kinase inhibition may more specifically prevent tau hyperphosphorylation, e.g., on PHF sites, which are key biomarkers of pathological changes in Alzheimer’s disease. The main features of our model are partial phosphorylation of tau residues and merging of random and sequential mechanisms of multisite phosphorylation within the framework of the probability-based approach assuming independent phosphorylation events.</p></div

Phosphorylation kinetics of S396 (purple) and S404 (green) of tau (A) or PKA-prephosphorylated tau (B) by GSK3β.

<p>Kinetics for pS404 with 95% confidence bands are represented. Errors of experimental values were not provided by the authors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192519#pone.0192519.ref020" target="_blank">20</a>].</p

Phosphorylation stoichiometry (the ratio of phosphorylated tau to total tau, mol P/mol) for (A) GSK3β and (B) CDK5 with unphosphorylated tau (gray) or PKA-prephosphorylated tau (black) [20].

<p>Experimental values are marked by points, and model predictions by lines. Two black and gray experimental points for CDK5 (B) coincide. Errors of experimental values were not provided by the authors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192519#pone.0192519.ref020" target="_blank">20</a>].</p

Heatmap representation of predicted sensitivity of tau residues and total tau phosphorylation (columns) to the reaction of (de)phosphorylation (row).

<p>Heatmap representation of predicted sensitivity of tau residues and total tau phosphorylation (columns) to the reaction of (de)phosphorylation (row).</p

Kinetics of tau phosphorylation at S404 by (A) GSK3β or (B) CDK5 with (black) or without (gray) PKA-prephosphorylation.

<p>The transient peak in (A) is caused by sequential phosphorylation (first S404, then S396) by GSK3β in contrast to kinetics of tau phosphorylation at S404 with CDK5 when residues S396 and S404 are phosphorylated independently by a random mechanism. Errors of experimental values were not provided by the authors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192519#pone.0192519.ref020" target="_blank">20</a>].</p

Schematic representation of the tau residues and kinases incorporated into the mathematical model.

<p>Schematic representation of the tau residues and kinases incorporated into the mathematical model.</p