Xanthine dehydrogenase AtXDH1 from Arabidopsis thaliana is a key enzyme in purine degradation where it oxidizes hypoxanthine to xanthine and xanthine to uric acid. Electrons released from these substrates are either transferred to NAD(+) or to molecular oxygen, thereby yielding NADH or superoxide, respectively. By an alternative activity, AtXDH1 is capable of oxidizing NADH with concomitant formation of NAD(+) and superoxide. Here we demonstrate that in comparison to the specific activity with xanthine as substrate, the specific activity of recombinant AtXDH1 with NADH as substrate is about 15-times higher accompanied by a doubling in superoxide production. The observation that NAD(+) inhibits NADH oxidase activity of AtXDH1 while NADH suppresses NAD(+)-dependent xanthine oxidation indicates that both NAD(+) and NADH compete for the same binding-site and that both sub-activities are not expressed at the same time. Rather, each sub-activity is determined by specific conditions such as the availability of substrates and co-substrates, which allows regulation of superoxide production by AtXDH1. Since AtXDH1 exhibits the most pronounced NADH oxidase activity among all xanthine dehydrogenase proteins studied thus far, our results imply that in particular by its NADH oxidase activity AtXDH1 is an efficient producer of superoxide also in vivo.

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Expression of the catabolic network in Escherichia coli is predominantly regulated, via oxygen availability, by the two-component system ArcBA. It has been shown that the kinase activity of ArcB is controlled by the redox state of two critical pairs of cysteines in dimers of the ArcB sensory kinase. Among the cellular components that control the redox state of these cysteines of ArcB are the quinones from the cytoplasmic membrane of the cell, which function in ‘respiratory’ electron transfer. This study is an effort to understand how the redox state of the quinone pool(s) is sensed by the cell via the ArcB kinase. We report the relationship between growth, quinone content, ubiquinone redox state, the level of ArcA phosphorylation, and the level of ArcA-dependent gene expression, in a number of mutants of E. coli with specific alterations in their set of quinones, under a range of physiological conditions. Our results provide experimental evidence for a previously formulated hypothesis that not only ubiquinone, but also demethylmenaquinone, can inactivate kinase activity of ArcB. Also, in a mutant strain that only contains demethylmenaquinone, the extent of ArcA phosphorylation can be modulated by the oxygen supply rate, which shows that demethylmenaquinone can also inactivate ArcB in its oxidized form. Furthermore, in batch cultures of a strain that contains ubiquinone as its only quinone species, we observed that the ArcA phosphorylation level closely followed the redox state of the ubiquinone/ubiquinol pool, much more strictly than it does in the wild type strain. Therefore, at low rates of oxygen supply in the wild type strain, the activity of ArcB may be inhibited by demethylmenaquinone, in spite of the fact that the ubiquinones are present in the ubiquinol form.

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The efficient redesign of bacteria for biotechnological purposes, such as biofuel production, waste disposal or specific biocatalytic functions, requires a quantitative systems-level understanding of energy supply, carbon, and redox metabolism. The measurement of transcript levels, metabolite concentrations and metabolic fluxes per se gives an incomplete picture. An appreciation of the interdependencies between the different measurement values is essential for systems-level understanding. Mathematical modeling has the potential to provide a coherent and quantitative description of the interplay between gene expression, metabolite concentrations, and metabolic fluxes. Escherichia coli undergoes major adaptations in central metabolism when the availability of oxygen changes. Thus, an integrated description of the oxygen response provides a benchmark of our understanding of carbon, energy, and redox metabolism. We present the first comprehensive model of the central metabolism of E. coli that describes steady-state metabolism at different levels of oxygen availability. Variables of the model are metabolite concentrations, gene expression levels, transcription factor activities, metabolic fluxes, and biomass concentration. We analyze the model with respect to the production capabilities of central metabolism of E. coli. In particular, we predict how precursor and biomass concentration are affected by product formation.