NADH: ubiquinone oxidoreductase (Complex I) is a proton pump in the electron transport chain that can produce a significant amounts of superoxide and hydrogen peroxide. While the flavin mononucleotide (FMN) is the putative site for hydrogen peroxide generation, sites responsible for superoxide are less certain. Here, data on Complex I kinetics and ROS generation are analyzed using a computational model to determine the sites responsible for superoxide. The analysis includes all the major redox centers: the FMN, iron-sulfur cluster N2, and semiquinone. Analysis reveals that the fully reduced FMN and semiquinone are the primary sources of superoxide, and the iron-sulfur cluster N2 produces none. The FMN radical only produces ROS when the quinone reductase site is blocked. Model simulations reveal ROS generation is maximized during reverse electron transport with both the FMN and semiquin one producing similar amounts of superoxide. In addition, the model successfully predicts the increase in ROS generation when the membrane potential is high and matrix pH is alkaline. Of the total ROS produced by Complex I, the majority originates from the FMN.

The thioredoxin system, which consists of a family of proteins, including thioredoxin (Trx), peroxiredoxin (Prx) and thioredoxin reductase (TrxR), plays a critical role in the defense against oxidative stress by removing harmful hydrogen peroxide (H2O2). Specifically, Trx donates electrons to Prx to remove H2O2 and then TrxR maintains the reduced Trx concentration with NADPH as the cofactor. Despite a great deal of kinetic information gathered on the removal of H2O2 by the Trx system from various sources/species, a mechanistic understanding of the associated enzymes is still not available. We address this issue by developing a thermodynamically-consistent mathematical model of the Trx system which entails mechanistic details and provides quantitative insights into the kinetics of the TrxR and Prx enzymes. Consistent with experimental studies, the model analyses of the available data show that both enzymes operate by a ping-pong mechanism. The proposed mechanism for TrxR, which incorporates substrate inhibition by NADPH and intermediate protonation states, well describes the available data and accurately predicts the bell-shaped behavior of the effect of pH on the TrxR activity. Most importantly, the model also predicts the inhibitory effects of the reaction products (NADP+ and Trx(SH)2) on the TrxR activity for which suitable experimental data are not available. The model analyses of the available data on the kinetics of Prx from mammalian sources reveal that Prx operates at very low H2O2 concentrations compared to their human parasite counterparts. Furthermore, the model is able to predict the dynamic overoxidation of Prx at high H2O2 concentrations, consistent with the available data. The integrated Prx-TrxR model simulations well describe the NADPH and H2O2 degradation dynamics and also show that the coupling of TrxR- and Prx-dependent reduction of H2O2 allowed ultrasensitive changes in the Trx concentration in response to changes in the TrxR concentration at high Prx concentrations. Thus, the model of this sort is very useful for integration into computational H2O2 degradation models to identify its role in physiological and pathophysiological functions.

Glutathione reductase (GR) catalyzes the reduction of oxidized glutathione (GSSG) to reduced glutathione (GSH) using NADPH as the reducing cofactor, and thereby maintains a constant GSH level in the system. GSH scavenges superoxide (O2·−) and hydroxyl radicals (OH·) non-enzymatically or by serving as an electron donor to several enzymes involved in reactive oxygen species (ROS) detoxification. In either case, GSH oxidizes to GSSG and is subsequently regenerated with the catalytic action of GR. Though GR kinetic mechanism has been extensively studied under different experimental conditions with variable substrates and products, the catalytic mechanism has not been studied in terms of a mechanistic model that accounts for the effects of the substrates and products on the reaction kinetics. The aim of the current study is therefore to develop a comprehensive mathematical model for the catalytic mechanism of GR. We use available experimental data on GR kinetics from various species/sources to develop the mathematical model and estimate the associated model parameters. The model simulations are consistent with the experimental observations that GR operates via both ping-pong and sequential branching mechanisms based on relevant concentrations of its reaction substrate GSSG. Furthermore, we show the observed pH-dependent substrate-inhibition of GR activity by GSSG and bi-modal behavior of GR activity with pH. The model presents a unique opportunity to understand the effects of products on the kinetics of GR. The model simulations show that under physiological conditions, where both substrates and products are present, the flux distribution depends on the concentrations of both GSSG and NADP+ with ping-pong flux operating at low levels and sequential flux dominating at higher levels. The kinetic model of GR may serve as a key module for the development of integrated models for ROS scavenging system to understand protection of cells under normal and oxidative stress conditions.

In order to provide timely treatment for organ damage initiated by therapeutic drugs or exposure to environmental toxicants, we first need to identify markers that provide an early diagnosis of potential adverse effects before permanent damage occurs. Specifically, the liver, as a primary organ prone to toxicants-induced injuries, lacks diagnostic markers that are specific and sensitive to the early onset of injury. Here, to identify plasma metabolites as markers of early toxicant-induced injury, we used a constraint-based modeling approach with a genome-scale network reconstruction of rat liver metabolism to incorporate perturbations of gene expression induced by acetaminophen, a known hepatotoxicant. A comparison of the model results against the global metabolic profiling data revealed that our approach satisfactorily predicted altered plasma metabolite levels as early as 5 h after exposure to 2 g/kg of acetaminophen, and that 10 h after treatment the predictions significantly improved when we integrated measured central carbon fluxes. Our approach is solely driven by gene expression and physiological boundary conditions, and does not rely on any toxicant-specific model component. As such, it provides a mechanistic model that serves as a first step in identifying a list of putative plasma metabolites that could change due to toxicant-induced perturbations.