Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer.

BACKGROUND: Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. METHODS: We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer. RESULTS: We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing. CONCLUSIONS: Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.

A novel variant of the 13C-methacetin liver function breath test that eliminates the confounding effect of individual differences in systemic CO2 kinetics.

The principle of dynamic liver function breath tests is founded on the administration of a 13C-labeled drug and subsequent monitoring of 13CO2 in the breath, quantified as time series delta over natural baseline 13CO2 (DOB) liberated from the drug during hepatic CYP-dependent detoxification. One confounding factor limiting the diagnostic value of such tests is that only a fraction of the liberated 13CO2 is immediately exhaled, while another fraction is taken up by body compartments from which it returns with delay to the plasma. The aims of this study were to establish a novel variant of the methacetin-based breath test LiMAx that allows to estimate and to eliminate the confounding effect of systemic 13CO2 distribution on the DOB curve and thus enables a more reliable assessment of the hepatic detoxification capacity compared with the conventional LiMAx test. We designed a new test variant (named "2DOB") consisting of two consecutive phases. Phase 1 is initiated by the intravenous administration of 13C-bicarbonate. Phase 2 starts about 30 min later with the intravenous administration of the 13C-labelled test drug. Using compartment modelling, the resulting 2-phasic DOB curve yields the rate constants for the irreversible elimination and the reversible exchange of plasma 13CO2 with body compartments (phase 1) and for the detoxification and exchange of the drug with body compartments (phase 2). We carried out the 2DOB test with the test drug 13C-methacetin in 16 subjects with chronic liver pathologies and 22 normal subjects, who also underwent the conventional LiMAx test. Individual differences in the systemic CO2 kinetics can lead to deviations up to a factor of 2 in the maximum of DOB curves (coefficient of variation CV ≈ 0.2) which, in particular, may hamper the discrimination between subjects with normal or mildly impaired detoxification capacities. The novel test revealed that a significant portion of the drug is not immediately metabolized, but transiently taken up into a storage compartment. Intriguingly, not only the hepatic detoxification rate but also the storage capacity of the drug, turned out to be indicative for a normal liver function. We thus used both parameters to define a scoring function which yielded an excellent disease classification (AUC = 0.95) and a high correlation with the MELD score (RSpearman = 0.92). The novel test variant 2DOB promises a significant improvement in the assessment of impaired hepatic detoxification capacity. The suitability of the test for the reliable characterization of the natural history of chronic liver diseases (fatty liver-fibrosis-cirrhosis) has to be assessed in further studies.

Physiologically based toxicokinetic models and in silico predicted partition coefficients to estimate tetrachlorodibenzo-p-dioxin transfer from feed into growing pigs.

Tetrachlorodibenzo-p-dioxin (TCDD) is a ubiquitous, toxic, persistent and bioaccumulative organic pollutant. TCDD can potentially enter the food chain through contaminated food of animal origin as a consequence of feed contamination. Prediction of the TCDD transfer from feed into animal products is thus important for human health risk assessment. Here, we develop several physiologically based toxicokinetic (PBTK) models of TCDD transfer from contaminated feed into growing pigs (Sus scrofa) exposed to doses ranging from 24.52 to 3269.25 ng of TCDD. We test the consequences of explicit dose-dependent absorption (DDA) versus the indirect effects of a self-induced liver metabolism (SIM). The DDA and SIM models showed similar fit to experimental data, although currently it is not possible to unequivocally make statement on a mechanistic preference. The performance of both toxicokinetic models was successfully evaluated using the 1999 Belgian case of contaminated fats for feeding. In combination with toxicokinetic models of other dioxin congeners, they can be used to formulate maximum allowance levels of dioxins in feedstuffs for pigs. Additionally, the implementation of in silico-predicted partition coefficients was explored as a useful alternative to predict TCDD tissue distribution in low-dose scenarios without recurring to animal experiments.

A multiscale modelling approach to assess the impact of metabolic zonation and microperfusion on the hepatic carbohydrate metabolism.

The capacity of the liver to convert the metabolic input received from the incoming portal and arterial blood into the metabolic output of the outgoing venous blood has three major determinants: The intra-hepatic blood flow, the transport of metabolites between blood vessels (sinusoids) and hepatocytes and the metabolic capacity of hepatocytes. These determinants are not constant across the organ: Even in the normal organ, but much more pronounced in the fibrotic and cirrhotic liver, regional variability of the capillary blood pressure, tissue architecture and the expression level of metabolic enzymes (zonation) have been reported. Understanding how this variability may affect the regional metabolic capacity of the liver is important for the interpretation of functional liver tests and planning of pharmacological and surgical interventions. Here we present a mathematical model of the sinusoidal tissue unit (STU) that is composed of a single sinusoid surrounded by the space of Disse and a monolayer of hepatocytes. The total metabolic output of the liver (arterio-venous glucose difference) is obtained by integration across the metabolic output of a representative number of STUs. Application of the model to the hepatic glucose metabolism provided the following insights: (i) At portal glucose concentrations between 6-8 mM, an intra-sinusoidal glucose cycle may occur which is constituted by glucose producing periportal hepatocytes and glucose consuming pericentral hepatocytes, (ii) Regional variability of hepatic blood flow is higher than the corresponding regional variability of the metabolic output, (iii) a spatially resolved metabolic functiogram of the liver is constructed. Variations of tissue parameters are equally important as variations of enzyme activities for the control of the arterio-venous glucose difference.

The relative importance of kinetic mechanisms and variable enzyme abundances for the regulation of hepatic glucose metabolism--insights from mathematical modeling.

BACKGROUND: Adaptation of the cellular metabolism to varying external conditions is brought about by regulated changes in the activity of enzymes and transporters. Hormone-dependent reversible enzyme phosphorylation and concentration changes of reactants and allosteric effectors are the major types of rapid kinetic enzyme regulation, whereas on longer time scales changes in protein abundance may also become operative. Here, we used a comprehensive mathematical model of the hepatic glucose metabolism of rat hepatocytes to decipher the relative importance of different regulatory modes and their mutual interdependencies in the hepatic control of plasma glucose homeostasis. RESULTS: Model simulations reveal significant differences in the capability of liver metabolism to counteract variations of plasma glucose in different physiological settings (starvation, ad libitum nutrient supply, diabetes). Changes in enzyme abundances adjust the metabolic output to the anticipated physiological demand but may turn into a regulatory disadvantage if sudden unexpected changes of the external conditions occur. Allosteric and hormonal control of enzyme activities allow the liver to assume a broad range of metabolic states and may even fully reverse flux changes resulting from changes of enzyme abundances alone. Metabolic control analysis reveals that control of the hepatic glucose metabolism is mainly exerted by enzymes alone, which are differently controlled by alterations in enzyme abundance, reversible phosphorylation, and allosteric effects. CONCLUSION: In hepatic glucose metabolism, regulation of enzyme activities by changes of reactants, allosteric effects, and reversible phosphorylation is equally important as changes in protein abundance of key regulatory enzymes.

Quantifying the contribution of the liver to glucose homeostasis: a detailed kinetic model of human hepatic glucose metabolism.

Despite the crucial role of the liver in glucose homeostasis, a detailed mathematical model of human hepatic glucose metabolism is lacking so far. Here we present a detailed kinetic model of glycolysis, gluconeogenesis and glycogen metabolism in human hepatocytes integrated with the hormonal control of these pathways by insulin, glucagon and epinephrine. Model simulations are in good agreement with experimental data on (i) the quantitative contributions of glycolysis, gluconeogenesis, and glycogen metabolism to hepatic glucose production and hepatic glucose utilization under varying physiological states. (ii) the time courses of postprandial glycogen storage as well as glycogen depletion in overnight fasting and short term fasting (iii) the switch from net hepatic glucose production under hypoglycemia to net hepatic glucose utilization under hyperglycemia essential for glucose homeostasis (iv) hormone perturbations of hepatic glucose metabolism. Response analysis reveals an extra high capacity of the liver to counteract changes of plasma glucose level below 5 mM (hypoglycemia) and above 7.5 mM (hyperglycemia). Our model may serve as an important module of a whole-body model of human glucose metabolism and as a valuable tool for understanding the role of the liver in glucose homeostasis under normal conditions and in diseases like diabetes or glycogen storage diseases.

HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology.

We present HepatoNet1, the first reconstruction of a comprehensive metabolic network of the human hepatocyte that is shown to accomplish a large canon of known metabolic liver functions. The network comprises 777 metabolites in six intracellular and two extracellular compartments and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high-quality evidence-based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint-based modeling techniques. Taking the hepatic detoxification of ammonia as an example, we show how the availability of nutrients and oxygen may modulate the interplay of various metabolic pathways to allow an efficient response of the liver to perturbations of the homeostasis of blood compounds.

IGERS: inferring Gibbs energy changes of biochemical reactions from reaction similarities.

Mathematical analysis and modeling of biochemical reaction networks requires knowledge of the permitted directionality of reactions and membrane transport processes. This information can be gathered from the standard Gibbs energy changes (DeltaG(0)) of reactions and the concentration ranges of their reactants. Currently, experimental DeltaG(0) values are not available for the vast majority of cellular biochemical processes. We propose what we believe to be a novel computational method to infer the unknown DeltaG(0) value of a reaction from the known DeltaG(0) value of the chemically most similar reaction. The chemical similarity of two arbitrary reactions is measured by the relative number (T) of co-occurring changes in the chemical attributes of their reactants. Testing our method across a validated reference set of 173 biochemical reactions with experimentally determined DeltaG(0) values, we found that a minimum reaction similarity of T = 0.6 is required to infer DeltaG(0) values with an error of <10 kJ/mol. Applying this criterion, our method allows us to assign DeltaG(0) values to 458 additional reactions of the BioPath database. We believe our approach permits us to minimize the number of DeltaG(0) measurements required for a full coverage of a given reaction network with reliable DeltaG(0) values.

Local oxygen homeostasis during various neuronal network activity states in the mouse hippocampus.

Cortical information processing comprises various activity states emerging from timed synaptic excitation and inhibition. However, the underlying energy metabolism is widely unknown. We determined the cerebral metabolic rate of oxygen (CMRO2) along a tissue depth of <0.3 mm in the hippocampal CA3 region during various network activities, including gamma oscillations and sharp wave-ripples that occur during wakefulness and sleep. These physiological states associate with sensory perception and memory formation, and critically depend on perisomatic GABA inhibition. Moreover, we modelled vascular oxygen delivery based on quantitative microvasculature analysis. (1) Local CMRO2 was highest during gamma oscillations (3.4 mM/min), medium during sharp wave-ripples, asynchronous activity and isoflurane application (2.0-1.6 mM/min), and lowest during tetrodotoxin application (1.4 mM/min). (2) Energy expenditure of axonal and synaptic signaling accounted for >50% during gamma oscillations. (3) CMRO2 positively correlated with number and synchronisation of activated synapses, and neural multi-unit activity. (4) The median capillary distance was 44 µm. (5) The vascular oxygen partial pressure of 33 mmHg was needed to sustain oxidative phosphorylation during gamma oscillations. We conclude that gamma oscillations featuring high energetics require a hemodynamic response to match oxygen consumption of respiring mitochondria, and that perisomatic inhibition significantly contributes to the brain energy budget.

The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks.

The dynamic behavior of metabolic networks is governed by numerous regulatory mechanisms, such as reversible phosphorylation, binding of allosteric effectors or temporal gene expression, by which the activity of the participating enzymes can be adjusted to the functional requirements of the cell. For most of the cellular enzymes, such regulatory mechanisms are at best qualitatively known, whereas detailed enzyme-kinetic models are lacking. To explore the possible dynamic behavior of metabolic networks in cases of lacking or incomplete enzyme-kinetic information, we present a computational approach based on structural kinetic modeling. We derive statistical measures for the relative impact of enzyme-kinetic parameters on dynamic properties (such as local stability) and apply our approach to the metabolism of human erythrocytes. Our findings show that allosteric enzyme regulation significantly enhances the stability of the network and extends its potential dynamic behavior. Moreover, our approach allows to differentiate quantitatively between metabolic states related to senescence and metabolic collapse of the human erythrocyte. We think that the proposed method represents an important intermediate step on the long way from topological network analysis to detailed kinetic modeling of complex metabolic networks.

Corrigendum to "Kinetic Modeling of the Mitochondrial Energy Metabolism of Neuronal Cells: The Impact of Reduced α-Ketoglutarate Dehydrogenase Activities on ATP Production and Generation of Reactive Oxygen Species".

[This corrects the article DOI: 10.1155/2012/757594.].

HEPATOKIN1 is a biochemistry-based model of liver metabolism for applications in medicine and pharmacology.

The epidemic increase of non-alcoholic fatty liver diseases (NAFLD) requires a deeper understanding of the regulatory circuits controlling the response of liver metabolism to nutritional challenges, medical drugs, and genetic enzyme variants. As in vivo studies of human liver metabolism are encumbered with serious ethical and technical issues, we developed a comprehensive biochemistry-based kinetic model of the central liver metabolism including the regulation of enzyme activities by their reactants, allosteric effectors, and hormone-dependent phosphorylation. The utility of the model for basic research and applications in medicine and pharmacology is illustrated by simulating diurnal variations of the metabolic state of the liver at various perturbations caused by nutritional challenges (alcohol), drugs (valproate), and inherited enzyme disorders (galactosemia). Using proteomics data to scale maximal enzyme activities, the model is used to highlight differences in the metabolic functions of normal hepatocytes and malignant liver cells (adenoma and hepatocellular carcinoma).

Renal oncocytoma characterized by the defective complex I of the respiratory chain boosts the synthesis of the ROS scavenger glutathione.

Renal oncocytomas are rare benign tumors of the kidney and characterized by a deficient complex I (CI) enzyme activity of the oxidative phosphorylation (OXPHOS) system caused by mitochondrial DNA (mtDNA) mutations. Yet, little is known about the underlying molecular mechanisms and alterations of metabolic pathways in this tumor. We compared renal oncocytomas with adjacent matched normal kidney tissues on a global scale by multi-omics approaches, including whole exome sequencing (WES), proteomics, metabolomics, and metabolic pathway simulation. The abundance of proteins localized to mitochondria increased more than 2-fold, the only exception was a strong decrease in the abundance for CI subunits that revealed several pathogenic heteroplasmic mtDNA mutations by WES. We also observed renal oncocytomas to dysregulate main metabolic pathways, shunting away from gluconeogenesis and lipid metabolism. Nevertheless, the abundance of energy carrier molecules such as NAD+, NADH, NADP, ATP, and ADP were significantly higher in renal oncocytomas. Finally, a substantial 5000-fold increase of the reactive oxygen species scavenger glutathione can be regarded as a new hallmark of renal oncocytoma. Our findings demonstrate that renal oncocytomas undergo a metabolic switch to eliminate ATP consuming processes to ensure a sufficient energy supply for the tumor.

Sequential metabolic phases as a means to optimize cellular output in a constant environment.

Temporal changes of gene expression are a well-known regulatory feature of all cells, which is commonly perceived as a strategy to adapt the proteome to varying external conditions. However, temporal (rhythmic and non-rhythmic) changes of gene expression are also observed under virtually constant external conditions. Here we hypothesize that such changes are a means to render the synthesis of the metabolic output more efficient than under conditions of constant gene activities. In order to substantiate this hypothesis, we used a flux-balance model of the cellular metabolism. The total time span spent on the production of a given set of target metabolites was split into a series of shorter time intervals (metabolic phases) during which only selected groups of metabolic genes are active. The related flux distributions were calculated under the constraint that genes can be either active or inactive whereby the amount of protein related to an active gene is only controlled by the number of active genes: the lower the number of active genes the more protein can be allocated to the enzymes carrying non-zero fluxes. This concept of a predominantly protein-limited efficiency of gene expression clearly differs from other concepts resting on the assumption of an optimal gene regulation capable of allocating to all enzymes and transporters just that fraction of protein necessary to prevent rate limitation. Applying this concept to a simplified metabolic network of the central carbon metabolism with glucose or lactate as alternative substrates, we demonstrate that switching between optimally chosen stationary flux modes comprising different sets of active genes allows producing a demanded amount of target metabolites in a significantly shorter time than by a single optimal flux mode at fixed gene activities. Our model-based findings suggest that temporal expression of metabolic genes can be advantageous even under conditions of constant external substrate supply.

Regulation of liver metabolism by the endosomal GTPase Rab5.

The liver maintains glucose and lipid homeostasis by adapting its metabolic activity to the energy needs of the organism. Communication between hepatocytes and extracellular environment via endocytosis is key to such homeostasis. Here, we addressed the question of whether endosomes are required for gluconeogenic gene expression. We took advantage of the loss of endosomes in the mouse liver upon Rab5 silencing. Strikingly, we found hepatomegaly and severe metabolic defects such as hypoglycemia, hypercholesterolemia, hyperlipidemia, and glycogen accumulation that phenocopied those found in von Gierke's disease, a glucose-6-phosphatase (G6Pase) deficiency. G6Pase deficiency alone can account for the reduction in hepatic glucose output and glycogen accumulation as determined by mathematical modeling. Interestingly, we uncovered functional alterations in the transcription factors, which regulate G6Pase expression. Our data highlight a requirement of Rab5 and the endosomal system for the regulation of gluconeogenic gene expression that has important implications for metabolic diseases.

Cytosolic processing of proteasomal cleavage products can enhance the presentation efficiency of MHC-1 epitopes.

The vertebrate immune system is able to detect abnormal body cells by the specific repertoire of 8 - 12 residues long peptides (= epitopes or peptide antigens) presented at the cell surface by the MHC-1 molecule complex. The generation of an epitope starts with the degradation of endogenous proteins into primary oligomeric fragments by cytosolic proteases, predominantly the proteasome. These primary fragments may be further attacked by various amino peptidases resident in the cytosol or, alternatively, may escape from this attack by entering the endoplasmic reticulum (ER) by the transporter associated with antigen presentation (TAP). To study the possible consequences of this scenario for the efficiency of antigen presentation we have applied kinetic modelling. The mathematical model comprises the generation of primary oligomeric fragments containing the definitive epitope, the successive N-terminal shortening of these primary fragments by cytosolic amino peptidases and the TAP-mediated transport of cytosolic peptides into the ER. Because the number of peptide molecules may become very small we have performed deterministic and stochastic simulations of the kinetic model. Our simulations show that cytosolic N-terminal trimming of primary fragments may drastically increase loading epitope precursors into the ER. In particular, a primary fragment generated with a low rate of TAP transport into the ER may nevertheless become a potent epitope precursor if at least one of its N-terminal trimming products will be efficiently transported.

A method for classifying metabolites in topological pathway analyses based on minimization of pathway number.

Metabolic pathway analysis based on the concept of elementary flux mode is a valuable tool for reconstruction of bacterial metabolisms and in predicting optimal conversion yields in biotechnology. However, pathway analysis of large and highly entangled metabolic networks meets the problem of combinatorial explosion of possible routes across the networks. Here we propose a method for coping with this problem by suitably classifying metabolites as external or internal. External metabolites are considered to have buffered concentrations while internal metabolites have to fulfil a balance condition at steady state. For many substances such as nutrients and excreted products, there are biochemical reasons to classify them as external. In addition, other substances (especially at central branching points) can operationally be considered external in order to avoid combinatorial explosion. We suggest to find such a classification of metabolites that minimizes the number of elementary flux modes (pathways). This is motivated by the objectives of finding such a description of the system that reduces as much as possible the amount of necessary data and of removing the ambiguity and arbitrariness in the classification of metabolites in an automated, systematic way. For networks of moderate size, the solution to this combinatorial minimization problem can be found by exhaustive search. To tackle also larger systems, a stochastic optimization program based on the Metropolis algorithm was developed. Both methods are applied, for illustration, to several reaction schemes including a larger network representing glutathione metabolism.

Implications of enzyme deficiencies on mitochondrial energy metabolism and reactive oxygen species formation of neurons involved in rotenone-induced Parkinson's disease: a model-based analysis.

Steadily growing experimental evidence suggests that mitochondrial dysfunction plays a key role in the age-dependent impairment of nerve cells underlying several neurodegenerative diseases. In particular, the citric acid cycle enzyme complex α-ketoglutarate dehydrogenase (KGDHC) and respiratory chain complex I of the respiratory chain often show reduced activities in the dopaminergic neurons involved in Parkinson's disease, both giving rise to an impaired mitochondrial energy metabolism as demonstrated in a number of in vitro studies with cell lines as well as isolated mitochondria. To understand the metabolic regulation underlying these experimental findings we used a detailed kinetic model of mitochondrial energy metabolism. First, we investigated the effect of complex I inhibition on energy production and formation of reactive oxygen species (ROS). Next, we applied the model to a situation where both KGDHC and complex I exhibit reduced activities. These calculations reveal synergistic effects with respect to the energy metabolism but antagonistic effects with respect to ROS formation: the drop in the ATP production capacity is more pronounced than at inhibition of either enzyme complex alone. Interestingly, however, the reduction state of the ROS-generating sites of the impaired complex I becomes significantly lowered if additionally the activity of the KGDHC is reduced. We discuss the pathophysiological consequences of these intriguing findings.

Assessment of hepatic detoxification activity: proposal of an improved variant of the (13)c-methacetin breath test.

Breath tests based on the administration of a (13)C-labeled drug and subsequent monitoring of (13)CO2 in the breath (quantified as DOB - delta over baseline) liberated from the drug during hepatic CPY-dependent detoxification are important tools in liver function diagnostics. The capability of such breath tests to reliably indicate hepatic CYP performance is limited by the fact that (13)CO2 is not exclusively exhaled but also exchanged with other compartments of the body. In order to assess this bias caused by variations of individual systemic CO2 kinetics we administered intravenously the test drug (13)C-methacetin to 25 clinically liver-healthy individuals and monitored progress curves of DOB and the plasma concentration of (13)C-methacetin. Applying compartment modelling we estimated for each individual a set of kinetic parameters characterizing the time-dependent exchange of the drug and of CO2 with the liver and non-hepatic body compartments. This analysis revealed that individual variations in the kinetics of CO2 may account for up to 30% deviation of DOB curve parameters from their mean at otherwise identical (13)C-methacetin metabolization rates. In order to correct for this bias we introduced a novel detoxification score which ideally should be assessed from the DOB curve of a 2-step test ("2DOB") which is initialized with the injection of a standard dose of (13)C-labeled bicarbonate (in order to provide information on the actual CO2 status of the individual) followed by injection of the (13)C-labeled test drug (the common procedure). Computer simulations suggest that the predictive power of the proposed 2DOB breath test to reliably quantity the CYP-specific hepatic detoxification activity should be significantly higher compared to the conventional breath test.

Kinetic Modeling of the Mitochondrial Energy Metabolism of Neuronal Cells: The Impact of Reduced α-Ketoglutarate Dehydrogenase Activities on ATP Production and Generation of Reactive Oxygen Species.

Reduced activity of brain α-ketoglutarate dehydrogenase complex (KGDHC) occurs in a number of neurodegenerative diseases like Parkinson's disease and Alzheimer's disease. In order to quantify the relation between diminished KGDHC activity and the mitochondrial ATP generation, redox state, transmembrane potential, and generation of reactive oxygen species (ROS) by the respiratory chain (RC), we developed a detailed kinetic model. Model simulations revealed a threshold-like decline of the ATP production rate at about 60% inhibition of KGDHC accompanied by a significant increase of the mitochondrial membrane potential. By contrast, progressive inhibition of the enzyme aconitase had only little impact on these mitochondrial parameters. As KGDHC is susceptible to ROS-dependent inactivation, we also investigated the reduction state of those sites of the RC proposed to be involved in ROS production. The reduction state of all sites except one decreased with increasing degree of KGDHC inhibition suggesting an ROS-reducing effect of KGDHC inhibition. Our model underpins the important role of reduced KGDHC activity in the energetic breakdown of neuronal cells during development of neurodegenerative diseases.