Exposure to PFAS chemicals induces sex-dependent alterations in key rate-limiting steps of lipid metabolism in liver steatosis.

Toxicants with the potential to bioaccumulate in humans and animals have long been a cause for concern, particularly due to their association with multiple diseases and organ injuries. Per- and polyfluoro alkyl substances (PFAS) and polycyclic aromatic hydrocarbons (PAH) are two such classes of chemicals that bioaccumulate and have been associated with steatosis in the liver. Although PFAS and PAH are classified as chemicals of concern, their molecular mechanisms of toxicity remain to be explored in detail. In this study, we aimed to identify potential mechanisms by which an acute exposure to PFAS and PAH chemicals can induce lipid accumulation and whether the responses depend on chemical class, dose, and sex. To this end, we analyzed mechanisms beginning with the binding of the chemical to a molecular initiating event (MIE) and the consequent transcriptomic alterations. We collated potential MIEs using predictions from our previously developed ToxProfiler tool and from published steatosis adverse outcome pathways. Most of the MIEs are transcription factors, and we collected their target genes by mining the TRRUST database. To analyze the effects of PFAS and PAH on the steatosis mechanisms, we performed a computational MIE-target gene analysis on high-throughput transcriptomic measurements of liver tissue from male and female rats exposed to either a PFAS or PAH. The results showed peroxisome proliferator-activated receptor (PPAR)-α targets to be the most dysregulated, with most of the genes being upregulated. Furthermore, PFAS exposure disrupted several lipid metabolism genes, including upregulation of fatty acid oxidation genes (Acadm, Acox1, Cpt2, Cyp4a1-3) and downregulation of lipid transport genes (Apoa1, Apoa5, Pltp). We also identified multiple genes with sex-specific behavior. Notably, the rate-limiting genes of gluconeogenesis (Pck1) and bile acid synthesis (Cyp7a1) were specifically downregulated in male rats compared to female rats, while the rate-limiting gene of lipid synthesis (Scd) showed a PFAS-specific upregulation. The results suggest that the PPAR signaling pathway plays a major role in PFAS-induced lipid accumulation in rats. Together, these results show that PFAS exposure induces a sex-specific multi-factorial mechanism involving rate-limiting genes of gluconeogenesis and bile acid synthesis that could lead to activation of an adverse outcome pathway for steatosis.

Probing Liver Injuries Induced by Thioacetamide in Human In Vitro Pooled Hepatocyte Experiments.

Animal studies are typically utilized to understand the complex mechanisms associated with toxicant-induced hepatotoxicity. Among the alternative approaches to animal studies, in vitro pooled human hepatocytes have the potential to capture population variability. Here, we examined the effect of the hepatotoxicant thioacetamide on pooled human hepatocytes, divided into five lots, obtained from forty diverse donors. For 24 h, pooled human hepatocytes were exposed to vehicle, 1.33 mM (low dose), and 12 mM (high dose) thioacetamide, followed by RNA-seq analysis. We assessed gene expression variability using heat maps, correlation plots, and statistical variance. We used KEGG pathways and co-expression modules to identify underlying physiological processes/pathways. The co-expression module analysis showed that the majority of the lots exhibited activation for the bile duct proliferation module. Despite lot-to-lot variability, we identified a set of common differentially expressed genes across the lots with similarities in their response to amino acid, lipid, and carbohydrate metabolism. We also examined efflux transporters and found larger lot-to-lot variability in their expression patterns, indicating a potential for alteration in toxicant bioavailability within the cells, which could in turn affect the gene expression patterns between the lots. Overall, our analysis highlights the challenges in using pooled hepatocytes to understand mechanisms of toxicity.

High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model.

To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.

A stochastic B cell affinity maturation model to characterize mechanisms of protection for tetravalent dengue vaccine constructs.

Dengue annually infects millions of people from a regionally and seasonally varying dengue virus population circulating as four distinct serotypes. Effective protection against dengue infection and disease requires tetravalent vaccine formulations to stimulate a balanced protective immune response to all four serotypes. However, this has been a challenge to achieve, and several clinical trials with different leading vaccine candidates have demonstrated unbalanced replication and interference of interindividual serotype components, leading to low efficacy and enhanced disease severity for dengue-naïve populations. Production of serotype-specific neutralizing antibodies is largely viewed as a correlate of protection against severe dengue disease. However, the underlying mechanisms that lead to these protective immune responses are not clearly elucidated. In this work, using a stochastic model of B cell affinity maturation, we tested different live-attenuated vaccine constructs with varied viral replication rates and contrasted the initiation and progress of adaptive immune responses during tetravalent vaccination and after dengue virus challenge. Comparison of our model simulations across different disease-severity levels suggested that individual production of high levels of serotype-specific antibodies together with a lower cross-reactive antibody are better correlates for protection. Furthermore, evolution of these serotype-specific antibodies was dependent on the percent of viral attenuation in the vaccine, and production of initial B cell and T cell populations pre- and post-secondary dengue infection was crucial in providing protective immunity for dengue-naïve populations. Furthermore, contrasting disease severity with respect to different dengue serotypes, our model simulations showed that tetravalent vaccines fare better against DENV-4 serotype when compared to other serotypes.

Assessing Kidney Injury Induced by Mercuric Chloride in Guinea Pigs with In Vivo and In Vitro Experiments.

Acute kidney injury, which is associated with high levels of morbidity and mortality, affects a significant number of individuals, and can be triggered by multiple factors, such as medications, exposure to toxic chemicals or other substances, disease, and trauma. Because the kidney is a critical organ, understanding and identifying early cellular or gene-level changes can provide a foundation for designing medical interventions. In our earlier work, we identified gene modules anchored to histopathology phenotypes associated with toxicant-induced liver and kidney injuries. Here, using in vivo and in vitro experiments, we assessed and validated these kidney injury-associated modules by analyzing gene expression data from the kidneys of male Hartley guinea pigs exposed to mercuric chloride. Using plasma creatinine levels and cell-viability assays as measures of the extent of renal dysfunction under in vivo and in vitro conditions, we performed an initial range-finding study to identify the appropriate doses and exposure times associated with mild and severe kidney injuries. We then monitored changes in kidney gene expression at the selected doses and time points post-toxicant exposure to characterize the mechanisms of kidney injury. Our injury module-based analysis revealed a dose-dependent activation of several phenotypic cellular processes associated with dilatation, necrosis, and fibrogenesis that were common across the experimental platforms and indicative of processes that initiate kidney damage. Furthermore, a comparison of activated injury modules between guinea pigs and rats indicated a strong correlation between the modules, highlighting their potential for cross-species translational studies.

Genomics and metabolomics of early-stage thioacetamide-induced liver injury: An interspecies study between guinea pig and rat.

To study the complex processes involved in liver injuries, researchers rely on animal investigations, using chemically or surgically induced liver injuries, to extrapolate findings and infer human health risks. However, this presents obvious challenges in performing a detailed comparison and validation between the highly controlled animal models and development of liver injuries in humans. Furthermore, it is not clear whether there are species-dependent and -independent molecular initiating events or processes that cause liver injury before they eventually lead to end-stage liver disease. Here, we present a side-by-side study of rats and guinea pigs using thioacetamide to examine the similarities between early molecular initiating events during an acute-phase liver injury. We exposed Sprague Dawley rats and Hartley guinea pigs to a single dose of 25 or 100 mg/kg thioacetamide and collected blood plasma for metabolomic analysis and liver tissue for RNA-sequencing. The subsequent toxicogenomic analysis identified consistent liver injury trends in both genomic and metabolomic data within 24 and 33 h after thioacetamide exposure in rats and guinea pigs, respectively. In particular, we found species similarities in the key injury phenotypes of inflammation and fibrogenesis in our gene module analysis for liver injury phenotypes. We identified expression of several common genes (e.g., SPP1, TNSF18, SERPINE1, CLDN4, TIMP1, CD44, and LGALS3), activation of injury-specific KEGG pathways, and alteration of plasma metabolites involved in amino acid and bile acid metabolism as some of the key molecular processes that changed early upon thioacetamide exposure and could play a major role in the initiation of acute liver injury.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.

Toxicant-Induced Metabolic Alterations in Lipid and Amino Acid Pathways Are Predictive of Acute Liver Toxicity in Rats.

Liver disease and disorders associated with aberrant hepatocyte metabolism can be initiated via drug and environmental toxicant exposures. In this study, we tested the hypothesis that gene and metabolic profiling can reveal commonalities in liver response to different toxicants and provide the capability to identify early signatures of acute liver toxicity. We used Sprague Dawley rats and three classical hepatotoxicants: acetaminophen (2 g/kg), bromobenzene (0.4 g/kg), and carbon tetrachloride (0.3 g/kg), to identify early perturbations in liver metabolism after a single acute exposure dose. We measured changes in liver genes and plasma metabolites at two time points (5 and 10 h) and used genome-scale metabolic models to identify commonalities in liver responses across the three toxicants. We found strong correlations for gene and metabolic profiles between the toxicants, indicative of similarities in the liver response to toxicity. We identified several injury-specific pathways in lipid and amino acid metabolism that changed similarly across the three toxicants. Our findings suggest that several plasma metabolites in lipid and amino acid metabolism are strongly associated with the progression of liver toxicity, and as such, could be targeted and clinically assessed for their potential as early predictors of acute liver toxicity.

Mechanism-based identification of plasma metabolites associated with liver toxicity.

Early diagnosis of liver injuries caused by drugs or occupational exposures is necessary to enable effective treatments and prevent liver failure. Whereas histopathology remains the gold standard for assessing hepatotoxicity in animals, plasma aminotransferase levels are the primary measures for monitoring liver dysfunction in humans. In this study, using Sprague Dawley rats, we investigated whether integrated analyses of transcriptomic and metabolomic data with genome-scale metabolic models (GSMs) could identify early indicators of injury and provide new insights into the mechanisms of hepatotoxicity. We obtained concurrent measurements of gene-expression changes in the liver and kidneys, and expression changes along with metabolic profiles in the plasma and urine, from rats 5 or 10 h after exposing them to one of two classical hepatotoxicants, acetaminophen (2 g/kg) or bromobenzene (0.4 g/kg). Global multivariate analyses revealed that gene-expression changes in the liver and metabolic profiles in the plasma and urine of toxicant-treated animals differed from those of controls, even at time points much earlier than changes detected by conventional markers of liver injury. Furthermore, clustering analysis revealed that both the gene-expression changes in the liver and the metabolic profiles in the plasma induced by the two hepatotoxicants were highly correlated, indicating commonalities in the liver toxicity response. Systematic GSM-based analyses yielded metabolites associated with the mechanisms of toxicity and identified several lipid and amino acid metabolism pathways that were activated by both toxicants and those uniquely activated by each. Our findings suggest that several metabolite alterations, which are strongly associated with the mechanisms of toxicity and occur within injury-specific pathways (e.g., of bile acid and fatty acid metabolism), could be targeted and clinically assessed for their potential as early indicators of liver damage.

Genome-Scale Model-Based Identification of Metabolite Indicators for Early Detection of Kidney Toxicity.

Identifying early indicators of toxicant-induced organ damage is critical to provide effective treatment. To discover such indicators and the underlying mechanisms of toxicity, we used gentamicin as an exemplar kidney toxicant and performed systematic perturbation studies in Sprague Dawley rats. We obtained high-throughput data 7 and 13 h after administration of a single dose of gentamicin (0.5 g/kg) and identified global changes in genes in the liver and kidneys, metabolites in the plasma and urine, and absolute fluxes in central carbon metabolism. We used these measured changes in genes in the liver and kidney as constraints to a rat multitissue genome-scale metabolic network model to investigate the mechanism of gentamicin-induced kidney toxicity and identify metabolites associated with changes in tissue gene expression. Our experimental analysis revealed that gentamicin-induced metabolic perturbations could be detected as early as 7 h postexposure. Our integrated systems-level analyses suggest that changes in kidney gene expression drive most of the significant metabolite alterations in the urine. The analyses thus allowed us to identify several significantly enriched injury-specific pathways in the kidney underlying gentamicin-induced toxicity, as well as metabolites in these pathways that could serve as potential early indicators of kidney damage.

Mining Public Toxicogenomic Data Reveals Insights and Challenges in Delineating Liver Steatosis Adverse Outcome Pathways.

Exposure to chemicals contributes to the development and progression of fatty liver, or steatosis, a process characterized by abnormal accumulation of lipids within liver cells. However, lack of knowledge on how chemicals cause steatosis has prevented any large-scale assessment of the 80,000+ chemicals in current use. To address this gap, we mined a large, publicly available toxicogenomic dataset associated with 18 known steatogenic chemicals to assess responses across assays (in vitro and in vivo) and species (i.e., rats and humans). We identified genes that were differentially expressed (DEGs) in rat in vivo, rat in vitro, and human in vitro studies in which rats or in vitro primary cell lines were exposed to the chemicals at different doses and durations. Using these DEGs, we performed pathway enrichment analysis, analyzed the molecular initiating events (MIEs) of the steatosis adverse outcome pathway (AOP), and predicted metabolite changes using metabolic network analysis. Genes indicative of oxidative stress were among the DEGs most frequently observed in the rat in vivo studies. Nox4, a pro-fibrotic gene, was down-regulated across these chemical exposure conditions. We identified eight genes (Cyp1a1, Egr1, Ccnb1, Gdf15, Cdk1, Pdk4, Ccna2, and Ns5atp9) and one pathway (retinol metabolism), associated with steatogenic chemicals and whose response was conserved across the three in vitro and in vivo systems. Similarly, we found the predicted metabolite changes, such as increases of saturated and unsaturated fatty acids, conserved across the three systems. Analysis of the target genes associated with the MIEs of the current steatosis AOP did not provide a clear association between these 18 chemicals and the MIEs, underlining the multi-factorial nature of this disease. Notably, our overall analysis implicated mitochondrial toxicity as an important and overlooked MIE for chemical-induced steatosis. The integrated toxicogenomics approach to identify genes, pathways, and metabolites based on known steatogenic chemicals, provide an important mean to assess development of AOPs and gauging the relevance of new testing strategies.

Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes.

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

Mechanistic identification of biofluid metabolite changes as markers of acetaminophen-induced liver toxicity in rats.

Acetaminophen (APAP) is the most commonly used analgesic and antipyretic drug in the world. Yet, it poses a major risk of liver injury when taken in excess of the therapeutic dose. Current clinical markers do not detect the early onset of liver injury associated with excess APAP-information that is vital to reverse injury progression through available therapeutic interventions. Hence, several studies have used transcriptomics, proteomics, and metabolomics technologies, both independently and in combination, in an attempt to discover potential early markers of liver injury. However, the casual relationship between these observations and their relation to the APAP mechanism of liver toxicity are not clearly understood. Here, we used Sprague-Dawley rats orally gavaged with a single dose of 2 g/kg of APAP to collect tissue samples from the liver and kidney for transcriptomic analysis and plasma and urine samples for metabolomic analysis. We developed and used a multi-tissue, metabolism-based modeling approach to integrate these data, characterize the effect of excess APAP levels on liver metabolism, and identify a panel of plasma and urine metabolites that are associated with APAP-induced liver toxicity. Our analyses, which indicated that pathways involved in nucleotide-, lipid-, and amino acid-related metabolism in the liver were most strongly affected within 10 h following APAP treatment, identified a list of potential metabolites in these pathways that could serve as plausible markers of APAP-induced liver injury. Our approach identifies toxicant-induced changes in endogenous metabolism, is applicable to other toxicants based on transcriptomic data, and provides a mechanistic framework for interpreting metabolite alterations.

Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat.

The liver-a central metabolic organ that integrates whole-body metabolism to maintain glucose and fatty-acid regulation, and detoxify ammonia-is susceptible to injuries induced by drugs and toxic substances. Although plasma metabolite profiles are increasingly investigated for their potential to detect liver injury earlier than current clinical markers, their utility may be compromised because such profiles are affected by the nutritional state and the physiological state of the animal, and by contributions from extrahepatic sources. To tease apart the contributions of liver and non-liver sources to alterations in plasma metabolite profiles, here we sought to computationally isolate the plasma metabolite changes originating in the liver during short-term fasting. We used a constraint-based metabolic modeling approach to integrate central carbon fluxes measured in our study, and physiological flux boundary conditions gathered from the literature, into a genome-scale model of rat liver metabolism. We then measured plasma metabolite profiles in rats fasted for 5-7 or 10-13 h to test our model predictions. Our computational model accounted for two-thirds of the observed directions of change (an increase or decrease) in plasma metabolites, indicating their origin in the liver. Specifically, our work suggests that changes in plasma lipid metabolites, which are reliably predicted by our liver metabolism model, are key features of short-term fasting. Our approach provides a mechanistic model for identifying plasma metabolite changes originating in the liver.

A thermodynamically-constrained mathematical model for the kinetics and regulation of NADPH oxidase 2 complex-mediated electron transfer and superoxide production.

Reactive oxygen species (ROS) play an important role in cell signaling, growth, and immunity. However, when produced in excess, they are toxic to the cell and lead to premature aging and a myriad of pathologies, including cardiovascular and renal diseases. A major source of ROS in many cells is the family of NADPH oxidase (NOX), comprising of membrane and cytosolic components. NOX2 is among the most widely expressed and well-studied NOX isoform. Although details on the NOX2 structure, its assembly and activation, and ROS production are well elucidated experimentally, there is a lack of a quantitative and integrative understanding of the kinetics of NOX2 complex, and the various factors such as pH, inhibitory drugs, and temperature that regulate the activity of this oxidase. To this end, we have developed here a thermodynamically-constrained mathematical model for the kinetics and regulation of NOX2 complex based on diverse published experimental data on the NOX2 complex function in cell-free and cell-based assay systems. The model incorporates (i) thermodynamics of electron transfer from NADPH to O2 through different redox centers of the NOX2 complex, (ii) dependence of the NOX2 complex activity upon pH and temperature variations, and (iii) distinct inhibitory effects of different drugs on the NOX2 complex activity. The model provides the first quantitative and integrated understanding of the kinetics and regulation of NOX2 complex, enabling simulation of diverse experimental data. The model also provides several novel insights into the NOX2 complex function, including alkaline pH-dependent inhibition of the NOX2 complex activity by its reaction product NADP+. The model provides a mechanistic framework for investigating the critical role of NOX2 complex in ROS production and its regulation of diverse cellular functions in health and disease. Specifically, the model enables examining the effects of specific targeting of various enzymatic sources of pathological ROS which could overcome the limitations of pharmacological efforts aimed at scavenging ROS which has resulted in poor outcomes of antioxidant therapies in clinical studies.

A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools.

GEnome-scale Network REconstructions (GENREs) mathematically describe metabolic reactions of an organism or a specific cell type. GENREs can be used with a number of constraint-based reconstruction and analysis (COBRA) methods to make computational predictions on how a system changes in different environments. We created a simplified GENRE (referred to as iSIM) that captures central energy metabolism with nine metabolic reactions to illustrate the use of and promote the understanding of GENREs and constraint-based methods. We demonstrate the simulation of single and double gene deletions, flux variability analysis (FVA), and test a number of metabolic tasks with the GENRE. Code to perform these analyses is provided in Python, R, and MATLAB. Finally, with iSIM as a guide, we demonstrate how inaccuracies in GENREs can limit their use in the interrogation of energy metabolism.

Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat.

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.

Modeling the detailed kinetics of mitochondrial cytochrome c oxidase: Catalytic mechanism and nitric oxide inhibition.

Cytochrome c oxidase (CcO) catalyzes the exothermic reduction of O2 to H2O by using electrons from cytochrome c, and hence plays a crucial role in ATP production. Although details on the enzyme structure and redox centers involved in O2 reduction have been known, there still remains a considerable ambiguity on its mechanism of action, e.g., the number of sequential electrons donated to O2 in each catalytic step, the sites of protonation and proton pumping, and nitric oxide (NO) inhibition mechanism. In this work, we developed a thermodynamically constrained mechanistic mathematical model for the catalytic action of CcO based on available kinetic data. The model considers a minimal number of redox centers on CcO and couples electron transfer and proton pumping driven by proton motive force (PMF), and accounts for the inhibitory effects of NO on the reaction kinetics. The model is able to fit well all the available kinetic data under diverse experimental conditions with a physiologically realistic unique parameter set. The model predictions show that: 1) the apparent Km of O2 varies considerably and increases from fully reduced to fully oxidized cytochrome c depending on pH and the energy state of mitochondria, and 2) the intermediate enzyme states depend on pH and cytochrome c redox fraction and play a central role in coupling mitochondrial respiration to PMF. The developed CcO model can easily be integrated into existing mitochondrial bioenergetics models to understand the role of the enzyme in controlling oxidative phosphorylation in normal and disease conditions.

Mechanistic characterization of the thioredoxin system in the removal of hydrogen peroxide.

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.