Kinetic Modeling of the Mitochondrial Energy Metabolism of Neuronal Cells: The Impact of Reduced α

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

structured representation of scientific evidence in the biomedical domain using Semantic Web techniques

Background Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. Results We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. Conclusions SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats

Computer Simulations Suggest a Key Role of Membranous Nanodomains in Biliary Lipid Secretion

The bile fluid contains various lipids that are secreted at the canalicular membrane of hepatocytes. As the secretion mechanism is still a matter of debate and a direct experimental observation of the secretion process is not possible so far, we used a mathematical model to simulate the extraction of the major bile lipids cholesterol, phosphatidylcholine and sphingomyelin from the outer leaflet of the canalicular membrane. Lipid diffusion was modeled as random movement on a triangular lattice governed by next-neighbor interaction energies. Phase separation in liquid-ordered and liquid-disordered domains was modeled by assigning two alternative ordering states to each lipid species and minimization of next-neighbor ordering energies. Parameterization of the model was performed such that experimentally determined diffusion rates and phases in ternary lipid mixtures of model membranes were correctly recapitulated. The model describes the spontaneous formation of nanodomains in the external leaflet of the canalicular membrane in a time window between 0.1 ms to 10 ms at varying lipid proportions. The extraction of lipid patches from the bile salt soluble nanodomain into the bile reproduced observed biliary phospholipid compositions for a physiologi-cal membrane composition. Comparing the outcome of model simulations with available experi-mental observations clearly favors the extraction of tiny membrane patches composed of about 100–400 lipids as the likely mechanism of biliary lipid secretion

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

A Conceptual Mathematical Model of the Dynamic Self-Organisation of Distinct Cellular Organelles

Formation, degradation and renewal of cellular organelles is a dynamic process based on permanent budding, fusion and inter-organelle traffic of vesicles. These processes include many regulatory proteins such as SNAREs, Rabs and coats. Given this complex machinery, a controversially debated issue is the definition of a minimal set of generic mechanisms necessary to enable the self-organization of organelles differing in number, size and chemical composition. We present a conceptual mathematical model of dynamic organelle formation based on interacting vesicles which carry different types of fusogenic proteins (FP) playing the role of characteristic marker proteins. Our simulations (ODEs) show that a de novo formation of non-identical organelles, each accumulating a different type of FP, requires a certain degree of disproportionation of FPs during budding. More importantly however, the fusion kinetics must indispensably exhibit positive cooperativity among these FPs, particularly for the formation of larger organelles. We compared different types of cooperativity: sequential alignment of corresponding FPs on opposite vesicle/organelles during fusion and pre-formation of FP-aggregates (equivalent, e.g., to SNARE clusters) prior to fusion described by Hill kinetics. This showed that the average organelle size in the system is much more sensitive to the disproportionation strength of FPs during budding if the vesicular transport system gets along with a fusion mechanism based on sequential alignments of FPs. Therefore, pre-formation of FP aggregates within the membranes prior to fusion introduce robustness with respect to organelle size. Our findings provide a plausible explanation for the evolution of a relatively large number of molecules to confer specificity on the fusion machinery compared to the relatively small number involved in the budding process. Moreover, we could speculate that a specific cooperativity which may be described by Hill kinetics (aggregates or Rab/SNARE complex formation) is suitable if maturation/identity switching of organelles play a role (bistability)

Dynamic Metabolic Zonation of the Hepatic Glucose Metabolism Is Accomplished by Sinusoidal Plasma Gradients of Nutrients and Hormones

Being the central metabolic organ of vertebrates, the liver possesses the largest repertoire of metabolic enzymes among all tissues and organs. Almost all metabolic pathways are resident in the parenchymal cell, hepatocyte, but the pathway capacities may largely differ depending on the localization of hepatocytes within the liver acinus-a phenomenon that is commonly referred to as metabolic zonation. Metabolic zonation is rather dynamic since gene expression patterns of metabolic enzymes may change in response to nutrition, drugs, hormones and pathological states of the liver (e.g., fibrosis and inflammation). This fact has to be ultimately taken into account in mathematical models aiming at the prediction of metabolic liver functions in different physiological and pathological settings. Here we present a spatially resolved kinetic tissue model of hepatic glucose metabolism which includes zone-specific temporal changes of enzyme abundances which are driven by concentration gradients of nutrients, hormones and oxygen along the hepatic sinusoids. As key modulators of enzyme expression we included oxygen, glucose and the hormones insulin and glucagon which also control enzyme activities by cAMP-dependent reversible phosphorylation. Starting with an initially non-zonated model using plasma profiles under fed, fasted and diabetic conditions, zonal patterns of glycolytic and gluconeogenetic enzymes as well as glucose uptake and release rates are created as an emergent property. We show that mechanisms controlling the adaptation of enzyme abundances to varying external conditions necessarily lead to the zonation of hepatic carbohydrate metabolism. To the best of our knowledge, this is the first kinetic tissue model which takes into account in a semi-mechanistic way all relevant levels of enzyme regulation

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

Metabolic heterogeneity of human hepatocellular carcinoma: implications for personalized pharmacological treatment

Metabolic reprogramming is a characteristic feature of cancer cells, but there is no unique metabolic program for all tumors. Genetic and gene expression studies have revealed heterogeneous inter- and intratumor patterns of metabolic enzymes and membrane transporters. The functional implications of this heterogeneity remain often elusive. Here, we applied a systems biology approach to gain a comprehensive and quantitative picture of metabolic changes in individual hepatocellular carcinoma (HCC). We used protein intensity profiles determined by mass spectrometry in samples of 10 human HCCs and the adjacent noncancerous tissue to calibrate Hepatokin1, a complex mathematical model of liver metabolism. We computed the 24-h profile of 18 metabolic functions related to carbohydrate, lipid, and nitrogen metabolism. There was a general tendency among the tumors toward downregulated glucose uptake and glucose release albeit with large intertumor variability. This finding calls into question that the Warburg effect dictates the metabolic phenotype of HCC. All tumors comprised elevated β-oxidation rates. Urea synthesis was found to be consistently downregulated but without compromising the tumor's capacity for ammonia detoxification owing to increased glutamine synthesis. The largest intertumor heterogeneity was found for the uptake and release of lactate and the size of the cellular glycogen content. In line with the observed metabolic heterogeneity, the individual HCCs differed largely in their vulnerability against pharmacological treatment with metformin. Taken together, our approach provided a comprehensive and quantitative characterization of HCC metabolism that may pave the way for a computational a priori assessment of pharmacological therapies targeting metabolic processes of HCC

Functional Consequences of Metabolic Zonation in Murine Livers: Insights for an Old Story

Background and Aims: Zone-dependent differences in expression of metabolic enzymes along the portocentral axis of the acinus are a long-known feature of liver metabolism. A prominent example is the preferential localization of the enzyme, glutamine synthetase, in pericentral hepatocytes, where it converts potentially toxic ammonia to the valuable amino acid, glutamine. However, with the exception of a few key regulatory enzymes, a comprehensive and quantitative assessment of zonal differences in the abundance of metabolic enzymes and, much more important, an estimation of the associated functional differences between portal and central hepatocytes is missing thus far. Approach and Results: We addressed this problem by establishing a method for the separation of periportal and pericentral hepatocytes that yields sufficiently pure fractions of both cell populations. Quantitative shotgun proteomics identified hundreds of differentially expressed enzymes in the two cell populations. We used zone-specific proteomics data for scaling of the maximal activities to generate portal and central instantiations of a comprehensive kinetic model of central hepatic metabolism (Hepatokin1). Conclusions: The model simulations revealed significant portal-to-central differences in almost all metabolic pathways involving carbohydrates, fatty acids, amino acids, and detoxification

Changes in Liver Mechanical Properties and Water Diffusivity During Normal Pregnancy Are Driven by Cellular Hypertrophy

During pregnancy, the body's hyperestrogenic state alters hepatic metabolism and synthesis. While biochemical changes related to liver function during normal pregnancy are well understood, pregnancy-associated alterations in biophysical properties of the liver remain elusive. In this study, we investigated 26 ex vivo fresh liver specimens harvested from pregnant and non-pregnant rats by diffusion-weighted imaging (DWI) and magnetic resonance elastography (MRE) in a 0.5-Tesla compact magnetic resonance imaging (MRI) scanner. Water diffusivity and viscoelastic parameters were compared with histological data and blood markers. We found livers from pregnant rats to have (i) significantly enlarged hepatocytes (26 ± 15%, p < 0.001), (ii) increased liver stiffness (12 ± 15%, p = 0.012), (iii) decreased viscosity (-23 ± 14%, p < 0.001), and (iv) increased water diffusivity (12 ± 11%, p < 0.001). In conclusion, increased stiffness and reduced viscosity of the liver during pregnancy are mainly attributable to hepatocyte enlargement. Hypertrophy of liver cells imposes fewer restrictions on intracellular water mobility, resulting in a higher hepatic water diffusion coefficient. Collectively, MRE and DWI have the potential to inform on structural liver changes associated with pregnancy in a clinical context