Reconstruction and flux analysis of coupling between metabolic pathways of astrocytes and neurons: application to cerebral hypoxia

Uncovering the Effect of SARS-CoV-2 on Liver Metabolism via Genome-Scale Metabolic Modeling for Reprogramming and Therapeutic Strategies

Genome-scale metabolic models (GEMs) are promising computational tools that contribute to elucidating host-virus interactions at the system level and developing therapeutic strategies against viral infection. In this study, the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on liver metabolism was investigated using integrated GEMs of human hepatocytes and SARS-CoV-2. They were generated for uninfected and infected hepatocytes using transcriptome data. Reporter metabolite analysis resulted in significant transcriptional changes around several metabolites involved in xenobiotics, drugs, arachidonic acid, and leukotriene metabolisms due to SARS-CoV-2 infection. Flux balance analysis and minimization of metabolic adjustment approaches unraveled possible virus-induced hepatocellular reprogramming in fatty acid, glycerophospholipid, sphingolipid cholesterol, and folate metabolisms, bile acid biosynthesis, and carnitine shuttle among others. Reaction knockout analysis provided critical reactions in glycolysis, oxidative phosphorylation, purine metabolism, and reactive oxygen species detoxification subsystems. Computational analysis also showed that administration of dopamine, glucosamine, D-xylose, cysteine, and (R)-3-hydroxybutanoate contributes to alleviating viral infection. In essence, the reconstructed host-virus GEM helps us understand metabolic programming and develop therapeutic strategies to battle SARS-CoV-2.

Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.

The role and potential therapeutic targets of astrocytes in central nervous system demyelinating diseases

Astrocytes play vital roles in the central nervous system, contributing significantly to both its normal functioning and pathological conditions. While their involvement in various diseases is increasingly recognized, their exact role in demyelinating lesions remains uncertain. Astrocytes have the potential to influence demyelination positively or negatively. They can produce and release inflammatory molecules that modulate the activation and movement of other immune cells. Moreover, they can aid in the clearance of myelin debris through phagocytosis and facilitate the recruitment and differentiation of oligodendrocyte precursor cells, thereby promoting axonal remyelination. However, excessive or prolonged astrocyte phagocytosis can exacerbate demyelination and lead to neurological impairments. This review provides an overview of the involvement of astrocytes in various demyelinating diseases, emphasizing the underlying mechanisms that contribute to demyelination. Additionally, we discuss the interactions between oligodendrocytes, oligodendrocyte precursor cells and astrocytes as therapeutic options to support myelin regeneration. Furthermore, we explore the role of astrocytes in repairing synaptic dysfunction, which is also a crucial pathological process in these disorders.

Mechanistic multiscale modelling of energy metabolism in human astrocytes reveals the impact of morphology changes in Alzheimer's Disease

Astrocytes with their specialised morphology are essential for brain homeostasis as metabolic mediators between blood vessels and neurons. In neurodegenerative diseases such as Alzheimer's disease (AD), astrocytes adopt reactive profiles with molecular and morphological changes that could lead to the impairment of their metabolic support and impact disease progression. However, the underlying mechanisms of how the metabolic function of human astrocytes is impaired by their morphological changes in AD are still elusive. To address this challenge, we developed and applied a metabolic multiscale modelling approach integrating the dynamics of metabolic energy pathways and physiological astrocyte morphologies acquired in human AD and age-matched control brain samples. The results demonstrate that the complex cell shape and intracellular organisation of energetic pathways determine the metabolic profile and support capacity of astrocytes in health and AD conditions. Thus, our mechanistic approach indicates the importance of spatial orchestration in metabolism and allows for the identification of protective mechanisms against disease-associated metabolic impairments.

Interplay between biochemical processes and network properties generates neuronal up and down states at the tripartite synapse

Neuronal up and down states have long been known to exist both in vitro and in vivo. A variety of functions and mechanisms have been proposed for their generation, but there has not been a clear connection between the functions and mechanisms. We explore the potential contribution of cellular-level biochemistry to the network-level mechanisms thought to underlie the generation of up and down states. We develop a neurochemical model of a single tripartite synapse, assumed to be within a network of similar tripartite synapses, to investigate possible function-mechanism links for the appearance of up and down states. We characterize the behavior of our model in different regions of parameter space and show that resource limitation at the tripartite synapse affects its ability to faithfully transmit input signals, leading to extinction-down states. Recovery of resources allows for "reignition" into up states. The tripartite synapse exhibits distinctive "regimes" of operation depending on whether ATP, neurotransmitter (glutamate), both, or neither, is limiting. Our model qualitatively matches the behavior of six disparate experimental systems, including both in vitro and in vivo models, without changing any model parameters except those related to the experimental conditions. We also explore the effects of varying different critical parameters within the model. Here we show that availability of energy, represented by ATP, and glutamate for neurotransmission at the cellular level are intimately related, and are capable of promoting state transitions at the network level as ignition and extinction phenomena. Our model is complementary to existing models of neuronal up and down states in that it focuses on cellular-level dynamics while still retaining essential network-level processes. Our model predicts the existence of a "final common pathway" of behavior at the tripartite synapse arising from scarcity of resources and may explain use dependence in the phenomenon of "local sleep." Ultimately, sleeplike behavior may be a fundamental property of networks of tripartite synapses.

Identification of Therapeutic Targets for Medulloblastoma by Tissue-Specific Genome-Scale Metabolic Model

Medulloblastoma (MB), occurring in the cerebellum, is the most common childhood brain tumor. Because conventional methods decline life quality and endanger children with detrimental side effects, computer models are needed to imitate the characteristics of cancer cells and uncover effective therapeutic targets with minimum toxic effects on healthy cells. In this study, metabolic changes specific to MB were captured by the genome-scale metabolic brain model integrated with transcriptome data. To determine the roles of sphingolipid metabolism in proliferation and metastasis in the cancer cell, 79 reactions were incorporated into the MB model. The pathways employed by MB without a carbon source and the link between metastasis and the Warburg effect were examined in detail. To reveal therapeutic targets for MB, biomass-coupled reactions, the essential genes/gene products, and the antimetabolites, which might deplete the use of metabolites in cells by triggering competitive inhibition, were determined. As a result, interfering with the enzymes associated with fatty acid synthesis (FAs) and the mevalonate pathway in cholesterol synthesis, suppressing cardiolipin production, and tumor-supporting sphingolipid metabolites might be effective therapeutic approaches for MB. Moreover, decreasing the activity of succinate synthesis and GABA-catalyzing enzymes concurrently might be a promising strategy for metastatic MB.

Kinetic Mathematical Modeling of Oxidative Phosphorylation in Cardiomyocyte Mitochondria

Oxidative phosphorylation (OXPHOS) is an oxygen-dependent process that consumes catabolized nutrients to produce adenosine triphosphate (ATP) to drive energy-dependent biological processes such as excitation-contraction coupling in cardiomyocytes. In addition to in vivo and in vitro experiments, in silico models are valuable for investigating the underlying mechanisms of OXPHOS and predicting its consequences in both physiological and pathological conditions. Here, we compare several prominent kinetic models of OXPHOS in cardiomyocytes. We examine how their mathematical expressions were derived, how their parameters were obtained, the conditions of their experimental counterparts, and the predictions they generated. We aim to explore the general landscape of energy production mechanisms in cardiomyocytes for future in silico models.

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.

Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

Cancer Recurrence and Omics: Metabolic Signatures of Cancer Dormancy Revealed by Transcriptome Mapping of Genome-Scale Networks

A major problem in medicine and oncology is cancer recurrence through the activation of dormant cancer cells. A system scale examination of metabolic dysregulations associated with the cancer dormancy offers promise for the discovery of novel molecular targets for cancer precision medicine, and importantly, for the prevention of cancer recurrence. In this study, we mapped the total mRNA sequencing-based transcriptomic data from dormant cancer cell lines and nondormant cancer controls onto a human genome-scale metabolic network by using a graph-based approach, and two mass balance-based approaches with one based on reaction activity/inactivity and the other one on flux changes. The gene expression datasets were accessed from Gene Expression Omnibus (GSE83142 and GSE114012). This analysis included two diverse cancer types, a liquid and a solid cancer, namely, acute lymphoblastic leukemia and colorectal cancer. For the dormant cancer state, we observed changes in major adenosine triphosphate-producing pathways, including the citric acid cycle, oxidative phosphorylation, and glycolysis/gluconeogenesis, indicating a reprogramming in the metabolism of dormant cells away from Warburg-based energy metabolism. All three computational approaches unanimously predicted that folate metabolism, pyruvate metabolism, and glutamate metabolism, as well as valine/leucine/isoleucine metabolism are likely dysregulated in cancer dormancy. These findings provide new insights and molecular pathway targets on cancer dormancy, comprehensively catalog dormancy-associated metabolic pathways, and inform future research aimed at prevention of cancer recurrence in particular.

Understanding Brain-Skeletal Muscle Crosstalk Impacting Metabolism and Movement

Metabolism and movement, among the critical determinants in the survival and success of an organism, are tightly regulated by the brain and skeletal muscle. At the cellular level, mitochondria -that powers life, and myosin - the molecular motor of the cell, have both evolved to serve this purpose. Although independently, the skeletal muscle and brain have been intensively investigated for over a century, their coordinated involvement in metabolism and movement remains poorly understood. Therefore, a fundamental understanding of the coordinated involvement of the brain and skeletal muscle in metabolism and movement holds great promise in providing a window to a wide range of life processes and in the development of tools and approaches in disease detection and therapy. Recent developments in new tools, technologies and approaches, and advances in computing power and machine learning, provides for the first time the opportunity to establish a new field of study, the 'Science and Engineering of Metabolism and Movement'. This new field of study could provide substantial new insights and breakthrough into how metabolism and movement is governed at the systems level in an organism. The design and approach to accomplish this objective is briefly discussed in this article.

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

Astrocyte-to-neuron transportation of enhanced green fluorescent protein in cerebral cortex requires F-actin dependent tunneling nanotubes

Tunneling nanotube (TNT), a dynamic cell–cell contact, is dependent on actin polymerization. TNTs are efficient in transporting ions, proteins and organelles intercellularly, which are important mechanisms in physiological and pathological processes. Reported studies on the existence and function of TNTs among neural cells focus on cultured cell for the convenience in detecting TNTs’ ultrastructure. In this study, the adeno-associated virus (AAV-GFAP-EGFP-p2A-cre) was injected into the cerebral cortex of knock-in mice ROSA26 GNZ. GFAP promoter initiated the expression of enhanced green fluorescent protein (EGFP) in infected astrocytes. At 10 days post injection (10 DPI), EGFP transferred from astrocytes in layer I–III to neurons in layer V. The dissemination of EGFP was not through endocytosis or exosome. Applying microscopes, we found that the intercellular transportation of EGFP through contact connection was F-actin dependent. Therefore, we concluded that EGFP transported from astrocytes to neurons in cortex via F-actin dependent TNTs. This study first proved that proteins transported intercellularly via TNTs in brain.

Use of a neuron-glia genome-scale metabolic reconstruction to model the metabolic consequences of the Arylsulphatase a deficiency through a systems biology approach

Metachromatic leukodystrophy (MLD) is a human neurodegenerative disorder characterized by progressive damage on the myelin band in the nervous system. MLD is caused by the impaired function of the lysosomal enzyme Arylsulphatase A (ARSA). The physiopathology mechanisms and the biochemical consequences in the brain of ARSA deficiency are not entirely understood. In recent years, the use of genome-scale metabolic (GEM) models has been explored as a tool for the study of the biochemical alterations in MLD. Previously, we modeled the metabolic consequences of different lysosomal storage diseases using single GEMs. In the case of MLD, using a glia GEM, we previously predicted that the metabolism of glycosphingolipids and neurotransmitters was altered. The results also suggested that mitochondrial metabolism and amino acid transport were the main reactions affected. In this study, we extended the modeling of the metabolic consequences of ARSA deficiency through the integration of neuron and glial cell metabolic models. Cell-specific models were generated from Recon2, and these were used to create a neuron-glial bi-cellular model. We propose a workflow for the integration of this type of model and its subsequent study. The results predicted the impairment pathways involved in the transport of amino acids, lipids metabolism, and catabolism of purines and pyrimidines. The use of this neuron-glial GEM metabolic reconstruction allowed to improve the prediction capacity of the metabolic consequences of ARSA deficiency, which might pave the way for the modeling of the biochemical alterations of other inborn errors of metabolism with central nervous system involvement.

Systematic investigation of mouse models of Parkinson's disease by transcriptome mapping on a brain-specific genome-scale metabolic network

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model organisms for neurodegenerative diseases, a brain-specific metabolic network model of mice has not yet been reconstructed. Here we reconstructed the first brain-specific metabolic network model of mice, iBrain674-Mm, by a homology-based approach, which consisted of 992 reactions controlled by 674 genes and distributed over 48 pathways. We validated the newly reconstructed network model by showing that it predicts healthy resting-state metabolic phenotypes of mouse brain compatible with the literature. We later used iBrain674-Mm to interpret various experimental mouse models of Parkinson's Disease (PD) at the transcriptome level. To this end, we applied a constraint-based modelling based biomarker prediction method called TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) to predict altered metabolite production from transcriptomic data. Systemic analysis of seven different PD mouse models by TIMBR showed that the neuronal levels of glutamate, lactate, creatine phosphate, neuronal acetylcholine, bilirubin and formate increased in most of the PD mouse models, whereas the levels of melatonin, epinephrine, astrocytic formate and astrocytic bilirubin decreased. Although most of the predictions were consistent with the literature, there were some inconsistencies among different PD mouse models, signifying that there is no perfect experimental model to reflect PD metabolism. The newly reconstructed brain-specific genome-scale metabolic network model of mice can make important contributions to the interpretation and development of experimental mouse models of PD and other neurodegenerative diseases.

In Silico Prediction of Metabolic Fluxes in Cancer Cells with Altered S-adenosylmethionine Decarboxylase Activity

This paper investigates the redistribution of metabolic fluxes in the cell with altered activity of S-adenosylmethionine decarboxylase (SAMdc, EC: 4.1.1.50), the key enzyme of the polyamine cycle and the common target for antitumor therapy. To address these goals, a stoichiometric metabolic model was developed that includes five metabolic pathways: polyamine, methionine, methionine salvage cycles, folic acid cycle, and the pathway of glutathione and taurine synthesis. The model is based on 51 reactions involving 57 metabolites, 31 of which are internal metabolites. All calculations were performed using the method of Flux Balance Analysis. The outcome indicates that the inactivation of SAMdc results in a significant increase in fluxes through the methionine, the taurine and glutathione synthesis, and the folate cycles. Therefore, when using therapeutic agents inactivating SAMdc, it is necessary to consider the possibility of cellular tumor metabolism reprogramming. S-adenosylmethionine affects serine methylation and activates serine-dependent de novo ATP synthesis. Methionine-depleted cell becomes methionine-dependent, searching for new sources of methionine. Inactivation of SAMdc enhances the transformation of S-adenosylmethionine to homocysteine and then to methionine. It also intensifies the transsulfuration process activating the synthesis of glutathione and taurine.

Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches

The growing importance of astrocytes in the field of neuroscience has led to a greater number of computational models devoted to the study of astrocytic functions and their metabolic interactions with neurons. The modeling of these interactions demands a combined understanding of brain physiology and the development of computational frameworks based on genomic-scale reconstructions, system biology, and dynamic models. These computational approaches have helped to highlight the neuroprotective mechanisms triggered by astrocytes and other glial cells, both under normal conditions and during neurodegenerative processes. In the present review, we evaluate some of the most relevant models of astrocyte metabolism, including genome-scale reconstructions and astrocyte-neuron interactions developed in the last few years. Additionally, we discuss novel strategies from the multi-omics perspective and computational models of other glial cell types that will increase our knowledge in brain metabolism and its association with neurodegenerative diseases.

Systems Biology Approaches to Understand the Host-Microbiome Interactions in Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.

A systems biology approach for studying neurodegenerative diseases

Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.

Multiple Pathways Involved in Palmitic Acid-Induced Toxicity: A System Biology Approach

Inflammation is a complex biological response to injuries, metabolic disorders or infections. In the brain, astrocytes play an important role in the inflammatory processes during neurodegenerative diseases. Recent studies have shown that the increase of free saturated fatty acids such as palmitic acid produces a metabolic inflammatory response in astrocytes generally associated with damaging mechanisms such as oxidative stress, endoplasmic reticulum stress, and autophagic defects. In this aspect, the synthetic neurosteroid tibolone has shown to exert protective functions against inflammation in neuronal experimental models without the tumorigenic effects exerted by sexual hormones such as estradiol and progesterone. However, there is little information regarding the specific mechanisms of tibolone in astrocytes during inflammatory insults. In the present study, we performed a genome-scale metabolic reconstruction of astrocytes that was used to study astrocytic response during an inflammatory insult by palmitate through Flux Balance Analysis methods and data mining. In this aspect, we assessed the metabolic fluxes of human astrocytes under three different scenarios: healthy (normal conditions), induced inflammation by palmitate, and tibolone treatment under palmitate inflammation. Our results suggest that tibolone reduces the L-glutamate-mediated neurotoxicity in astrocytes through the modulation of several metabolic pathways involved in glutamate uptake. We also identified a set of reactions associated with the protective effects of tibolone, including the upregulation of taurine metabolism, gluconeogenesis, cPPAR and the modulation of calcium signaling pathways. In conclusion, the different scenarios studied in our model allowed us to identify several metabolic fluxes perturbed under an inflammatory response and the protective mechanisms exerted by tibolone.

Computational singular perturbation analysis of brain lactate metabolism

Lactate in the brain is considered an important fuel and signalling molecule for neuronal activity, especially during neuronal activation. Whether lactate is shuttled from astrocytes to neurons or from neurons to astrocytes leads to the contradictory Astrocyte to Neuron Lactate Shuttle (ANLS) or Neuron to Astrocyte Lactate Shuttle (NALS) hypotheses, both of which are supported by extensive, but indirect, experimental evidence. This work explores the conditions favouring development of ANLS or NALS phenomenon on the basis of a model that can simulate both by employing the two parameter sets proposed by Simpson et al. (J Cereb. Blood Flow Metab., 27:1766, 2007) and Mangia et al. (J of Neurochemistry, 109:55, 2009). As most mathematical models governing brain metabolism processes, this model is multi-scale in character due to the wide range of time scales characterizing its dynamics. Therefore, we utilize the Computational Singular Perturbation (CSP) algorithm, which has been used extensively in multi-scale systems of reactive flows and biological systems, to identify components of the system that (i) generate the characteristic time scale and the fast/slow dynamics, (ii) participate to the expressions that approximate the surfaces of equilibria that develop in phase space and (iii) control the evolution of the process within the established surfaces of equilibria. It is shown that a decisive factor on whether the ANLS or NALS configuration will develop during neuronal activation is whether the lactate transport between astrocytes and interstitium contributes to the fast dynamics or not. When it does, lactate is mainly generated in astrocytes and the ANLS hypothesis is realised, while when it doesn't, lactate is mainly generated in neurons and the NALS hypothesis is realised. This scenario was tested in exercise conditions.

Fundamentals of the modern theory of the phenomenon of "pain" from the perspective of a systematic approach. Neurophysiological basis. Part 1: A brief presentation of key subcellular and cellular ctructural elements of the central nervous system

The phenomenon of “pain” is a psychophysiological phenomenon that is actualized in the mind of a person as a result of the systemic response of his body to certain external and internal stimuli. The heart of the corresponding mental processes is certain neurophysiological processes, which in turn are caused by a certain form of the systemic structural and functional organization of the central nervous system (CNS). Thus, the systemic structural and functional organization of the central nervous system of a person, determining the corresponding psychophysiological state in a specific time interval, determines its psycho-emotional states or reactions manifested by the pain phenomenon. The nervous system of the human body has a hierarchical structure and is a morphologically and functionally complete set of different, interconnected, nervous and structural formations. The basis of the structural formations of the nervous system is nervous tissue. It is a system of interconnected differentials of nerve cells, neuroglia and glial macrophages, providing specific functions of perception of stimulation, excitation, generation of nerve impulses and its transmission. The neuron and each of its compartments (spines, dendrites, catfish, axon) is an autonomous, plastic, active, structural formation with complex computational properties. One of them – dendrites – plays a key role in the integration and processing of information. Dendrites, due to their morphology, provide neurons with unique electrical and plastic properties and cause variations in their computational properties. The morphology of dendrites: 1) determines – a) the number and type of contacts that a particular neuron can form with other neurons; b) the complexity, diversity of its functions; c) its computational operations; 2) determines – a) variations in the computational properties of a neuron (variations of the discharges between bursts and regular forms of pulsation); b) back distribution of action potentials. Dendritic spines can form synaptic connection – one of the main factors for increasing the diversity of forms of synaptic connections of neurons. Their volume and shape can change over a short period of time, and they can rotate in space, appear and disappear by themselves. Spines play a key role in selectively changing the strength of synaptic connections during the memorization and learning process. Glial cells are active participants in diffuse transmission of nerve impulses in the brain. Astrocytes form a three-dimensional, functionally “syncytia-like” formation, inside of which there are neurons, thus causing their specific microenvironment. They and neurons are structurally and functionally interconnected, based on which their permanent interaction occurs. Oligodendrocytes provide conditions for the generation and transmission of nerve impulses along the processes of neurons and play a significant role in the processes of their excitation and inhibition. Microglial cells play an important role in the formation of the brain, especially in the formation and maintenance of synapses. Thus, the CNS should be considered as a single, functionally “syncytia-like”, structural entity. Because the three-dimensional distribution of dendritic branches in space is important for determining the type of information that goes to a neuron, it is necessary to consider the three-dimensionality of their structure when analyzing the implementation of their functions.

Computational prediction of changes in brain metabolic fluxes during Parkinson's disease from mRNA expression

Background

Parkinson’s disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson’s disease (PD) brain.

Results

We use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer’s disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity.

Conclusion

Our method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson’s disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.

Genome-Scale Brain Metabolic Networks as Scaffolds for the Systems Biology of Neurodegenerative Diseases: Mapping Metabolic Alterations

Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.

A Flux Balance of Glucose Metabolism Clarifies the Requirements of the Warburg Effect

The Warburg effect, or aerobic glycolysis, is marked by the increased metabolism of glucose to lactate in the presence of oxygen. Despite its widespread prevalence in physiology and cancer biology, the causes and consequences remain incompletely understood. Here, we show that a simple balance of interacting fluxes in glycolysis creates constraints that impose the necessary conditions for glycolytic flux to generate lactate as opposed to entering into the mitochondria. These conditions are determined by cellular redox and energy demands. By analyzing the constraints and sampling the feasible region of the model, we further study how cell proliferation rate and mitochondria-associated NADH oxidizing and ATP producing fluxes are interlinked. Together this analysis illustrates the simplicity of the origins of the Warburg effect by identifying the flux distributions that are necessary for its instantiation.

GAD1 Upregulation Programs Aggressive Features of Cancer Cell Metabolism in the Brain Metastatic Microenvironment

The impact of altered amino acid metabolism on cancer progression is not fully understood. We hypothesized that a metabolic transcriptome shift during metastatic evolution is crucial for brain metastasis. Here, we report a powerful impact in this setting caused by epigenetic upregulation of glutamate decarboxylase 1 (GAD1), a regulator of the GABA neurotransmitter metabolic pathway. In cell-based culture and brain metastasis models, we found that downregulation of the DNA methyltransferase DNMT1 induced by the brain microenvironment-derived clusterin resulted in decreased GAD1 promoter methylation and subsequent upregulation of GAD1 expression in brain metastatic tumor cells. In a system to dynamically visualize cellular metabolic responses mediated by GAD1, we monitored the cytosolic NADH:NAD⁺ equilibrium in tumor cells. Reducing GAD1 in metastatic cells by primary glia cell coculture abolished the capacity of metastatic cells to utilize extracellular glutamine, leading to cytosolic accumulation of NADH and increased oxidative status. Similarly, genetic or pharmacologic disruption of the GABA metabolic pathway decreased the incidence of brain metastasis in vivo Taken together, our results show how epigenetic changes in GAD1 expression alter local glutamate metabolism in the brain metastatic microenvironment, contributing to a metabolic adaption that facilitates metastasis outgrowth in that setting. Cancer Res; 77(11); 2844-56. ©2017 AACR.

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.

Computational Flux Balance Analysis Predicts that Stimulation of Energy Metabolism in Astrocytes and their Metabolic Interactions with Neurons Depend on Uptake of K+ Rather than Glutamate

Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rates of energy consumption and oxidative metabolism in these glial cells. In the brain, almost all energy is consumed by the Na⁺/K⁺ ATPase, which hydrolyzes 1 ATP to move 3 Na⁺ outside and 2 K⁺ inside the cells. Astrocytes are commonly thought to be primarily involved in transmitter glutamate cycling, a mechanism that however only accounts for few % of brain energy utilization. In order to examine the participation of astrocytic energy metabolism in brain ion homeostasis, here we attempted to devise a simple stoichiometric relation linking glutamatergic neurotransmission to Na⁺ and K⁺ ionic currents. To this end, we took into account ion pumps and voltage/ligand-gated channels using the stoichiometry derived from available energy budget for neocortical signaling and incorporated this stoichiometric relation into a computational metabolic model of neuron-astrocyte interactions. We aimed at reproducing the experimental observations about rates of metabolic pathways obtained by ¹³C-NMR spectroscopy in rodent brain. When simulated data matched experiments as well as biophysical calculations, the stoichiometry for voltage/ligand-gated Na⁺ and K⁺ fluxes generated by neuronal activity was close to a 1:1 relationship, and specifically 63/58 Na⁺/K⁺ ions per glutamate released. We found that astrocytes are stimulated by the extracellular K⁺ exiting neurons in excess of the 3/2 Na⁺/K⁺ ratio underlying Na⁺/K⁺ ATPase-catalyzed reaction. Analysis of correlations between neuronal and astrocytic processes indicated that astrocytic K⁺ uptake, but not astrocytic Na⁺-coupled glutamate uptake, is instrumental for the establishment of neuron-astrocytic metabolic partnership. Our results emphasize the importance of K⁺ in stimulating the activation of astrocytes, which is relevant to the understanding of brain activity and energy metabolism at the cellular level.

A possible role of microglia-derived nitric oxide by lipopolysaccharide in activation of astroglial pentose-phosphate pathway via the Keap1/Nrf2 system

BACKGROUND

Toll-like receptor 4 (TLR4) plays a pivotal role in the pathophysiology of stroke-induced inflammation. Both astroglia and microglia express TLR4, and endogenous ligands produced in the ischemic brain induce inflammatory responses. Reactive oxygen species (ROS), nitric oxide (NO), and inflammatory cytokines produced by TLR4 activation play harmful roles in neuronal damage after stroke. Although astroglia exhibit pro-inflammatory responses upon TLR4 stimulation by lipopolysaccharide (LPS), they may also play cytoprotective roles via the activation of the pentose phosphate pathway (PPP), reducing oxidative stress by glutathione peroxidase. We investigated the mechanisms by which astroglia reduce oxidative stress via the activation of PPP, using TLR4 stimulation and hypoxia in concert with microglia.

METHODS

In vitro experiments were performed using cells prepared from Sprague-Dawley rats. Coexisting microglia in the astroglial culture were chemically eliminated using L-leucine methyl ester (LME). Cells were exposed to LPS (0.01 μg/mL) or hypoxia (1 % O2) for 12-15 h. PPP activity was measured using [1-(14)C]glucose and [6-(14)C]glucose. ROS and NO production were measured using 2',7'-dichlorodihydrofluorescein diacetate and diaminofluorescein-FM diacetate, respectively. The involvement of nuclear factor-erythroid-2-related factor 2 (Nrf2), a cardinal transcriptional factor under stress conditions that regulates glucose 6-phosphate dehydrogenase, the rate-limiting enzyme of PPP, was evaluated using immunohistochemistry.

RESULTS

Cultured astroglia exposed to LPS elicited 20 % increases in PPP flux, and these actions of astroglia appeared to involve Nrf2. However, the chemical depletion of coexisting microglia eliminated both increases in PPP and astroglial nuclear translocation of Nrf2. LPS induced ROS and NO production in the astroglial culture containing microglia but not in the microglia-depleted astroglial culture. LPS enhanced astroglial ROS production after glutathione depletion. U0126, an upstream inhibitor of mitogen-activated protein kinase, eliminated LPS-induced NO production, whereas ROS production was unaffected. U0126 also eliminated LPS-induced PPP activation in astroglial-microglial culture, indicating that microglia-derived NO mediated astroglial PPP activation. Hypoxia induced astroglial PPP activation independent of the microglia-NO pathway. Elimination of ROS and NO production by sulforaphane, a natural Nrf2 activator, confirmed the astroglial protective mechanism.

CONCLUSIONS

Astroglia in concert with microglia may play a cytoprotective role for countering oxidative stress in stroke.

Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma

Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in in-vitro and in-vivo experiments. The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM.

Understanding the physiology of the ageing individual: computational modelling of changes in metabolism and endurance

Ageing and lifespan are strongly affected by metabolism. The maximal possible uptake of oxygen is not only a good predictor of performance in endurance sports, but also of life expectancy. Figuratively speaking, healthy ageing is a competitive sport. Although the root cause of ageing is damage to macromolecules, it is the balance with repair processes that is decisive. Reduced or intermittent nutrition, hormones and intracellular signalling pathways that regulate metabolism have strong effects on ageing. Homeostatic regulatory processes tend to keep the environment of the cells within relatively narrow bounds. On the other hand, the body is constantly adapting to physical activity and food consumption. Spontaneous fluctuations in heart rate and other processes indicate youth and health. A (homeo)dynamic aspect of homeostasis deteriorates with age. We are now in a position to develop computational models of human metabolism and the dynamics of heart rhythm and oxygen transport that will advance our understanding of ageing. Computational modelling of the connections between dietary restriction, metabolism and protein turnover may increase insight into homeostasis of the proteins in our body. In this way, the computational reconstruction of human physiological processes, the Physiome, can help prevent frailty and age-related disease.

Housing Complexity Alters GFAP-Immunoreactive Astrocyte Morphology in the Rat Dentate Gyrus

Rats used in research are typically housed singly in cages with limited sensory stimulation. There is substantial evidence that housing rats in these conditions lead to numerous neuroanatomical and behavioral abnormalities. Alternatively, rats can be housed in an enriched environment in which rats are housed in groups and given room for exercise and exploration. Enriched environments result in considerable neuroplasticity in the rodent brain. In the dentate gyrus of the hippocampus, enriched environments evoke especially profound neural changes, including increases in the number of neurons and the number of dendritic spines. However, whether changes in astrocytes, a type of glia increasingly implicated in mediating neuroplasticity, are concurrent with these neural changes remains to be investigated. In order to assess morphological changes among astrocytes of the rat dentate gyrus, piSeeDB was used to optically clear 250 μm sections of tissue labeled using GFAP immunohistochemistry. Confocal imaging and image analysis were then used to measure astrocyte morphology. Astrocytes from animals housed in EE demonstrated a reduced distance between filament branch points. Furthermore, the most complex astrocytes were significantly more complex among animals housed in EE compared to standard environments.

Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling

Sampling of the optimal flux space using modified LHS gives a more uniform coverage than Monte-Carlo Sampling. Analysis of the flux data shows that majority of variation in the flux distribution pattern within the space arises due to the presence of few alternate pathways.

A constraint-based modelling approach to metabolic dysfunction in Parkinson's disease

One of the hallmarks of sporadic Parkinson's disease is degeneration of dopaminergic neurons in the pars compacta of the substantia nigra. The aetiopathogenesis of this degeneration is still not fully understood, with dysfunction of many biochemical pathways in different subsystems suggested to be involved. Recent advances in constraint-based modelling approaches hold great potential to systematically examine the relative contribution of dysfunction in disparate pathways to dopaminergic neuronal degeneration, but few studies have employed these methods in Parkinson's disease research. Therefore, this review outlines a framework for future constraint-based modelling of dopaminergic neuronal metabolism to decipher the multi-factorial mechanisms underlying the neuronal pathology of Parkinson's disease.

Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain

Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer’s disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.

Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit

Modelling of metabolic networks is a powerful tool to analyse the behaviour of developing plant organs, including fruits. Guided by our current understanding of heterotrophic metabolism of plant cells, a medium-scale stoichiometric model, including the balance of co-factors and energy, was constructed in order to describe metabolic shifts that occur through the nine sequential stages of Solanum lycopersicum (tomato) fruit development. The measured concentrations of the main biomass components and the accumulated metabolites in the pericarp, determined at each stage, were fitted in order to calculate, by derivation, the corresponding external fluxes. They were used as constraints to solve the model by minimizing the internal fluxes. The distribution of the calculated fluxes of central metabolism were then analysed and compared with known metabolic behaviours. For instance, the partition of the main metabolic pathways (glycolysis, pentose phosphate pathway, etc.) was relevant throughout fruit development. We also predicted a valid import of carbon and nitrogen by the fruit, as well as a consistent CO2 release. Interestingly, the energetic balance indicates that excess ATP is dissipated just before the onset of ripening, supporting the concept of the climacteric crisis. Finally, the apparent contradiction between calculated fluxes with low values compared with measured enzyme capacities suggest a complex reprogramming of the metabolic machinery during fruit development. With a powerful set of experimental data and an accurate definition of the metabolic system, this work provides important insight into the metabolic and physiological requirements of the developing tomato fruits.

Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network

Network-oriented analysis is essential to identify those parts of a cell affected by a given perturbation. The effect of neurodegenerative perturbations in the form of diseases of brain metabolism was investigated by using a newly reconstructed brain-specific metabolic network. The developed stoichiometric model correctly represents healthy brain metabolism, and includes 630 metabolic reactions in and between astrocytes and neurons, which are controlled by 570 genes. The integration of transcriptome data of six neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, multiple sclerosis, schizophrenia) with the model was performed to identify reporter features specific and common for these diseases, which revealed metabolites and pathways around which the most significant changes occur. The identified metabolites are potential biomarkers for the pathology of the related diseases. Our model indicated perturbations in oxidative stress, energy metabolism including TCA cycle and lipid metabolism as well as several amino acid related pathways, in agreement with the role of these pathways in the studied diseases. The computational prediction of transcription factors that commonly regulate the reporter metabolites was achieved through binding-site analysis. Literature support for the identified transcription factors such as USF1, SP1 and those from FOX families are known from the literature to have regulatory roles in the identified reporter metabolic pathways as well as in the neurodegenerative diseases. In essence, the reconstructed brain model enables the elucidation of effects of a perturbation on brain metabolism and the illumination of possible machineries in which a specific metabolite or pathway acts as a regulatory spot for cellular reorganization.

Cellular and molecular mechanisms in kidney fibrosis

Fibrosis is a characteristic feature of all forms of chronic kidney disease. Deposition of pathological matrix in the interstitial space and within the walls of glomerular capillaries as well as the cellular processes resulting in this deposition are increasingly recognized as important factors amplifying kidney injury and accelerating nephron demise. Recent insights into the cellular and molecular mechanisms of fibrogenesis herald the promise of new therapies to slow kidney disease progression. This review focuses on new findings that enhance understanding of cellular and molecular mechanisms of fibrosis, the characteristics of myofibroblasts, their progenitors, and molecular pathways regulating both fibrogenesis and its resolution.

Glycine receptors in nervous tissue and their functional role

The literature data on glycine metabolism in neural tissue, mitochondrial Gly-cleaving system, Gly-catching system in neural and glial cells are summarized. The peculiarities of localization and distribution of specific glycine receptors and binding-sites in nervous tissue of mammals are described. Four types of glycine-binding receptors are described: own specific glycine receptor (Gly-R), ionotropic receptor, which binds N-methyl-D-aspartate selectively (NMDA-R), and ionotropic receptors of g-aminobutyrate (GABA A -R, GABA С -R). The feutures of glycine effects in neuroglial cultures are discussed

Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective

Background

The energetics of cerebral activity critically relies on the functional and metabolic interactions between neurons and astrocytes. Important open questions include the relation between neuronal versus astrocytic energy demand, glucose uptake and intercellular lactate transfer, as well as their dependence on the level of activity.

Results

We have developed a large-scale, constraint-based network model of the metabolic partnership between astrocytes and glutamatergic neurons that allows for a quantitative appraisal of the extent to which stoichiometry alone drives the energetics of the system. We find that the velocity of the glutamate-glutamine cycle (V_cyc) explains part of the uncoupling between glucose and oxygen utilization at increasing V_cyc levels. Thus, we are able to characterize different activation states in terms of the tissue oxygen-glucose index (OGI). Calculations show that glucose is taken up and metabolized according to cellular energy requirements, and that partitioning of the sugar between different cell types is not significantly affected by V_cyc. Furthermore, both the direction and magnitude of the lactate shuttle between neurons and astrocytes turn out to depend on the relative cell glucose uptake while being roughly independent of V_cyc.

Conclusions

These findings suggest that, in absence of ad hoc activity-related constraints on neuronal and astrocytic metabolism, the glutamate-glutamine cycle does not control the relative energy demand of neurons and astrocytes, and hence their glucose uptake and lactate exchange.

To supplement or not to supplement: a metabolic network framework for human nutritional supplements

Flux balance analysis and constraint based modeling have been successfully used in the past to elucidate the metabolism of single cellular organisms. However, limited work has been done with multicellular organisms and even less with humans. The focus of this paper is to present a novel use of this technique by investigating human nutrition, a challenging field of study. Specifically, we present a steady state constraint based model of skeletal muscle tissue to investigate amino acid supplementation's effect on protein synthesis. We implement several in silico supplementation strategies to study whether amino acid supplementation might be beneficial for increasing muscle contractile protein synthesis. Concurrent with published data on amino acid supplementation's effect on protein synthesis in a post resistance exercise state, our results suggest that increasing bioavailability of methionine, arginine, and the branched-chain amino acids can increase the flux of contractile protein synthesis. The study also suggests that a common commercial supplement, glutamine, is not an effective supplement in the context of increasing protein synthesis and thus, muscle mass. Similar to any study in a model organism, the computational modeling of this research has some limitations. Thus, this paper introduces the prospect of using systems biology as a framework to formally investigate how supplementation and nutrition can affect human metabolism and physiology.

Inhaled carbon monoxide provides cerebral cytoprotection in pigs

Carbon monoxide (CO) at low concentrations imparts protective effects in numerous preclinical small animal models of brain injury. Evidence of protection in large animal models of cerebral injury, however, has not been tested. Neurologic deficits following open heart surgery are likely related in part to ischemia reperfusion injury that occurs during cardiopulmonary bypass surgery. Using a model of deep hypothermic circulatory arrest (DHCA) in piglets, we evaluated the effects of CO to reduce cerebral injury. DHCA and cardiopulmonary bypass (CPB) induced significant alterations in metabolic demands, including a decrease in the oxygen/glucose index (OGI), an increase in lactate/glucose index (LGI) and a rise in cerebral blood pressure that ultimately resulted in increased cell death in the neocortex and hippocampus that was completely abrogated in piglets preconditioned with a low, safe dose of CO. Moreover CO-treated animals maintained normal, pre-CPB OGI and LGI and corresponding cerebral sinus pressures with no change in systemic hemodynamics or metabolic intermediates. Collectively, our data demonstrate that inhaled CO may be beneficial in preventing cerebral injury resulting from DHCA and offer important therapeutic options in newborns undergoing DHCA for open heart surgery.