Analysis Of Metabolic Systems Research Articles

Cell Geometry and Membrane Protein Crowding Constrain Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy.

A metabolic theory is presented for predicting maximum growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on the intersection of cell geometry, membrane protein crowding, and metabolism. The importance of cytosolic macromolecular crowding on phenotype has been established in the literature but the importance of surface area has been largely overlooked due to incomplete knowledge of membrane properties. We demonstrate that the capacity of the membrane to host proteins increases with growth rate offsetting decreases in surface area-to-volume ratios (SA:V). This increase in membrane protein is hypothesized to be essential to competitive Escherichia coli phenotypes. The presented membrane-centric theory uses biophysical properties and metabolic systems analysis to successfully predict the phenotypes of E. coli K-12 strains, MG1655 and NCM3722, which are genetically similar but have SA:V ratios that differ up to 30%, maximum growth rates on glucose media that differ by 40%, and overflow phenotypes that start at growth rates that differ by 80%. These analyses did not consider cytosolic macromolecular crowding, highlighting the distinct properties of the presented theory. Cell geometry and membrane protein crowding are significant biophysical constraints on phenotype and provide a theoretical framework for improved understanding and control of cell biology.

Pulmonary hypertension – an explorative metabolomic analysis of the endothelium

Abstract Background Pulmonary hypertension (PH) is classified into five clinical diagnostic groups, including group 1 (idiopathic pulmonary arterial hypertension (IPAH) and connective tissue disease-associated PAH (CTD-aPAH)) and group 4 (chronic thromboembolic pulmonary hypertension (CTEPH)). PH is a progressive, life-threatening, incurable disease. The pathological mechanisms underlying PH remain elusive; recent evidence has revealed that abnormal metabolic activities in the endothelium may play a crucial role. Purpose Introducing a novel approach for studying PH endothelial function, building on genome-scale metabolic (GEM) reconstruction of the endothelial cell (EC) to investigate intracellular metabolism. Methods This prospective observational study includes 35 PH patients with either PAH or CTEPH. We have previously developed a platform to study the endothelium cell (EC) by reconstructing the most comprehensive endothelial genome-scale metabolic model (EC-GEM). Since the EC is in direct contact with the blood flow, we can adjust the EC-GEM by measuring extracellular metabolites from the blood and calculating the intracellular metabolic flux in the EC. We conducted individual EC-GEM models, i.e., in total 35 different models. Additionally, each model was widely analyzed; in total, 190 different intracellular activities were investigated. This enables us to develop a heatmap of the intracellular activities at individual patient levels. Patients with similar intracellular endothelial activities were combined. Results Four distinct intracellular phenotypes were identified; all were independent of the clinical classification of PH. We observed clinically relevant differences in the NT-pro BNP levels and the WHO functional classification between these phenotypes. Also, different important cellular activities were identified separating the four phenotypes. Two phenotypes were characterized by catabolic crisis and extreme citric acid cycle turnover for energy production. However, one of these also suffered from oxidative stress. Conclusion In PH patients metabolic systems analysis identified four metabolic phenotypes that correlated with NT-pro BNP levels and the WHO functional classification but were independent of the clinical diagnosis. This novel finding may enable us to identify metabolic therapeutic targets specific to the respective phenotypes and thus promote personalized medicine in patients with PH. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): This study was funded by Novo Nordisk Foundation research grant NNF20OC0064556 and the Heart Center Research Council, Copenhagen University Hospital, Rigshospitalet, provided to Jørn Carlsen

Metabolic systems analysis identifies a novel mechanism contributing to shock in patients with endotheliopathy of trauma (EoT) involving thromboxane A2 and LTC4

PurposeEndotheliopathy of trauma (EoT), as defined by circulating levels of syndecan-1 ≥ 40 ng/mL, has been reported to be associated with significantly increased transfusion requirements and a doubled 30-day mortality. Increased shedding of the glycocalyx points toward the endothelial cell membrane composition as important for the clinical outcome being the rationale for this study. ResultsThe plasma metabolome of 95 severely injured trauma patients was investigated by mass spectrometry, and patients with EoT vs. non-EoT were compared by partial least square-discriminant analysis, identifying succinic acid as the top metabolite to differentiate EoT and non-EoT patients (VIP score = 3). EoT and non-EoT patients’ metabolic flux profile was inferred by integrating the corresponding plasma metabolome data into a genome-scale metabolic network reconstruction analysis and performing a functional study of the metabolic capabilities of each group. Model predictions showed a decrease in cholesterol metabolism secondary to impaired mevalonate synthesis in EoT compared to non-EoT patients. Intracellular task analysis indicated decreased synthesis of thromboxanA2 and leukotrienes, as well as a lower carnitine palmitoyltransferase I activity in EoT compared to non-EoT patients. Sensitivity analysis also showed a significantly high dependence of eicosanoid-associated metabolic tasks on alpha-linolenic acid as unique to EoT patients. ConclusionsModel-driven analysis of the endothelial cells’ metabolism identified potential novel targets as impaired thromboxane A2 and leukotriene synthesis in EoT patients when compared to non-EoT patients. Reduced thromboxane A2 and leukotriene availability in the microvasculature impairs vasoconstriction ability and may thus contribute to shock in EoT patients. These findings are supported by extensive scientific literature; however, further investigations are required on these findings.

An Improved Genome Sequence Resource of Bipolaris maydis, Causal Agent of Southern Corn Leaf Blight.

An Improved Genome Sequence Resource of Bipolaris maydis, Causal Agent of Southern Corn Leaf Blight.

Metabolic Systems Analysis of Shock-Induced Endotheliopathy (SHINE) in Trauma

Investigate the endothelial cell phenotype (s) that causes Shock-Induced Endotheliopathy in trauma. We have studied more than 2750 trauma patients and identified that patients with high circulating syndecan-1 (endothelial glycocalyx damage marker) in plasma have an increased mortality rate compared with patients with lower levels. Notably, we found that patients suffering from the same trauma severity could develop significantly different degrees of endothelial dysfunction as measured by syndecan-1. Prospective observational study of 20 trauma patients admitted to a Level 1 Trauma Centre and 20 healthy controls. Admission plasma syndecan-1 level and mass spectrometry were measured and analyzed by computational network analysis of our genome-scale metabolic model of the microvascular endothelial cell function. Trauma patients had a significantly different endothelial metabolic profile compared with controls. Among the patients, 4 phenotypes were identified. Three phenotypes were independent of syndecan-1 levels. We developed genome-scale metabolic models representative of the observed phenotypes. Within these phenotypes, we observed differences in the cell fluxes from glucose and palmitate to produce Acetyl-CoA, and secretion of heparan sulfate proteoglycan (component of syndecan-1). We confirm that trauma patients have a significantly different metabolic profile compared with controls. A minimum of 4 shock-induced endotheliopathy phenotypes were identified, which were independent of syndecan-1level (except 1 phenotype) verifying that the endothelial response to trauma is heterogeneous and most likely driven by a genetic component. Moreover, we introduced a new research tool in trauma by using metabolic systems biology, laying the foundation for personalized medicine.

Visual FTBL Generator: Visual Generation Tool for 13C-FLUX File

Abstract 13C metabolic flux analysis (13C-MFA) has achieved increasing significance in quantitative metabolic system analysis in recent years. In 13C metabolic flux analysis, 13C-FLUX software is a major analytical tool. The software’s input script is primarily expressed in textual form without visual presentation of the structure of the entire metabolic network, thus error-prone in manual input. To solve this problem, we have developed a visual FTBL generator (VFG, available at http://47.100.98.220/vfg/index.jsp in a Google or Firefox browser)for MFA that eliminates the tedious, error-prone text entry mode and provides a user-friendly graphical interface and simple visual reaction generation functions.

Kinetic modeling of the Calvin cycle identifies flux control and stable metabolomes in Synechocystis carbon fixation.

Biological fixation of atmospheric CO2 via the Calvin-Benson-Bassham cycle has massive ecological impact and offers potential for industrial exploitation, either by improving carbon fixation in plants and autotrophic bacteria, or by installation into new hosts. A kinetic model of the Calvin-Benson-Bassham cycle embedded in the central carbon metabolism of the cyanobacterium Synechocystis sp. PCC 6803 was developed to investigate its stability and underlying control mechanisms. To reduce the uncertainty associated with a single parameter set, random sampling of the steady-state metabolite concentrations and the enzyme kinetic parameters was employed, resulting in millions of parameterized models which were analyzed for flux control and stability against perturbation. Our results show that the Calvin cycle had an overall high intrinsic stability, but a high concentration of ribulose 1,5-bisphosphate was associated with unstable states. Low substrate saturation and high product saturation of enzymes involved in highly interconnected reactions correlated with increased network stability. Flux control, that is the effect that a change in one reaction rate has on the other reactions in the network, was distributed and mostly exerted by energy supply (ATP), but also by cofactor supply (NADPH). Sedoheptulose 1,7-bisphosphatase/fructose 1,6-bisphosphatase, fructose-bisphosphate aldolase, and transketolase had a weak but positive effect on overall network flux, in agreement with published observations. The identified flux control and relationships between metabolite concentrations and system stability can guide metabolic engineering. The kinetic model structure and parameterizing framework can be expanded for analysis of metabolic systems beyond the Calvin cycle.

Systems analysis of metabolism in platelet concentrates during storage in platelet additive solution.

Platelets (PLTs) deteriorate over time when stored within blood banks through a biological process known as PLT storage lesion (PSL). Here, we describe the refinement of the biochemical model of PLT metabolism, iAT-PLT-636, and its application to describe and investigate changes in metabolism during PLT storage. Changes in extracellular acetate and citrate were measured in buffy coat and apheresis PLT units over 10 days of storage in the PLT additive solution T-Sol. Metabolic network analysis of these data was performed alongside our prior metabolomics data to describe the metabolism of fresh (days 1-3), intermediate (days 4-6), and expired (days 7-10) PLTs. Changes in metabolism were studied by comparing metabolic model flux predictions of iAT-PLT-636 between stages and between collection methods. Extracellular acetate and glucose contribute most to central carbon metabolism in PLTs. The anticoagulant citrate is metabolized in apheresis-stored PLTs and is converted into aconitate and, to a lesser degree, malate. The consumption of nutrients changes during storage and reflects altered PLT activation profiles following their collection. Irrespective of the collection method, a slowdown in oxidative phosphorylation takes place, consistent with mitochondrial dysfunction during PSL. Finally, the main contributors to intracellular ammonium and NADPH are highlighted. Future optimization of flux through these pathways provides opportunities to address intracellular pH changes and reactive oxygen species, which are both of importance to PSL. The metabolic models provide descriptions of PLT metabolism at steady state and represent a platform for future PLT metabolic research.

Metabolic systems analysis of LPS induced endothelial dysfunction applied to sepsis patient stratification

Endothelial dysfunction contributes to sepsis outcome. Metabolic phenotypes associated with endothelial dysfunction are not well characterised in part due to difficulties in assessing endothelial metabolism in situ. Here, we describe the construction of iEC2812, a genome scale metabolic reconstruction of endothelial cells and its application to describe metabolic changes that occur following endothelial dysfunction. Metabolic gene expression analysis of three endothelial subtypes using iEC2812 suggested their similar metabolism in culture. To mimic endothelial dysfunction, an in vitro sepsis endothelial cell culture model was established and the metabotypes associated with increased endothelial permeability and glycocalyx loss after inflammatory stimuli were quantitatively defined through metabolomics. These data and transcriptomic data were then used to parametrize iEC2812 and investigate the metabotypes of endothelial dysfunction. Glycan production and increased fatty acid metabolism accompany increased glycocalyx shedding and endothelial permeability after inflammatory stimulation. iEC2812 was then used to analyse sepsis patient plasma metabolome profiles and predict changes to endothelial derived biomarkers. These analyses revealed increased changes in glycan metabolism in sepsis non-survivors corresponding to metabolism of endothelial dysfunction in culture. The results show concordance between endothelial health and sepsis survival in particular between endothelial cell metabolism and the plasma metabolome in patients with sepsis.

GENOMICS AND IMAGING DATA INFORMED METABOLOMICS: A BIOCHEMICAL ROADMAP FOR ALZHEIMER DISEASE

Increasing evidence supports that Alzheimer disease (AD) is a metabolic disease with high diabetes co-morbidity and a wide range of metabolic perturbations occurring early in the disease process. While genetic risk clearly plays a role in AD, the gut microbiome, exposome and diet also exert influences on brain metabolic health. Advances in analytical chemistry enable simultaneous measurement of 1000's of metabolites that can be integrated into tissue-specific metabolic networks enabling a new kind of metabolic systems analysis of AD and where peripheral and central metabolic changes can be connected. We will highlight developments in the metabolomics field and large initiatives within Accelerated Medicine Partnership (AMP-AD) and MOVE AD where metabolomics data is being generated and informed by genomics and imaging data. Fasting baseline serum samples from Alzheimer's Disease Neuroimaging Initiative ADNI1 (199 CN, 356 LMCI and 175 AD participants) were analyzed using targeted (AbsoluteIDQ-p180 and Biocrates Bile Acid Kit) and non-targeted metabolomics and lipidomics platforms (UPLC-TOF MS; UPLC-QTOF MS). ADNI GO/2 (150 CN, 100 SMC, 100 EMCI, 150 LMCI, and 150 mild AD) and Rotterdam studies were used for validation of findings. Partial correlation networks revealed changes in sphingomyelins and ether-containing phosphatidylcholines in preclinical biomarker-defined AD stages providing mechanistic insights about abeta and tau pathology and pointing to changes in membrane structure and function. Subsequent changes in acylcarnitines and several amines, including the branched chain amino acid valine and α-aminoadipic acid correlated with imaging changes and cognitive decline pointing to a role of mitochondrial energetics. Several metabolic changes related to liver enzymatic functions and gut microbiome activity correlated with imaging and cognitive changes highlighting a possible role for gut liver brain axis in disease pathogenesis. Meta-analysis across metabolomics studies reveals common biochemical findings when evaluated within a metabolic network context. Metabolomics identified key disease-related metabolic changes and disease-progression-related changes some related to liver and gut microbiome activity. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery. Genomics and imaging data inform metabolomics data and together they provide a powerful systems approach for the study of AD.

Phenotypic innovation through recombination in genome-scale metabolic networks.

Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a well-understood genotype-phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experimentally validated computational method that can predict metabolic phenotypes from metabolic genotypes. We show that recombination is much more likely to create novel metabolic abilities than random changes in chemical reactions of a metabolic network. We also find that phenotypic innovation is more likely when recombination occurs between parents that are genetically closely related, phenotypically highly diverse, and viable on few rather than many carbon sources. Survival on a new carbon source preferentially involves reactions that are superessential, that is, essential in many metabolic networks. We validate our observations with data from 61 reconstructed prokaryotic metabolic networks. Our systematic and quantitative analysis of metabolic systems helps understand how recombination creates innovation.

Technical Challenges in Mass Spectrometry-Based Metabolomics.

Metabolomics is a strategy for analysis, and quantification of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scientific research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10-100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and specific instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism.

Analysis and modelling of glucose metabolism in diabetic Göttingen minipigs

This paper introduces an improved physiological animal model with diabetic Göttingen minipigs, which focuses on the application of human therapy devices and metabolic system analysis. Based on measurement data-sets collected by metabolic test procedures, a new mathematical minipig model is developed. The model consists of 16 differential equations and describes the diabetic porcine glucose metabolism according to the human model published by Sorensen. In the future, the mathematical model will be used as a basis for controller design and the physiological model for experimental control performance evaluation.

Comparative Metabolic Systems Analysis of Pathogenic Burkholderia

Burkholderia cenocepacia and Burkholderia multivorans are opportunistic drug-resistant pathogens that account for the majority of Burkholderia cepacia complex infections in cystic fibrosis patients and also infect other immunocompromised individuals. While they share similar genetic compositions, B. cenocepacia and B. multivorans exhibit important differences in pathogenesis. We have developed reconciled genome-scale metabolic network reconstructions of B. cenocepacia J2315 and B. multivorans ATCC 17616 in parallel (designated iPY1537 and iJB1411, respectively) to compare metabolic abilities and contextualize genetic differences between species. The reconstructions capture the metabolic functions of the two species and give insight into similarities and differences in their virulence and growth capabilities. The two reconstructions have 1,437 reactions in common, and iPY1537 and iJB1411 have 67 and 36 metabolic reactions unique to each, respectively. After curating the extensive reservoir of metabolic genes in Burkholderia, we identified 6 genes essential to growth that are unique to iPY1513 and 13 genes uniquely essential to iJB1411. The reconstructions were refined and validated by comparing in silico growth predictions to in vitro growth capabilities of B. cenocepacia J2315, B. cenocepacia K56-2, and B. multivorans ATCC 17616 on 104 carbon sources. Overall, we identified functional pathways that indicate B. cenocepacia can produce a wider array of virulence factors compared to B. multivorans, which supports the clinical observation that B. cenocepacia is more virulent than B. multivorans. The reconciled reconstructions provide a framework for generating and testing hypotheses on the metabolic and virulence capabilities of these two related emerging pathogens.

A topological characterization of medium-dependent essential metabolic reactions.

Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, we analyze the essentiality of metabolic reactions depending on the growth medium and identify the topological footprint of these reactions. We find that the typical topological context of a medium-dependent essential reaction is systematically different from that of a globally essential reaction. In particular, we observe systematic differences in the distribution of medium-dependent essential reactions across three-node subgraphs (the network motif signature of medium-dependent essential reactions) compared to globally essential or globally redundant reactions. In this way, we provide evidence that the analysis of metabolic systems on the few-node subgraph scale is meaningful for explaining dynamic patterns. This topological characterization of medium-dependent essentiality provides a better understanding of the interplay between reaction deletions and environmental conditions.

Role of phosphate in the central metabolism of two lactic acid bacteria – a comparative systems biology approach

Lactic acid-producing bacteria survive in distinct environments, but show common metabolic characteristics. Here we studied the dynamic interactions of the central metabolism in Lactococcus lactis, extensively used as a starter culture in the dairy industry, and Streptococcus pyogenes, a human pathogen. Glucose-pulse experiments and enzymatic measurements were performed to parameterize kinetic models of glycolysis. Significant improvements were made to existing kinetic models for L. lactis, which subsequently accelerated the development of the first kinetic model of S. pyogenes glycolysis. The models revealed an important role for extracellular phosphate in the regulation of central metabolism and the efficient use of glucose. Thus, phosphate, which is rarely taken into account as an independent species in models of central metabolism, should be considered more thoroughly in the analysis of metabolic systems in the future. Insufficient phosphate supply can lead to a strong inhibition of glycolysis at high glucose concentrations in both species, but this was more severe in S. pyogenes. S. pyogenes is more efficient at converting glucose to ATP, showing a higher tendency towards heterofermentative energy metabolism than L. lactis. Our comparative systems biology approach revealed that the glycolysis of L. lactis and S. pyogenes have similar characteristics, but are adapted to their individual natural habitats with respect to phosphate regulation.

Metabolic systems analysis to advance algal biotechnology

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Network analysis can direct microbial development efforts towards successful strategies and enable quantitative fine-tuning of the network for optimal product yields while maintaining the robustness of the production microbe. Metabolic modeling yields insights into microbial function, guides experiments by generating testable hypotheses, and enables the refinement of knowledge on the specific organism. While the application of such analytical approaches to algal systems is limited to date, metabolic network analysis can improve understanding of algal metabolic systems and play an important role in expediting the adoption of new biofuel technologies.

Understanding your inhibitions: effects of GABA and GABAA receptor modulation on brain cortical metabolism

A targeted neuropharmacological, (1)H/(13)C NMR spectroscopy and multivariate statistical approach was used to examine the effects of exogenous GABA and ligands at the GABA(A) receptor family on brain metabolism in the Guinea pig cortical tissue slice. All ligands at GABA(A) receptors generated metabolic patterns which were distinct from one another with the major variance in the data arising because of metabolic work (shown by net flux into Krebs cycle byproducts and increased metabolic pool sizes). Three major clusters of metabolic signatures were identified which corresponded to: (i) activity at phasic (synaptic) GABA(A) receptors, dominated by alpha1-containing receptors and responsive to GABA at 10 micromol/L; (ii) activity at perisynaptic receptors, dominated by response to high (40 micromol/L) GABA and the superagonist 4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridine-3-ol hydrochloride, and C, activity at extrasynaptic receptors, dominated by response to low (0.1-1.0 micromol/L) GABA, zolpidem (400 nmol/L) and the non-specific allosteric modulator RO19-4603 (1 nmol/L). These results highlight the utility of a different but robust approach to study of the GABAergic system using metabolic systems analysis.

Understanding human metabolic physiology: a genome-to-systems approach

The intricate nature of human physiology renders its study a difficult undertaking, and a systems biology approach is necessary to understand the complex interactions involved. Network reconstruction is a key step in systems biology and represents a common denominator because all systems biology research on a target organism relies on such a representation. With the recent development of genome-scale human metabolic networks, metabolic systems analysis is now possible and has initiated a shift towards human systems biology. Here, we review the important aspects of reconstructing a bottom-up human metabolic network, the network's role in modeling human physiology and the necessity for a community-based consensus reconstruction of human metabolism to be established.

Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major.

Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systems-based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof-of-concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage-specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.