Profiling of dynamic changes in hypermetabolic livers

The liver plays an important role in the overall negative nitrogen balance leading to muscle wasting commonly observed in patients following many conditions, including severe injury, cancer, and diabetes. In order to study changes in liver metabolism during the establishment of such catabolic states, we used a rat skin burn injury model that induces hypermetabolism and muscle wasting. At various times during the first week following the injury, livers were isolated and perfused in a recirculating system under well-defined conditions. We applied a steady-state metabolic flux analysis model of liver metabolism and then used k-means clustering to objectively group together reaction flux time profiles. We identified six distinct groups of reactions that were differentially responsive: (1) pentose phosphate pathway (PPP); (2) amino acid oxidation reactions leading to the formation of tricarboxylic acid (TCA) cycle intermediates; (3) gluconeogenesis; (4) TCA-cycle and mitochondrial oxidation; (5) lipolysis, beta-oxidation, and ketone body formation; and (6) urea-cycle. Burn injury sequentially upregulated the urea-cycle, the PPP, and the TCA-cycle, in order, while beta-oxidation and gluconeogenesis remained unchanged. The upregulation of the PPP was transient, whereas the rise in urea- and TCA-cycle fluxes was sustained. An ATP balance predicted an increased production of ATP and energy expenditure starting on day 3 post-burn, which correlated with the induction of the oxidative phosphorylation uncoupler uncoupling protein-2. We conclude that metabolic profiling using flux analysis and clustering analysis is a useful methodology to characterize the differential activation of metabolic pathways in perfused organs and to identify specific key pathways that are sensitive to a stimulus or insult without making a priori assumptions.     

Dynamic flux balance analysis of diauxic growth in Escherichia coli

Flux Balance Analysis (FBA) has been used in the past to analyze microbial metabolic networks. Typically, FBA is used to study the metabolic flux at a particular steady state of the system. However, there are many situations where the reprogramming of the metabolic network is important. Therefore, the dynamics of these metabolic networks have to be studied. In this paper, we have extended FBA to account for dynamics and present two different formulations for dynamic FBA. These two approaches were used in the analysis of diauxic growth in Escherichia coli. Dynamic FBA was used to simulate the batch growth of E. coli on glucose, and the predictions were found to qualitatively match experimental data. The dynamic FBA formalism was also used to study the sensitivity to the objective function. It was found that an instantaneous objective function resulted in better predictions than a terminal-type objective function. The constraints that govern the growth at different phases in the batch culture were also identified. Therefore, dynamic FBA provides a framework for analyzing the transience of metabolism due to metabolic reprogramming and for obtaining insights for the design of metabolic networks.     

α-ketobutyrate links alterations in cystine metabolism to glucose oxidation in mtDNA mutant cells

Pathogenic mutations in the mitochondrial genome (mtDNA) impair organellar ATP production, requiring mutant cells to activate metabolic adaptations for survival. Understanding how metabolism adapts to clinically relevant mtDNA mutations may provide insight into cellular strategies for metabolic flexibility. In this study, we use ¹³C isotope tracing and metabolic flux analysis to investigate central carbon and amino acid metabolic reprogramming in isogenic cells containing mtDNA mutations. We identify alterations in glutamine and cystine transport which indirectly regulate mitochondrial metabolism and electron transport chain function. Metabolism of cystine can promote glucose oxidation through the transsulfuration pathway and the production of α-ketobutyrate. Intriguingly, activating or inhibiting α-ketobutyrate production is sufficient to modulate both glucose oxidation and mitochondrial respiration in mtDNA mutant cells. Thus, cystine-stimulated transsulfuration serves as an adaptive mechanism linking glucose oxidation and amino acid metabolism in the setting of mtDNA mutations.     

Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures—Systems biology‐based interpretation using genome‐scale metabolic flux balance model and multivariate data analysis

Genome‐scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745–753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome‐scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163–1173, 2016     

Synergy between (13)C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli

Genome-based Flux Balance Analysis (FBA) and steady-state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here, genome-derived models of Escherichia coli (E. coli) metabolism were used for FBA and 13C-MFA analyses of aerobic and anaerobic growths of wild-type E. coli (K-12 MG1655) cells. Validated MFA flux maps reveal that the fraction of maintenance ATP consumption in total ATP production is about 14% higher under anaerobic (51.1%) than aerobic conditions (37.2%). FBA revealed that an increased ATP utilization is consumed by ATP synthase to secrete protons from fermentation. The TCA cycle is shown to be incomplete in aerobically growing cells and submaximal growth is due to limited oxidative phosphorylation. An FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but the most-frequently predicted values of internal fluxes yielded from sampling the feasible space differ substantially from MFA-derived fluxes.     

A hybrid kinetic and constraint-based model of leaf metabolism allows predictions of metabolic fluxes in different environments

While flux balance analysis (FBA) provides a framework for predicting steady-state leaf metabolic network fluxes, it does not readily capture the response to environmental variables without being coupled to other modelling formulations. To address this, we coupled an FBA model of 903 reactions of soybean (Glycine max) leaf metabolism with e-photosynthesis, a dynamic model that captures the kinetics of 126 reactions of photosynthesis and associated chloroplast carbon metabolism. Successful coupling was achieved in an iterative formulation in which fluxes from e-photosynthesis were used to constrain the FBA model and then, in turn, fluxes computed from the FBA model used to update parameters in e-photosynthesis. This process was repeated until common fluxes in the two models converged. Coupling did not hamper the ability of the kinetic module to accurately predict the carbon assimilation rate, photosystem II electron flux, and starch accumulation of field-grown soybean at two CO₂ concentrations. The coupled model also allowed accurate predictions of additional parameters such as nocturnal respiration, as well as analysis of the effect of light intensity and elevated CO₂ on leaf metabolism. Predictions included an unexpected decrease in the rate of export of sucrose from the leaf at high light, due to altered starch–sucrose partitioning, and altered daytime flux modes in the tricarboxylic acid cycle at elevated CO₂. Mitochondrial fluxes were notably different between growing and mature leaves, with greater anaplerotic, tricarboxylic acid cycle and mitochondrial ATP synthase fluxes predicted in the former, primarily to provide carbon skeletons and energy for protein synthesis.     

Structural Control of Metabolic Flux

Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions. To meet the accompanying change in metabolic demands, the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state, which is characterized by reaction fluxes and metabolite concentrations. These state transitions are the effect of simultaneously manipulating fluxes through several reactions. While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control, its applications are restricted by the limited availability of kinetic information. Here, we introduce structural metabolic control as a framework to examine individual reactions'' potential to control metabolic functions, such as biomass production, based on structural modeling. The capability to carry out a metabolic function is determined using flux balance analysis (FBA). We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality (FC). This framework is based on the Shapley value from cooperative game theory and FBA, and we demonstrate its superior ability to assign “share of control” to individual reactions with respect to metabolic functions and environmental conditions. A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control. We propose a Monte Carlo algorithm to estimate FCs for large networks, based on the enumeration of elementary flux modes. We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions.     

Branched-Chain Aminotransferases Control TORC1 Signaling in Saccharomyces cerevisiae

The conserved target of rapamycin complex 1 (TORC1) integrates nutrient signals to orchestrate cell growth and proliferation. Leucine availability is conveyed to control TORC1 activity via the leu-tRNA synthetase/EGOC-GTPase module in yeast and mammals, but the mechanisms sensing leucine remain only partially understood. We show here that both leucine and its α-ketoacid metabolite, α-ketoisocaproate, effectively activate the yeast TORC1 kinase via both EGOC GTPase-dependent and -independent mechanisms. Leucine and α-ketoisocaproate are interconverted by ubiquitous branched-chain aminotransferases (BCAT), which in yeast are represented by the mitochondrial and cytosolic enzymes Bat1 and Bat2, respectively. BCAT yeast mutants exhibit severely compromised TORC1 activity, which is partially restored by expression of Bat1 active site mutants, implicating both catalytic and structural roles of BCATs in TORC1 control. We find that Bat1 interacts with branched-chain amino acid metabolic enzymes and, in a leucine-dependent fashion, with the tricarboxylic acid (TCA)-cycle enzyme aconitase. BCAT mutation perturbed TCA-cycle intermediate levels, consistent with a TCA-cycle block, and resulted in low ATP levels, activation of AMPK, and TORC1 inhibition. We propose the biosynthetic capacity of BCAT and its role in forming multicomplex metabolons connecting branched-chain amino acids and TCA-cycle metabolism governs TCA-cycle flux to activate TORC1 signaling. Because mammalian mitochondrial BCAT is known to form a supramolecular branched-chain α-keto acid dehydrogenase enzyme complex that links leucine metabolism to the TCA-cycle, these findings establish a precedent for understanding TORC1 signaling in mammals.     

Carbon flux through tricarboxylic acid cycle in rat renal tubules

Our aim was to delineate the effect(s) of chronic metabolic acidosis on renal TCA-cycle metabolism. Renal tubules isolated from control and chronically acidotic rats were incubated at pH 7.4 with either 2 mM [2,3-13C]pyruvate or [2-13C]acetate. GC-MS and/or 13C-NMR were utilized to monitor the flux of 13C through pyruvate dehydrogenase, pyruvate carboxylase and the TCA-cycle. With either, precursor acidosis was associated with significantly decreased formation of 13C-labelled citrate, malate, aspartate and alanine and increased formation of glucose, lactate and acetyl-CoA as compared with the control. The results indicate that adaptation of renal metabolism to chronic metabolic acidosis is associated with diminished flux through citrate synthetase and concomitantly increased flux through pyruvate carboxylase. The data suggest that depletion of TCA-cycle intermediates and enhanced ammoniagenesis in the kidney of chronically acidotic rats may be regulated at the site of mitochondrial citrate-condensing enzyme.     

Biochemical thresholds for pathological presentation of ATP synthase deficiencies

Mitochondrial ATP synthase is responsible for production of the majority of cellular ATP. Disorders of ATP synthase in humans can be caused by numerous mutations in both structural subunits and specific assembly factors. They are associated with variable pathogenicity and clinical phenotypes ranging from mild to the most severe mitochondrial diseases. To shed light on primary/pivotal functional consequences of ATP synthase deficiency, we explored human HEK 293 cells with a varying content of fully assembled ATP synthase, selectively downregulated to 15–80% of controls by the knockdown of F₁ subunits γ, δ and ε. Examination of cellular respiration and glycolytic flux revealed that enhanced glycolysis compensates for insufficient mitochondrial ATP production while reduced dissipation of mitochondrial membrane potential leads to elevated ROS production. Both insufficient energy provision and increased oxidative stress contribute to the resulting pathological phenotype. The threshold for manifestation of the ATP synthase defect and subsequent metabolic remodelling equals to 10–30% of residual ATP synthase activity. The metabolic adaptations are not able to sustain proliferation in a galactose medium, although sufficient under glucose-rich conditions. As metabolic alterations occur when the content of ATP synthase drops below 30%, some milder ATP synthase defects may not necessarily manifest with a mitochondrial disease phenotype, as long as the threshold level is not exceeded.     

Capturing the essence of a metabolic network: A flux balance analysis approach

As genome-scale metabolic reconstructions emerge, tools to manage their size and complexity will be increasingly important. Flux balance analysis (FBA) is a constraint-based approach widely used to study the metabolic capabilities of cellular or subcellular systems. FBA problems are highly underdetermined and many different phenotypes can satisfy any set of constraints through which the metabolic system is represented.Two of the main concerns in FBA are exploring the space of solutions for a given metabolic network and finding a specific phenotype which is representative for a given task such as maximal growth rate. Here, we introduce a recursive algorithm suitable for overcoming both of these concerns. The method proposed is able to find the alternate optimal patterns of active reactions of an FBA problem and identify the minimal subnetwork able to perform a specific task as optimally as the whole.Our method represents an alternative to and an extension of other approaches conceived for exploring the space of solutions of an FBA problem. It may also be particularly helpful in defining a scaffold of reactions upon which to build up a dynamic model, when the important pathways of the system have not yet been well-defined.     

Predictive Potential of Flux Balance Analysis of Saccharomyces cerevisiae Using as Optimization Function Combinations of Cell Compartmental Objectives

Background

The main objective of flux balance analysis (FBA) is to obtain quantitative predictions of metabolic fluxes of an organism, and it is necessary to use an appropriate objective function to guarantee a good estimation of those fluxes.

Methodology

In this study, the predictive performance of FBA was evaluated, using objective functions arising from the linear combination of different cellular objectives. This approach is most suitable for eukaryotic cells, owing to their multiplicity of cellular compartments. For this reason, Saccharomyces cerevisiae was used as model organism, and its metabolic network was represented using the genome-scale metabolic model iMM904. As the objective was to evaluate the predictive performance from the FBA using the kind of objective function previously described, substrate uptake and oxygen consumption were the only input data used for the FBA. Experimental information about microbial growth and exchange of metabolites with the environment was used to assess the quality of the predictions.

Conclusions

The quality of the predictions obtained with the FBA depends greatly on the knowledge of the oxygen uptake rate. For the most of studied classifications, the best predictions were obtained with “maximization of growth”, and with some combinations that include this objective. However, in the case of exponential growth with unknown oxygen exchange flux, the objective function “maximization of growth, plus minimization of NADH production in cytosol, plus minimization of NAD(P)H consumption in mitochondrion” gave much more accurate estimations of fluxes than the obtained with any other objective function explored in this study.     

Parametric analysis of metabolic fluxes of α-amylase and protease-producing Bacillus subtilis

Parametric analysis was applied for a metabolic flux model for the fed-batch culture of Bacillus subtilis producing recombinant α-amylase and protease. The metabolic flux model was formulated as a linear programming problem consisting of 49 reactions (decision variables) and 50 metabolites (equality constraints). This study was aimed to determine the response of the metabolic fluxes and objective function value of minimizing the difference between ATP consumption and ATP production (ATP balance). With regard to intracellular metabolite accumulation, the objective function value was least sensitive to variation in succinate and most sensitive to variation in malate. Amongst the variations in the accumulation rates of extracellular metabolites, the objective function value was least sensitive to variation in glutamate and most sensitive to variation in starch hydrolysis and triglyceride synthesis. A 10% variation in metabolite accumulation rates caused a maximum of 13.8% variation (standard error = 3.8%) in the objective function value.     

Metabolic stasis in an ancient symbiosis: genome-scale metabolic networks from two <Emphasis Type="Italic">Blattabacterium cuenoti</Emphasis> strains,primary endosymbionts of cockroaches

BACKGROUND: Cockroaches are terrestrial insects that strikingly eliminate waste nitrogen as ammonia instead of uric acid. Blattabacterium cuenoti (Mercier 1906) strains Bge and Pam are the obligate primary endosymbionts of the cockroaches Blattella germanica and Periplaneta americana, respectively. The genomes of both bacterial endosymbionts have recently been sequenced, making possible a genome-scale constraint-based reconstruction of their metabolic networks. The mathematical expression of a metabolic network and the subsequent quantitative studies of phenotypic features by Flux Balance Analysis (FBA) represent an efficient functional approach to these uncultivable bacteria. RESULTS: We report the metabolic models of Blattabacterium strains Bge (iCG238) and Pam (iCG230), comprising 296 and 289 biochemical reactions, associated with 238 and 230 genes, and 364 and 358 metabolites, respectively. Both models reflect both the striking similarities and the singularities of these microorganisms. FBA was used to analyze the properties, potential and limits of the models, assuming some environmental constraints such as aerobic conditions and the net production of ammonia from these bacterial systems, as has been experimentally observed. In addition, in silico simulations with the iCG238 model have enabled a set of carbon and nitrogen sources to be defined, which would also support a viable phenotype in terms of biomass production in the strain Pam, which lacks the first three steps of the tricarboxylic acid cycle. FBA reveals a metabolic condition that renders these enzymatic steps dispensable, thus offering a possible evolutionary explanation for their elimination. We also confirm, by computational simulations, the fragility of the metabolic networks and their host dependence. CONCLUSIONS: The minimized Blattabacterium metabolic networks are surprisingly similar in strains Bge and Pam, after 140 million years of evolution of these endosymbionts in separate cockroach lineages. FBA performed on the reconstructed networks from the two bacteria helps to refine the functional analysis of the genomes enabling us to postulate how slightly different host metabolic contexts drove their parallel evolution.     

Influence of reaction and diffusion on spatial organization of mitochondria and effectiveness factors in skeletal muscle cell design

A mathematical model is developed to analyze the influence of chemical reaction and diffusion processes on the intracellular organization of mitochondria in skeletal muscle cells. The mathematical modeling approach uses a reaction-diffusion analysis of oxygen, ATP, and ADP involved in energy metabolism and mitochondrial function as governed by oxygen supply, volume fraction of mitochondria, and rates of reaction. Superimposed upon and coupled to the continuum species material balances is a cellular automata (CA) approach governing mitochondrial life cycles in response to the metabolic state of the cell. The effectiveness factor (η), defined as the ratio of reaction rate in the system with finite rates of diffusion to those in the absence of any diffusion limitation is used to assess diffusional constraints in muscle cells. The model shows the dramatic effects that the governing parameters have on the mitochondrial cycle of life and death and how these effects lead to changes in the distribution patterns of mitochondria observed experimentally. The model results showed good agreement with experimental results on mitochondrial distributions in mammalian muscle fibers. The η increases as the mitochondrial population is redistributed toward the fiber periphery in response to a decreased availability of oxygen. Modification of the CA parameters so that the mitochondrial lifecycle is more sensitive to the oxygen concentration caused larger mitochondrial shifts to the edge of the cell with smaller changes in oxygen concentration, and thus also lead to increased values of η. The present study shows that variation in oxygen supply, muscle activity and mitochondrial ATP supply influence the η and are the important parameters that can cause diffusion limitations. In order to prevent diffusion constraints, the cell resorts to shifts in their mitochondrial population towards the cell periphery, thus increasing η.     

Diseases caused by mutations in mitochondrial DNA

Mitochondrial diseases associated with mutations within mitochondrial genome are a subgroup of metabolic disorders since their common consequence is reduced metabolic efficiency caused by impaired oxidative phophorylation and shortage of ATP. Although the vast majority of mitochondrial proteins (approximately 1500) is encoded by nuclear genome, mtDNA encodes 11 subunits of respiratory chain complexes, 2 subunits of ATP synthase, 22 tRNAs and 2 rRNAs. Up to now, more than 250 pathogenic mutations have been described within mtDNA. The most common are point mutations in genes encoding mitochondrial tRNAs such as 3243A-->G and 8344T-->G that cause, respectively, MELAS (mitochondrial encephalopathy, lactic acidosis and stroke-like episodes) or MIDD (maternally-inherited diabetes and deafness) and MERRF (myoclonic epilepsy with ragged red fibres) syndromes. There have been also found mutations in genes encoding subunits of ATP synthase such as 8993T-->G substitution associated with NARP (neuropathy, ataxia and retinitis pigmentosa) syndrome. It is worth to note that mitochondrial dysfunction can also be caused by mutations within nuclear genes coding for mitochondrial proteins.     

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol⁻¹, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.     

The role of mitochondrial respiration in physiological and evolutionary adaptation

Aerobic mitochondria serve as the power sources of eukaryotes by producing ATP through oxidative phosphorylation (OXPHOS). The enzymes involved in OXPHOS are multisubunit complexes encoded by both nuclear and mitochondrial DNA. Thus, regulation of respiration is necessarily a highly coordinated process that must organize production, assembly and function of mitochondria to meet an organism's energetic needs. Here I review the role of OXPHOS in metabolic adaptation and diversification of higher animals. On a physiological timescale, endocrine-initiated signaling pathways allow organisms to modulate respiratory enzyme concentration and function under changing environmental conditions. On an evolutionary timescale, mitochondrial enzymes are targets of natural selection, balancing cytonuclear coevolutionary constraints against physiological innovation. By synthesizing our knowledge of biochemistry, physiology and evolution of respiratory regulation, I propose that we can now explore questions at the interface of these fields, from molecular translation of environmental cues to selection on mitochondrial haplotype variation.     

Two mutations in mitochondrial ATP6 gene of ATP synthase,related to human cancer,affect ROS,calcium homeostasis and mitochondrial permeability transition in yeast

The relevance of mitochondrial DNA (mtDNA) mutations in cancer process is still unknown. Since the mutagenesis of mitochondrial genome in mammals is not possible yet, we have exploited budding yeast S. cerevisiae as a model to study the effects of tumor-associated mutations in the mitochondrial MTATP6 gene, encoding subunit 6 of ATP synthase, on the energy metabolism. We previously reported that four mutations in this gene have a limited impact on the production of cellular energy. Here we show that two mutations, Atp6-P163S and Atp6-K90E (human MTATP6-P136S and MTATP6-K64E, found in prostate and thyroid cancer samples, respectively), increase sensitivity of yeast cells both to compounds inducing oxidative stress and to high concentrations of calcium ions in the medium, when Om45p, the component of porin complex in outer mitochondrial membrane (OM), was fused to GFP. In OM45-GFP background, these mutations affect the activation of yeast permeability transition pore (yPTP, also called YMUC, yeast mitochondrial unspecific channel) upon calcium induction. Moreover, we show that calcium addition to isolated mitochondria heavily induced the formation of ATP synthase dimers and oligomers, recently proposed to form the core of PTP, which was slower in the mutants. We show the genetic evidence for involvement of mitochondrial ATP synthase in calcium homeostasis and permeability transition in yeast. This paper is a first to show, although in yeast model organism, that mitochondrial ATP synthase mutations, which accumulate during carcinogenesis process, may be significant for cancer cell escape from apoptosis.