An interval approach for dealing with flux distributions and elementary modes activity patterns

This work introduces the use of an interval representation of fluxes. This representation can be useful in two common situations: (a) when fluxes are uncertain due to the lack of accurate measurements and (b) when the flux distribution is partially unknown. In addition, the interval representation can be used for other purposes such as dealing with inconsistency or representing a range of behaviour. Two main problems are addressed. On the one hand, the translation of a metabolic flux distribution into an elementary modes or extreme pathways activity pattern is analysed. In general, there is not a unique solution for this problem but a range of solutions. To represent the whole solution region in an easy way, it is possible to compute the alpha-spectrum (i.e., the range of possible values for each elementary mode or extreme pathway activity). Herein, a method is proposed which, based on the interval representation of fluxes, makes it possible to compute the alpha-spectrum from an uncertain or even partially unknown flux distribution. On the other hand, the concept of the flux-spectrum is introduced as a variant of the metabolic flux analysis methodology that presents some advantages: applicable when measurements are insufficient (underdetermined case), integration of uncertain measurements, inclusion of irreversibility constraints and an alternative procedure to deal with inconsistency. Frequently, when applying metabolic flux analysis the available measurements are insufficient and/or uncertain and the complete flux distribution cannot be uniquely calculated. The method proposed here allows the determination of the ranges of possible values for each non-calculable flux, resulting in a flux region called flux-spectrum. In order to illustrate the proposed methods, the example of the metabolic network of CHO cells cultivated in stirred flasks is used.     

Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism

Elementary mode analysis is a useful metabolic pathway analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering.     

Fundamental Escherichia coli biochemical pathways for biomass and energy production: creation of overall flux states

We have previously shown that the metabolism for most efficient cell growth can be realized by a combination of two types of elementary modes. One mode produces biomass while the second mode generates only energy. The identity of the four most efficient biomass and energy pathway pairs changes, depending on the degree of oxygen limitation. The identification of such pathway pairs for different growth conditions offers a pathway-based explanation of maintenance energy generation. For a given growth rate, experimental aerobic glucose consumption rates can be used to estimate the contribution of each pathway type to the overall metabolic flux pattern. All metabolic fluxes are then completely determined by the stoichiometries of involved pathways defining all nutrient consumption and metabolite secretion rates. We present here equations that permit computation of network fluxes on the basis of unique pathways for the case of optimal, glucose-limited Escherichia coli growth under varying levels of oxygen stress. Predicted glucose and oxygen uptake rates and some metabolite secretion rates are in remarkable agreement with experimental observations supporting the validity of the presented approach. The entire most efficient, steady-state, metabolic rate structure is explicitly defined by the developed equations without need for additional computer simulations. The approach should be generally useful for analyzing and interpreting genomic data by predicting concise, pathway-based metabolic rate structures.     

Estradiol stimulates the biosynthetic pathways of breast cancer cells: detection by metabolic flux analysis

Selective estrogen receptor (ER) modulators are highly successful breast cancer therapies, but they are not effective in patients with ER negative and selective estrogen receptor modulator (SERM)-resistant tumors. Understanding the mechanisms of estrogen-stimulated proliferation may provide a route to design estrogen-independent therapies that would be effective in these patients. In this study, metabolic flux analysis was used to determine the intracellular fluxes that are significantly affected by estradiol stimulation in MCF-7 breast cancer cells. Intracellular fluxes were calculated from nuclear magnetic resonance (NMR)-generated isotope enrichment data and extracellular metabolite fluxes, using a specific flux analysis algorithm. The metabolic pathway model used by the algorithm includes glycolysis, the tricarboxylic acid cycle (TCA cycle), the pentose phosphate pathway, glutamine catabolism, pyruvate carboxylase, and malic enzyme. The pathway model also incorporates mitochondrial compartmentalization and reversible trans-mitochondrial membrane reactions to more accurately describe the role of mitochondria in cancer cell proliferation. Flux results indicate that estradiol significantly increases carbon flow through the pentose phosphate pathway and increases glutamine consumption. In addition, intra-mitochondrial malic enzyme was found to be inactive and the malate-aspartate shuttle (MAS) was only minimally active. The inactivity of these enzymes indicates that glutamine is not oxidized within mitochondria, but is consumed primarily to provide biosynthetic precursors. The excretion of glutamine carbons from the mitochondria has the secondary effect of limiting nicotinamide adenine dinucleotide (NADH) recycle, resulting in NADH buildup in the cytosol and the excretion of lactate. The observed dependence of breast cancer cells on pentose phosphate pathway activity and glutamine consumption for estradiol-stimulated biosynthesis suggests that these pathways may be targets for estrogen-independent breast cancer therapies.     

In silico investigation of novel biological pathways: The role of CD200 in regulation of T cell priming in experimental autoimmune encephalomyelitis

The use of simulation to investigate biological domains will inevitably lead to the need to extend existing simulations as new areas of these domains become more fully understood. Such simulation extensions can entail the incorporation of additional cell types, molecules or molecular pathways, all of which can exert a profound influence on the simulation behaviour. Where the biological domain is not well characterised, a structured development methodology must be employed to ensure that the extended simulation is well aligned with its predecessor. We develop and discuss such a methodology, relying on iterative simulation development and sensitivity analysis. The utility of this methodology is demonstrated using a case study simulation of experimental autoimmune encephalomyelitis (EAE), a murine T cell-mediated autoimmune disease model of multiple sclerosis, where it is used to investigate the activity of an additional regulatory pathway. We discuss how application of this methodology guards against creating inappropriate simulation representations of the biology when investigating poorly characterised biological mechanisms.     

Choke point analysis of metabolic pathways in E.histolytica: a computational approach for drug target identification

With the Entamoeba genome essentially complete, the organism can be studied from a whole genome standpoint. The understanding of cellular mechanisms and interactions between cellular components is instrumental to the development of new effective drugs and vaccines. Metabolic pathway analysis is becoming increasingly important for assessing inherent network properties in reconstructed biochemical reaction networks. Metabolic pathways illustrate how proteins work in concert to produce cellular compounds or to transmit information at different levels. Identification of drug targets in E. histolytica through metabolic pathway analysis promises to be a novel approach in this direction. This article focuses on the identification of drug targets by subjecting the Entamoeba genome to BLAST with the e-value inclusion threshold set to 0.005 and choke point analysis. A total of 86.9 percent of proposed drug targets with biological evidence are chokepoint reactions in Entamoeba genome database.     

In vivo overproduction of the pentafunctional arom polypeptide in Aspergillus nidulans affects metabolic flux in the quinate pathway

Summary The shikimate pathway and the quinic acid utilisation (QUT) pathway of Aspergillus nidulans and other fungi share the two common metabolic intermediates, 3-dehydroquinic acid (DHQ) and dehydroshikimic acid (DHS), which are interconverted by two isoenzymes, catabolic 3-dehydroquinase, (cDHQase) and biosynthetic dehydroquinase (bDHQase). bDHQase is one of five consecutive enzymatic activities associated with the pentafunctional arom protein encoded by the complex AROM locus, whereas cDHQase is encoded by the single-function QUTE gene, one of seven genes comprising the QUT gene cluster in A. nidulans, which is required for the catabolism of quinate to protocatechuate. We addressed the question of how much (if any) leakage there is of the two common substrates between the two pathways, by increasing the concentration of the arom protein in vivo by means of recombinant DNA technology. We demonstrated that constitutive overproduction of the arom protein by 12-fold in the presence of quinate inhibits germination of conidiospores, but showed that 12-fold quinate-inducible overproduction of arom protein does not have this effect. In addition we showed that a qutE mutant (lacking cDHQase) can grow with quinic acid as sole carbon source when the arom protein is overproduced fivefold. The data are most simply interpreted as simple competition for common substrates by the enzymes of the two pathways and demonstrate that any channelling function of the arom protein in vivo is relatively leaky.     

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and ¹³C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering.     

Mass flux balance-based model and metabolic pathway engineering analysis for serine alkaline protease synthesis by Bacillus licheniformis

A mass flux balance-based stoichiometric model of Bacillus licheniformis for the serine alkaline protease (SAP) fermentation process has been established. The model considers 147 reaction fluxes, and there are 105 metabolites that are assumed to be in pseudo-steady state. Metabolic flux distributions were obtained from the solution of the model based on the minimum SAP accumulation rate assumption in B. licheniformis in combination with the off-line extracellular analyses of the metabolites that were the sole carbon source citrate, dry cell, organic acids, amino acids, and SAP; variations in the intracellular fluxes were demonstrated for the three periods of the batch bioprocess. The flux distribution maps showed that the cells completed the TCA cycle and utilized the gluconeogenesis pathway, pentose phosphate pathway, and anaplerotic reactions throughout the fermentation; however, the glycolysis pathway was inactive in all the periods of the fermentation. The flux values toward SAP increased throughout the bioprocess and slightly decreased in the last period; however, SAP selectivity values were almost the same in Periods II and III and higher than Period I. The diversions in the pathways and certain metabolic reactions depending on the bioprocess periods are also presented and the results indicated that the intracellular amino acid fluxes played an important role in the SAP fermentation process.     

Reactive oxygen species regulate oxygen-sensitive potassium flux in rainbow trout erythrocytes

In the present study, we have investigated if reactive oxygen species are involved in the oxygen-dependent regulation of potassium-chloride cotransport activity in trout erythrocyte membrane. An increase in the oxygen level caused an increase in chloride-sensitive potassium transport (K(+)-Cl(-) cotransport). 5 mM hydrogen peroxide caused an increase in K(+)-Cl(-) cotransport at 5% oxygen. The increase in flux could be inhibited by adding extracellular catalase in the incubation. Pretreatment of the cells with mercaptopropionyl glycine (MPG), a scavenger of reactive oxygen species showing preference for hydroxyl radicals, abolished the activation of the K(+)-Cl(-) cotransporter by increased oxygen levels. The inhibition by MPG was reversible, and MPG could not inhibit the activation of transporter by the sulfhydryl reagent, N-ethylmaleimide, indicating that the effect of MPG was due to the scavenging of reactive oxygen species and not to the reaction of MPG with the cotransporter. Copper ions, which catalyze the production of hydroxyl radicals in the Fenton reaction, activated K(+)-Cl(-) cotransport significantly at hypoxic conditions (1% O(2)). These data suggest that hydroxyl radicals, formed from O(2) in close vicinity to the cell membrane, play an important role in the oxygen-dependent activation of the K(+)-Cl(-) cotransporter.     

Dynamic metabolic flux analysis (DMFA): a framework for determining fluxes at metabolic non-steady state

Metabolic flux analysis (MFA) is a key tool for measuring in vivo metabolic fluxes in systems at metabolic steady state. Here, we present a new method for dynamic metabolic flux analysis (DMFA) of systems that are not at metabolic steady state. The advantages of our DMFA method are: (1) time-series of metabolite concentration data can be applied directly for estimating dynamic fluxes, making data smoothing and estimation of average extracellular rates unnecessary; (2) flux estimation is achieved without integration of ODEs, or iterations; (3) characteristic metabolic phases in the fermentation data are identified automatically by the algorithm, rather than selected manually/arbitrarily. We demonstrate the application of the new DMFA framework in three example systems. First, we evaluated the performance of DMFA in a simple three-reaction model in terms of accuracy, precision and flux observability. Next, we analyzed a commercial glucose-limited fed-batch process for 1,3-propanediol production. The DMFA method accurately captured the dynamic behavior of the fed-batch fermentation and identified characteristic metabolic phases. Lastly, we demonstrate that DMFA can be used without any assumed metabolic network model for data reconciliation and detection of gross measurement errors using carbon and electron balances as constraints.     

CrxOS maintains the self-renewal capacity of murine embryonic stem cells

Embryonic stem (ES) cells maintain pluripotency by self-renewal. Several homeoproteins, including Oct3/4 and Nanog, are known to be key factors in maintaining the self-renewal capacity of ES cells. However, other genes required for the mechanisms underlying this process are still unclear. Here we report the identification by in silico analysis of a homeobox-containing gene, CrxOS, that is specifically expressed in murine ES cells and is essential for their self-renewal. ES cells mainly express the short isoform of endogenous CrxOS. Using a polyoma-based episomal expression system, we demonstrate that overexpression of the CrxOS short isoform is sufficient for maintaining the undifferentiated morphology of ES cells and stimulating their proliferation. Finally, using RNA interference, we show that CrxOS is essential for the self-renewal of ES cells, and provisionally identify foxD3 as a downstream target gene of CrxOS. To our knowledge, ours is the first delineation of the physiological role of CrxOS in ES cells.     

Genome-scale reconstruction and <Emphasis Type="Italic">in silico</Emphasis> analysis of the <Emphasis Type="Italic">Clostridium acetobutylicum</Emphasis> ATCC 824 metabolic network

To understand the metabolic characteristics of Clostridium acetobutylicum and to examine the potential for enhanced butanol production, we reconstructed the genome-scale metabolic network from its annotated genomic sequence and analyzed strategies to improve its butanol production. The generated reconstructed network consists of 502 reactions and 479 metabolites and was used as the basis for an in silico model that could compute metabolic and growth performance for comparison with fermentation data. The in silico model successfully predicted metabolic fluxes during the acidogenic phase using classical flux balance analysis. Nonlinear programming was used to predict metabolic fluxes during the solventogenic phase. In addition, essential genes were predicted via single gene deletion studies. This genome-scale in silico metabolic model of C. acetobutylicum should be useful for genome-wide metabolic analysis as well as strain development for improving production of biochemicals, including butanol. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. J. L. and H. Y. equally contributed to this work.     

The infection biology of Fusarium graminearum: defining the pathways of spikelet to spikelet colonisation in wheat ears

Fusarium graminearum is one of the main causal agents of Fusarium Ear Blight on wheat. How the pathogen colonises the entire ear is not known. There is controversy over whether this mycotoxin producing pathogenic fungus invades wheat floral tissue using a necrotrophic or another mode of nutrition. A detailed microscopic investigation has revealed how wild-type fungal hyphae, of the sequenced strain PH-1, colonised susceptible wheat ears and spread from spikelet to spikelet. At the advancing infection front, colonisation of the host cortex occurred ahead of any vascular colonisation and the hyphae adapted to the available intercellular space between host cells. Intercellular hyphae then became abundant and host cells lost their entire cellular contents just prior to intracellular colonisation. No host cells died ahead of the infection. However, while these deep cortex infections progressed, just below the surface the highly photosynthetic chlorenchyma cells were observed to have died prior to colonisation. Behind the infection front, hyphae were abundant in the vasculature and the cortex, often growing through the pit fields of thick walled cells. This high level of inter- and intracellular fungal colonisation resulted in the collapse of the non-lignified cell-types. In this middle zone of infection, hyphal diameters were considerably enlarged. Far behind the infection front inter- and intracellular hyphae were devoid of contents and had often collapsed. At later stages of infection, the pathogen switched from predominately vertical to lateral growth and accumulated below the surface of the rachis. Here the lignified host cell walls became heavily degraded and hyphae ruptured the epidermis and produced an aerial mycelium.     

Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3‐hydroxypropanoate

Sustainable microbial production of high‐value organic compounds such as 3‐hydroxypropanoate (3HP) is becoming an increasingly attractive alternative to organic syntheses that utilize petrochemical feedstocks. We applied the Biochemical Network Integrated Computational Explorer (BNICE) framework to the automated design and evaluation of novel biosynthetic routes for the production of 3HP from pyruvate. Among the pathways generated by the BNICE framework were all of the known pathways for the production of 3HP as well as numerous novel pathways. The pathways generated by BNICE were ranked based on four criteria: pathway length, thermodynamic feasibility, maximum achievable yield to 3HP from glucose, and maximum achievable activity at which 3HP can be produced. Four pathways emerged from this ranking as the most promising for the biosynthesis of 3HP, and three of these pathways, including the shortest pathways discovered, were novel. We also discovered novel routes for the biosynthesis of 28 commercially available compounds that are currently produced exclusively through organic synthesis. Examination of the optimal pathways for the biosynthesis of these 28 compounds in E. coli revealed pyruvate and succinate to be ideal intermediates for achieving high product yields from glucose. Biotechnol. Bioeng. 2010; 106: 462–473. © 2010 Wiley Periodicals, Inc.     

Identification of kinin-related peptides in the disease vector, Rhodnius prolixus

We have used an in silico approach to identify a gene from the blood-gorging vector, Rhodnius prolixus, that is predicted to produce an insect kinin prepropeptide. The prepropeptide is 398 amino acids in length and can potentially produce a large number of kinin-related peptides following post-translational processing. A comparison with other insect kinin precursor sequences demonstrates greatest conservation at the C-terminal region of the kinin peptides. Multiple peptides predicted from the kinin gene are phenotypically expressed in R. prolixus, as revealed by MALDI-TOF MS MS, including 12 kinins and one kinin precursor peptide (KPP). Six of these peptides are characterized by the typical insect kinin C-terminal motif FX₁X₂WGamide and five of these are also found as truncated forms. Five peptides were identified with an atypical, though similar, FX₁X₂WAamide C-terminus. There is also peptide with a C-terminal DDNGamide motif and a number of non-amidated peptides.     

Biochemical and structural characterization of the paralogous benzoate CoA ligases from Burkholderia xenovorans LB400: defining the entry point into the novel benzoate oxidation (box) pathway

Xenobiotic aromatic compounds represent one of the most significant classes of environmental pollutants. A novel benzoate oxidation (box) pathway has been identified recently in Burkholderia xenovorans LB400 (referred to simply as LB400) that is capable of assimilating benzoate and intimately tied to the degradation of polychlorinated biphenyls (PCBs). The box pathway in LB400 is present in two paralogous copies (boxM and boxC) and encodes eight enzymes with the first committed step catalyzed by benzoate CoA ligase (BCL). As a first step towards delineating the biochemical role of the box pathway in LB400, we have carried out functional studies of the paralogous BCL enzymes (BCLM and BCLC) with 20 different putative substrates. We have established a structural rationale for the observed substrate specificities on the basis of a 1.84 A crystal structure of BCLM in complex with benzoate. These data show that, while BCLM and BCLC display similar overall substrate specificities, BCLM is significantly more active towards benzoate and 2-aminobenzoate with tighter binding (Km) and a faster reaction rate (Vmax). Despite these clear functional differences, the residues that define the substrate-binding site in BCLM are completely conserved in BCLC, suggesting that second shell residues may play a significant role in substrate recognition and catalysis. Furthermore, comparison of the active site of BCLM with the recently solved structures of 4-chlorobenzoate CoA ligase and 2, 3-dihydroxybenzoate CoA ligase offers additional insight into the molecular features that mediate substrate binding in adenylate-forming enzymes. This study provides the first biochemical characterization of a Box enzyme from LB400 and the first structural characterization of a Box enzyme from any organism, and further substantiates the concept of distinct roles for the two paralogous box pathways in LB400.