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Bioprocess Engineering • Braz. J. Chem. Eng. 35 (3) • Jul-Sep 2018 • https://doi.org/10.1590/0104-6632.20180353s20170327 linkcopy

Open-access MODELING THE CORE METABOLISM OF Komagataeibacter hansenii ATCC 23769 TO EVALUATE NANOCELLULOSE BIOSYNTHESIS

AuthorshipSCIMAGO INSTITUTIONS RANKINGS

    Abstract

    Genome-scale metabolic models based on a combination of genome sequence and biochemical information have strongly influenced the field of systems biology. However, basic principles of the operation of metabolic networks, in particular the central metabolism can be easily studied in smaller metabolic (core) models. Komagataeibacter hansenii ATCC 23769 has been used for bacterial nanocellulose (BNC) biosynthesis, and the recent availability of its genome sequence allowed the development of a metabolic model. The core metabolic model was constructed from an initial draft metabolic reconstruction including 74 reactions and 68 metabolites that provides insights for a better understanding of K. hansenii metabolic pathways. The applicability of the model is finally demonstrated by applying the FBA approach, and the in silico simulation successfully predicted the minimal medium and the growing abilities on different substrates. This core model can facilitate system-level metabolic analysis as well as developments for improving BNC production.

    Keywords: Komagataeibacter hansenii; Bacterial nanocellulose; Core metabolic model; Flux balance analysis

    INTRODUCTION

    Metabolic models have a promising ability to describe cellular phenotypes accurately and to relate the annotated genome sequence to the physiological functions of a cell (Covert et al., 2001; Kim et al., 2015). There is an extensive diversity of unexplored metabolism encoded into the genomes of microorganisms and a huge gap in understanding the link between the genetic information and the resulting phenotype (Blank and Ebert, 2013; Mahadevan et al., 2011). Metabolic models are based on a network of chemical reactions that characterize the vast metabolic network of an organism (Almaas et al., 2004; Shimizu, 2009; Wiechert, 2002). These networks may be used to generate metabolic states for a given set of environmental conditions.

    Genomes of several bacterial strains have been sequenced and annotated, providing information that has been used alongside biochemical and physiological data to reconstruct metabolic networks (Huang et al., 2014; Loira et al., 2012; Terzer et al., 2009; Zhang and Hua, 2015). A comprehensive protocol was developed to describe each step necessary to build a high-quality genome-scale metabolic reconstruction (Thiele and Palsson, 2010). This protocol was properly structured for large-scale metabolic networks and well-studied organisms when several experimental evidences are available to allow the required significant manual curation (Becker et al., 2007; Cheng et al., 2009). Although well-curated genome-scale models were developed, some microorganisms do not have sufficient information available about their metabolic features. In this case, reducing those models to a certain core or module while keeping key elements or/and important functional properties, i.e., to construct metabolic core models, can be a suitable way to study and understand basic principles of the central metabolism. In core models, the reactions and pathways are chosen to represent the most well-known and widely studied metabolic pathways (Orth, 2010). However, for the construction of a core and representative model for organisms with little reported information on their metabolic capabilities, some adaptations in the current protocol were necessary.

    The combination of metabolic network reconstruction and constraint-based modeling provides a rich information set from which one can build mathematical models of biological interest (Barabási and Oltvai, 2004). Moreover, computational tools have been developed to predict fluxes in biochemical networks, thereby integrating different fields such as systems biology, bioinformatics and metabolic engineering (Fernández-Castané et al., 2014; Ishii et al., 2004; McCloskey et al., 2013). Flux Balance Analysis (FBA) has been successfully applied to obtain growth predictions, theoretical product yields and for a global estimation of flux distribution within the metabolism of different organisms (Grafahrend-Belau et al., 2014; Reed, 2012). Critical steps in FBA are the reconstruction of a metabolic network, followed by mass balance, imposition of constraints, choice of a suitable (biologically relevant) objective function and linear optimization (Angeles-Martinez and Theodoropoulos, 2016; Orth et al., 2010; Raman and Chandra, 2009). Simulation results can be a useful guide for metabolic engineering (Liu et al., 2014; Simeonidis and Price, 2015). Currently, a popular tool for investigating complex metabolic models is the constraint-based reconstruction and analysis (COBRA), a MATLAB® (MathWorks Inc.) toolbox (Becker et al., 2007; Schellenberger et al., 2011). Our group has developed a set of computational systems biology tools, called GEnSys (Genomic Engineering System), which comprises several modules that allow analysis and simulation of biochemical reaction networks, for instance, flux balance analysis (FBA) (Bagnariolli et al., 2010).

    Komagataeibacter hansenii ATCC 23769 (formerly Gluconacetobacter hansenii) (Iyer et al., 2010; Yamada et al., 2012) produces, as a result of the fermentation process, a microstructured nanocellulose with high purity (Benziman et al., 1980; Deinema and Zevenhuizen, 1971; Ross et al., 1991). Bacterial nanocellulose (BNC) is a potential material for medical applications and it has been commonly applied as wound dressing and temporary skin replacement (Cheng et al., 2009; Hutchens et al., 2007; Jorfi and Foster, 2015; Jozala et al., 2016). Given the importance of bacterial nanocellulose-based biomaterials in tissue engineering, an in silico core metabolic model of K. hansenii can provide new strategic insights into the BNC synthesis and be useful in the study of typical synthesis conditions, such as different growth media, environmental conditions and formation of bioproducts. K. hansenii is not the most commonly studied model bacterium for BNC production, such as K. xylinum, however it has the ability, as well, to produce nanocellulose (Ramana et al., 2000; Ruka et al., 2012; Zeng et al., 2011).

    Here, a core metabolic model of K. hansenii ATCC 23769 was developed, based on the draft assembly of the genome of this bacterium (GenBank accession no. CM000920 and taxonomy ID: 714995) (Iyer et al., 2010). Through simulation, relevant physiological scenarios were studied. The FBA approach was performed to simulate different conditions and maximize specific reactions to understand the effects of nanocellulose production and distribution of cellular fluxes by varying three carbon sources: glucose, mannitol and glycerol under minimal nutritional requirements. These carbon sources were chosen because they are known to lead to differences in the nanocellulose microstructure, which have different fiber densities (Mikkelsen et al., 2009; Ruka et al., 2012). This in silico model can facilitate system-level metabolic analysis and allow experiments with K. hansenii growing in a defined medium that enables controlled experiments since the exact composition of nutrients is known.

    MATERIALS AND METHODS

    Draft reconstruction

    The reconstruction process of the K. hansenii core metabolic model involved the following steps, as outlined in Figure 1: (1) creation of a draft model (draft reconstruction); (2) reconstruction of a detailed model (manual curation to build the core model); (3) conversion into a mathematical format (FBA; mathematical formulation); (4) analysis of the network (model simulation).

    Figure 1
    Schematic illustration of the network reconstruction. The four steps used in the present work are: 1) creation of a draft model using automated resources, 2) manual curation to construct the core model, 3) conversion of the model into a mathematical format and, 4) biological analysis of the network through simulations.

    The annotated data of the draft genome sequence of K. hansenii (NCBI ID 714995; ACCESSION NZ CM000920, 3636659 bp) (Iyer et al., 2010) were used in two different platforms that can map genes to reactions in an automated manner and allow exporting all reactions and metabolites to a SBML file, to create two different drafts models. The first one was the software Pathway Tools (SRI International), version 16.5 (Karp et al., 2002, 2009; Paley et al., 2012) that gives a raw draft model file as a result. The second one was the web-based resource, called Model SEED (Devoid et al., 2013), where the assembled genome sequence is annotated by the RAST server, which provides a semi-automated curation of the draft model. The set of reactions from the drafts contains incorrect or unbalanced stoichiometry, missing reactions and mistakes from the annotated sequence and neither of these approaches replace a careful manual curation. The resources used during the reconstruction are summarized in Table 1.

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    Table 1
    Online resources for the reconstruction of the metabolic network of K hansenii.

    Manual curation

    The basic principles of the operation of metabolic networks, in particular of the central metabolism, can sometimes be more easily studied in smaller scale models, known as core models (Hädicke and Klamt, 2017). As the goal is to construct a core model that represents the central metabolism of K. hansenii in detail, the reactions and metabolites involved in glycolysis, pentose phosphate pathway, Entner-Doudoroff pathway, tricarboxylic acid cycle, and key reactions of the electron transport chain were included in the model. To improve network connectivity and decrease the number of dead-end metabolites, the reactions not inferred in the automatic drafts models were added during manual curation. Such reactions include: (1) spontaneous reactions; (2) extracellular transport reactions; (3) intracellular transport reactions; and (4) exchange reactions, which allows specific molecules through the system and environment and (5) reactions of cellulose biosynthesis common to microorganism producers.

    Biochemistry textbooks and biochemical digital databases, including KEGG (Kanehisa et al., 2006, 2010), BRENDA (Scheer et al., 2011), ExPASy (Gasteiger et al., 2003) and the platform IMG (Integrated Microbial Genomes) (Markowitz et al., 2012) were used to verify the reactions. A biochemical thermodynamics calculator, eQuilibrator (Flamholz et al., 2012), was used to check the reversibility and stoichiometry of the reactions. The metabolic reactions in the model were organized into two compartments (cytoplasm and extracellular) based on the localization of associated enzymes. Then, we organized the reactions into pathways/subsystems. For each metabolite, the charge, formula and identification were compiled from the KEGG database.

    The last step was the incorporation of a biomass reaction. In order to represent growth, the core K. hansenii model includes a biomass reaction, which drains precursor metabolites from the network. The biomass composition data for K. hansenii used in this study was obtained from the literature (Edirisinghe et al., 2016). The reaction of the biomass included internal protons and water (Appendix - Table A1). The amount of water required is equal to the amount of hydrolyzed ATP to satisfy the ATP growth requirement. The ATP hydrolysis results in the production of one proton, while using NADPH as NADH consumes one proton, resulting in the production of protons in the biomass reaction. All precursors were added to the molecules to perform oxidation (NAD), reduction (NADPH) and provide energy (ATP), resulting in 1 mol of biomass, which is the amount of biomass produced with these compounds. Missing reactions (referred to as gaps) that resulted in dead-end metabolites and prevented the computational simulation of cell growth were identified and filled in. The procedure was continued until all the biomass components were include. This ensures that the reduced network contains at least all protected reactions and, additionally, a set of biosynthesis routes that produces all components consumed by the biomass synthesis reaction. In total, 74 reactions and their reactants have been protected in the central metabolism as listed in the Appendix (Tables A1 and A2).

    The in silico minimal medium composition capable of supporting growth of K. hansenii chosen was the Yamanaka medium (Yamanaka et al., 1989), which is composed of 50 g∙L-1 (carbon source), 5 g∙L-1 (nitrogen source) and 3 g∙L-1 (phosphate source). Three different carbon sources (glucose, mannitol and glycerol) were used to calculate the carbon flux through different pathways. The uptake rates of nitrogen and phosphate sources were determined according to the composition of the medium and we used experimental data in continuous culture since the FBA approach assumes steady state and generates predictions that are consistent with continuous culture. Biomass concentration and dilution rate values were estimated to calculate and infer the maximum uptake rates of nitrogen ((NH4)2SO4) and phosphate sources (KH2PO4) in the model.

    Flux Balance Analysis (FBA)

    The metabolic flux distribution of the core model of K. hansenii was calculated using FBA. With this approach, it was possible to obtain the optimal solution for the intracellular fluxes by optimizing an objective function. The core model was converted into a mathematical representation known as a stoichiometric matrix. The stoichiometric matrix (S) consists of rows of metabolites and columns of reactions, and is the basis from which all constraints-based modeling is carried out. The converted core model is expressed as a stoichiometric model represented by a pseudo steady-state system of mass balance equations dc/dt = S ∙ v = 0, where v corresponds to a vector of all reaction fluxes in the network (Feist, 2009; Orth et al., 2010). To identify optimal solutions in the vast solution space, we defined FBA objective functions to solve the system of linear equations that represent the mass balance constraints. In this study, we evaluated four scenarios for biologically meaningful predictions: (i) maximization of biomass yield; (ii) maximization of nanocellulose synthesis, product of greatest interest derived from the bacterium K. hansenii; (iii) the maximization of the external metabolites to evaluate the balance consistency; and (iv) the ability to synthesize precursors of biomass by adding demand reactions. Moreover, metabolic flux distribution was estimated under limitations of some nutrients, such as phosphate and nitrogen source.

    The constraints for the upper and lower bounds of reversible and irreversible reactions were defined as -∞ ≤ v i ≤ ∞ and 0 ≤ v i ≤ ∞, respectively. For irreversible reactions, the lower bound was set to zero and for reversible reactions, lower and upper bounds were typically set to arbitrarily large values. Besides defining the directions of all metabolic reactions, these constraints were used to specify a maximum flux through a given reaction or to specify a measured substrate uptake rate. Exchange reactions were added to enable uptake and secretion of extracellular metabolites for simulations. The stoichiometry and the reversibility of each reaction, together with the steady state assumption for the internal metabolites, allow defining a region of feasible flux distribution.

    Model simulation

    The core metabolic network of K. hansenii was built and loaded into MATLAB® (MathWorks Inc.) using functions available in the GEnSys toolbox (Bagnariolli et al., 2010). The GenSys Toolbox is available upon request. By using Flux Balance Analysis (FBA), we investigated the core metabolic network of K. hansenii through simulations. The flux values were expressed in mmol∙gDW-1∙h-1. For the simulation of aerobic growth on minimal medium, we allowed the following external metabolites to freely enter and leave the network: O2, H+, CO2, H2O, NH4+ and PO43-. With the minimal medium, each carbon source was allowed to enter into the in silico core model one by one by adding exchange reactions (if there was no corresponding one) for the sake of simulating the growth under different environmental conditions.

    The core metabolic model construction is an integrated process. Through experimentally determined biochemical characteristics of K. hansenii combined with computer modeling provided advances to understand what happens inside a cell through in silico simulation (Figure 2).

    Figure 2
    Biochemical analysis and computer modeling to advance the understanding of what happens inside a cell. The core metabolic network is modified in the context of other physiological constraints to produce a mathematical model, which can be used to generate quantitatively testable hypotheses in silico. Depending on the culture conditions, K. hansenii is able to synthesize BNC with different shapes, such as membranes, spheres and vessels.

    From the annotated genome sequence, a core metabolic model of K. hansenii was constructed to comprehend the mechanisms and synthesis of bacterial nanocellulose. This core model can facilitate system-level metabolic analysis as well as developments for improving BNC production.

    RESULTS AND DISCUSSION

    Metabolic model reconstruction

    The initial draft of the core reconstruction was built from the annotated genome of K. hansenii ATCC 23769. The chromosomal sequence contains 3,547,122 bp, with a GC content of 59%. The genome contains 3,351 genes, of which 3,308 are protein-encoding genes, accounting for 84% of the genome. There are 43 genes for tRNAs and two rRNA loci. The genes encoding proteins involved in cellulose synthesis are in operons consisting of acsAB (GXY_04277; GXY_08864), acsC (GXY_04282; GXY_08869) and acsD (GXY_04292). The two drafts generated by Pathway Tools and Model SEED were initial mapping processes that list the rough data of the metabolites and reactions of this organism. They were stored in both SBML and XLS formats and were used to manage all the consolidated data.

    From a topological analysis, a metabolic network can be interpreted as a bipartite graph, consisting of two sets of nodes that represent metabolites and reactions, respectively. The two disjoint sets of nodes are connected by a set of (directed or undirected) edges, specifying which metabolites participate in a reaction. This graph represents the visualization of the stoichiometric matrix, known as sparse matrix, since most of the coefficients are zero (Figure 3).

    Figure 3
    Sparse matrix of the K. hansenii core model. The matrix contains 329 non-zero elements (blue points), with 4,703 zero elements (white points). Its sparsity is 93.5%, and its density is 6.5%.

    Our results revealed a sparsity matrix, with 4,703 zero elements which correspond to 93.5% of sparsity. The non-zero (nz) elements or null space correspond to the density of the matrix, which is this case represents 6.5% (nz=329). A sparsity matrix indicates that most substrates participate in only a few reactions, whereas a small number of metabolites, such as ATP, NADPH, NADH, participate in a very large number of reactions. These more interconnected metabolites play important roles in the metabolic network: for example, the stability in the transport of such metabolites inside or outside the network can affect the organization of regulatory mechanisms. Generally, metabolic networks are considered to be sparse and sparsity has been used as a criteria for inferring linear network models.

    In silico capabilities of the K. hansenii core metabolic model

    Model simulations were carried out to obtain insights on the metabolic network and the flux distribution. The metabolic flux analysis combines a set of measured fluxes (often extracellular), with a constraint-based model to obtain an estimate of all the fluxes. In this case, the uptake and consumption rates for all three carbon sources and for nitrogen and phosphate sources were set, as shown in Table 2.

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    Table 2
    Composition and boundary conditions of the minimal growth medium for the simulations. The carbon source uptake rates were set and the following external metabolites were allowed to freely enter and leave the network. All the equations are from extracellular [e] compartment to the cytoplasm [c].

    The network contains 79 metabolites and 74 fluxes. Of those, 68 are internal metabolites, resulting in 6 degrees of freedom and the stoichiometric matrix consisted of 68 rows and 74 columns. The matrix included exchange reactions to allow metabolites to be taken up or excreted to the extracellular medium, and transport reactions to allow the uptake of metabolites. Because the number of measured fluxes is less than the degrees of freedom of the matrix, the solution space will have infinite solutions. To determinate the optimal solution, an objective function was used to obtain a linear programming problem. The four physiological scenarios of interest were defined as objective functions: (i) maximization of biomass yield; (ii) maximization of nanocellulose synthesis; (iii) maximization of the external metabolites; and (iv) the ability to synthesize precursors of biomass by adding demand reactions. Each objective function was tested with the addition of constraints (Table 2) to identify which one was the most appropriate for predicting fluxes by FBA.

    With the FBA technique, the carbon sources were chosen to evaluate the capability of K. hansenii to grow on each carbon source supplemented in the minimal medium. Glucose and mannitol uptake rates were 10 mmol∙gDW-1∙h-1 and glycerol uptake rate was 20 mmol∙gDW-1∙h-1. Uptake rates were established based on the C-mol of each carbon source. Glucose and mannitol are six-carbon sources, while glycerol is a three-carbon source. The rates were obtained by dividing the concentration (g(L-1) of each source by its molar mass (g(mol-1), and then multiplying it by the specific dilution rate (h-1) divided by biomass concentration (gDW(L-1). Also, for the correct mass balance of the model, glycerol needed to enter twice as much as the other carbon sources.

    In order to analyze the metabolic flux for these different carbon sources at the same dilution rate and to prevent the wash out, a dilution rate of 0.05·h was chosen for the experiments, based on previous studies which revealed a high growth yield using a low dilution rate (Olijve and Kok, 1979). For simulation of aerobic growth on Yamanaka medium, the following external metabolites were allowed to freely enter and leave the network: O2, H+, CO2, H2O, NH4+ and PO43-, except for the carbon sources. Since the nutrients, such as nitrogen and phosphate, are not considered unlimited, the maximum uptake rates of nitrogen and phosphate were calculated to determine which combination results in an optimal growth of bacterial nanocellulose. A maximum uptake rate of 1.26 mmol∙gDW-1∙h-1 and 0.78 mmol∙gDW-1∙h-1, for nitrogen and phosphate, respectively, were set as lower boundaries (-1.26 ≤ v ≤ 1000 mmol∙gDW-1∙h-1) and (-0.78 ≤ v ≤ 1000 mmol∙gDW-1∙h-1). The oxygen uptake was set as a virtually unlimited flux (-1000 ≤ v ≤ 1000 mmol∙gDW-1∙h-1), because K. hansenii is an aerobic bacterium.

    The COBRA Toolbox generated the Systems Biology Markup Language (SBML) file of the core model. The XML-based data format is presented in the Supplementary file I "sbml_coremodel". The FBA method computes the maximal growth yield achievable in the core metabolic model by maximizing the biomass reaction flux (v74). By maximizing the biomass reaction (Figure 4) the carbon flux was used for the bacterial growth, without any production of cellulose (reaction v5) (See reactions in the appendix - Table A1). This is a biological representative scenario since there are strains that do not produce cellulose (Iguchi et al., 2000).

    Figure 4
    Representation of the most important metabolic fluxes resulting from FBA analysis using the biomass reaction as the objective function. By maximizing this reaction, we proved that all carbon flux was used for bacterial growth without any production of BNC. Glucose as carbon source presented the highest specific growth rates per hour, µ = 1.93. Mannitol and glycerol showed lower growth rates, µ = 0.72 and µ = 0.84, respectively.

    Flux distribution using glycerol as a carbon source revealed that the pentose phosphate pathway was not favored (Figure 4). The fraction of carbon directed to the pentose phosphate pathway was growth-rate dependent. The specific growth rates per hour were µ = 1.93 in glucose, and lower in mannitol, µ = 0.72 and µ = 0.84 in glycerol, under minimal nutritional requirements. These results indicated that the bacterium has the metabolic machinery needed to use all those carbon sources for growth competence. This is the first reported in silico prediction of K. hansenii metabolic capabilities under a minimal medium growth condition. We have tested the hypothesis that K. hansenii uses its metabolism to grow at a maximal rate using the core metabolic model. Based on this hypothesis, further studies should be performed to describe the quantitative relationship between glucose uptake rate, oxygen uptake rate, and maximal cellular growth rate.

    The second scenario was the maximization of nanocellulose synthesis. In this case, we used this reaction of BNC production (v5) as objective function. During the analysis, the results revealed that this flux is a suitable objective function and predicts the theoretical yield of nanocellulose, since the biomass flux (cell growth) was zero under that constraint. Thus, the majority of the carbon flux is directed to the production of nanocellulose, and there was no carbon used to produce biomass. The theoretical nanocellulose yield was calculated per mol of carbon of the substrate consumed: 0.95 C-mol/C-mol of glucose, 0.5 C-mol/C-mol of mannitol and 0.6 C-mol/C-mol of glycerol, on a Carbon-mol base. These results can be explained by the metabolism of K. hansenii. Glucose is easily transported through the cell membrane and incorporated into the nanocellulose biosynthetic pathway (Oikawa et al., 1995; Ross et al., 1991). Mannitol is known to be converted to fructose, and then metabolized by this organism to produce BNC, while glucose and fructose are transported through the cell membrane and incorporated into the cellulose biosynthetic pathway. Glycerol, a three-carbon sugar, on the other hand, is introduced into metabolic pathways at the triose phosphate level. The oxidation of triose phosphate is a primary reaction in this organism for the channeling of sugar carbon from the pentose phosphate pathway (PPP) into the tricarboxylic acid cycle (TCA cycle). Biosynthesis of bacterial nanocellulose depends on two amphibolic pathways (anabolism and catabolism): PPP for the oxidation of carbohydrates and TCA for the oxidation of organic acids and related compounds (Brown et al., 1976; Oikawa et al., 1995; Ross et al., 1991).

    This could explain the lower theoretical nanocellulose yield using mannitol and glycerol, compared to glucose as carbon source. In terms of BNC yields, there is a variation depending on the strain, the composition of the medium and the operating conditions, such as static or agitated culture, temperature, oxygen and pH (Jozala et al., 2016; Keshk and Sameshima, 2005; Ruka et al., 2012). The core model is consistent with experimental data, since this bacterium can synthesize BNC with all these carbon sources. The central metabolic pathway for the three carbon sources varies in many aspects like the pathway used for catabolism of carbon sources and production of extra-cellular metabolites, as shown in Figure 5.

    Figure 5
    Representation of the core metabolic network of K. hansenii and the input of three carbon sources: glucose (GLC), mannitol (MANN) and glycerol (GLYC). The microfibrils of BNC and biomass are represented as output. Metabolite abbreviations and reaction details are provided in the Appendix (Tables A1 and A2).

    The inability to metabolize glucose (GLC) via the Embden-Meyerhof pathway in K. hansenii lies in the fact that it lacks phosphofructokinase, which is required for glycolysis (Velasco-Bedrán and López-Isunza, 2007; Zhong et al., 2014). Gluconeogenesis occurs from oxaloacetate (OAA) via pyruvate (PYR), because of the unusual regulation of the enzymes oxaloacetate decarboxylase and pyruvate phosphate dikinase. Thus, cellulose arises in this organism from a metabolic pool of hexose phosphate that is sustained directly by the phosphorylation of exogenous hexoses and indirectly via the pentose phosphate and the gluconeogenic pathways.

    The glucose catabolism involves its conversion to glyceraldehyde-3-phosphate and pyruvate via the Enter-Doudoroff enzymes 6-phosphogluconate dehydrogenase and 2-dehydro-3-deoxyphosphogluconate aldolase. Depending on physiological conditions, glucose is converted into 6-phosphogluconate (6PGC) by one of two routes, one of which is oxidative and the other is phosphorylative. The direct oxidative route involves oxidation of glucose to gluconate (GLCN) and gluconokinase. Alternatively, the phosphorylative route involves uptake of glucose by an inducible transport system. Once inside the organism, glucose is phosphorylated by glucokinase and then converted to 6-phosphogluconate by glucose-6-phosphate dehydrogenase. One important metabolite that influenced the nanocellulose synthesis is the gluconic acid production, which our core model predicted, in accordance with previous studies (Hwang et al., 1999; Ishihara et al., 2002; Liu et al., 2016). Our results showed that the three carbon sources, glucose, mannitol and glycerol can be used by K. hansenii under minimal nutritional requirements. To the best of our knowledge, no previous studies reported a core metabolic model of K. hansenii ATCC 23769. Two metabolic networks of Gluconacetobacter xylinus E25 were developed, the first by Ross (Ross et al., 1991) consisted in 42 reactions, and the second by Zhong and co-workers (Zhong et al., 2013), adapted from Ross' model, consisted of 26 reactions. Zhong and coworkers (2013) performed a metabolic flux analysis (MFA) to compare the metabolic flux distribution. However, neither of these two networks was built based on the genome sequence and a flux balance analysis performed.

    The third scenario related to the maximization of external metabolites evaluated the balance consistency. For example, to maximize carbon dioxide (CO2_out), 10 mmol∙gDW-1∙h-1 of glucose was fed, resulting in 60 mmol∙gDW-1∙h-1of CO2. This is stoichiometrically consistent, given that glucose has six carbons and the carbon dioxide molecule has only one. The mass balance was checked using all external metabolites.

    In the fourth scenario, the ability to synthesize precursors of biomass by adding demand reactions was performed. To verify the fluxes distribution under nutrient deprived conditions, nitrogen, phosphate and oxygen uptake were limited, which means those fluxes were set to zero. Under oxygen limitation conditions the bacterial growth rate, nanocellulose production and all the other main function were null, as expected, confirming aerobic functionality. Limitation of nitrogen and/or phosphate sources was shown to be insufficient to prevent bacterial growth. According to Ross (Ross et al., 1991), in Acetobacter xylinum washed cells, deprived of a nitrogen source, production of nanocellulose continues when supplied with an adequate carbon substrate and does not depend on net protein synthesis. The excess of available carbon substrate and limitations in other nutrients, such as nitrogen or phosphate, could promote nanocellulose synthesis. Flux consistency implies that each one of the metabolite precursors was produced by the bacterium. By including a demand reaction (reaction that consumes the compound without producing anything) for each metabolite of the biomass reaction, and optimizing demand reaction fluxes, results revealed that the core model could predict each of the biomass constituents, for all carbon sources used.

    CONCLUSIONS

    In this study, a core metabolic model of K. hansenii ATCC 23769 was developed. The network was constructed by using automatic reconstruction and an iterative process of manual curation based on genomic and bibliome databases. This curated core model accounts for 68 metabolites and 74 reactions and represents an up-to-date database that encompasses the knowledge available in public databases, scientific publications and textbooks on the metabolism of this bacteria.

    Flux balance analysis of the model was applied under different physiological scenarios and predicted quantitative relationships between input rates of nutrients, output rates of products and bacterial growth rate. A simplified model could answer simple biological questions and the central carbon metabolism addressed key metabolites. Moreover, the in silico core model successfully predicted the growing abilities on different substrates and gave insights of the use of minimal medium capable to support BNC production. With the increased interest in BNC, the in silico model presented here will be a valuable tool for fundamental research, serving as a starting point for metabolic engineering approaches. The core model is one important step for understanding the nanocellulose production process and contributes to the general knowledge of microbial function and physiology with computational analysis.

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    ACKNOWLEDGEMENTS

    This work was supported by the Financer of Studies and Projects - FINEP (grant 550084/2014-2 MCT/FINEP-COENG), by the Coordination of Improvement of Higher Education Personnel - CAPES (scholarship grant 1118675/2012-1- Samara Silva de Souza) and by the National Council for Scientific and Technological Development - CNPq (grant 402901/2013-4 - MCTI/CNPq/CT-Biotec and scholarship grant 158853/2012-1 CNPq GM/GD - Julia de Vasconcellos Castro). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

    APPENDIX

    Thumbnail
    Table A1
    List of all biochemical reactions of the core model network.
    Thumbnail
    Table A2
    List of all metabolites of the core model network.

    Publication Dates

    • Publication in this collection
      Jul-Sep 2018

    History

    • Received
      21 June 2017
    • Reviewed
      19 Aug 2017
    • Accepted
      01 Sept 2017
    Creative Common - by 4.0
    This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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