genome-scale analysis of mannheimia succiniciproducens metabolism

ACCELERATED PUBLICATION

Genome-Scale Analysis of Mannheimiasucciniciproducens Metabolism

Tae Yong Kim,1,2 Hyun Uk Kim,1,2 Jong Myoung Park,1,2 Hyohak Song,1,2 Jin Sik Kim,1,2

Sang Yup Lee1,2,3

1Department of Chemical and Biomolecular Engineering (BK21 Program), Metabolic and

Biomolecular Engineering National Research Laboratory, Korea Advanced Institute of

Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of

Korea; telephone: þ82-42-869-3930; fax: þ82-42-869-3910; e-mail: [email protected] for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea

Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon,

Republic of Korea3Department of BioSystems, BioProcess Engineering Research Center and Bioinformatics

Research Center, Korea Advanced Institute of Science and Technology, Guseong-dong,

Yuseong-gu, Daejeon, Republic of Korea

Received 12 January 2007; accepted 8 March 2007

Published online 2 April 2007 in Wiley InterScience (www.interscience.wiley.com). DO
I 10.1002/bit.21433
ABSTRACT: Mannheimia succiniciproducens MBEL55E iso-lated from bovine rumen is a capnophilic gram-negativebacterium that efficiently produces succinic acid, an indust-rially important four carbon dicarboxylic acid. In order todesign a metabolically engineered strain which is capable ofproducing succinic acid with high yield and productivity, itis essential to optimize the whole metabolism at the systemslevel. Consequently, in silico modeling and simulation of thegenome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineeringexperiments. The genome-scale metabolic network ofM. succiniciproducens consisting of 686 reactions and 519metabolites was constructed based on reannotation andvalidation experiments. With the reconstructed model,the network structure and key metabolic characteristicsallowing highly efficient production of succinic acid weredeciphered; these include strong PEP carboxylation,branched TCA cycle, relative weak pyruvate formation,the lack of glyoxylate shunt, and non-PTS for glucoseuptake. Constraints-based flux analyses were then carriedout under various environmental and genetic conditions tovalidate the genome-scale metabolic model and to decipherthe altered metabolic characteristics. Predictions based onconstraints-based flux analysis were mostly in excellentagreement with the experimental data. In silico knockoutstudies allowed prediction of new metabolic engineeringstrategies for the enhanced production of succinic acid.This genome-scale in silico model can serve as a platformfor the systematic prediction of physiological responses of

This article contains Supplementary Material available via the Internet at http://

www.interscience.wiley.com/jpages/0006-3592/suppmat.

Correspondence to: S.Y. Lee

� 2007 Wiley Periodicals, Inc.

M. succiniciproducens to various environmental and geneticperturbations and consequently for designing rational stra-tegies for strain improvement.

Biotechnol. Bioeng. 2007;97: 657–671.

� 2007 Wiley Periodicals, Inc.

KEYWORDS: Mannheimia succiniciproducens; genome-scalemetabolic model; model validation; constraints-based fluxanalysis

Introduction

Succinic acid is a four-carbon dicarboxylic acid used as animportant chemical in food, agricultural, chemical andpharmaceutical industries (Song and Lee, 2006). It is anintermediate of TCA cycle and one of fermentation productsof several anaerobic and facultative microorganisms. Thebest known succinic acid producers include Anaerobiospir-illum succiniciproducens, Actinobacillus succinogenes andMannheimia succiniciproducens (Davis et al., 1976; Guettleret al., 1999; Lee et al., 2002; Van der Werf et al., 1997).M. succiniciproducens MBEL55E is a gram-negative rumenbacterium, which produces succinic acid as one of the majorfermentation products.We recently determined its completegenome sequence and constructed a small-scale metabolic

Biotechnology and Bioengineering, Vol. 97, No. 4, July 1, 2007 657

network, which enabled us to understand its genomic andmetabolic characteristics to some extent (Hong et al., 2004).The genome of M. succiniciproducens consists of 2,314,078bp with a GC content of 42.5%. It contains 2,384 openreading frames (ORFs) with an average length of 873 bp. Theinitial investigation of the genome suggested thatM. succiniciproducens has unique metabolic characteristicsof a capnophilic rumen bacterium; it is well adapted to theoxygen-free but CO2 abundant rumen environment, whichallows highly efficient production of succinic acid.

Since the initial construction of a small-scale metabolicnetwork composed of 373 reactions and 352 metabolites(Hong et al., 2004), the genome was reannotated based onthe updated databases, and further molecular biological andfermentation studies were conducted (Lee et al., 2006). Inorder to design a metabolically engineered strain which iscapable of producing succinic acid with high yield andproductivity, it is essential to optimize the wholemetabolism at the systems level. Since it is not possible tocarry out all the necessary experiments to achieve this goal,in silico modeling and simulation of the genome-scalemetabolic network will facilitate strain improvement byallowing selection of key experiments to perform (Alperet al., 2005; Lee et al., 2005b).

Here, we report reconstruction of the expandedand validated genome-scale metabolic network ofM. succiniciproducens comprised of 686 reactions and519 metabolites. The biomass composition ofM. succiniciproducens was experimentally determined tomake the in silico simulation results more realistic.Constraints-based flux analysis (Price et al., 2004) wascarried out under various environmental and in silico geneknockout conditions to systematically decipher the meta-bolic characteristics. In addition, metabolic network analysiswas performed to understand the inherent networkproperties.

Materials and Methods

Source of the Orthologous Data

We used the updated version of Clusters of OrthologousGroups (COG) database (Tatusov et al., 2001) for thecomparison and analysis of the annotation results (Supple-mentary materials 1–3). Updated version of the databasecontains 4,873 groups of orthologous genes obtained from66 unicellular species and 7 eukaryotic species.

Updated Annotation of M. succiniciproducens

Reannotation was performed in a similar procedure des-cribed in the initial annotation of M. succiniciproducensgenome (Hong et al., 2004). We also employed non-redundant sequence database during the reannotationprocess. Those genes annotated differently were analyzed

658 Biotechnology and Bioengineering, Vol. 97, No. 4, July 1, 2007

further by experiments to assign the correct functions(Supplementary materials 1–3). Here the experimentsinclude investigation of the glucose uptake system, enzymeassays, and the results from our previous publication (Leeet al., 2006).

Phylogenetic Relationships

Phylogenetic analysis was carried out using the 16S rRNAsequences of the organisms obtained from the NCBI andRibosomal DB Project II. The sequences were stored inFASTA format for the multiple sequence alignment.Alignment of multiple sequences was performed using theClustalX software v.1.83 (Jeanmougin et al., 1998). Theneighbor-joining trees were generated using NJPlotWin95software and the .phr files obtained from the ClustalXsoftware. The branch lengths and bootstrap values (1,000iterations) were represented on each branch of the map(Supplementary material 4).

Reconstruction of the Metabolic Network

A set of biochemical reactions that have been annotated tobe operative in this organism were collected from the KyotoEncyclopedia of Genes and Genomes (KEGG) (Kanehisaet al., 2006). The initial version of the reconstructedmetabolic network based on the information present inKEGG was further refined by considering the reannotationresults and our previous publications on cultivation ofM. succiniciproducens (Hong et al., 2004; Lee et al., 2002,2006) and by performing more cultivation experimentsunder various conditions. Reannotation data (Supplemen-tary materials 1 and 2) and previous publications onM. succiniciproducens were referred to confirm the functionsof enzymes, cofactor requirement, and transport reactionswhen the corresponding data in the database wereambiguous. Further experiments were performed toexamine fermentation patterns including metabolitesproduction rates (Supplementary material 5), majorbiomass compositions (Supplementary material 6), glucoseuptake system (Supplementary material 9), and filling gapsin the metabolic network (Supplementary material 11).Analytical methods are detailed below in this section and inSupplementary materials. For transport reactions, theTransportDB (Ren and Paulsen, 2005) was employed; thisis because the transport reactions are not sufficientlydescribed in typical metabolic pathway databases. Detailedreconstruction process is described in the main text.

Fermentation

M. succiniciproducens MBEL55E (KCTC 0769BP; KoreanCollection for Type Cultures, Daejeon, Korea) stored as aglycerol stock at �708C was used. A seed culture wasprepared in a 500 mL Erlenmeyer flask containing 250 mL of

DOI 10.1002/bit

MMH3 medium plus 5 g � L�1 of glucose under CO2

atmosphere. In order to ensure anaerobic condition, thecultivation was conducted in an anaerobic chamber(ThermoForma, Marietta, OH) at 398C. The MMH3medium contains per liter: 5 g yeast extract, 1 g NaCl,0.02 g CaCl2�2H2O, 0.2 g MgCl2�6H2O, and 8.709g K2HPO4. Batch culture was carried out in a 6.6 L Bioflo3000 fermentor (New Brunswick Scientific Co., Edison, NJ)that contains 2.25 L of MMH3 medium plus 20 g � L�1 ofglucose and 10% (v/v) inoculum. The agitation speed waskept at 200 rpm. The pHwas adjusted at 6.5� 0.1 using 28%(v/v) ammonia solution. Foaming was controlled by theaddition of Antifoam 289 (Sigma, St. Louis, MO). Thefermentor was flushed with CO2 gas, scrubbed free of oxygenby passing it through an oxygen trap (Agilent, Waldbronn,Germany) at a flow rate of 0.25 vvm. Chemostat cultureswere carried out at the dilution rates of 0.15 and 0.30 h�1 bycontinuous feeding of the sterilized MMH3 medium plus20 g � L�1 of glucose and simultaneous removal of culturalbroth using peristaltic pumps at equal rates (Cole-Parmer,Vernon Hills, IL). The steady states were determined bymonitoring the constant concentrations of biomass, glucose,and organic acids in the fermentor for five consecutivesamples taken at 1–3 h intervals (Supplementary material 5).

Analytical Procedures

The concentrations of glucose and organic acids weredetermined by high-performance liquid chromatography(Varian ProStar 210, Palo Alto, CA) equipped with UV/VIS(Varian ProStar 320, Palo Alto, CA) and RI (Shodex RI-71,Tokyo, Japan) detectors. A MetaCarb 87H column (300mm� 7.8 mm, Varian) was isocratically eluted with 0.01N H2SO4 at 608C and a flow rate of 0.6 mL �min�1.The OD600 was measured using an Ultrospec 3000spectrophotometer (Pharmacia Biotech, Uppsala, Sweden)to monitor the cell concentration. Cell concentrationdefined as gram dry cell weight (DW) per liter wascalculated from the pre-determined standard curve relatingthe OD600 to DW (1 OD600¼ 0.451 g DW per liter).

Enzyme Assays

Enzyme activities of PckA, Ppc, and MaeB were measuredspectrophotometrically in a temperature-controlled spec-trophotometer (SpectraMax M2, Molecular Devices Co.,Sunnyvale, CA). All the assays were performed at 308C usingthe cell extracts prepared from the cells grown under 75%(v/v) CO2 and 25% (v/v) N2 condition. Reactions weremonitored by following the production or extinction ofNAD(P)H (extinction coefficient¼ 6.23 mM�1 � cm�1) at340 nm. The activities of PckA, Ppc, and MaeB weremeasured as described elsewhere (Kim et al., 2004; Peng andShimizu, 2003; Terada et al., 1991). One unit (U) of enzymeactivity was defined as the amount of enzyme necessary tocatalyze the conversion of 1 mmol of substrate per min into

Kim et al.: Reconstruction and Analysis of the

specific products. Specific activity was defined as units permg of protein.

Biomass Composition

The biomass composition was experimentally determined tomake the simulation results more realistic. Even though thebiomass composition changes under different physiologicalconditions, it should not be a problem as it has beendemonstrated that these small changes in biomass composi-tion little affect the simulation results (Varma et al., 1993).Compositions of carbohydrates, amino acids, and fatty acidsin M. succiniciproducens from batch culture in a complexmedium were experimentally measured at Korea BasicScience Institute (Daejeon, Korea). The neutral sugars fromcell extracts were analyzed using CarboPac PA1 (4.5 mm�250 mm) and CarboPac PA1 cartridge (4.5 mm� 50 mm)with Bio-LC DX-600 (Dionex, Sunnyvale, CA). Sixteen mMNaOH was used as a mobile phase at flow rate of 1.0 mL�min�1. Obtained data were then analyzed with PeakNet on-line software. Amino acid compositions were determined bythe Waters HPLC systems (Waters, Milford, MA) thatconsist of two 510 HPLC pumps, gradient controller, 717automatic sampler, 996 photodiode array detector, andMillennium 32 chromatography manager together withWaters pico-tag column (3.9 mm� 300 mm). Absorbanceat 254 nm was measured. The compositions of cellular fattyacids were determined by gas chromatographic analysis(6890 GC system, Agilent Technologies, Palo Alto, CA) offatty acid methyl esters (Peltroche-Llacsahuanga et al.,2000). Data for other components were either adopted fromthe literature or reasonably assumed as described inSupplementary material 6.

Constraints-Based Flux Analysis

In order to perform constraints-based flux analysis, internalmetabolites are first balanced under the assumption ofpseudo-steady state (Edwards et al., 1999; Gombert andNielsen, 2000). This results in a stoichiometric modelSij � vj¼ 0, in which Sij is a stoichiometric coefficient of ametabolite i in the jth reaction and vj is the flux of thejth reaction given in mmol g �DW�1 � h�1. The resultantbalanced reaction model is, however, almost always under-determined in calculating the flux distribution due toinsufficient measurements or constraints. Thus, linearprogramming (LP), subject to the constraints of massconservation, reaction thermodynamics and metaboliccapacity, was carried out to determine the fluxes as follows(Edwards et al., 1999; Stephanopoulos et al., 1998; Varmaand Palsson, 1994a):

Maximize/minimize:

Z ¼X

j2Jcjvj (1)

Genome-Scale Metabolic Network of Mannheimia succiniciproducens 659

Biotechnology and Bioengineering. DOI 10.1002/bit

Subject to:

X

j2JSijvj ¼ bi; 8i 2 I (2)

ai � vj � bj; 8j 2 J (3)

where cj is weight of the reaction j. The objective function, Z,is usually the maximization of biomass formation rate, butcan be variously defined (i.e., maximization of themetabolite formation). bi is the net transport flux ofmetabolite i. If this metabolite is an intermediate, bi is zero asin Sij � vj¼ 0 above. aj and bj are the lower and upper boundsof the flux of the jth reaction, respectively. Herein, the flux ofany irreversible reaction is considered to be positive; thenegative flux signifies the reverse direction of the reaction.Hence, the lower bound (aj) for the flux of irreversiblereaction j should be set to zero or a positive value, whereasthe flux of the jth reversible reaction is not constrained(aj¼�1 and bj¼1) or can be constrained within anyrange according to the capacity limitation.

In the case of gene deletion simulations, the fluxes of thereactions encoded by the genes to be deleted were set to zerobefore carrying out constraints-based flux analysis. Thetrade-off curves of M. succiniciproducens and E. coli weregenerated based on the simulation results obtained incomplex media under anaerobic condition. In many cases,energetic parameters should be directly included in themetabolic model for correctly predicting growth rate. Manymetabolic reactions require the consumption of ATP with orwithout contributing to the net synthesis of biomass, andthese reactions are represented by the energetic parameters.Thus, the energetic parameters, including growth associatedmaintenance energy (GAME) and non-growth associat-ed maintenance energy (NGAME) terms were calculatedand included in the model (Borodina et al., 2005; Varma andPalsson, 1994b). GAME (g � g DW�1) is represented in theform of ATP in the biomass equation, while NGAME(mmol � g DW�1 � h�1) is incorporated into the modelthrough an independent ATP dissipation reaction. First,GAME andNGAMEwere set to arbitrary values. Cell growthrate and succinic acid production rate obtained during thechemostat at one dilution rate were provided as constraintsduring the simulation with the objective function ofminimizing the glucose uptake rate. This was repeated forat least one more dilution rate. Then, the slope of a lineconnecting the calculated glucose uptake rates was obtainedfrom the plot of glucose uptake rate versus dilution rate.This value was compared with that of a line connecting theexperimentally determined glucose uptake rates. If dis-crepancy is observed between the two, GAME value wasaltered and the above procedure was repeated until theybecame consistent. After determining the GAME value, anarbitrary value of NGAME was then repeatedly changeduntil the correct y-intercept value (in the plot of glucoseuptake rate vs. dilution rate) is obtained. The GAME and


NGAME values for M. succiniciproducens in a complexmedium were 7.80 and 15.90 mmol � g DW�1 � h�1. Thosefor E. coli were set as 26.73 and 9.52 mmol � g DW�1 � h�1

(Aristidou et al., 1999). The glucose uptake rates of 14.46and 7.64 mmol � g DW�1 � h�1 were used as constraintsduring the simulation of M. succiniciproducens and E. coli,respectively, which correspond to the values obtained fromthe mid-exponential phase. Constraints for other transpor-ters set for the simulation ofM. succiniciproducens are shownin Supplementary materials 7 and 8 and those for E. coliwereset similarly. When generating the trade-off curves forM. succiniciproducens and E. coli, the objective function ofmaximizing the biomass formation was used under varyingsuccinic acid production rates from its maximum value tozero.

Simulation of LK, LPK and LPK7 Mutant Strains

We previously developed several knockout strains throughmetabolic engineering to improve succinic acid production(Lee et al., 2006). These mutant strains were characterized insilico and compared with the actual fermentation results:the~ldhA LK strain, the~ldhA~pflB LPK strain, and the~ldhA ~pflB ~pta ~ackA LPK7 strain. The fluxes of theknocked-out reactions were constrained to zero. In eachstrain, the GAME and NGAME terms were calculated sincethey are supposed to be different due to different genotypes.The ratio of NGAME to GAME in the wild-type MBEL55Estrain was found to be 2.04 (¼15.90 mmol � g DW�1 � h�1

NGAME/7.80 g � g DW�1 GAME; please note that thediscrepancy in the units does not matter here). These valuesof NGAME and GAME were varied at this fixed ratio untilthe calculated fluxes match with the experimental data. Theuse of the fixed ratio of NGAME to GAME is based on thefact that they increase proportionally (Borodina et al., 2005;Feist et al., 2006). The resulting GAME (g � g DW�1) andNGAME (mmol � g DW�1 � h�1) values for each strain are asfollows: 6.63 and 13.52 for in silico LK, 14.00 and 28.54 forin silico LPK, 11.70 and 23.85 for in silico LPK_1, 6.50 and13.25 for in silico LPK7 and 4.70 and 9.58 for in silicoLPK7_1. In silico LPK_1 and LPK7_1 represent the in silicomodels where additional constraints were added (see Resultsand Discussion).

Results and Discussion

General Features of the In SilicoM. succiniciproducensMetabolic Model

The reconstructed genome-scale metabolic network consistsof 686 metabolic reactions (638 unique reactions) and 519metabolites (Table I). The number of biochemical conver-sions is 565, and that of transporters is 121. This newlyupdated model contains 313 more reactions and 167 moremetabolites than the small-scale model we reportedpreviously (Hong et al., 2004). This expansion of the insilico metabolic network is an effort to examine the

DOI 10.1002/bit

Table I. Features of the in silico metabolic model of

M. succiniciproducens.

Features Number

Genome feature

Genome size (base pairs, bp) 2,314,078

No. of open reading frames (ORFs) 2,384

In silico metabolic model

No. of reactions (redundant) included in the model 686

No. of biochemical reactions 565

No. of transport reactions 121

No. of reactions (unique) included in the modela 638

No. of metabolites 519

No. of ORFs assigned in metabolic network 425

ORF coverageb (%) 17.83

aThe same redundant reactions catalyzed by an isozyme is counted asone.

bThe number of ORFs incorporated in the genome-scale model dividedby the total number of ORFs in the genome of M. succiniciproducens.

metabolic characteristics of M. succiniciproducens morerealistically. Among the reactions in the model, 78.6% ofthem (539 out of 686 reactions) are assigned with ORFs inthe metabolic network. The features of this model aresummarized in Table I. Reactions that could not be assignedwith specific ORFs were also incorporated into the model forone of the following reasons. First, it is assumed that themissing links in a given pathway exist if cells can grow in amedium lacking the component that the correspondingpathway produces. Second, some biochemical reactionsneed to be employed regardless of any biochemical orgenomic evidence in order for the in silico cell to generatebiomass. This is a problem mainly caused by the waybiomass formation reaction is defined in the model, andbecomes a mathematical problem, in which some of thepathways have to be connected, without gaps, towards thestoichiometric biomass-forming reaction. The reactioncatalyzed by lipid-A-disaccharide synthase (2.4.1.182) isone such example. The in silico model allows cell growthonly if this reaction is involved, and otherwise does not showany cell growth. All of these reactions are listed inSupplementary materials 7 and 8.

Subsequent reconstruction and validation of the meta-bolic network involved (i) incorporation of the reannotationresults of M. succiniciproducens genome, (ii) refinement ofthe metabolic network by examining the phenotypes ofknockout strains, determining the reversibility of reactions,and by incorporating fermentation results for filling gaps inthe pathways, and (iii) characterization and simulation ofthe reconstructed metabolic network to extract usefulinformation regarding various metabolic capabilities. Inaddition, we estimated the impact of knocking out eachreaction on both cell growth and succinic acid productionusing the in silico strain. This allowed identification of thecore knockout targets for enhanced succinic acid produc-tion. An accompanying metabolic reaction list (Supple-


mentary materials 7 and 8) shows the connectivity ofmetabolites among reactions in the network. Overall schemeof the reconstruction and validation of the genome-scalemodel, and its use in metabolic engineering is depicted inFigure 1.

Reannotation of M. succiniciproducens Genome andClusters of Orthologous Groups (COG) Analysis

All the genes in the genome were reannotated using theupdated COG (Tatusov et al., 2001) and non-redundant(Benson et al., 2005) databases. Also, phylogenetic relation-ship was examined by comparing the 16S rRNA sequences of48 organisms with that of M. succiniciproducens (Supple-mentary materials 1–3). Several clusters of genes which havethe same COG numbers did not match the same functions inthe non-redundant database. In most bacteria, a largenumber of homologous proteins belong to the same COGgroup, but the orthology of proteins is quite often notproperly assigned using the bidirectional best hits. Amongthe most notable ones are those involved in transportreactions (e.g., ATP-binding cassette (ABC) transportersand phosphotransferase (PTS) systems), signaling reactions(e.g., two-component systems and transcription factors),and processes related to the carbohydrate and amino acidconversions. These results are summarized in Supplemen-tary material 3.

In such cases, the annotation results should be validatedby experiments after cross-checking with reference data-bases. For example, the glucose uptake system ofM. succiniciproducens was predicted to be phosphotransfer-ase system (PTS) when the COG database was used forthe annotation. However, the annotation results basedon the non-redundant database suggested thatM. succiniciproducens utilizes a different glucose uptakesystem. In order to confirm the glucose uptake system ofM. succiniciproducens, we performed 14C-labeled glucosephosphorylation assay as described in Lee and Blaschek(2001) and Supplementary material 9. In this experiment,we employed the cell extracts of E. coli W3110 andM. succiniciproducens MBEL55E to phosphorylate 14C-labeled glucose with phosphoenolpyruvate (PEP) or ATP asa phosphate donor. The results showed that E. coli efficientlyphosphorylated glucose using both PEP and ATP asphosphate donors, whereas M. succiniciproducens efficientlytransferred phosphate from ATP to glucose, but poorly fromPEP. Based on these results, we concluded that, unlikeE. coli,M. succiniciproducens does not utilize PTS for glucosetransport and possesses an enzyme that acts as a glucokinase,the gene of which needs to be identified experimentally (Leeet al., 2005a). Furthermore, the activities of key enzymesrelated to the CO2 fixation were also measured to confirmthe functions of the corresponding genes. PckA, Ppc, andMaeB were subjected to the enzyme assay, and theirpresences were confirmed by observing the specific activitiesof 1.23, 0.078, and 0.00268 U mg protein�1, respectively.



Figure 1. Procedure for the reconstruction of a genome-scale metabolic network and its application to metabolic engineering. (i) Automatic reconstruction of metabolic

network using sequence-homology based annotation data (1–2–3–4). (ii) Fine-tuning of the metabolic network with more specifically investigated gene–protein-reaction

relationship using comparative genomics (4–7–8–2). (iii) Manual curation of the metabolic network using literatures, databases and comparative genomics to correct errors and fill

gaps in the pathways (5–6–8–2). The model building process can be further supported by experimental observations on the transport of various molecules. (iv) Model validation by

comparison with the experimental data (9–10–11). If the simulation results do not agree with the experimental data, the model needs to be further refined until the satisfactory

agreement is observed. (v) Systems-level metabolic engineering of organism by combining experimental and in silico approaches (12–13).

Refinement of the Genome-Scale Metabolic Network ofM. succiniciproducens

Initial Construction of M. succiniciproducensMetabolic Network

As an initial step, all the enzymatic reactions were extractedfrom the KEGG (Kanehisa et al., 2006) and employed for thereconstruction of genome-scale metabolic network of


M. succiniciproducens. KEGG is a practical and excellentdatabase for the initial reconstruction process as it visuallydisplays the automatically reconstructed metabolic networkof the organism and contains comprehensive informationon the genes, enzymes and metabolites. However, cautionshould be given as the database contains a number ofambiguous information as pointed by Borodina et al.(2005); these include inaccurately defined substrate specifi-cities, undefined directionality of enzymatic reactions,

DOI 10.1002/bit

insufficient description of the ORFs regarding whether theyare isozymes or subunits of the enzyme complex, insufficientinformation on cofactor specificity, and missing links in themetabolic pathways.

To solve these problems and consequently to upgrade theinitial version of the metabolic model, the annotation resultswere thoroughly revisited and verified. For instance, theKEGG shows that M. succiniciproducens possesses a malicenzyme (EC 1.1.1.40), but does not specify whether it isNAD or NADP dependent. This ambiguity could beremoved by referring to the COG-based genome annotationdata, which identified that M. succiniciproducens possessesan NADP-dependent malic enzyme. Other importantbiological features of M. succiniciproducens acquired fromthe reannotation data and incorporated in the model areexplained in the next two sections. As a conclusion, thiscross-verification process reshaped the initially built modeltowards a more solid organism-specific one by eliminatingthe ambiguities frequently encountered in the pathwaydatabases.

In addition to this approach, the metabolic network wasfurther refined by incorporating experimentally confirmedreactions. Because of the lack of experimental data onM. succiniciproducens in the literature, we conductednecessary experiments to determine its basic biochemicaland physiological properties. The compositions of carbohy-drates, amino acids, nucleic acids, and fatty acids inM. succiniciproducens cells were thus determined (Table II;Supplementary material 6). This is important as thestoichiometric reaction that describes the generation andconsumption of biomass constituents needs to be properlyformulated for more accurate constraints-based fluxanalysis. Also, the production rates of metabolites and thegrowth rates were measured in various batch and chemostatcultures (Supplementary material 5). These fermentationdata were employed for the validation of simulation results.The measured glucose uptake rate was used as a constraintduring the simulation. MetaCyc (Caspi et al., 2006) wasreferred to determine the reversibility of biochemicalreactions in the model.

Succinic Acid Production in M. succiniciproducens

Figure 2 presents the central metabolic pathways ofM. succiniciproducens and E. coli in a comparative manner.Although both M. succiniciproducens and E. coli producesuccinic acid, the corresponding capability ofM. succiniciproducens appears to be better than that ofE. coli. First of all, M. succiniciproducens possesses a strongPEP carboxykinase activity allowing efficient carboxylationof PEP to oxaloacetate (Lee et al., 2006).M. succiniciproducens lacks in succinate dehydrogenase(sdhABCD), which converts succinate into fumarate.Furthermore, fumarate functions as a major electronacceptor, mainly from menaquinone inM. succiniciproducens, and thus metabolic reaction isstrongly driven towards the production of succinic acid.


This reaction is catalyzed by fumarate reductase (frdABCD).Other genetic differences leading to different metaboliccharacteristics in two bacteria were also found.M. succiniciproducens does not possess PEP synthase (ppsA),but does possess oxaloacetate decarboxylase (oadABG).M. succiniciproducens does not possess the aceAB genes, andthus does not operate the glyoxylate shunt. Thus, the centralmetabolism ofM. succiniciproducens can be characterized bystrong PEP carboxylation, branched TCA cycle, relativelyless strong pyruvate formation, the lack of glyoxylate shunt,and the use of non-PTS for glucose uptake.

The Respiratory System Under Anaerobic Growth

Biosynthetic transformations involving oxidation–reduc-tion reactions play important roles in microbial metabolism,especially in the production of industrially importantorganic acids. In general, the electron donors are oxidizedby the substrate-specific dehydrogenases, which transferelectrons from electron donors to mobile ubiquinone,demethylmenaquinone, or menaquinone. Then, electronsare transferred to electron acceptors by the reactionscatalyzed by various reductases. The main quinone foranaerobic respiratory system has been known to bemenaquinone, and this was also true for M. succinicipro-ducens. The genes responsible for the biosynthesis ofubiquinone are missing in M. succiniciproducens (Honget al., 2004).

NADH and formate seem to serve as internal electrondonors since the dehydrogenases oxidize them and reducemenaquinone in the cytoplasmic membrane. The abilityof microorganisms to utilize H2 as an electron donor isalso important to attain redox balance in the productionof organic acids. We previously reported thatM. succiniciproducens can use H2 as an external electrondonor for anaerobic respiration (Hong et al., 2004), butcould not specifically describe how cells actually uptake andtransport H2 to the electron acceptors. We found in thiswork that this organism possesses genes putatively encodingthe hydrogenases: MS2360, MS2361, MS2362, MS2363,MS2364, and MS2365 genes. Hydrogenase is a membrane-bound enzyme which splits dihydrogen molecules intoprotons. Electrons derived from H2 are delivered tomenaquinones, and then to electron acceptors. Fumarateis a major electron acceptor in anaerobically growingM. succiniciproducens and upon its reduction is converted tosuccinic acid. These oxidation–reduction reactions wereproperly incorporated into the genome-scale metabolicnetwork of M. succiniciproducens.

Filling Gaps in the Metabolic Network

In silico metabolic network constructed by the aboveprocedure inevitably contains gaps that need to be filled in.These gaps may or may not be correct because of the possibleerroneous predictions from bioinformatic analyses. In orderto verify the gaps in the pathways, the nutritional



Table

II.

BiomasscompositionofM.succiniciprodu

censa.

Biomasscomponent

Biomasscomponent

Biomasscomponent

Protein

(0.506

g�g

DW)

mmol�gprotein

DNA(0.028

g�g

�1DW

�1)

mmol�gDNA

Cofactors

andvitamins(0.030

g�g

DW)

mmol�gcofactors

andvitamins

Alanine

1.186

dAMP

0.931

Pyridoxine

0.657

Arginine

0.372

dCMP

0.688

CoenzymeA

0.145

Asparagine

0.455

dTMP

0.931

Flavinadeninedinucleotide

0.141

Aspartate

0.455

dGMP

0.688

Flavinmononucleotide

0.243

Cysteine

0.044

RNA(0.2

g�g

DW)

mmol�gRNA

Menaquinone

0.360

Glutamate

0.534

AMP

0.675

NAD

0.167

Glutamine

0.534

GMP

0.682

NADP

0.149

Glycine

0.931

CMP

0.961

Tetrahydrofolate

0.249

Histidine

0.220

UMP

0.813

Thiamin

0.419

Isoleucine

0.515

Phospholipid

(0.049

g�g

DW)

mmol�gphospholipids

Lipopolysaccharide(0.018

g�g

DW)

mmol�glipopolysaccharide

Leucine

0.766

Phosphatidylethanolamine

0.648

KDO(2)-lipid

A0.140

lysine

0.452

Phosphatidylglycerol

0.162

ADP- L-glycero-D-m

anno-heptose

0.420

Methionine

0.136

Fatty

acidsin

phospholipids

mol�moltotalfattyacids

UDPglucose

0.280

Phenylalanine

0.326

Capricacid

(c10)

0.006

CDP-Ethanolamine

0.280

Proline

0.733

Lauricacid

(c12)

0.022

CMP-2-keto-3-deoxyoctanoate

0.420

Serine

0.414

Myristicacid

(c14)

0.287

Carbohydrate

(0.018

g�g

DW)

mmol�gcarbohydrate

Threonine

0.549

Myristoleic

acid

(c14:1)

0.001

N-A

cetylglucosamine

1.897

Tryptophane

0.003

Palmitic

acid

(c16)

0.260

Galactose

3.794

Tyrosine

0.111

Palmitoleic

acid

(c16:1)

0.403

Peptidoglycan

(0.025

g�g

DW)

mmol�gpeptidoglycan

Valine

0.788

Stearicacid

(c18)

0.004

Ash

(0.126

g�g

DW)

Oleic

acid

(c18:1)

0.017

a More

detailedinform

ationonthebiomasscompositionisavailablein

Supplementary

material6.


DOI 10.1002/bi
t

Figure 2. Comparison of central metabolic pathways between M. succiniciproducens and E. coli. Gene names in black color indicate enzymes that are present in both

M. succiniciproducens and E. coli. Grey-colored gene names are those that exist only in E. coli and not in M. succiniciproducens. Genes in the box form a multi-subunit enzyme

complex.

requirement of M. succiniciproducens was examined.Starting with a chemically defined medium containingglucose, salts, 20 amino acids, 13 vitamins and 5 nucleotides,various media lacking in one or more of the componentswere designed. By the cultivation ofM. succiniciproducens inthese media, the essential components required for thenormal growth of M. succiniciproducens could be identified(results not shown). For those components that need to beprovided for the growth of M. succiniciproducens, themissing links in the corresponding metabolic pathways were


left disconnected. By doing so, the in silico cell cannotgenerate biomass without transporting these essentialcomponents into the cell using the transporters. A goodexample is the pathway for the biosynthesis of pantothenateand coenzyme A (CoA). According to the KEGG, the firstthree enzymatic reactions catalyzed by 3-methyl-2-oxobu-tanoate hydroxymethyltransferase (EC 2.1.2.11), 2-dehy-dropantoate 2-reductase (EC 1.1.1.169) and pantoate-beta-alanine ligase (EC 6.3.2.1) are missing, and thusM. succiniciproducens cannot synthesize pantothenate. This



could be appropriately reflected in the in silico metabolicnetwork by incorporating the transporter for pantothenate.

Those missing links in the pathways that lead to thebiosynthesis of compounds which were found to be non-essential for growth were assumed to be present (con-nected). Therefore, the in silico cell can generate biomass byintracellularly synthesizing these compounds. One of therelevant cases is the de novo biosynthetic pathway ofpyrimidine. Genome annotation data suggested that twoinitial enzymatic reactions catalyzed by aspartate carba-moyltransferase (EC 2.1.3.2) and dihydroorotase (EC3.5.2.3) were missing (Supplementary material 11). BecauseM. succiniciproducens grew well in a medium withoutpyrimidine, and all the other de novo pyrimidine-biosynthetic reactions were identified, these two initialsteps were assumed to exist inM. succiniciproducens. The fulllist of reactions that do not have assigned ORFs is providedin Supplementary material 11.

Characterization and Simulation of the ReconstructedGenome-scale Metabolic Network

Metabolic Network Analysis

To elucidate the correlation between the structural proper-ties and the functional behaviors of the genome-scalemetabolic network of M. succiniciproducens, the inherentnetwork properties (Barabasi and Oltvai, 2004) wereanalyzed and compared with those of E. coli (Reed et al.,2003). The results of metabolic network analysis are shownin Supplementary material 12. The M. succiniciproducensnetwork showed power-law distribution of metabolites asobserved in E. coli (Barabasi and Oltvai, 2004; Jeong et al.,2000). The average number of reactions participated by eachmetabolite inM. succiniciproducens was 2.82, which is lowerthan that (3.21) in E. coli. During the analysis, the universalmetabolites such as CO2, phosphate, ATP, NADH andNADPH were not considered, because they are connected toso many reactions that they do not represent distinctfeatures of real metabolic pathways (Ma and Zeng, 2003)(Supplementary material 12). The network diameter ofM. succiniciproducens was found to be smaller than that ofE. coli (11 vs. 13). All of these findings indicate that themetabolic network ofM. succiniciproducens is more compactthan E. coli. If we consider functional redundancyrepresented by alternative pathways to form a specificmetabolite, it can be concluded that theM. succiniciproducens genome has less functional redun-dancy than the E. coli genome as reflected by its smallergenome size.

Metabolite connectivity can be used to determine therelative importance of metabolites included in the metabolicnetwork and to unveil the metabolic characteristics of anorganism. Thus, the connectivities of all metabolites in thegenome-scale metabolic network of M. succiniciproducenswere examined and compared with those determined in


E. coli (Reed et al., 2003) and S. cerevisiae (Forster et al.,2003). We excluded proton and water for this analysisbecause of the discrepancy in three models; proton andwater were considered in the E. colimodel whereas they werenot considered in the S. cerevisiae model. As shown inSupplementary material 13, the metabolites involved inenergy metabolism such as ATP, ADP and phosphateshowed the highest connectivity in all three organismsindicating that they are the most important constituents forvarious metabolic functions. A notable finding is therelatively higher frequency of CO2 in the network ofM. succiniciproducens (2.29%), compared with those inE. coli (1.85%) and S. cerevisiae (1.83%). This findingsupports the explanation that M. succiniciproducens hasdeveloped its metabolic system to adapt to the CO2-richrumen condition (65% of total gas in the rumen) despite itsrelatively small genome size.

Validation of the Genome-Scale Metabolic Model

The reconstructed genome-scale metabolic network wasvalidated by comparing the results of constraints-based fluxanalysis with the actual fermentation data (Table III;Supplementary material 5) (Lee et al., 2006). Before thesimulation, the energetic parameters, including GAME andNGAME terms in the model, were calculated based on thedata from the chemostat culture (Supplementary material5), following the methodology described elsewhere (Bor-odina et al., 2005; Varma and Palsson, 1994b). The GAMEvalue of 7.8 g � g DW�1 is equivalent to 15.38mmol � g DW�1, which is comparable to the NGAME value(15.90 mmol � g DW�1 � h�1) for the wild-type straininvestigated. In other studies (Feist et al., 2006; Forsteret al., 2003; Reed et al., 2003), GAME is generally larger thanNGAME. Relatively large NGAME seems to be needed inour system because the amounts of acids that were producedand excreted (pumped out) were much more than that ofbiomass formed. All simulations were carried out using theunique (non-redundant) set of metabolic network consistedof 638 reactions and 519 metabolites (Table I). First, thesimulation results were compared with the batch fermenta-tion data obtained under CO2-rich anaerobic condition. Forthe simulation, the glucose uptake rate of 14.46 mmol � gDW�1 . h�1, which is the experimentally measured valueat the exponential growth phase (2–4 h; Supplementarymaterial 5), was used as a constraint. It should be mentionedthat the comparison was made for the cells at theexponential phase because the objective function ofmaximizing cell growth rate was used during the simulation.As can be seen from Table III, the production rates ofsuccinic, acetic, formic, lactic, pyruvic and malic acidscalculated by the simulation matched quite well with thefermentation data. Furthermore, the in silico specific growthrate was also in good agreement with the experimentalobservation. These results suggest that the in silico genome-scale metabolic model properly represent the real metabo-lism of M. succiniciproducens.

DOI 10.1002/bit

Table III. Comparison of the in silico simulation results with the fermentation profiles of wild-type and mutant strains of M. succiniciproducens.

Strain Specific growth rate (h�1)

Rate of substrate or product formation (mmol g DW h�1)

Glucose Succinic acid Acetic acid Formic acid Lactic acid Malic acid Pyruvic acid

MBEL55E 0.65 �14.46 10.62 8.79 11.84 0.03 0.00 0.00

In silico MBEL55Ea 0.65 �14.46 13.32 12.90 13.81 0.00 0.00 0.00

LK 0.48 �11.10 9.48 8.05 4.05 0.25 0.03 �0.08

In silico LKb 0.48 �11.10 10.25 9.94 10.62 0.00 0.00 0.00

LPK 0.33 �15.72 12.75 2.18 0.00 0.82 4.24 6.84

In silico LPKc 0.33 �15.72 20.20 9.86 0.20 0.00 0.00 0.00

In silico LPK_1d 0.33 �15.72 18.31 5.43 0.00 0.00 0.00 6.25

LPK7 0.33 �10.80 13.46 0.00 0.00 0.00 0.00 10.11

In silico LPK7e 0.33 �10.80 17.68 0.00 0.00 0.00 0.00 0.00

In silico LPK7_1f 0.33 �10.80 10.71 0.00 0.00 0.00 0.00 9.41

aAll the GAME (g � g DW�1)and NGAME (mmol � g DW�1 h�1) values were recalculated for each strain. In in silico MBEL55, the growth associatedmaintenance (GAME) and non-growth associated maintenance (NGAME) parameters were calculated based on the data from the chemostat culture(Supplementary materials 6–8). GAME and NGAME values are 7.80 and 15.90, respectively, in the wild-type strain.

b~ldhA (lactate dehydrogenase). The GAME and NGAME values are 6.63 and 13.52, respectively.c~ldhA ~pflB (pyruvate formate lyase). The GAME and NGAME values are 14.0 and 28.54, respectively.dThe GAME andNGAME values are 11.7 and 23.85, respectively. The upper limits of the fluxes of pyruvate dehydrogenase and PEP carboxylase were set to

be 5.69 and 18.93 mmol � g DW�1 � h�1, respectively.e~ldhA ~pflB ~pta-ackA (phosphate acetyltransferase - acetate kinase). The GAME and NGAME values are 6.50 and 13.25, respectively.fThe GAME and NGAME values are 4.70 and 9.58, respectively. The upper limits of the fluxes of pyruvate dehydrogenase and PEP carboxylase were set to

be 0.27 and 11.26 mmol g DW�1 h�1, respectively.

Genetic Perturbation

Systematic gene deletion studies have often been carried outto understand the functions of the genes and alteredmetabolic characteristics. In order to determine the effects ofsingle gene deletion on the metabolic characteristics, wesystematically knocked out every gene one at a time and alsomultiple genes during the constraints-based flux analysis byproviding a constraint that sets the flux through a particularreaction to zero, as detailed in this section. This approachallows rapid prediction of the consequences of gene deletionand thus identification of the genes to be manipulated bymetabolic engineering to achieve a desired goal (Alper et al.,2005; Burgard et al., 2003; Lee et al., 2005a).

The phenotypic changes of M. succiniciproducens upongene deletion were examined both in minimal and complexmedia (Supplementary material 14). We first set appropriateconstraints for the transporters to realistically reflect themedium composition (Supplementary materials 7 and 8). Ifthe deletion of a certain reaction causes no cell growth, thereaction is considered essential. In the case that the deletionof a reaction causes decreased cell growth rate but notno-growth, the reaction is considered partially essential.Finally, if the deletion of a reaction does not affect cellgrowth, the reaction is classified as non-essential. WhenM. succiniciproducens was grown in a minimal medium, 251reactions were computationally predicted to be essential.When a complex medium was used, 179 reactions werepredicted to be essential. The number of partially essentialand non-essential reactions was 27 and 360 in a minimalmedium, and 33 and 426 in a complexmedium, respectively.The reactions in the M. succiniciproducens metabolism werecategorized into 8 metabolic subsystems according to thefunctional groups (Supplementary material 14). The


number of essential reactions in the nucleotide and aminoacid metabolism was considerably less when a complexmedium was used. This is a well-predicted phenomenon ascells can complement these compounds from the complexmedium, and thus cell growth is not affected even thoughcells can no longer intracellularly synthesize them due to thegene deletion.

We next examined the effects of gene deletion on thesuccinic acid production rate. Figure 3 shows thecomparative performance limits of the in silico geneknockout mutants of M. succiniciproducens and E. coli withrespect to the succinic acid production rate and the cellgrowth rate. These trade-off curves were obtained byconstraints-based flux analysis with an objective function ofmaximizing the cell growth rate at various values of succinicacid production rate; the cell growth rate was calculated at aspecific fixed succinic acid production rate, which wasaltered from the maximum value of 25.28 to zerommol � g DW�1 � h�1 in the case of M. succiniciproducens(point 1 in Fig. 3A). Fermentation data taken from theexponential phase were used for the simulation of in silicoM. succiniciproducens metabolic network. The measuredglucose uptake rate, which was used as a constraint, was14.46 mmol � g DW�1 � h�1 (Supplementary material 5). Theglucose uptake rate of E. coli in a complex medium was set to7.64 mmol � g DW�1 � h�1 (Aristidou et al., 1999). Theresulting trade-off curves show the succinic acid productionrate and cell growth rate under all possible physiologicalstates. The outermost boundary is the domain of thephysiologically plausible states of the wild-typeM. succiniciproducens MBEL55E strain. The maximum cellgrowth rate of 0.65 h�1 (point 2 in Fig. 3A) and themaximum succinic acid production rate of 25.28 mmol � gDW�1 � h�1 were predicted using the objective functions of



Figure 3. Trade-off curves of M. succiniciproducens (A) and E. coli (B). They represent the cell growth rate and the succinic acid production rate of the wild-type and the

gene deletion mutants. It should be noted that single gene deletion was performed for all genes, while the genes selected for multiple gene knockout simulations were based on the

previous experiments to compare the experimental and simulation results. Each color indicates different gene deletion mutants. (A) Black: wild type or ~ldhA, red: ~ackA or ~pta,

blue: ~pflB or ~ldhA~pflB, green: ~ldhA~pflB~pta-ackA. (B) Black: wild type, blue: ~ptsG~pykFA, red: ~pflB~ldhA, green: ~pflB~ldhA~adhE, gray: ~ptsG~pykFA

~pta~ackA. It is notable that the ~ldhA mutant strain of M. succiniciproducens also shows the same trade-off curve. The trade-off curves for only several mutants are shown as

representatives. The numbered points are: 1, the maximum succinic acid production rate; 2, the maximum cell growth rate; 3, the optimal physiological state in which minimally

reduced cell growth rate is accompanied by maximally improved succinic acid production rate. [Color figure can be seen in the online version of this article, available at

www.interscience.wiley.com.]

maximizing the cell growth rate and succinic acidproduction rate, respectively (Fig. 3A). Our ultimate goalis to maximize succinic acid production rate, but this isachievable at the cost of the reduced cellular growth. Hence,there is a trade-off between the succinic acid production rateand cell growth rate. In this study, we conducted simulationsof all single gene deletion mutants (Fig. 4) and experimen-tally obtained strains (LK, LPK, and LPK7). Simulation onthe changes in metabolic fluxes caused by gene deletionshowed that there exist several good operating states inwhich the cell growth rate is minimally reduced while thesuccinic acid production rate is maximally increased(Fig. 3A). One of these points observed in ~pta, ~ack,or ~ldhA ~pflB ~pta-ackA strain (point 3 in Fig. 3A) isrepresented by 27.50% reduced cell growth rate comparedwith the wild-type MBEL55E, but 92.59% of the maximumpossible succinic acid production rate. The achievement ofenhanced succinic acid production rate by gene knockout(s)inM. succiniciproducens is remarkable compared with E. coli.In E. coli, one of the best combinatorial knockout strainspreviously predicted for the efficient production of succinicacid is~ldhA~pflB~adhE strain (Burgard et al., 2003). Inthe case of this mutant strain, the cell growth rate is reducedby 57.7% from the maximum cell growth rate (0.22 h�1) ofthe wild-type strain, while the succinic acid production rateincreases up to 9.76 mmol � g DW�1 � h�1 (point 3 inFig. 3B), which is equivalent to 78.9% of the maximumpossible succinic acid production rate of 12.37 mmol � gDW�1 � h�1. Similarly, the trade-off curves of several other


E. coli mutants also showed enhanced succinic acidproduction rates (Burgard et al., 2003; Lee et al., 2005a),but the extent of increase was significantly less than themutants ofM. succiniciproducens. In short, the positive effectof gene deletion(s) on succinic acid production rate wasmore dramatic in M. succiniciproducens than E. coli.Furthermore, the absolute metabolic performance is alsomuch greater in M. succiniciproducens as its glucoseconsumption rate is higher and its metabolism allowshigher succinic acid flux than E. coli under anaerobiccondition. This is reflected in the trade-off curves shown inFigure 3. The trade-off curves for all single gene mutants canbe found in Figure 4. As can be seen from Figure 4B, groupswere categorized based on the cell growth rate (h�1) whereasclusters were categorized based on the succinic acidproduction rate (mmol � g DW�1 � h�1) at the maximumcell growth rate. These simulations on generating the trade-off curves were carried out by deleting non- and partiallyessential reactions (458 out of 638 reactions) that do notresult in zero cell growth rates. A full list of genes knocked-out in these simulations is available in Supplementarymaterial 15. Most of single gene knockout mutants (97.5%)showed the trade-off curve belonging to the cluster 1 ofgroup A, which were able to grow at a growth rate of greaterthan 0.6 h�1. It can be seen that the optimal succinic acidproducing mutant strains ~pta, ~ack, and ~ldhA ~pflB~pta-ackA strains (point 3 in Fig. 3A), belong to the cluster1 of group C, which shows the minimal decrease of cellgrowth rate accompanied by the large increase of succinic

DOI 10.1002/bit

Figure 4. Trade-off curves of all single gene mutants. (A) Trade-off curves for the M. succiniciproducens mutant strains in which the indicated central metabolic pathway

gene was knocked-out. In each graph, X-axis refers to the cell growth rate and Y-axis the succinic acid production rate. Each graph is labeled to show which cluster it belongs to, as

shown in (B). A.1 corresponds to cluster 1 in group A. Likewise, B.1 and B.2 correspond to clusters 1 and 2 in group B, respectively. C.1, C.2, and C.3 indicate clusters 1, 2, and 3 in

group C, respectively. In (B), groups are categorized based on the cell growth rate (h�1) whereas clusters are categorized based on the succinic acid production rate

(mmol � g DW�1 � h�1) at the maximum cell growth rate. The cell growth rates are: group A, greater than 0.6 h�1; group B, between 0.5 and 0.6 h�1; group C, between 0.4 and 0.5 h�1;

group D, between 0.3 and 0.4 h�1; group E, less than 0.1 h�1. These simulations on generating the trade-off curves were carried out by deleting non- and partially essential reactions

(458 out of 638 reactions) that do not result in zero cell growth rates. The number of reactions belonging to each cluster is shown in percentage with respect to the total number of

458 reactions individually knocked-out. A full list of genes knocked-out in these simulations is available in Supplementary material 15.

Kim et al.: Reconstruction and Analysis of the Genome-Scale Metabolic Network of Mannheimia succiniciproducens 669


acid production rate. This type of analysis should help usidentify the gene deletion candidates for desired metabolicengineering.

We also examined whether the in silico metabolicnetwork can correctly predict the growth rate andmetabolite production rates for the knockout mutantstrains constructed in our previous study (Lee et al.,2006). Table III summarizes the simulation results ofmutant strains, in which GAME and NGAME wererecalculated for each strain. Again, the glucose uptake ratemeasured at the exponential phase was used as the onlyconstraint during the simulation. First, the LK strain, whichis an ldhAmutant strain of MBEL55E, was examined. The insilico LK cell was created by setting the flux of lactatedehydrogenase reaction to zero. The predicted specificgrowth rate and the production rates of succinic and aceticacids were in good agreement with the actual fermentationresults (Table III). There was some deviation between thepredicted and measured values of the formic acidproduction rate. However, the in silico model correctlypredicted that there was no formation of lactic, pyruvic, andmalic acids. Also, the flux distribution obtained bysimulation suggests that PEP is converted to succinic acidthrough oxaloacetate, malate and fumarate catalyzed by PEPcarboxykinase (pckA), malate dehydrogenase, fumarase, andfumarate reductase, which agrees with the experimentalobservation (Lee et al., 2006) (Supplementary material 16).We also confirmed that the in silico knockout of the pckAgene strongly hampered cell growth and succinic acidproduction, as experimentally observed (Lee et al., 2006)(Supplementary material 16). All of these results suggest thatthe in silico metabolic model constructed in this studypredicts the real metabolism of M. succiniciproducens andintracellular flux distribution quite well.

We then extended our study to more severely engineeredstrains: the LPK strain which lacks in the ldhA and pflBgenes, and the LPK7 strain which lacks in the ldhA, pflB, ptaand ackA genes. For each mutant, we designed an in silicocell by constraining the fluxes of the correspondingknockout reactions to zero (designated as in silico LPKand in silico LPK7, respectively, in Table III). Somediscrepancies were observed between the calculated andexperimentally measured values in these strains. Mostnotably, the secretion of pyruvic acid could not be predictedby the simulation of the in silico models. This is likely due tothe lack of appropriate constraints in the model reflectingregulations. In order to find constraints that can reflect theseunknown mechanisms, an extra constraint was applied tothe pyruvic acid secreting reaction in the in silico LPK7model; the actual pyruvic acid excretion rate from thefermentation results (Table III) was first provided as aconstraint during the simulation. Then, the reactionsshowing significantly different fluxes between the previousand modified simulations were identified. It was found thatthe fluxes of the reactions catalyzed by pyruvate dehy-drogenase complex (aceEF and lpdA) and PEP carboxyki-nase (pckA) were notably lower in the modified simulation


results. Thus, we provided additional constraints of upperlimit flux values (Table III footnotes) for the aforemen-tioned two reactions. Then, the constraint of pyruvic acidproduction rate was removed again to examine the truepredictability of the model (designated as in silico LPK7_1).The simulation results obtained with these new constraintswere consistent with the actual fermentation data, includingcorrect prediction of pyruvic acid excretion. This procedureshould be useful for providing new constraints for thesimulation reflecting some of the unknown metaboliccharacteristics.

Likewise, we applied the same procedure to in silico LPKand provided constraints for the same set of reactions as inin silico LPK7. The values of the upper limit fluxes (Table IIIfootnotes) were different from those provided in in silicoLPK7_1 to reflect the unique profile observed in the LPKstrain. By doing so, the in silico LPK strain, producedpyruvic acid, as observed experimentally (in silico LPK_1 inTable III). However, production of a small amount of formicacid and no production of malic acid were inconsistent withthe experimental data. Investigation of the flux distributionsuggested that this small amount of formic acid wasproduced from the metabolism of ‘‘one carbon poolinvolving folate’’ that makes use of tetrahydrofolate ratherthan central carbon metabolism. Our current version of themodel does not account for this type of metaboliccharacteristics. Inability to predict malic acid productionfurther suggests that it is necessary to provide morephysiologically relevant constraints and update the modeliteratively in order to improve the simulation performance.Nonetheless, the current in silico model predicts themetabolic phenotypes very well, and thus should be usefulfor deciphering metabolic characteristics under variousconditions.

Conclusions

In this study, we reconstructed the genome-scale metabolicnetwork of M. succiniciproducens based on the newlyobtained information from the reannotation and validationexperiments. In particular, we provided several proceduresfor filling-in the gaps and fine-tuning the network.Constraints-based flux analysis under various conditionssuggested that the genome-scale metabolic model representsthe real cellular metabolism correctly. In silico geneknockout simulations allowed identification of genes tobe knocked out for the enhanced production of succinicacid. Interestingly, the trade-off curves obtained forM. succiniciproducens and E. coli were much different uponthe deletion of the same set of genes. The simulation resultsof in silico gene knockout mutants matched the actualfermentation results obtained with these mutant strainsquite well, but the formation of some metabolites could notbe correctly predicted. We thus suggested a new strategy ofproviding new constraints to solve this problem to someextent. Taken together, the genome-scale model of

DOI 10.1002/bit

M. succiniciproducens represents the real metabolism verywell, and thus should be useful for various in silicosimulations. Furthermore, the detailed procedure forreconstructing, validating and simulating the genome-scalemetabolic model described here should be useful fordeveloping and fine-tuning the genome-scale metabolicmodels for other organisms.

We thank Kwang Ho Lee and Sang Jun Lee for their contribution to

PTS/glucokinase (14C-labeled glucose phosphorylation) assay, and

Sung Won Lim for his contribution to experiments on biomass

composition. This work was supported by the Genome-Based Inte-

grated Bioprocess Development Project of the Ministry of Science and

Technology. Further supports by the LG Chem Chair Professorship,

IBM-SUR program, Microsoft, and the Center for Ultramicrochem-

ical Process Systems sponsored by KOSEF are appreciated.

References

Alper H, Jin YS, Moxley JF, Stephanopoulos G. 2005. Identifying gene

targets for the metabolic engineering of lycopene biosynthesis in

Escherichia coli. Metab Eng 7(3):155–164.

Aristidou AA, San KY, Bennett GN. 1999. Metabolic flux analysis of

Escherichia coli expressing the Bacillus subtilis acetolactate synthase

in batch and continuous cultures. Biotechnol Bioeng 63(6):737–749.

Barabasi AL, Oltvai ZN. 2004. Network biology: Understanding the cell’s

functional organization. Nat Rev Genet 5(2):101–113.

Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. 2005.

GenBank. Nucleic Acids Res 33(Database issue): D34–D38.

Borodina I, Krabben P, Nielsen J. 2005. Genome-scale analysis of Strepto-

myces coelicolor A3(2) metabolism. Genome Res 15(6):820–829.

Burgard AP, Pharkya P, Maranas CD. 2003. Optknock: A bilevel program-

ming framework for identifying gene knockout strategies for microbial

strain optimization. Biotechnol Bioeng 84(6):647–657.

Caspi R, Foerster H, Fulcher CA, Hopkinson R, Ingraham J, Kaipa P,

Krummenacker M, Paley S, Pick J, Rhee SY, et al. 2006. MetaCyc: A

multiorganism database of metabolic pathways and enzymes. Nucleic

Acids Res 34(Database issue): D511–D5116.

Davis CP, Cleven D, Brown J, Balish E. 1976. Anaerobiospirillum, a new

genus of spiral-shaped bacteria. Int J Syst Bacteriol 26:498–504.

Edwards JS, Ramakrishna R, Schilling CH, Palsson BO. 1999.Metabolic flux

balance analysis. In: Lee SY, Papoutsakis ET, editors. Metabolic engi-

neering. New York: Marcel Dekker Inc., p 13–57.

Feist AM, Scholten JC, Palsson BO, Brockman FJ, Ideker T. 2006. Modeling

methanogenesis with a genome-scale metabolic reconstruction of

Methanosarcina barkeri. Mol Syst Biol 2: 2006.0004.

Forster J, Famili I, Fu P, Palsson BO, Nielsen J. 2003. Genome-scale

reconstruction of the Saccharomyces cerevisiae metabolic network.

Genome Res 13(2):244–253.

Gombert AK, Nielsen J. 2000. Mathematical modeling of metabolism. Curr

Opin Biotechnol 11:180–186.

Guettler MV, Rumler D, JainMK. 1999. Actinobacillus succinogenes sp. nov.,

a novel succinic-acid-producing strain from the bovine rumen. Int J

Syst Bacteriol 49(Pt 1): 207–216.

Hong SH, Kim JS, Lee SY, In YH, Choi SS, Rih JK, Kim CH, Jeong H, Hur

CG, Kim JJ. 2004. The genome sequence of the capnophilic rumen

bacterium Mannheimia succiniciproducens. Nat Biotechnol 22(10):

1275–1281.

Jeanmougin F, Thompson JD, Higgins DG, Gibson TJ. 1998. Multiple

sequence alignment with ClustalX. Trends Biochem Sci 23:403–405.

Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL. 2000. The large-

scale organization of metabolic networks. Nature 407(6804):651–

654.

Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S,

Katayama T, Araki M, Hirakawa M. 2006. From genomics to chemical


genomics: new developments in KEGG. Nucleic Acids Res 34(Database

issue): D354–D357.

Kim P, Laivenieks M, McKinlay J, Vieille C, Gregory Zeikus J. 2004.

Construction of a shuttle vector for the overexpression of re-

combinant proteins in Actinobacillus succinogenes. Plasmid 51(2):

108–115.

Lee J, Blaschek HP. 2001. Glucose uptake in Clostridium beijerinckii

NCIMB 8052 and the solvent-hyperproducing mutant BA101. Appl

Environ Microbiol 67(11):5025–5031.

Lee PC, Lee SY, Hong SH, Chang HN. 2002. Isolation and characterization

of a new succinic acid-producing bacterium, Mannheimia succinici-

producensMBEL55E, from bovine rumen. Appl Microbiol Biotechnol

58(5):663–668.

Lee SJ, Lee DY, Kim TY, Kim BH, Lee J, Lee SY. 2005a. Metabolic

engineering of Escherichia coli for enhanced production of succinic

acid, based on genome comparison and in silico gene knockout

simulation. Appl Environ Microbiol 71(12):7880–7887.

Lee SY, Lee DY, Kim TY. 2005b. Systems biotechnology for strain improve-

ment. Trends Biotechnol 23(7):349–358.

Lee SJ, Song H, Lee SY. 2006. Genome-based metabolic engineering of

Mannheimia succiniciproducens for succinic acid production. Appl

Environ Microbiol 72(3):1939–1948.

Ma HW, Zeng AP. 2003. The connectivity structure, giant strong compo-

nent and centrality of metabolic networks. Bioinformatics 19(11):

1423–1430.

Peltroche-Llacsahuanga H, Schmidt S, Lutticken R, Haase G. 2000. Dis-

criminative power of fatty acid methyl ester (FAME) analysis using the

microbial identification system (MIS) for Candida (Torulopsis) glab-

rata and Saccharomyces cerevisiae. Diagn Microbiol Infect Dis

38(4):213–221.

Peng L, Shimizu K. 2003. Global metabolic regulation analysis for Escher-

ichia coli K12 based on protein expression by 2-dimensional electro-

phoresis and enzyme activity measurement. Appl Microbiol Biotechnol

61(2):163–178.

Price ND, Reed JL, Palsson BO. 2004. Genome-scale models of microbial

cells: Evaluating the consequences of constraints. Nat Rev Microbiol

2(11):886–897.

Reed JL, Vo TD, Schilling CH, Palsson BO. 2003. An expanded genome-

scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol

4(9):R54.

Ren Q, Paulsen IT. 2005. Comparative analyses of fundamental differences

in membrane transport capabilities in prokaryotes and eukaryotes.

PLoS Comput Biol 1(3):e27.

SongH, Lee SY. 2006. Production of succinic acid by bacterial fermentation.

Enzyme Microb Technol 39:352–361.

Stephanopoulos GN, Aristidou AA, Nielsen J. 1998. Metabolic engineer-

ing—Principles and methodologies. San Diego: Academic Press.

Tatusov RL, Natale DA, Garkavtsev IV, Tatusova TA, Shankavaram UT,

Rao BS, Kiryutin B, Galperin MY, Fedorova ND, Koonin EV.

2001. The COG database: New developments in phylogenetic classifi-

cation of proteins from complete genomes. Nucleic Acids Res 29(1):

22–28.

Terada K, Murata T, Izui K. 1991. Site-directed mutagenesis of phosphoe-

nolpyruvate carboxylase from E. coli: The role of His579 in the catalytic

and regulatory functions. J Biochem (Tokyo) 109(1):49–54.

Van der Werf MJ, Guettler MV, Jain MK, Zeikus JG. 1997. Environmental

and physiological factors affecting the succinate product ratio during

carbohydrate fermentation by Actinobacillus sp. 130Z. Arch Microbiol

167(6):332–342.

Varma A, Palsson BO. 1994a. Metabolic flux balancing: Basic concepts,

scientific and practical use. Bio-Technology 12:994–998.

Varma A, Palsson BO. 1994b. Stoichiometric flux balance models quanti-

tatively predict growth and metabolic by-product secretion in wild-

type Escherichia coli W3110. Appl Environ Microbiol 60(10):3724–

3731.

Varma A, Boesch BW, Palsson BO. 1993. Stoichiometric interpretation of

Escherichia coli glucose catabolism under various oxygenation rates.

Appl Environ Microbiol 59(8):2465–2473.



genome-scale analysis of mannheimia succiniciproducens metabolism

Documents