Genome-scale reconstruction and in silico analysis of...

GENOMICS AND PROTEOMICS

Genome-scale reconstruction and in silico analysisof the Clostridium acetobutylicum ATCC 824 metabolic network

Joungmin Lee & Hongseok Yun & Adam M. Feist &Bernhard Ø. Palsson & Sang Yup Lee

Received: 20 June 2008 /Revised: 2 August 2008 /Accepted: 4 August 2008 / Published online: 29 August 2008# Springer-Verlag 2008

Abstract To understand the metabolic characteristics ofClostridium acetobutylicum and to examine the potentialfor enhanced butanol production, we reconstructed thegenome-scale metabolic network from its annotated ge-nomic sequence and analyzed strategies to improve itsbutanol production. The generated reconstructed networkconsists of 502 reactions and 479 metabolites and was usedas the basis for an in silico model that could computemetabolic and growth performance for comparison withfermentation data. The in silico model successfullypredicted metabolic fluxes during the acidogenic phaseusing classical flux balance analysis. Nonlinear program-ming was used to predict metabolic fluxes during thesolventogenic phase. In addition, essential genes were

predicted via single gene deletion studies. This genome-scale in silico metabolic model of C. acetobutylicumshould be useful for genome-wide metabolic analysis aswell as strain development for improving production ofbiochemicals, including butanol.

Keywords Genome-scale metabolic network . In silico .

Metabolic flux analysis .Clostridium acetobutylicum .

Butanol

Introduction

Clostridium acetobutylicum is a Gram-positive anaerobicbacterium that produces several solvents including acetone,butanol, and ethanol. Due to demand for solvents in thechemical industry and during the two World Wars in thetwentieth century, acetone–butanol–ethanol (ABE) fermen-tation using Clostridium spp. had been well developed andused on a large scale. However, biobutanol production byC. acetobutylicum stopped for the most part in the middleof twentieth century (with a few exceptions) due to therapid growth of the petrochemical industry (Dürre 2007;Jones and Woods 1986; Zverlov et al. 2006). As the oilprice sharply increased together with our environmentalconcerns, biobutanol production has come into focus as analternative to gasoline. Therefore, interest in solvent-producing clostridia has come back to the forefront.

Since native solventogenic clostridia produce by-productssuch as acetone (or isopropanol), acetate, and butyrate (Jonesand Woods 1986), it needs to be metabolically engineered toimprove butanol yield. The lack of an efficient geneinactivation system and its complex physiology, includingthe relationship between sporulation and solventogenesis,makes the metabolic engineering of C. acetobutylicum

Appl Microbiol Biotechnol (2008) 80:849–862DOI 10.1007/s00253-008-1654-4

Electronic supplementary material The online version of this article(doi:10.1007/s00253-008-1654-4) contains supplementary material,which is available to authorized users.

J. L. and H. Y. equally contributed to this work.

J. Lee :H. Yun : S. Y. Lee (*)Metabolic and Biomolecular Engineering National ResearchLaboratory, Department of Chemical & Biomolecular Engineering(BK21 Program), BioProcess Engineering Research Center,Center for Systems and Synthetic Biotechnology and Institutefor the BioCentury, KAIST,Daejeon 305-701, Republic of Koreae-mail: [email protected]

A. M. Feist : B. Ø. PalssonDepartment of Bioengineering, University of California,San Diego, La Jolla, CA 92093-0412, USA

S. Y. LeeDepartment of Bio and Brain Engineering, Department ofBiological Sciences and Bioinformatics Research Center, KAIST,Daejeon 305-701, Republic of Korea

http://dx.doi.org/10.1007/s00253-008-1654-4

challenging, even though C. acetobutylicum has been studiedas a model organism of ABE fermentation for severaldecades. Fortunately, two reports of gene inactivationsystems using retrotransposition have recently appeared(Heap et al. 2007; Shao et al. 2007). Sporulation ofClostridium spp. is somewhat similar to that of Bacillusspp., but the relationship between sporulation and solventproduction in C. acetobutylicum is not clear (Paredes et al.2005). For these reasons, it might be attractive to producebutanol using Escherichia coli, a well-characterized bacteri-um with the best established genetic manipulation tools.However, there have been only two reports on butanolproduction using engineered E. coli through the introductionof a clostridial pathway, and the yield of butanol is relativelylow compared to that of C. acetobutylicum (Atsumi et al.2008; Inui et al. 2008). These results suggest that it might bedifficult to improve butanol production with classicalmetabolic engineering approaches.

Recent advances in genomics made it possible for us toreconstruct “genome-scale” metabolic network of an or-ganism (Francke et al. 2005; Reed et al. 2006a). Since thefirst genome-scale metabolic model of E. coli appeared(Edwards and Palsson 2000), similar reconstructions wereperformed for a number of organisms across all threephylogenetic domains (Becker and Palsson 2005; Borodinaet al. 2005; Duarte et al. 2004; Feist et al. 2006, 2007; Kimet al. 2007; Oh et al. 2007; Reed et al. 2006a; Thiele et al.2005). A useful and popular tool to investigate suchreconstructions is flux balance analysis (FBA). FBA isbased on linear programming and is used to evaluate fluxdistribution in a metabolic network under governingconstraints (Schilling et al. 1999). Using this formalism,gene deletion simulations can provide qualitative andquantitative predictions of network robustness and theproduction rates of specific metabolites. Such predictionshave proven useful in strain improvement for metaboliteproduction (Feist and Palsson 2008; Park et al. 2007, 2008).

There have been a limited number of attempts tocomputationally analyze the metabolism ofC. acetobutylicumATCC 824. A simple stoichiometric model of 14 reactionsincluding glycolytic, acidogenic, and solventogenic path-ways has been formulated (Desai et al. 1999b). Dynamicmodeling and simulation of ABE fermentation were alsocarried out (Shinto et al. 2007).

In this paper, we report reconstruction of a genome-scalemetabolic model for C. acetobutylicum from its annotationdata and experimental results and the characteristics of C.acetobutylicum metabolism using FBA and a nonlinearapproach. Recently, while we were preparing the manu-script, a separate genome-scale model of C. acetobutylicumhas appeared (Senger and Papoutsakis 2008a, b). Thetwo reconstructions and modeling approaches were alsocompared.

Materials and methods

Metabolic network reconstruction

A metabolic network model of C. acetobutylicum ATCC824 was constructed using the combined information frommany different sources such as public databases, literature,and experiments (see Fig. 1). The primary genomeannotation of C. acetobutylicum ATCC 824 was obtainedfrom NCBI (http://www.ncbi.nlm.nih.gov/), and it was usedas a guideline to build gene–protein–reaction (GPR)relationships (Reed et al. 2006a). In addition, for efficientnetwork reconstruction, we collected the annotation datafrom various databases, including BioSilico (Hou et al.2004), KEGG (Kanehisa et al. 2006), TIGR (http://www.tigr.org), and MetaCyc (Caspi et al. 2006). Information onmost biochemical reactions was obtained from the Bio-Silico and KEGG databases and was used to develop thedraft reconstruction as outlined in Francke et al. (2005).

Determination of biomass composition

The formulation of the biomass objective function is based onmetabolites called precursors that are converted into thebuilding blocks of each major macromolecule of the cell, suchas amino acids, nucleic acids, and fatty acids (see Feist et al.2007 for a detailed explanation). These building blocks thenpolymerize into macromolecules of cellular biomass (Stepha-nopoulos et al. 1998). In our modeling, biomass compositionwas determined from various reported data (Supplementarydata 1) and experimental results. Macromolecular composi-tion and solute pools were assumed to be the same with thatof Bacillus subtilis (Oh et al. 2007). The composition ofnucleotides was averaged based on its genome sequence(Borodina et al. 2005). Lipid composition and teichoic acidcomposition was determined based on literature (see Supple-mentary data 2). Amino acid and cell wall compositions wereobtained by analyzing the cells ofC. acetobutylicum collectedfrom the batch culture in a chemically defined medium.Amino acid composition in total cytoplasmic proteins wasexperimentally measured at Korea Basic Science Institute(Daejeon, Korea). Cell wall composition was determined atDeutche Sammlung von Mikroorganismen und ZellkulturenGmbH (Braunschweig, Germany).

Gap filling procedure

In general, draft models cannot work properly as a “viable”in silico cell. The draft model contains incorrect andinsufficient metabolic pathways due to the incompletenessof database information from which it was built. The mostfrequently observed inaccuracies in the database informa-tion are reaction reversibility, missing enzymes, different

850 Appl Microbiol Biotechnol (2008) 80:849–862

http://www.ncbi.nlm.nih.gov/

http://www.tigr.org

http://www.tigr.org

notations for the same metabolites and genes, and cofactorspecificities. Thus, draft models should be revised througha gap-filling process using the published cell-specific dataavailable in the literature and additional experimental data(Breitling et al. 2008).

Maintenance coefficients are also required for the use ofthe biomass objective function in FBA. For gap fillingalone, the value of biomass production rate, i.e., specificgrowth rate, is not important, and only the binary outcomeof being able to produce the specific precursors outlined inthe function is important. Thus, we need suitable values forgrowth-associated maintenance (GAM) and non-growth-associated maintenance (NGAM) energies. As these values

for C. acetobutylicum are unknown, we first assumed themto be 40 mmol ATP per gram dry cell weight (DCW) forGAM and 5 mmol/g DCW per hour for NGAM based onprevious analyses for similar organisms (Feist et al. 2006,2007; Oh et al. 2007). These values were used only duringgap-filling process, and the actual values were determinedfor the final genome-scale model (see below).

Estimation of maintenance energy and its influenceto acid-producing ratio

As described above, there are two kinds of cellular maintenancecoefficients, GAM and NGAM. These parameters can be

Fig. 1 The process utilized to reconstruct the metabolic network of C.acetobutylicum. 1 The C. acetobutylicum ATCC 824 annotation wasused along with several databases to generate an automatedreconstruction. 2 Next, based on the automated reconstruction, a draftmodel was developed and curated using various literature andexperimental data sources. Gaps in the automated reconstruction werefilled during this step. 3 Results from two-phase analysis werecompared with experimental data to validate the content and modelingapproach. 4 After validation, the curated model, CacMBEL502, wasutilized for phenotype predictions of in silico knockout strains and

genome-wide metabolic engineering applications of C. acetobutyli-cum. This process was similar to the method outlined previously (Feistet al. 2006). Right panel of the figure shows flow chart diagram fortwo-phase flux analysis using nonlinear programming. First, typicalFBA using linear programming was carried out for acidogenic phase.In this case, maximization of cellular growth was the objectivefunction. Acetate and butyrate production rates in the acidogenicsimulation were used as additional constraints for nonlinear solvento-genic constraints. See text for detailed explanation of the nonlinearprogramming

Appl Microbiol Biotechnol (2008) 80:849–862 851

derived from chemostat culture data (Varma and Palsson 1994).Currently, there is no report on the GAM and NGAM valuesfor C. acetobutylicum. Thus, we used the method ofestimating GAM and NGAM established for Methanosarcinabarkeri (Feist et al. 2006). For this maintenance energycalculation, glucose was used as the primary substrate andwas set to be 10 mmol/g DCW per hour based onexperimental data (Monot et al. 1984).

Flux balance analysis

FBA is a useful method for interrogating metabolicnetworks in which constraints are imposed by stoichiome-try in a chemical network (Edwards et al. 1999). The netsum of all production and consumption fluxes is set to zerofor each internal metabolite (that is, pseudo-steady state).This steady-state approximation is generally valid becausethe metabolite concentrations tend to reach equilibriummuch faster compared to genetic regulation (Schilling et al.1999). Additional constraints can also be introduced asinequalities of metabolites, which correspond to measuredor imposed values. All flux vectors satisfying the con-straints above define a feasible space that is a convex set inN (total number of fluxes)-dimensional space of fluxes foran underdetermined system, which is a typical case in themodeling of cellular metabolic networks (Bonarius et al.1997; Schilling et al. 2000). Linear programming isgenerally used to characterize the set of points in thefeasible space that maximize or minimize a given linearobjective function (Schilling et al. 1999).

Stoichiometric model of C. acetobutylicumin solventogenic phase

C. acetobutylicum typically has two distinct phases ofproduct formation: acidogenesis and solventogenesis.Acidogenesis occurs in the exponential cell growth phasewith the production of acetate and butyrate, which are toxicto the cell at high concentrations. During solventogenesis,cell growth becomes stationary, and cells uptake acetate andbutyrate to form ABE.

For a better understating of C. acetobutylicum metabo-lism, the genome-scale model was used to analyze theacidogenic and solventogenic phases. However, there aretwo major problems in modeling of solventogenic phase:cyclic pathways and objective function. Specifically, sol-ventogenesis involves the cyclic pathway for both uptake ofacids and production of ABE that represents the mainmetabolism of acid and solvent production (Fig. 3).However, FBA based on linear programming generatesmultiple solutions due to the cyclic pathway, that is, aclosed loop of fluxes brings no net change. To resolve this,a nonlinear constraint for uptake fluxes of acetate and

butyrate from the available kinetic and selectivity informa-tion was proposed (Desai et al. 1999b). The uptake fluxeswere calculated from the experimentally measured acidconcentrations.

Maximizing the biomass formation is not suitable as theobjective function because solventogenesis occurs in thestationary phase. Recently, the modified method of mini-mization of metabolic adjustment was presented (Luo et al.2006; Segre et al. 2002). We thus developed the stoichio-metric model of C. acetobutylicum in solventogenic phasewith an assumption that C. acetobutylicum follows theminimal fluctuation of the profile of metabolite concen-trations between acidogenic and solventogenic phasesexcept for the related fluxes in the production of acidsand solvents (Fig. 1).

Minimize:

vacid � vsol��

��2þ vsolbu upv

acidac out � 0:315vsolac upv

acidbu out

� �2

Subject to:

S � v ¼ 0; vmin � v � vmax

where the first term of objective function is a sum ofleast-squared residuals and the other term is a nonlinearconstraint as previously reported (Desai et al. 1999b). vacid

is the flux vector in acidogenic phase which is calculatedunder the objective function as maximizing cell growthrate; vsol is the desired flux vector in solventogenic phase;vsolbu up is the desired butyrate uptake flux in solventogenicphase; vacidac out is the calculated acetate secretion flux inacidogenic phase; vsolac up is the desired acetate uptake flux insolventogenic phase; vacidbu out is the calculated butyratesecretion flux in acidogenic phase; S denotes the stoichio-metric matrix; v denotes the flux vector; and vmin and vmax

correspond to the upper and lower bounds of v.

Results

Genome-scale reconstruction of C. acetobutylicummetabolic network

The genome-scale metabolic network of C. acetobutylicumhas been reconstructed, curated, and validated againstavailable experimental data (Desai et al. 1999a; Monot etal. 1984). As described above, a draft model wasreconstructed from the annotation data of C. acetobutylicumusing the procedure summarized in Fig. 1. The features ofC. acetobutylicum genome and basic properties of gap-filled model are shown in Table 1. The model comprises479 metabolites and 502 reactions. Of these reactions, 431reactions have GPR relationships where 432 genes areinvolved (Supplementary data 1). The sequence annotation


from KEGG was the main source of information used toreconstruct the network. For cases that were not annotatedin KEGG, other databases (e.g., MetaCyc) were used (Reedet al. 2006a).

The C. acetobutylicum ATCC 824 genome consists of achromosomal DNAwith 3,672 open reading frames (ORFs)and a megaplasmid with 176 ORFs. Thus, the ORFcoverage of the reconstruction is approximately 11% ofthe total identified ORFs. This value is lower than in thereconstructions of similar organisms, especially B. subtilis(approximately 19%; Oh et al. 2007).

Reconstruction of C. acetobutylicum central metabolism

In this and the following sections, we outline key cellularmetabolic pathways in C. acetobutylicum and give exam-ples on gap-filling process during the reconstruction. C.acetobutylicum is able to utilize various sugars such asglucose and lactose via the phosphotransferase system(PTS; Tangney and Mitchell 2007; Yu et al. 2007). Basedon the annotation in KEGG, C. acetobutylicum is able touptake glucose, fructose, mannose, mannitol, maltose, andN-acetyl-D-glucosamine via PTS. Phosphoenolpyruvate-dependent sucrose PTS was characterized previously(Tangney and Mitchell 2000), but it was not annotated inKEGG. Thus, we included the reaction for sucrose PTSbased on the literature information. Utilization of variouscarbon sources by C. acetobutylicum has been reportedpreviously (Keis et al. 2001). We compared the in silicoprediction on the use of carbon sources with experimentaldata (Table 2). A complete list of filled reactions withoutGPR associations is available in Supplementary data 1. Inaddition, it was reported that C. acetobutylicum couldutilize xylose and arabinose as sole carbon sources (Ounineet al. 1983). However, the candidate for xylose isomerasecould not be found via BLAST search. There are two

possible reasons. The first is that C. acetobutylicum mightcontain a xylose isomerase belonging to a novel family. Thesecond is that C. acetobutylicum might have a fungalpathway for xylose utilization. The former hypothesis couldnot be tested because complete biochemical analyses arerequired. Instead, we tested the latter using BLAST. Weused the amino acid sequences of the NAD(P)H-dependentD-xylose reductase (XYL1) gene (Amore et al. 1991) and D-xylulose reductase (XYL2) gene (Kotter et al. 1990) of axylose-utilizing yeast Pichia stipitis in order to search forthe corresponding genes in C. acetobutylicum. Interestingly,genes with similar amino acid sequences were found(Table 3). Based on these results, we added reactionscatalyzed by aldose reductase and xylitol dehydrogenase.For arabinose utilization, ribulokinase is required, althoughit was not annotated in the C. acetobutylicum genome.Xylulose kinase of C. acetobutylicum shows somewhathigh similarity with B. subtilis ribulokinase (Table 3). Thus,we assumed that xylulose kinase of C. acetobutylicum isable to catalyze the ribulokinase reaction.

Imported sugars are metabolized into pyruvate throughglycolysis or the pentose phosphate pathway (PPP) inC. acetobutylicum. Glycolytic enzymes from glucose 6-phosphate to pyruvate were annotated in C. acetobutylicumATCC 824 genome. Pyruvate is converted into acetyl-CoAby pyruvate:ferredoxin oxidoreductase (Dürre 2007; Jonesand Woods 1986), and then acetyl-CoA is used for fattyacid synthesis and acid/solvent formation. Major pathwaysin C. acetobutylicum based on its genome annotation areshown in Fig. 3. Ferredoxin:NAD(P)H oxidoreductase is animportant enzyme for redox balance of Clostridium species.In several species of Clostridium, such as C. beijerinckii andC. botulinum, ferredoxin:NADP+ reductase was annotated.BLAST searches of the protein of these genes against all C.

Table 2 Comparison of carbon utilization between experimental andin silico data

Carbon sources Experimentalevidencesa

In silico growthin draft model

In silico growthin final model

Glucose + + +Maltose + + +Sucrose + − +Lactose + + +Mannose + + +Rhamnose − − −Arabinose + − +Ribose − − −Xylose + − +Glycerol Weak growth − −Mannitol + + +

a From Keis et al. 2001

Table 1 Basic features of C. acetobutylicum genome and metabolicreconstruction

Features Number

Genome featuresTotal genome size (bp) 4,132,880Chromosome 3,940,880Megaplasmid (pSOL1) 192,000Open reading frames (ORFs) 3,848In silico reconstructionGenes 432Reactions 502Associated with genes 431Not associated with genes 71Metabolites 479ORF coverage (%) 11.2


acetobutylicum proteins yielded low scores. However, it isreasonable to assume that the ferredoxin:NAD(P)H oxidore-ductase reaction exists based on literature (Jones and Woods1986; Jungermann et al. 1971; Petitdemange et al. 1977).The enzymes responsible for the PPP, which is responsiblefor producing NAD(P)H in many non-photosynthetic organ-isms, were not annotated. Instead, NAD(P)H is generatedfrom reduced ferredoxin (Jones and Woods 1986). Riboki-nase was not annotated in C. acetobutylicum. However, thereaction of ribokinase was added to the draft model throughcomputational evidence, as no biomass production waspredicted without the ribokinase reaction.

The citric acid cycle of genus Clostridium has not beencharacterized experimentally. However, aconitase and iso-citrate dehydrogenase were annotated in KEGG despitethese enzymes not being reported in the first release of theC. acetobutylicum genome. It was proposed that pyruvate isconverted to oxaloacetate by pyruvate carboxylase (Nöllinget al. 2001). This enzyme showed high similarity to the B.subtilis Pyc protein, which showed pyruvate carboxylaseactivity (Wang et al. 2006). Citric acid synthase (E.C.2.3.3.1) was not annotated in KEGG. In addition, fumaratereductase or succinate dehydrogenase (E.C. 1.3.99.1),succinyl-CoA synthetase (E.C. 6.2.1.4 or 6.2.1.5), and α-ketoglutarate dehydrogenase complex were also missing inthe C. acetobutylicum genome (Nölling et al. 2001). Thecitric acid cycle can be either reductive or oxidative(Huynen et al. 1999), and the gaps could be filled foreither direction. It is reasonable to assume that C.acetobutylicum has a reductive citric acid cycle, since theinternal environment of C. acetobutylicum is kept at lowredox potential. Furthermore, an important function of thecitric acid cycle is producing precursors of some aminoacids, oxaloacetate, and α-ketoglutarate. C. acetobutyli-cum is able to grow using ammonium as a sole nitrogensource. Based on these considerations, we filled the gapsin the citric acid cycle so that α-ketoglutarate can besynthesized from oxaloacetate (the reductive branch).Nölling et al. (2001) hypothesized that butyrate:acetoacetateCoA-transferase (CAP0163-0164) might also utilize succinatefor succinyl-CoA production and that 2-oxoacid:ferredoxin

oxidoreductase could catalyze α-ketoglutarate formation fromsuccinyl-CoA. Thus, we added these reactions and filled gaps.

One important feature of C. acetobutylicum metabolismis solvent production from sugars. A more extensiveexplanation of enzymes in ABE fermentation is availableelsewhere (Dürre 2007). Most genes in solventogenicpathway were properly annotated in the KEGG databaseexcept for crotonase. The crotonase gene (crt, CAC2712)was annotated functionally as E.C. 4.2.1.55, which converts(R)-3-hydroxybutyryl-CoA to crotonyl-CoA. On the otherhand, 3-hydroxybutyryl-CoA dehydrogenase (E.C.1.1.1.157) produces (S)-3-hydroxybutyryl-CoA from ace-toacetyl-CoA and was included in the functional annota-tion. Therefore, one of two genes was not correctlyannotated and thus should be corrected. Since Hbd fromC. acetobutylicum produces (S)-3-hydroxybutyryl-CoA(Lee et al. 2008), we assumed that Crt of C. acetobutylicumconverts (S)-3-hydroxybutyryl-CoA into crotonyl-CoA.The butyryl-CoA dehydrogenase (bcd, CAC2711) is alsoimportant, as it converts crotonyl-CoA to butyryl-CoA. Arecent report revealed that oxidized ferredoxin is reducedduring the reduction of crotonyl-CoA to butyryl-CoA byBcd enzyme complex in Clostridium kluyveri (Li et al.2008) using 2 NADH, and thus, the reaction stoichiometryof butyryl-CoA dehydrogenase was corrected based on thisresult.

Reconstruction and curation of pathways to biomass

It has been shown experimentally that C. acetobutylicumcan grow using ammonium and sulfate as the sole nitrogenand sulfur source, respectively (Monot et al. 1982). Thegaps present in the metabolic pathways from basicprecursors, such as glucose, ammonium, sulfate, andphosphate, to major biomass components were filled sothat the in silico cell can grow using these precursors.

Genes associated with nitrogen assimilation and fixationwere well annotated for C. acetobutylicum in KEGG. Insulfur assimilation, sulfite reductase is required, but it wasnot annotated in KEGG. In fact, C. acetobutylicum genomehas anaerobic sulfite reductase genes (asrA, CAC1513;

Table 3 Hypothetical ORF annotations based on BLAST and gap-filling performed on the in silico model

Locus Original annotation Suggested annotation E value Source Comment

CAC1958 Predicted aldo/keto reductase,YTBE/YVGN B. subtilis ortholog

NAD(P)H-dependentD-xylose reductase (XR)

3.00E−47 Pichia stipitis

CAC3375 Alcohol dehydrogenase D-Xylulose reductase 7.00E−18 Pichia stipitisCAC2612 Xylulose kinase Ribulokinase 6.00E−33 Bacillus subtilis Putative bifunctionalCAC2388 N-acetylornithine aminotransferase Ornithine-oxo-acid transaminase 4.00E−68 Bacillus subtilis Putative bifunctionalCAC3657 NADP-dependent glyceraldehyde-

3-phosphate dehydrogenase1-Pyrroline-5-carboxylate dehydrogenase 2.00E−59 Bacillus subtilis Putative bifunctional


asrB, CAC1514; asrC, CAC1515). Since the stoichiometryof these genes is not known (E.C. 1.8.1.-), it was assumedthat the stoichiometry is the same as that of sulfite reductasein E. coli.

In our draft model, most amino acids could besynthesized, except L-alanine and L-proline. L-Proline canbe synthesized from either L-glutamate via L-1-pyrroline-5-carboxylate:NAD+ oxidoreductase (E.C. 1.5.1.12) or L-ornithine via L-ornithine:2-oxo-acid aminotransferase (E.C.2.6.1.13). The genes responsible for these pathways wereannotated in B. subtilis genome, and thus, we carried outBLAST using these queries. Interestingly, the candidatesfor both genes were found (Table 3). Therefore, we addedboth reactions in the draft model. There are threepossibilities for alanine biosynthesis: (1) transamination ofpyruvate using L-aspartate or L-glutamate as an aminegroup donor; (2) decarboxylation of L-aspartate; and (3)transamination of pyruvate by L-ornithine:2-oxo-acid ami-notransferase. In case of (1) and (2), we could not find anycandidates based on BLAST search. Thus, L-ornithine:2-oxo-acid aminotransferase (E.C. 2.6.1.13) reaction wasused for the gap filling in L-alanine biosynthesis.

GPR relationships in peptidoglycan and cell membranemetabolism were rather incomplete, and this is a typicalfeature observed in the initial reconstruction of metabolicnetworks (Reed et al. 2006a). Thus, we determined whichtype of peptidoglycan C. acetobutylicum has; it was typeA1γ. Based on this result, the pathway necessary tosynthesize peptidoglycan was included as one equation(often called a lumped reaction). The biosynthesis pathwaysfor the formation of additional biomass components havenot been elucidated. Thus, we used appropriate assump-tions. For example, clostridia have plasmalogens in theirmembrane lipids, which have 1-alkenyl chains in the sn-1position of a glycerol backbone. The oxygen-dependentbiosynthesis pathway for plasmalogens is well character-ized in eukaryotes (35). However, all Clostridium speciesare strict anaerobes and cannot use this pathway. Therefore,we assumed that fatty aldehydes are similar to fatty acids inthat they have the same length and number of double bondsand, further, that the biosynthesis of plasmalogens is thesame with that of diacylglycerol phosphate. These reactionswere thus included in the model. The entire cellularcomposition of C. acetobutylicum is shown in Supplemen-tary data 2.

Investigation on the acidogenic phase

Following reconstruction and functional testing to producethe cellular biomass components in C. acetobutylicum,FBA was performed to determine flux distribution in theacidogenic phase. In this case, the reactions responsible forformation of butanol, ethanol, and acetone from acetate and

butyrate were excluded. Maximization of biomass forma-tion was used as an objective function during thissimulation because the acidogenic phase is associated withthe growth phase. The flux distributions were obtained byFBA, and the results were compared with the fermentationresults, both on synthetic medium (Monot et al. 1984) andcomplex medium (Desai et al. 1999a).

In order to perform FBA, the parameters representingcellular maintenance, so-called GAM and NGAM energies,are required. However, these parameters are unknownbecause there has been no study on the maintenancerequirements of C. acetobutylicum. Thus, we decided toestimate the GAM and NGAM in a similar manner to M.barkeri (Feist et al. 2006). However, the situation of C.acetobutylicum was somewhat different from M. barkeri inthat the stoichiometry of clostridial hydrogenase is wellcharacterized. Therefore, we investigated the effect ofmaintenance parameters to acetate and butyrate formationrates, since the acid-producing pathway is responsible forgenerating ATP, with different net amounts for acetate andbutyrate (Jones and Woods 1986). The GAM and NGAMvalues are theoretically independent of each other, but itwas shown that the values of these parameters changeproportionally (Borodina et al. 2005).

First, the NGAM was assigned to be 2.5% of the GAMas in M. barkeri (Feist et al. 2006). Then, we monitored thechanges in the specific growth rate and the ratios of acetateproduction rate and butyrate production rate at a glucoseuptake rate of 10 mmol h−1 g−1 DCW as the GAM valuewas varied from 0 to 160 mmol ATP per gram DCW(Fig. 2). The specific growth rate decreased as GAM wasincreased. Acetate formation rate was increased relative tobutyrate formation rate as GAM was increased. This resultagrees with our general intuition. If 1 mol of glucose iscompletely converted to acetate, 4 mol of ATP would begenerated, whereas 3 mol of ATP would be generated if1 mol of glucose is completely converted to butyrate (Jonesand Woods 1986). Thus, the rate of acetate formationincreases to supply ATP for growth as GAM increases. Bycomparing the ratio of acid formation rate to the growth ratewith experimental results, we estimated the GAM of C.acetobutylicum. As shown in Fig. 2, the simulation resultsusing the GAM values of 25–55 mmol ATP per gram DCWshowed good agreement with the experimentally deter-mined growth rate and acid formation rates; thus, we usedthe GAM value of 40 mmol ATP per gram DCW for therest of the simulations.

For the validation of the model, we performed thesimulation on the buk knockout strain and compared theresults with the experimental data obtained with the bukknockout C. acetobutylicum strain PJC4BK (Green et al.1996). PJC4BK has been shown to produce more butanolthan the wild-type strain (Harris et al. 2000). In batch


fermentation, PJC4BK showed increased production ofacetate and decreased production of butyrate (Green et al.1996). Production of a small amount of butyrate might bedue to the existence of a second butyrate kinase (Huang etal. 2000). The growth of PJC4BK strain was slightly slowerthan the wild-type strain (Harris et al. 2000).

The initial simulation of our model without butyratekinase reaction suggested no cell growth. However, whenthe solventogenic (ABE) reactions were included into themodel, the results of simulation on in silico buk knockoutstrain showed good agreement with the results reported byHarris et al. (2000; Table 4 and Fig. 3a). Accordingly, weinvestigated the acidogenic fluxes using the model contain-ing solventogenic reactions. Because the specific growthrate was not reported in Harris et al. (2000), more detailedcomparison was difficult. The acetone flux in our modelwas higher than that obtained with the simple model used

by Harris et al. (2000), since FBAwas carried out using thecondition of synthetic medium. In this case, various aminoacids were produced from precursors in the citric acidcycle, which accompanied production of acetoacetate fromsuccinate. In addition, the acid production ratio is differentfrom that obtained with the simple model. This might bepartly because the maintenance energy in our model wasevaluated based on the fermentation results obtained in asynthetic medium.

Simulation of our model showed no flux of ferredoxin:NADH oxidoreductase. This was mainly due to thereduction of ferredoxin by butyryl-CoA dehydrogenasereaction in our model as recently suggested (Li et al.2008), which was not reflected in that of Harris et al.(2000). In our model, NADPH is generated by reducedferredoxin. As described above, C. acetobutylicumappeared to not have oxidative PPP enzymes. NADPH isan important cofactor for various biosynthesis pathwayswhich a simple model could not take into consideration.From these results, we could suggest that the ferredoxin ofC. acetobutylicum is the link that interconnects NADHmetabolism and NADPH metabolism. Taken together, ourgenome-scale metabolic model allows more detailed un-derstanding of C. acetobutylicum metabolism comparedwith the simple model developed earlier.

Investigation on the solventogenic phase using nonlinearprogramming

Calculation of in vivo fluxes in the solventogenic pathwayis difficult to deal with because the stoichiometric matrix ofsolventogenic clostridia has singularity due to two CoAtransferase reactions. Thus, it cannot be solved to generateunique solutions for acetate and butyrate uptake rates usinglinear programming (Desai et al. 1999b). To resolve thesingularity of the stoichiometric matrix, nonlinear program-ming, which determines the uptake rates of acetate andbutyrate with respect to their extracellular concentration,was used along with data from the previous reports on themetabolic flux analyses of C. acetobutylicum in this growthphase. However, a limitation of this method is that it is notpredictive, and internal metabolic fluxes should be calcu-

Fig. 2 Estimation of the growth-associated maintenance (GAM)energy from experimental data (Monot et al. 1984). The ratio ofacetate/butyrate production rates (acid production ration; computedfrom rPTAAK and rPTBBK fluxes) and the specific growth rate fromgiven glucose uptake rates vary as the value of the GAM is increasedor decreased. The specific growth rate and the acid production ratiowere compared to experimental data reported previously. From thiscomparison, the GAM value consistent with experimental data couldbe estimated and was indicated as a gray box. rPTAAK, the flux ofphosphate acetyltransferase and acetate kinase; rPTBBK, the flux ofphosphate butyryltransferase and butyrate kinase

Table 4 Comparison of computation with experimental data for acidogenic growth phase

Strain Model Specific growthrate (h−1)

Rate of substrate uptake or product formationa

Glucose Acetate Butyrate Hydrogen Acetone Butanol Ethanol

WT Harris et al. N.R. 4.02 1.85 2.28 8.36 0.23 0.55 0.06WT CacMBEL489 0.12 4.02 0.96 2.61 7.10 0.10 0.00 0.00PJC4BK Harris et al. N.R. 6.74 5.62 0.79 12.92 0.029 2.69 0.69PJC4BK CacMBEL489 0.20 6.74 5.14 0 10.22 0.18 2.63 0.00

a In the case of Harris et al. (2000), the unit of the rates is mM [OD 600]−1 h−1 and is mmol [g DCW]−1 h−1 in the case of our model


lated without the concentrations of products. Thus, we useda modification of a simple model using a nonlinearprogramming method reported previously (Desai et al.1999b). As described in “Materials and methods”, weassumed that the cell follows the minimal fluctuation of theprofiles of metabolite concentrations between acidogenicand solventogenic phases except for the related fluxes in theproduction of acids and solvents. In addition, we usedacetate and butyrate production rates instead of theirconcentrations in order to evaluate the acid uptake rates.The results from these computations are shown in Fig. 3band Table 5.

In a different analysis, we investigated the relationship ofhydrogenase and solvent production, since the hydrogenaseis important for redox balancing of C. acetobutylicum

(Fig. 4). Simulation results suggested that the butanolproduction rate increased as the hydrogenase flux wasdecreased.

Gene deletion analysis

Metabolic engineering (Bailey 1991) of microorganismoften requires deletion of genes from the metabolicnetwork. However, in some cases, gene deletion causessevere problem, such as inhibition of cell growth. Despite alack of experimental evidence, investigating gene essenti-ality has significant potential for use and guidance in themetabolic engineering of C. acetobutylicum. Therefore, weconducted a single gene deletion study to predict essentialgenes and reactions of C. acetobutylicum in synthetic

Fig. 3 A computed flux distribution for reactions in the key metabolicpathway of C. acetobutylicum. Relative fluxes are represented as formof thickness of reaction arrow. Double dotted line and green boxindicate zero fluxes of corresponding reactions and pathways. a Fluxdistribution in acidogenic phase. b Flux distribution in solventogenicphase. E.C. numbers describing each reaction are shown in the box onthe reaction arrow. White boxes indicate that the enzymes (genes)were verified to be correctly annotated in KEGG database (Kanehisa

et al. 2006). Gray boxes indicate that the genes were assigned properlyin KEGG, but the E.C. numbers were incorrectly assigned. Blackboxes indicate that the enzymes responsible for the reactions weredeficient in the genome of C. acetobutylicum, but are thought to bepresent based on the biochemical and physiological studies publishedin the literature. See the main text for details. In this case, E.C.numbers in the box were presumed based on literature


medium. GPR associations provide the mapping betweengenes and reactions in the reconstructed network. Adetailed procedure of this analysis is described elsewhere(Kim et al. 2007). From this analysis, 194 reactions and 27reactions, corresponding to 158 and 32 genes, werepredicted to be essential and partially essential, respectively.The complete list of essential and partially essential genesis available in Supplementary data 3. This analysis is aninitial step towards identifying genetic targets for model-driven metabolic engineering of C. acetobutylicum (Lee etal. 2007b; Park et al. 2007).

Discussion

We have presented a genome-scale reconstruction andanalysis of the genome-scale C. acetobutylicum ATCC824 metabolic network. The network was constructed in a

three-step process: (1) automatic reconstruction of meta-bolic network; (2) construction and manual curation of themodel; and (3) completion of the model (Fig. 1).

As described above, the ORF coverage of our model islower than the genome-scale model of B. subtilis, whichbelongs to the same phylum (Table 1). This implies that thefunctional annotation of C. acetobutylicum is much lesscomplete than that of B. subtilis. Generally, functionalannotation is carried out based on biochemical character-ization of the proteins and statistical analysis of homologieswith previously characterized proteins or motif sequences(Lee et al. 2007a). Enzymes or proteins that have novelstructure or motifs might not be correctly annotated. Clostrid-ial proteins and enzymes are less studied and characterizedbiochemically than those of model bacteria, such as E. coliand B. subtilis, and many clostridial genes show distinctevolutionary distances with B. subtilis (Nölling et al. 2001).Thus, further molecular and biochemical studies on the

Fig. 3 Continued


enzymes and proteins in C. acetobutylicum are required toidentify novel enzymes and proteins for better annotation.Model-driven analyses are emerging as useful tools in theseefforts (Fuhrer et al. 2007; Reed et al. 2006b).

Immediately prior to the submission of this manuscript, asimilar metabolic reconstruction of C. acetobutylicum hasappeared and has been analyzed using different approachesfrom those presented in this paper (Senger and Papoutsakis2008a, b). Differences in the reconstructions lie in themethod of reconstruction and the computational approachesused to interrogate the models. Similarities between the tworeconstructions and validation processes also exist. Where-as the reconstruction of the Senger and Papoutsakis (2008a,b) network was done through a semi-automated and reverseengineering algorithm, the network presented here wasreconstructed through a more manually curated method.However, both reconstructions are based largely on theKEGG database (Kanehisa et al. 2006), and both have been

functionally tested to produce the major biomass compo-nents. Additionally, they are similar in size, with thisreconstruction having a greater number of metabolites (479versus 422), but a smaller number of reactions (502 versus552) and genes (432 versus 474).

Furthermore, there are some differences between ourreconstructed network and that of Senger and Papoutsakis.Senger and Papoutsakis (2008a, b) suggested that the ureacycle and amino acid pathways of C. acetobutylicumcontribute to the biosynthesis of α-ketoglutarate in theincomplete citric acid cycle where the reactions that convertfumarate into α-ketoglutarate are disconnected in themetabolic network. However, our model follows thesuggestion of Nölling et al. (2001) that the reductivepathway from pyruvate to α-ketoglutarate is likely to beconnected. It is expected that such differences would beresolved as more biochemical studies on C. acetobutylicumare conducted.

The major methodological differences between this workand that of Senger and Papoutsakis (2008a, b) lie in theanalysis of the reconstructed metabolic networks. Whereasboth models have been tested by FBA, Senger andPapoutsakis (2008a, b) utilize a genetic algorithm tointegrate proton concentrations into modeling simulations.This approach showed good agreement with experimentaldata under various pH conditions. As described herein, weutilize a nonlinear approach to understand and predict thesolventogenic phase of C. acetobutylicum where butanol isproduced, a novel approach. Going forward, it will beuseful to compare the independently generated reconstruc-tions and modeling methods to develop a universalgenome-scale metabolic model and an integrated approachto model-driven engineering of C. acetobutylicum. Therewere some gaps in our draft model, which initially made thein silico C. acetobutylicum incapable of growing in in silicomedium. To resolve this, the gaps in the pathway fromsubstrate and other nutrients to biomass were filled withvarious literature information, biomass composition, andhomology-driven search (Table 3). Novel hypotheticalannotations were elucidated from the reconstruction ofmetabolic network in Pseudomonas aeruginosa (Overheardet al. 2008). We also successfully employed some hypo-

Fig. 4 The relationship between hydrogenase and solvent produc-tion rates using nonlinear flux analysis. As the hydrogenase fluxwas decreased, the butanol production rate increased. This result isconsistent with the previous report where hydrogenase activity wasinhibited by increasing partial pressure of carbon monoxide (Kimet al. 1984), except that ethanol production rate showed nosignificant change; solid line, butanol; dashed line, acetone;dashed-dotted line, ethanol

Table 5 Comparison of computation with experimental data for solventogenic phase

Strain Model Specific growthrate (h−1)

Rate of substrate uptake or product formationa

Glucose Acetate Butyrate Hydrogen Acetone Butanol Ethanol

WT Harris et al. N.R. 0.73 0.26 −0.04 1.09 0.29 0.47 0.04WT CacMBEL489 0.00 0.71 0.00 0.25 0.99 0.25 0.38 0.03PJC4BK Harris et al. N.R. 1.13 0.06 0.05 0.88 0.33 0.75 0.23PJC4BK CacMBEL489 0.00 1.11 0.14 0.00 0.76 0.14 0.79 0.04

a In the case of Harris et al. (2000), the unit of the rates is mM [OD 600]−1 h−1 and is mmol [g DCW]−1 h−1 in the case of our model


thetical annotations based on homology-driven search,which suggests that a reconstruction of metabolic networkof an organism can be used as a tool for functionalannotation.

In general, the maintenance energy is obtained fromchemostat culture at varying dilution rates. The mainte-nance energy of C. acetobutylicum in synthetic mediumwas calculated as previously reported (Feist et al. 2006).Assuming that the NGAM has a linear relationship with theGAM, the appropriate range of maintenance coefficientscould be determined by comparing the ratios of acetate andbutyrate fluxes and specific growth rate (Fig. 2). Onceappropriate maintenance energy was determined, the fluxesin acidogenic phase could be examined in both wild-typeand butyrate kinase mutant strains (Fig. 3a and Table 4). Asdescribed above, our in silico buk knockout strain initiallyshowed no growth. As we described above, inclusion ofsolventogenic pathways during the simulation solved thisproblem. The reason for this might be attributed to redoxbalance. It has been known that excess reducing equivalentsin the form of NADH produced in glycolysis needs to beoxidized with ferredoxin. However, as recently reportedand consequently incorporated in our metabolic model,butyryl-CoA dehydrogenase reaction is a key step toremoving excess reducing power in the acidogenic phasethrough reduction of oxidized ferredoxin (Li et al. 2008),the reduced form of which is used for biomass formationthrough introversion to NADPH and for the generation ofmolecular hydrogen (Jones and Woods 1986). One mightthink that oxidized ferredoxin can be reduced by NADH::ferredoxin oxidoreductase. However, this reaction is ther-modynamically unfavorable (Herrmann et al. 2008). Thus,it is most reasonable at this time to assume that ferredoxinis reduced together with the reduction of crotonyl-CoA bybutyryl-CoA dehydrogenase as in C. kluyveri (Li et al.2008). Our model reflects this.

In silico inactivation of butyrate kinase reaction mightmake the flux of butyryl-CoA dehydrogenase zero, andelectrons from excess NADH could not be transferred to anelectron acceptor. To test this hypothesis, we allowed theflexibility for NAD+ and NADH by adding the reactions ofremoving NADH and supplying NAD. Adding these tworeactions recovered the growth of in silico cell. Thus, atleast two possibilities exist. The first is that excess reducingequivalents in the form of NADH might be transferred toother metabolites, for instance, in the conversion ofpyruvate into lactate. The second possibility is that thereduction of ferredoxin occurs with NAD(P)H, althoughthis reaction is thermodynamically unfavorable. The evi-dence for the first hypothesis was found in a previous report(Harris et al. 2000). Metabolic flux analysis of PJC4BKshowed higher butanol and ethanol fluxes than those ofATCC 824 strain even in earlier acidogenic stage.

We also investigated the flux distribution in thesolventogenic phase (Table 5 and Fig. 3b) using nonlinearapproach (see Fig. 1). Simulation of our model showedsimilar trend of metabolic shift to that observed using theprevious simple model developed by Papoutsakis’ group.Compared with the results obtained using the simple model,the butanol production rate was similar, but acetone andethanol production rates were lower. A difference in ourapproach was that in our model, acetate and butyrate wereassumed to be at steady state. This neglected the effect ofextracellular acid, and acetone production rate was totallydependent on acetate and butyrate production. This mightbe a part of the reason that acetone production rate was low.The reason for lower ethanol production rate obtained inour simulation might be due to the reduced availability ofNADH for ethanol formation, as our metabolic modelincorporates the new finding that 1 NADH is used to reduceferredoxin in butyryl-CoA dehydrogenase reaction.

Next, the effect of hydrogen flux on solventogenicmetabolismwas investigated because the formation of butanolrequires reducing equivalents, and hydrogen production is oneway of removing excess reducing equivalents. Our simulationresults showing that butanol production increased withdecreased hydrogen production are consistent with theprevious finding that butanol production increased by inhibit-ing hydrogenase activity by carbon monoxide (Kim et al.1984). This confirms that decreasing hydrogenase flux is oneof the strategies used to enhance the butanol-producingcapability of C. acetobutylicum.

Finally, we conducted in silico gene deletion studies insynthetic medium and found candidates for essential genes(Supplementary data 3). This is important as it providesinitial guidelines for selecting gene knockout targets. Sincean electroporation protocol for C. acetobutylicum wasdeveloped (Mermelstein et al. 1992), it has been possibleto perform metabolic engineering by gene overexpression(Harris et al. 2000; Nair et al. 1994). In contrast, geneinactivation is more laborious. Although the inactivation ofone or more genes is required for enhanced butanolproduction or even homo-butanol production, not all genesinvolved in acid and solvent production have beeninactivated so far. Gene knockout by homologous recom-bination using suicide plasmids has been attempted, andseveral genes were inactivated by Campbell-like integration(Green and Bennett 1996). However, these knockoutmutants are not reconstructed easily due to little recombi-nation frequencies. As described in “Introduction”, themethod using L1.LtrB intron was developed recently (Heapet al. 2007; Shao et al. 2007). However, there are not yetenough number of reports on the phenotypic characteriza-tion or fermentation data of mutants generated by ClosTronor TargeTron system. This makes it difficult to validate themetabolic network of C. acetobutylicum as a tool for


predicting knockout targets. Despite this, gene deletionstudies using the genome-scale model can be used as aguideline for metabolic engineering C. acetobutylicum.

The characteristics of the in silico model were assessedand presented using various computational tools. Themodel was validated with experimental data reported inthe literature. As for other bacteria, the genome-scalemetabolic model of C. acetobutylicum is expected to beuseful for genome-wide analysis as well as to designmetabolic engineering strategies for improving solventsproduction. The present study is a step towards understand-ing the metabolism of C. acetobutylicum in genome-scale,and there is room for the refinement of this model as thegenome and metabolic and gene regulatory networks of C.acetobutylicum are further characterized.

Acknowledgments We wish to express thanks to Peter Schumannand Jong-Soon Choi for the determination of cell wall and amino acidcomposition, respectively. Also, we are grateful to Jin Young Lee andYu Sin Jang for discussion on cell cultivation and analyticalprocedure. Finally, we thank Tae Yong Kim and Hyun Uk Kim fordiscussion on flux balance analysis. This work was supported by theKorea–Australia Collaborative Research Project on the Developmentof Sucrose-Based Bioprocess Platform (N02071165) from the KoreanMinistry of Knowledge Economy. Further support by LG Chem ChairProfessorship and Microsoft are appreciated.

References

Amore R, Kotter P, Kuster C, Ciriacy M, Hollenberg CP (1991)Cloning and expression in Saccharomyces cerevisiae of the NAD(P)H-dependent xylose reductase-encoding gene (XYL1) fromthe xylose-assimilating yeast Pichia stipitis. Gene 109:89–97

Atsumi S, Cann AF, Connor MR, Shen CR, Smith KM, BrynildsenMP, Chou KJ, Hanai T, Liao JC (2008) Metabolic engineeringof Escherichia coli for 1-butanol production. doi:101016/jymben200708003

Bailey JE (1991) Toward a science of metabolic engineering. Science252:1668–1675

Becker SA, Palsson BØ (2005) Genome-scale reconstruction of themetabolic network in Staphylococcus aureus N315: an initialdraft to the two-dimensional annotation. BMC Microbiol 5:8–19

Bonarius H, Schmid G, Tramper J (1997) Flux analysis of under-determined metabolic networks: the quest for the missingconstraints. Trends Biotechnol 15:308–314

Borodina I, Krabben P, Nielsen J (2005) Genome-scale analysis ofStreptomyces coelicolor A3(2) metabolism. Genome Res 15:820–829

Breitling R, Vitkup D, BarrettMP (2008) New surveyor tools for chartingmicrobial metabolic maps. Nat Rev Microbiol 6:156–161

Caspi R, Foerster H, Fulcher CA, Hopkinson R, Ingraham J, Kaipa P,Krummenacker M, Paley S, Pick J, Rhee SY, Tissier C, Zhang P,Karp PD (2006) MetaCyc: a multiorganism database of meta-bolic pathways and enzymes. Nucleic Acids Res 34:D511–D516

Desai RP, Harris LM, Welker NE, Papoutsakis ET (1999a) Metabolicflux analysis elucidates the importance of the acid-formationpathways in regulating solvent production by Clostridiumacetobutylicum. Metab Eng 1:206–213

Desai RP, Nielsen LK, Papoutsakis ET (1999b) Stoichiometricmodeling of Clostridium acetobutylicum fermentations withnon-linear constraints. J Biotechnol 71:191–205

DuarteNC,HerrgardMJ, PalssonBØ (2004) Reconstruction and validationof Saccharomyces cerevisiae iND750, a fully compartmentalizedgenome-scale metabolic model. Genome Res 14:1298–1309

Dürre P (2007) Biobutanol: an attractive biofuel. Biotechnol J 2:1525–1534

Edwards JS, Palsson BØ (2000) The Escherichia coli MG1655 insilico metabolic genotype: its definition, characteristics, andcapabilities. Proc Natl Acad Sci U S A 97:5528–5533

Edwards JS, Ramakrishna R, Schilling CH, Palsson BØ (1999)Metabolic flux balance analysis. In: Lee SY, Papoutsakis ET (eds)Metabolic engineering. Marcel Dekker, New York, pp 13–57

Feist AM, Palsson BO (2008) The growing scope of applications ofgenome-scale metabolic reconstructions using Escherichia coli.Nat Biotechnol 26:659–667

Feist AM, Scholten JC, Palsson BØ, Brockman FJ, Ideker T (2006)Modeling methanogenesis with a genome-scale metabolic recon-struction of Methanosarcina barkeri. Mol Syst Biol 2:20060004

Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, KarpPD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ (2007) Agenome-scale metabolic reconstruction for Escherichia coli K-12MG1655 that accounts for 1260 ORFs and thermodynamicinformation. Mol Syst Biol 3:121–138

Francke C, Siezen RJ, Teusink B (2005) Reconstructing the metabolicnetwork of a bacterium from its genome. Trends Microbiol13:550–558

Fuhrer T, Chen L, Sauer U, Vitkup D (2007) Computational predictionand experimental verification of the gene encoding the NAD+/NADP+-dependent succinate semialdehyde dehydrogenase inEscherichia coli. J Bacteriol 189:8073–8078

Green EM, Bennett GN (1996) Inactivation of an aldehyde/alcoholdehydrogenase gene from Clostridium acetobutylicum ATCC824. Appl Biochem Biotechnol 57–58:213–221

Green EM, Boynton ZL, Harris LM, Rudolph FB, Papoutsakis ET,Bennett GN (1996) Genetic manipulation of acid formationpathways by gene inactivation in Clostridium acetobutylicumATCC 824. Microbiology 142(Pt 8):2079–2086

Harris LM, Desai RP, Welker NE, Papoutsakis ET (2000) Character-ization of recombinant strains of the Clostridium acetobutylicumbutyrate kinase inactivation mutant: need for new phenomeno-logical models for solventogenesis and butanol inhibition?Biotechnol Bioeng 67:1–11

Heap JT, Pennington OJ, Cartman ST, Carter GP, Minton NP (2007)The ClosTron: a universal gene knock-out system for the genusClostridium. J Microbiol Methods 70:452–464

Herrmann G, Jayamani E, Mai G, Buckel W (2008) Energyconservation via electron-transferring flavoprotein in anaerobicbacteria. J Bacteriol 190:784–791

Hou BK, Kim JS, Jun JH, Lee DY, Kim YW, Chae S, Roh M, In YH,Lee SY (2004) BioSilico: an integrated metabolic databasesystem. Bioinformatics 20:3270–3272

Huang KX, Huang S, Rudolph FB, Bennett GN (2000) Identification andcharacterization of a second butyrate kinase from Clostridiumacetobutylicum ATCC 824. J Mol Microbiol Biotechnol 2:33–38

Huynen MA, Dandekar T, Bork P (1999) Variation and evolution of thecitric-acid cycle: a genomic perspective. Trends Microbiol 7:281–291

Inui M, Suda M, Kimura S, Yasuda K, Suzuki H, Toda H, YamamotoS, Okino S, Suzuki N, Yukawa H (2008) Expression ofClostridium acetobutylicum butanol synthetic genes in Escher-ichia coli. Appl Microbiol Biotechnol 77:1305–1316

Jones DT, Woods DR (1986) Acetone–butanol fermentation revisited.Microbiol Rev 50:484–524

Jungermann K, Leimenstoll G, Rupprecht E, Thauer RK (1971)Demonstration of NADH-ferredoxin reductase in two saccharo-lytic Clostridia. Arch Mikrobiol 80:370–372

Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M,Kawashima S, Katayama T, Araki M, Hirakawa M (2006) From


http://dx.doi.org/101016/jymben200708003

http://dx.doi.org/101016/jymben200708003

genomics to chemical genomics: new developments in KEGG.Nucleic Acids Res 34:D354–D357

Keis S, Shaheen R, Jones DT (2001) Emended descriptions ofClostridium acetobutylicum and Clostridium beijerinckii, anddescriptions of Clostridium saccharoperbutylacetonicum sp. nov.and Clostridium saccharobutylicum sp. nov. Int J Syst EvolMicrobiol 51:2095–2103

Kim BH, Bellows P, Datta R, Zeikus JG (1984) Control of carbon andelectron flow in Clostridium acetobutylicum fermentations: utiliza-tion of carbon monoxide to inhibit hydrogen production and toenhance butanol yields. Appl Environ Microbiol 48:764–770

Kim TY, Kim HU, Park JM, Song H, Kim JS, Lee SY (2007)Genome-scale analysis of Mannheimia succiniciproducens me-tabolism. Biotechnol Bioeng 97:657–671

Kotter P, Amore R, Hollenberg CP, Ciriacy M (1990) Isolation andcharacterization of the Pichia stipitis xylitol dehydrogenase gene,XYL2, and construction of a xylose-utilizing Saccharomycescerevisiae transformant. Curr Genet 18:493–500

Lee D, Redfern O, Orengo C (2007a) Predicting protein function fromsequence and structure. Nat Rev Mol Cell Biol 8:995–1005

Lee KH, Park JH, Kim TY, Kim HU, Lee SY (2007b) Systemsmetabolic engineering of Escherichia coli for L-threonineproduction. Mol Syst Biol 3:149

Lee SH, Park SJ, Lee SY, Hong SH (2008) Biosynthesis ofenantiopure (S)-3-hydroxybutyric acid in metabolically engi-neered Escherichia coli. Appl Microbiol Biotechnol 79:633–641

Li F, Hinderberger J, Seedorf H, Zhang J, Buckel W, Thauer RK (2008)Coupled ferredoxin and crotonyl coenzyme A (CoA) reductionwith NADH catalyzed by the butyryl-CoA dehydrogenase/Etfcomplex from Clostridium kluyveri. J Bacteriol 190:843–850

Luo RY, Liao S, Tao GY, Li YY, Zeng S, Li YX, Luo Q (2006) Dynamicanalysis of optimality in myocardial energy metabolism undernormal and ischemic conditions. Mol Syst Biol 2:2006.0031

Mermelstein LD, Welker NE, Bennett GN, Papoutsakis ET (1992)Expression of cloned homologous fermentative genes in Clostrid-ium acetobutylicum ATCC 824. Biotechnology (NY) 10:190–195

Monot F, Martin JR, Petitdemange H, Gay R (1982) Acetone andbutanol production by Clostridium acetobutylicum in a syntheticmedium. Appl Environ Microbiol 44:1318–1324

Monot F, Engasser JM, Petitdemange H (1984) Influence of pH andundissociated butyric acid on the production of acetone andbutanol in batch cultures of Clostridium acetobutylicum. ApplMicrobiol Biotechnol 19:422–426

Nair RV, Bennett GN, Papoutsakis ET (1994) Molecular character-ization of an aldehyde/alcohol dehydrogenase gene from Clos-tridium acetobutylicum ATCC 824. J Bacteriol 176:871–885

Nölling J, Breton G, Omelchenko MV, Makarova KS, Zeng Q,Gibson R, Lee HM, Dubois J, Qiu D, Hitti J, Wolf YI, Tatusov RL,Sabathe F, Doucette-Stamm L, Soucaille P, Daly MJ, BennettGN, Koonin EV, Smith DR (2001) Genome sequenceand comparative analysis of the solvent-producing bacteriumClostridium acetobutylicum. J Bacteriol 183:4823–4838

Overheard MA, Puchalka J, Fryer KE, Martins dos Santos VA, Papin JA(2008) Genome-scale metabolic network analysis of the opportu-nistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol190:2790–2803

Oh YK, Palsson BØ, Park SM, Schilling CH, Mahadevan R (2007)Genome-scale reconstruction of metabolic network in Bacillussubtilis based on high-throughput phenotyping and gene essentialitydata. J Biol Chem 282:28791–28799

Ounine K, Petitdemange H, Raval G, Gay R (1983) Acetone–butanolproduction from pentoses by Clostridium acetobutylicum. BiotechnolLett 5:605–610

Paredes CJ, Alsaker KV, Papoutsakis ET (2005) A comparativegenomic view of clostridial sporulation and physiology. Nat RevMicrobiol 3:969–978

Park JH, Lee KH, Kim TY, Lee SY (2007) Metabolic engineering ofEscherichia coli for the production of L-valine based ontranscriptome analysis and in silico gene knockout simulation.Proc Natl Acad Sci U S A 104:7797–7802

Park JH, Lee SY, Kim TY, Kim HU (2008) Application of systemsbiology for bioprocess development. Trends Biotechnol 26:404–412

Petitdemange H, Cherrier C, Bengone JM, Gay R (1977) Study of theNADH and NADPH-ferredoxin oxidoreductase activities inClostridium acetobutylicum. Can J Microbiol 23:152–160

Reed JL, Famili I, Thiele I, Palsson BØ (2006a) Towards multidi-mensional genome annotation. Nat Rev Genet 7:130–141

Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD,Bui OT, Knight EM, Fong SS, Palsson BO (2006b) Systemsapproach to refining genome annotation. Proc Natl Acad Sci U SA 103:17480–17484

Schilling CH, Edwards JS, Palsson BØ (1999) Toward metabolicphenomics: analysis of genomic data using flux balances.Biotechnol Prog 15:288–295

Schilling CH, Edwards JS, Letscher D, Palsson BØ (2000) Combiningpathway analysis with flux balance analysis for the comprehen-sive study of metabolic systems. Biotechnol Bioeng 71:286–306

Segre D, Vitkup D, Church GM (2002) Analysis of optimality innatural and perturbed metabolic networks. Proc Natl Acad Sci US A 99:15112–15117

Senger RS, Papoutsakis ET (2008a) Genome-scale model forClostridium acetobutylicum. Part 1: metabolic network resolutionand analysis. Biotechnol Bioeng (in press)

Senger RS, Papoutsakis ET (2008b) Genome-scale model forClostridium acetobutylicum. Part 2: development of specificproton flux states and numerically-determined sub-systems.Biotechnol Bioeng (in press)

Shao L, Hu S, Yang Y, Gu Y, Chen J, Yang Y, Jiang W, Yang S (2007)Targeted gene disruption by use of a group II intron (targetron)vector in Clostridium acetobutylicum. Cell Res 17:963–965

Shinto H, Tashiro Y, Yamashita M, Kobayashi G, Sekiguchi T, HanaiT, Kuriya Y, Okamoto M, Sonomoto K (2007) Kinetic modelingand sensitivity analysis of acetone–butanol–ethanol production. JBiotechnol 131:45–56

Stephanopoulos GN, Aristidou AA, Nielsen J (1998) Metabolicengineering: principles and methodologies. Academic, New York

Tangney M, Mitchell WJ (2000) Analysis of a catabolic operon forsucrose transport and metabolism in Clostridium acetobutylicumATCC 824. J Mol Microbiol Biotechnol 2:71–80

Tangney M, Mitchell WJ (2007) Characterisation of a glucosephosphotransferase system in Clostridium acetobutylicum ATCC824. Appl Microbiol Biotechnol 74:398–405

Thiele I, Vo TD, Price ND, Palsson BØ (2005) Expanded metabolicreconstruction of Helicobacter pylori (iIT341 GSM/GPR): an insilico genome-scale characterization of single- and double-deletion mutants. J Bacteriol 187:5818–5830

Varma A, Palsson BØ (1994) Stoichiometric flux balance modelsquantitatively predict growth and metabolic by-product secretionin wild-type Escherichia coli W3110. Appl Environ Microbiol60:3724–3731

Wang Q, Wu C, Chen T, Chen X, Zhao X (2006) Expression ofgalactose permease and pyruvate carboxylase in Escherichia coliptsG mutant increases the growth rate and succinate yield underanaerobic conditions. Biotechnol Lett 28:89–93

Yu Y, Tangney M, Aass HC, Mitchell WJ (2007) Analysis of themechanism and regulation of lactose transport and metabolism inClostridium acetobutylicum ATCC 824. Appl Environ Microbiol73:1842–1850

Zverlov VV, Berezina O, Velikodvorskaya GA, Schwarz WH (2006)Bacterial acetone and butanol production by industrial fermenta-tion in the Soviet Union: use of hydrolyzed agricultural waste forbiorefinery. Appl Microbiol Biotechnol 71:587–597


Genome-scale reconstruction and in silico analysis of...

Documents

Transcript of Genome-scale reconstruction and in silico analysis of...