Flux balance analysis of different carbon source fermentation with hydrogen producing Clostridium...

Bioresource Technology xxx (2014) xxx–xxx

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Bioresource Technology

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Short Communication

Flux balance analysis of different carbon source fermentationwith hydrogen producing Clostridium butyricum using Cell NetAnalyzer

http://dx.doi.org/10.1016/j.biortech.2014.10.0700960-8524/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail addresses: [email protected] (R. Rafieenia), [email protected]

(S.R. Chaganti).1 Tel.: +98 9123273185; fax: +98 2144289181.

Please cite this article in press as: Rafieenia, R., Chaganti, S.R. Flux balance analysis of different carbon source fermentation with hydrogen producintridium butyricum using Cell Net Analyzer. Bioresour. Technol. (2014), http://dx.doi.org/10.1016/j.biortech.2014.10.070

Razieh Rafieenia a,1, Subba Rao Chaganti b,⇑a Biotechnology Group, Department of Chemical Engineering, Islamic Azad University of Iran, Science and Research Branch, Tehran, Iranb Great Lakes Institute for Environmental Research, University of Windsor, 401 Sunset Ave., Windsor, Ontario N9B 3P4, Canada

h i g h l i g h t s

� A metabolic network model was developed for H2 production using six carbon sources.� H2 yields and growth ability estimations were well correlated with experiments results.� Sucrose and trehalose supported the maximum growth and H2 yields.

a r t i c l e i n f o

Article history:Received 6 July 2014Received in revised form 13 October 2014Accepted 14 October 2014Available online xxxx

Keywords:Clostridium butyricumHydrogenFlux balance analysisCarbon sourceCell Net Analyzer

a b s t r a c t

A metabolic network model for Clostridium butyricum was developed using six different carbon sources(sucrose, fructose, galactose, mannose, trehalose and ribose) to study the fermentative H2 production.The model was used for investigation of H2 production and the ability of growth on different substratesto predict the maximum abilities for fermentative H2 production that each substrate can support. NADHfluxes were calculated by the model as an important cofactor affecting on H2 production. Butyrate andacetate production were used as model assumptions and biomass formation was chosen as the objectivefunction for flux analysis calculations. Among the substrates selected, sucrose and trehalose supportedthe maximum growth and H2 yields. The Cell Net Analyzer metabolic network model developed for H2

estimation revealed good correlation with experimental data and could be further used to study the effectof environmental conditions and substrates concentration on H2 yield.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Dark fermentation using Clostridium species is a promisingprocess for hydrogen production among other chemical andbiological methods as it guarantees higher product yields andlower process costs (Mathews and Wang, 2009).In addition, othervaluable byproducts such as acetic acid and butyric acid can beproduced during H2 production. As a renewable energy, H2 canbe a promising source which can decrease the air pollution causedby fossil fuels. ForH2 production through dark fermentation whichis the most convenient method for this aim, Clostridium butyricumis a high producer among some other Clostridium sp. (Hawkes et al.,2002).

Different methods are utilized for optimization of fermentationprocesses including experimental design, strain, improvement andmathematical modeling. Constraint-based metabolic models canbe utilized to analyze the cellular functions in different conditions.These models are profitable to offer metabolic engineering strate-gies for increasing desired metabolites production. Metabolic fluxanalysis is a useful method for modeling of microbial processesin and predicts optimal fluxes during growth and metabolites pro-duction. Furthermore, it could be used to study the effect of envi-ronmental changes (pH, temperature, etc.) on productivity and cellgrowth. Since all these predictions can be done in silico, the cost ofthe industrial operations could be decreased compared to othermethods for improving product yield. In addition, due to elimina-tion of unfruitful experiments less time would be needed for opti-mization process.

Although many studies have investigated effect of differentcarbon sources on H2 yields, there are only a very few studieswhich have studied Clostridium sp. fermentation using different

g Clos-

http://dx.doi.org/10.1016/j.biortech.2014.10.070

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2 R. Rafieenia, S.R. Chaganti / Bioresource Technology xxx (2014) xxx–xxx

substrates and most of them are focused on mixed microbial cul-tures (Junghare et al., 2012; Li et al., 2008; Ren et al., 2008;Chong et al., 2009; Cheng et al., 2013). Most of the studies on math-ematical modeling of microorganisms are focused on using purecarbohydrate cultures and to our knowledge there is no compre-hensive report about metabolic modeling of C. butyricum and fluxdistribution using different monosaccharide and disaccharides.However, several studies have been done on flux analysis for H2

production using glucose as the carbon substrate for Clostridiumsp. and mixed cultures (Chaganti et al., 2011; Lalman et al.,2013; Cai et al., 2010; Cheng et al., 2013).

In the present study, metabolic flux distribution through meta-bolic pathways of C. butyricum has been studied for monosaccha-ride and disaccharides. The main objective of this study is todevelop a metabolic model which could predict the ability of dif-ferent carbon substrate for H2 production and show the fluxdistribution.

2. Methods

2.1. Model Construction and verification

Clostridium butyricum is one of the best strains for H2 produc-tion because of high product yields and its ability to grow on differ-ent simple and complex substrates. Metabolic modeling is anincreasingly widespread method which can be used for predictionof metabolic fluxes and maximum product yields with stoichiom-etric information of metabolic reactions. Metabolic models havebeen previously used for Clostridium sp. by other researchers forinvestigation of fermentative hydrogen production using glucoseas the carbon substrate (Cai et al., 2010; Junghare et al., 2012;Cheng et al., 2013). In the present study, metabolic pathways andreactions associated with different sugars degradation wereextracted from KEGG and BioCyc databases and also a genomebased metabolic model (Senger and papoutsakis, 2008) to beincluded in the model. In the case of biomass formation equation,the data was extracted from the work by Cai et al. (2010). A list ofreactions used in the model is given in Appendix B. The analysis fordetermination of metabolic fluxes was carried out by Cell Net Ana-lyzer (CNA) which is a comprehensive toolbox for MATLAB (Math-works Inc.) and Linear Programming method was used for fluxoptimization.

In this study maximization of growth was chosen as the objec-tive function because it is the most used objective function foroptimization of metabolic fluxes (Varma and Palsson, 1994). Since

Fig. 1. Stoichiometric matrix for sucrose as carbon substrate (red, green and blue colors innumber indicates the compound involved in total number of reactions. Number in the brthe references to color in this figure legend, the reader is referred to the web version of

Please cite this article in press as: Rafieenia, R., Chaganti, S.R. Flux balance analytridium butyricum using Cell Net Analyzer. Bioresour. Technol. (2014), http://d

the number of reactions exceeds the number of metabolites inmost metabolic networks, some of the fluxes should be determinedexperimentally and constraints for fluxes should be applied. Ace-tate and butyrate are the major soluble products in fermentationof C. butyricum, however lactate and ethanol are likely to be pro-duced in some cases (Junghare et al., 2012; Plangklang et al.,2012). In addition, for irreversible reactions the lower constraintfor metabolic fluxes was set to be zero.

2.2. Effect of different substrates on growth and H2 production

In order to compare the effect of different substrates on growthand H2 yield, Flux analysis was performed using Cell Net Analyzer.For verification of the model some of the exchange fluxes should bedefined and used as the constraint for the metabolic model. Fourdifferent monosaccharide (fructose, ribose, galactose and man-nose) and two disaccharides (trehalose and sucrose) were chosenas carbon substrates. In this study the data reported by Junghareet al. (2012) including substrate concentration and soluble metab-olites production (acetate and butyrate) was used as modelassumptions. H2 yields obtained by the model were compared withreported yields. In order to simulate the experimental conditionsto assess the model accuracy in prediction the H2 yields, the con-centration of each carbon source was assumed to be 10 g L�1

(Junghare et al., 2012). The stoichiometric matrix for sucrose isshown in Fig. 1.

3. Results and discussion

3.1. Flux distribution for different substrates

Fig. 2 shows the metabolic pathways of C. butyricum for 6 differ-ent carbon substrates. Possible byproducts have been shown,though in this study, acetate and butyrate are the major solublemetabolites. Table 1 summarizes the H2 yields obtained by themodel and those measured ones. The difference between the pre-dicted and measured values is due to biomass formation equationwhich was assumed to be similar to Clostridium acetobutylicumbecause no equation could be found in the literature for C. butyri-cum. As it can be seen, the lowest biomass was obtained for ribose(0.05 g mol�1 ribose) and followed by galactose (0.07 g mol�1 gal-actose) and mannose (0.1 g mol�1 mannose). In comparison withmonosaccharides, disaccharides (trehalose and sucrose) supporthigher biomass production (0.21 and 0.3 g mol�1 substrate respec-tively). The lower biomass yields for monosaccharides compared to

dicate substrate, product and intermediates respectively. For right side number firstackets indicates input, output and intermediate respectively.) (For interpretation ofthis article.)

sis of different carbon source fermentation with hydrogen producing Clos-x.doi.org/10.1016/j.biortech.2014.10.070


SUC6P

58 59

TREGALSUCαGLC

RIB GAL6P βGLCFRC

GLC6P RU5P

R5PMAN MAN6P X5PF6P

DHAP F1, 6P

GA3P

NAD+

NADHOAA PEP

PRALAC

CIT

PYRFd

NAD+

FdH H2

NADHAKG

AcCoABA AA

ETH

NADHNAD+ NADH

NAD+

NADHNAD+ NADH

NAD+

2NADPH2NADP

Fig. 2. Metabolic pathways of C. butyricum for different carbon sources.

Table 1Model predicted and experiment hydrogen, acetate, butyrate and biomass fordifferent carbon substrates.

Substrate Acetate(mol)b

Butyrate(mol)b

Biomass(g)

H2a

Model Measured

Fructose 20.9 17.1 15.48 1.41 1.7Mannose 31.9 33.8 10.74 1.7 1.34Galactose 37.3 48.4 7.043 2.01 1.74Ribose 34.5 43.6 5.01 1.65 1.69Trehalose 38.3 37.1 30.72 2.81 3.22Sucrose 66.1 66.4 21.49 3.56 2.98

a mol H2/mol substrate.b All the values are per 100 mol of substrate.

R. Rafieenia, S.R. Chaganti / Bioresource Technology xxx (2014) xxx–xxx 3

disaccharides are due to lower fluxes towards biomass compo-nents (R17, R20, R29, R35 and R38). Higher carbon fluxes to thesereactions lead to higher growth and therefore are beneficial for H2

production.In mixed cultures many different byproducts are expected to be

produced during H2 production (Chaganti et al., 2011). However,for C. butyricum cultures, acetate and butyrate and in some caseslactate are the major soluble metabolites (Cai et al., 2010; Wanget al., 2008). According to work done by Junghare et al. (2012)


other byproducts were insignificant in cultures of C. butyricumusing different carbon substrates. Hence, their fluxes were set tobe zero in the metabolic model.

Maximum theoretical H2 yields for mannose, galactose andfructose are 4 and 2 mol H2 per 1 mol hexose when acetate andbutyrate are the end product respectively (Eqs. (1) and (2))(Thauer et al., 1977).

C6H12O6 þ 2H2O! 2CH3COOHþ 2CO2 þ 4H2 ð1Þ

C6H12O6 ! CH3ðCH2Þ2COOHþ 2CO2 þ 2H2 ð2Þ

Similarly, sucrose and trehalose can be converted to 4 mol ofacetate and 8 mol H2 (Eq. (3)) or alternatively to 2 mol of butyrateand 4 mol of H2 (Eq. (4)) (Fang and Liu, 2002).

C12H22O11 þ 5H2O! 4CH3COOHþ 4CO2 þ 8H2 ð3Þ

C12H22O11 þH2O! 2CH3ðCH2Þ2COOHþ 4CO2 þ 4H2 ð4Þ

Ribose has the lowest theoretical H2 yields with 3.3 mol H2/molpentose with acetate and 1.67 mol H2/mol pentose with butyrate(Eqs. (5) and (6)) (Saripan and Reungsang, 2014).

C5H10O5 þ 1:67H2O! 1:67CH3COOHþ 1:67CO2 þ 3:3H2 ð5Þ

C5H10O5 ! 0:83CH3ðCH2Þ2COOHþ 1:67CO2 þ 1:67H2 ð6Þ



0

1

2

3

4

5

6

Fructose Galactose Mannose Sucrose Ribose Trehalose H2 y

ield

(mol

.mol

-1 su

bstra

te)

Substrate

Model Experimental

Fig. 4. Maximum theoretical H2 yields predicted by the model compared toexperimental values.


Pyruvate is a significant node in metabolic network of C. butyr-icum. It is the branchpoint which controls flux distribution towardslactate or acetyl-CoA which is the origin for other end products(acetate, butyrate and ethanol). Lactate and ethanol production isunfavorable for H2 production while acetyl-CoA increases the fluxtowards reduced ferrodoxine and therefore H2 (Cheng et al., 2013).

For galactose, majority of flux from glucose-6-phosphate (G6P)was toward Embden–Mayerhof–Paranas (EMP) pathway (R12)instead of Pentose Phosphate (PP) pathway (R21) and the flux ratioof R12/R21 was about 11. For Mannose and fructose, R12 flux wasnegative which means that the reaction proceeds backwards.Ribose which its degradation starts at PP pathway R12 flux is zeroand fructose-6-phosphate (F6P) needed for EMP pathway is sup-plied from reactions R27 and R28 which are not significant forother substrates. Sucrose and trehalose had higher PP fluxes com-pared to monosaccharides with 25.07 and 35.84 respectively. Sim-ilarly, EMP fluxes for disaccharides at pyruvate node were abouttwo times of them for monosaccharides.

3.2. Fluxes through NADH producing pathways

As it can be seen from R40 (Appendix A) NADH is a cofactorwhich its higher production leads to higher H2 yields for all sub-strates (Fig. 3). This reaction determines the NADH converted toFdH2 which then leads to H2 production. Hence, reactions relatedto NADH producing pathways affect directly on H2 yields. Fig. 3shows NADH fluxes produced through different reactions (R16,R29 and R38) using different substrates. It can be seen that R16is the major NADH producing reaction for all substrates whichdirect the flux through glycolytic pathway. Sucrose and trehalosehave the highest R29 fluxes among substrates. This is the reactionwhich converts pyruvate to acetyl-CoA and is one of the majorroutes for NADH production. Similarly, R16 fluxes were 354.92and 335.56 per 100 mol substrate for sucrose and trehalose respec-tively compared to 158.1 for ribose which had the lowest fluxamong all substrates. For reaction R38 which produces a-keto glut-arate (one of the biomass components) trehalose had the highestflux among all substrates (19.31) followed by sucrose (13.5).

It should be noted that because of anaerobic fermentation of C.butyricum, three carboxylic acid (TCA) cycle is inactive. Hence,a-keto glutarate is produced from pyruvate and oxaloacetate(R38) and fluxes through reactions R36 and R37 are zero.

3.3. Maximization of H2 yields

Production of volatile fatty acids in fermentation of C. butyricumis strongly related to substrates (Wang et al., 2008). It has been

0

50

100

150

200

250

300

350

400

R16 R29 R38

Flux

(mol

)

Reaction number

Fructose

Galactose

Mannose

Ribose

Sucrose

Trehalose

Fig. 3. NADH fluxes produced through reactions R16, R29 and R38 per 100 mol ofsubstrate.


shown that maximum H2 yields are associated with maximum ace-tate production (Chen et al., 2006). Therefore, maximization of ace-tate production was chosen as the objective function and biomassfor each substrate was set to the values in Table 1. Maximum the-oretical H2 yields for different substrates are shown in Fig. 4. Max-imum yields of 4.96 mol H2/mol sucrose and 3.65 mol H2/moltrehalose were predicted by the model. Higher H2 yield for sucrosein comparison with monosaccharides have been proved by previ-ous studies that reported high product yields when sucrose wasused in the fermentation of C. butyricum or mixed cultures(Wang et al., 2008; Lo et al., 2008).

In order to increase H2 yields in practice, important factorsaffecting on acetate and therefore H2 yields should be recognizedand considered for experiments. Several studies have shown highinitial substrate concentrations lead to lower acetate and H2 yields(Cai et al., 2010; Ren et al., 2008). Initial pH is the other factor oncarbon distribution towards end products. Fermentation of xyloseby Clostridium tyrobutyricum showed lower H2 yields in lower pHconditions where lactate was one the dominant products (Zhuand Yang, 2004). Lactate production is unfavorable for high H2

yields as it consumes large amounts of NADH necessary for H2

production.In addition to culture conditions, overexpression of acetate

kinase (AK) and Phosphotransacetylase (PTA) and knockout inPhosphotransbutyrylase (PTB) and butyrate kinase (BK) is a meta-bolic engineering strategy which results in high H2 yields since allthe carbon flux from acetyl CoA could be directed towards acetateformation.

4. Conclusion

In this study a metabolic model was developed for the first timeto study the anaerobic fermentation of different carbon sources forH2 production by C. butyricum. H2 yields obtained by metabolicmodel are acceptable when comparing with experimental studies.Though, if biomass formation equation could be determined for C.butyricum, the predicted data by model will be much more close toexperimental results. The model confirms that disaccharides (tre-halose and sucrose) are better carbon sources for H2 productioncompared to monosaccharides. It can be utilized further for inves-tigation of other important factors for enhanced H2 production,metabolic engineering and strain improvement using differentsubstrates.

Appendix A.

List of metabolic reactions included in the FBA model.



R. Rafieenia, S.R. Chaganti / Bioresource Technology xxx (2014) xxx–xxx 5

Ptr

R1. SUC ? S6P

lease cite this article in press as: Rafieeniidium butyricum using Cell Net Analyzer

R24. RU5P M R5P
R2. RIB ? R5P R25. RU5P M X5P R3. Gal ? Gal6P R26.
R5P + X5P M GA3P + S7P
R4. FRC ? F6P R27.
E4P + X5P ? F6P + GA3P
R5. Tre ? B-Glc + A-Glc R28.
GA3P + S7P ? E4P + F6P
R6. Man ? Man6P R29.
PYR ? AcCoA + NADH + CO2

R7. B-Glc M A-Glc
R30. AcCoA ? AC R8. B-Glc ? G6P R31.
2AcCoA + 2NADH ? BU
R9. Man6P ? F6P R32. NADH + PYR ? LAC R10. Gal6P ? G6P R33.
AcCoA + 2NADH ? ETH
R11. SUC6P ? G6P + FRC R34. LAC + NADH ? PRA R12. G6P M F6P R35. PEP + CO2 M OAA R13. F6P ? F1,6P R36. AcCoA + OAA ? ICIT R14. F1,6P M DHAP + GA3P R37. ICIT M AKG + NADH R15. DHAP M GA3P R38.
OAA + PYR ? AKG + NADH+ NADPH + 2CO2

R16. GA3P M 1,3PG + NADH
R39. NADPH M NADH R17. 1,3PG M 3PG R40. NADH M FdH2
R18. 3PG M 2PG
R41. FdH2 ? H2
R19. 2PG M PEP
R42. 1.239 3 PG + 1.01AcCoA + 0.6285AKG + 0.297
R20. PEP M PYR
E4P + 0.1788 F6P + 7.6152NADPH
R21. G6p M D6PGL + NADPH
+ 1.4452 OAA + 0.6411PEP + 1.6666 PYR + 0.5726
R22. D6PGL ? D6PGC
R5P ? Bio + 1.31685 NADH R23. D6PGC ? NADPH+Ru5P
+ CO2

Appendix B.

List of metabolites included in the model.

SUC
Sucrose S6P Sucrose-6-phosphate FRC Fructose RIB Ribose TRE Trehalose MAN Mannose GAL Galactose GAL6P Galactose-6-phosphate F6P Fructose 6-phosphate G6P Glucose6-phosphate b-Glucose Beta-glucose a-Glucose Alpha-glucose M6P Manose-6-phosphate F1,6P D-fructose 1,6-bisphosphate DHAP Dihydroxyacetone phosphate GA3P Glyceraldehyde 3-phosphate 1,3PG 3-Phospho-D-glyceroyl phosphate 3PG 3-Phospho-D-glycerate
a, R., Chaganti, S.R. Flux balance analysi. Bioresour. Technol. (2014), http://dx.d

2PG

s of different carbon souoi.org/10.1016/j.biortec

2-Phospho-D-glycerate
PEP Phosphoenolpyruvate PYR Pyruvate AcCoA Acetyl CoA FdH2 Reduced ferredoxin NADH Reduced-nicotinamide adenine
dinucleotide
NADPH Reduced-nicotinamide adenine
dinucleotide phosphate
OAA Oxaloacetate AKG A-ketoglutarate CO2 Carbon dioxide E4P D-erythrose 4-phosphate Ru5P D-ribulose 5-phosphate R5P D-ribose 5-phosphate X5P Xylulose 5-phosphate S7P D-sedoheptulose 7-phosphate ICIT Isocitrate BU Butyrate H2 Hydrogen AC Acetate LAC Lactate PRA Propionate Bio Biomass
Appendix C. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.biortech.2014.10.070.

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