Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

BMC Systems Biology

Ryan S Senger¹  Benjamin G Freedman¹  Jiun Y Yen¹  Michael J McAnulty¹ 

[1]Biological Systems Engineering Department, Virginia Tech, Blacksburg, VA 24061, USA

关键词: systems biology;    metabolic engineering;    flux balance analysis;    flux ratio;    clostridia;    Genome-scale model;   

Others  :  1144478
DOI  :  10.1186/1752-0509-6-42

 received in 2012-01-08, accepted in 2012-05-14,  发布年份 2012

【 摘 要 】

Background

Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico.

Results

A new method called “

f

lux

b

alance

a

nalysis with flux

ratio

s (FBrAtio)” was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO₂ and (viii) H₂. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production.

Conclusions

FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

【 授权许可】

   
2012 McAnulty et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Papoutsakis ET: Equations and calculations for fermentations of butyric acid bacteria. Biotechnol Bioeng 1984, 26:174-187.
• [2]Monot F, Martin JR, Petitdemange H, Gay R: Acetone and butanol production by Clostridium acetobutylicum in a synthetic medium. Appl Environ Microbiol 1982, 44:1318-1324.
• [3]Girbal L, Soucaille P: Regulation of Clostridium-acetobutylicum metabolism as revealed by mixed-substrate steady-state continuous cultures - Role of Nadh/Nad ratio and Atp Pool. J Bacteriol 1994, 176:6433-6438.
• [4]Papoutsakis ET, Desai RP, Nielsen LK: Stoichiometric modeling of Clostridium acetobutylicum fermentations with non-linear constraints. J Biotechnol 1999, 71:191-205.
• [5]Calik P, Ozdamar TH: Bioreaction network flux analysis for industrial microorganisms: A review. Rev Chem Eng 2002, 18:553-596.
• [6]Sillers R, Chow A, Tracy B, Papoutsakis ET: Metabolic engineering of the non-sporulating, non-solventogenic Clostridium acetobutylicum strain M5 to produce butanol without acetone demonstrate the robustness of the acid-formation pathways and the importance of the electron balance. Metab Eng 2008, 10:321-332.
• [7]Desai RP, Harris LM, Welker NE, Papoutsakis ET: Metabolic flux analysis elucidates the importance of the acid-formation pathways in regulating solvent production by Clostridium acetobutylicum. Metab Eng 1999, 1:206-213.
• [8]Desai RP, Nielsen LK, Papoutsakis ET: Stoichiometric modeling of Clostridium acetobutylicum fermentations with non-linear constraints. J Biotechnol 1999, 71:191-205.
• [9]Lee J, Yun H, Feist AM, Palsson BO, Lee SY: Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network. Appl Microbiol Biotechnol 2008, 80:849-862.
• [10]Senger RS: Biofuel production improvement with genome-scale models: The role of cell composition. Biotechnol J 2010, 5:671-685.
• [11]Senger RS, Papoutsakis ET: Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis. Biotechnol Bioeng 2008, 101:1036-1052.
• [12]Senger RS, Papoutsakis ET: Genome-scale model for Clostridium acetobutylicum: Part II. Development of specific proton flux states and numerically determined sub-systems. Biotechnol Bioeng 2008, 101:1053-1071.
• [13]Milne CB, Kim PJ, Eddy JA, Price ND: Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology. Biotechnol J 2009, 4:1653-1670.
• [14]Mahadevan R, Palsson BO, Lovley DR: In situ to in silico and back: elucidating the physiology and ecology of Geobacter spp. using genome-scale modelling. Nat Rev Microbiol 2011, 9:39-50.
• [15]Feist AM, Palsson BO: The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat Biotechnol 2008, 26:659-667.
• [16]Feist AM, Herrgard MJ, Thiele I, Reed JL, Palsson BO: Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 2009, 7:129-143.
• [17]Papin JA, Price ND, Wiback SJ, Fell DA, Palsson BO: Metabolic pathways in the post-genome era. Trends Biochem Sci 2003, 28:250-258.
• [18]Papin JA, Stelling J, Price ND, Klamt S, Schuster S, Palsson BO: Comparison of network-based pathway analysis methods. Trends Biotechnol 2004, 22:400-405.
• [19]Price ND, Papin JA, Schilling CH, Palsson BO: Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol 2003, 21:162-169.
• [20]Price ND, Reed JL, Palsson BO: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004, 2:886-897.
• [21]Reed JL, Famili I, Thiele I, Palsson BO: Towards multidimensional genome annotation. Nat Rev Genet 2006, 7:130-141.
• [22]Teusink B, Wiersma A, Molenaar D, Francke C, de Vos WM, Siezen RJ, Smid EJ: Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model. J Biol Chem 2006, 281:40041-40048.
• [23]Gonzalez O, Gronau S, Falb M, Pfeiffer F, Mendoza E, Zimmer R, Oesterhelt D: Reconstruction, modeling & analysis of Halobacterium salinarum R-1 metabolism. Mol Biosyst 2008, 4:148-159.
• [24]Oliveira AP, Nielsen J, Forster J: Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol 2005, 5:39. BioMed Central Full Text
• [25]Gianchandani EP, Papin JA, Price ND, Joyce AR, Palsson BO: Matrix formalism to describe functional states of transcriptional regulatory systems. PLoS Comput Biol 2006, 2:e101.
• [26]Chandrasekaran S, Price ND: Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 2010, 107:17845-17850.
• [27]Reed JL, Vo TD, Schilling CH, Palsson BO: An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 2003, 4:R54. BioMed Central Full Text
• [28]Archer CT, Kim JF, Jeong H, Park JH, Vickers CE, Lee SY, Nielsen LK: The genome sequence of E. coli W (ATCC 9637): comparative genome analysis and an improved genome-scale reconstruction of E. coli. BMC Genomics 2011, 12:9. BioMed Central Full Text
• [29]Perumal D, Samal A, Sakharkar KR, Sakharkar MK: Targeting multiple targets in Pseudomonas aeruginosa PAO1 using flux balance analysis of a reconstructed genome-scale metabolic network. J Drug Target 2011, 19:1-13.
• [30]Kim HU, Kim TY, Lee SY: Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Mol Biosyst 2010, 6:339-348.
• [31]AbuOun M, Suthers PF, Jones GI, Carter BR, Saunders MP, Maranas CD, Woodward MJ, Anjum MF: Genome scale reconstruction of a Salmonella metabolic model: comparison of similarity and differences with a commensal Escherichia coli strain. J Biol Chem 2009, 284:29480-29488.
• [32]Rambaut A, Pybus OG, Nelson MI, Viboud C, Taubenberger JK, Holmes EC: The genomic and epidemiological dynamics of human influenza A virus. Nature 2008, 453:615-619.
• [33]Heinemann M, Kummel A, Ruinatscha R, Panke S: In silico genome-scale reconstruction and validation of the Staphylococcus aureus metabolic network. Biotechnol Bioeng 2005, 92:850-864.
• [34]Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO: In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng 2005, 91:643-648.
• [35]Kuepfer L, Sauer U, Blank LM: Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Res 2005, 15:1421-1430.
• [36]Burgard AP, Pharkya P, Maranas CD: Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 2003, 84:647-657.
• [37]Pharkya P, Burgard AP, Maranas CD: OptStrain: a computational framework for redesign of microbial production systems. Genome Res 2004, 14:2367-2376.
• [38]Ranganathan S, Suthers PF, Maranas CD: OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput Biol 2010, 6:e1000744.
• [39]Jones SW, Paredes CJ, Tracy B, Cheng N, Sillers R, Senger RS, Papoutsakis ET: The transcriptional program underlying the physiology of clostridial sporulation. Genome Biol 2008, 9:R114. BioMed Central Full Text
• [40]Mermelstein LD, Papoutsakis ET: In vivo methylation in Escherichia coli by the Bacillus subtilis phage phi 3 T I methyltransferase to protect plasmids from restriction upon transformation of Clostridium acetobutylicum ATCC 824. Appl Environ Microbiol 1993, 59:1077-1081.
• [41]Green EM, Boynton ZL, Harris LM, Rudolph FB, Papoutsakis ET, Bennett GN: Genetic manipulation of acid formation pathways by gene inactivation in Clostridium acetobutylicum ATCC 824. Microbiology 1996, 142:2079-2086.
• [42]Harris LM, Desai RP, Welker NE, Papoutsakis ET: Characterization of recombinant strains of the Clostridium acetobutylicum butyrate kinase inactivation mutant: need for new phenomenological models for solventogenesis and butanol inhibition? Biotechnol Bioeng 2000, 67:1-11.
• [43]Tummala SB, Junne SG, Papoutsakis ET: antisense RNA downregulation of coenzyme a transferase combined with alcohol-aldehyde dehydrogenase overexpression leads to predominantly alcohologenic Clostridium acetobutylicum fermentations. J Bacteriol 2003, 185:3644-3653.
• [44]Sillers R, Al-Hinai MA, Papoutsakis ET: Aldehyde–alcohol dehydrogenase and/or thiolase overexpression coupled with CoA transferase downregulation lead to higher alcohol titers and selectivity in Clostridium acetobutylicum fermentations. Biotechnol Bioeng 2009, 102:38-49.
• [45]Fischer E, Sauer U: Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC-MS. European journal of biochemistry / FEBS 2003, 270:880-891.
• [46]Fischer E, Zamboni N, Sauer U: High-throughput metabolic flux analysis based on gas chromatography–mass spectrometry derived 13 C constraints. Anal Biochem 2004, 325:308-316.
• [47]Sauer U, Lasko DR, Fiaux J, Hochuli M, Glaser R, Szyperski T, Wuthrich K, Bailey JE: Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism. J Bacteriol 1999, 181:6679-6688.
• [48]Buhler B, Park JB, Blank LM, Schmid A: NADH availability limits asymmetric biocatalytic epoxidation in a growing recombinant Escherichia coli strain. Appl Environ Microbiol 2008, 74:1436-1446.
• [49]Sauer U, Hatzimanikatis V, Bailey JE, Hochuli M, Szyperski T, Wuthrich K: Metabolic fluxes in riboflavin-producing Bacillus subtilis. Nat Biotechnol 1997, 15:448-452.
• [50]Tummala SB, Welker NE, Papoutsakis ET: Design of antisense RNA constructs for downregulation of the acetone formation pathway of Clostridium acetobutylicum. J Bacteriol 2003, 185:1923-1934.
• [51]Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27-30.
• [52]Crown SB, Indurthi DC, Ahn WS, Choi J, Papoutsakis ET, Antoniewicz MR: Resolving the TCA cycle and pentose-phosphate pathway of Clostridium acetobutylicum ATCC 824: Isotopomer analysis, in vitro activities and expression analysis. Biotechnol J 2011, 6:300-305.
• [53]Amador-Noguez D, Feng XJ, Fan J, Roquet N, Rabitz H, Rabinowitz JD: Systems-level metabolic flux profiling elucidates a complete, bifurcated tricarboxylic acid cycle in Clostridium acetobutylicum. J Bacteriol 2010, 192:4452-4461.
• [54]Jankowski MD, Henry CS, Broadbelt LJ, Hatzimanikatis V: Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys J 2008, 95:1487-1499.
• [55]Henry CS, Broadbelt LJ, Hatzimanikatis V: Thermodynamics-based metabolic flux analysis. Biophys J 2007, 92:1792-1805.
• [56]Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2007, 2:727-738.
• [57]Edwards JS, Ibarra RU, Palsson BO: In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 2001, 19:125-130.
• [58]Harris LM, Blank L, Desai RP, Welker NE, Papoutsakis ET: Fermentation characterization and flux analysis of recombinant strains of Clostridium acetobutylicum with an inactivated solR gene. J Ind Microbiol Biotechnol 2001, 27:322-328.
• [59]Meyer CL, Papoutsakis ET: Increased levels of ATP and NADH are associated with increased solvent production in continuous cultures of Clostridium acetobutylicum. Appl Environ Microbiol 1989, 30:450-459.