期刊论文详细信息
BMC Systems Biology
Proteomics-based metabolic modeling and characterization of the cellulolytic bacterium Thermobifida fusca
Stephen S Fong2  Yu Deng1  John A Wilkins3  Oleg Krokhin3  Dmitriy Shamshurin3  Victor Spicer3  J Paul Brooks2  Niti Vanee2 
[1] Kansas State University, Olathe, USA;Virginia Commonwealth University, Richmond, USA;University of Manitoba, Winnipeg, Canada
关键词: Biofuel;    Mevalonate Pathway;    DXP Pathway;    Terpenoids Biosynthesis Pathway;    Proteomics Profiling;    Thermobifida fusca;    Actinomycete;    Constraint Based Modeling;    Flux Balance Analysis;    Metabolic Modeling;   
Others  :  1159578
DOI  :  10.1186/s12918-014-0086-2
 received in 2014-04-24, accepted in 2014-07-14,  发布年份 2014
PDF
【 摘 要 】

Background

Thermobifida fusca is a cellulolytic bacterium with potential to be used as a platform organism for sustainable industrial production of biofuels, pharmaceutical ingredients and other bioprocesses due to its capability of potential to convert plant biomass to value-added chemicals. To best develop T. fusca as a bioprocess organism, it is important to understand its native cellular processes. In the current study, we characterize the metabolic network of T. fusca through reconstruction of a genome-scale metabolic model and proteomics data. The overall goal of this study was to use multiple metabolic models generated by different methods and comparison to experimental data to gain a high-confidence understanding of the T. fusca metabolic network.

Results

We report the generation of three versions of a metabolic model of Thermobifida fusca sp. XY developed using three different approaches (automated, semi-automated, and proteomics-derived). The model closest to in vivo growth was the proteomics-derived model that consists of 975 reactions involving 1382 metabolites and account for 316 EC numbers (296 genes). The model was optimized for biomass production with the optimal flux of 0.48 doublings per hour when grown on cellobiose with a substrate uptake rate of 0.25 mmole/h. In vivo activity of the DXP pathway for terpenoid biosynthesis was also confirmed using real-time PCR.

Conclusions

iTfu296 provides a platform to understand and explore the metabolic capabilities of the actinomycete T. fusca for the potential use in bioprocess industries for the production of biofuel and pharmaceutical ingredients. By comparing different model reconstruction methods, the use of high-throughput proteomics data as a starting point proved to be the most accurate to in vivo growth.

【 授权许可】

   
2014 Vanee et al.; licensee BioMed Central

【 预 览 】
附件列表
Files Size Format View
20150409021535573.pdf 1860KB PDF download
Figure 10. 15KB Image download
Figure 9. 30KB Image download
Figure 8. 45KB Image download
Figure 7. 26KB Image download
Figure 6. 88KB Image download
Figure 5. 22KB Image download
Figure 4. 26KB Image download
Figure 3. 48KB Image download
Figure 2. 31KB Image download
Figure 1. 64KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

【 参考文献 】
  • [1]Wilson DB: Studies of Thermobifida fusca plant cell wall degrading enzymes. Chem Rec 2004, 4:72-82.
  • [2]Ghangas GS, Wilson DB: Cloning of the thermomonospora fusca endoglucanase E2 Gene in streptomyces lividans: affinity purification and functional domains of the cloned gene product. Appl Environ Microbiol 1988, 54:2521-2526.
  • [3]Irwin DC, Zhang S, Wilson DB: Cloning, expression and characterization of a family 48 exocellulase, Cel48A, from Thermobifida fusca. Eur J Biochem 2000, 267:4988-4997.
  • [4]Spiridonov NA, Wilson DB: Regulation of biosynthesis of individual cellulases in Thermomonospora fusca. J Bacteriol 1998, 180:3529-3532.
  • [5]Kukolya J, Nagy I, Laday M, Toth E, Oravecz O, Marialigeti K, Hornok L: Thermobifida cellulolytica sp. nov., a novel lignocellulose-decomposing actinomycete. Int J Syst Evol Microbiol 2002, 52:1193-1199.
  • [6]Lee J, Postmaster A, Soon HP, Keast D, Carson KC: Siderophore production by actinomycetes isolates from two soil sites in Western Australia. Biometals 2012, 25:285-296.
  • [7]Takahashi S, Toyoda A, Sekiyama Y, Takagi H, Nogawa T, Uramoto M, Suzuki R, Koshino H, Kumano T, Panthee S, Dairi T, Ishikawa J, Ikeda H, Sakaki Y, Osada H: Reveromycin A biosynthesis uses RevG and RevJ for stereospecific spiroacetal formation. Nat Chem Biol 2011, 7:461-468.
  • [8]Niraula NP, Kim SH, Sohng JK, Kim ES: Biotechnological doxorubicin production: pathway and regulation engineering of strains for enhanced production. Appl Microbiol Biotechnol 2010, 87:1187-1194.
  • [9]Cane DE, Ikeda H: Exploration and mining of the bacterial terpenome. Acc Chem Res 2012, 45:463-472.
  • [10]Citron CA, Gleitzmann J, Laurenzano G, Pukall R, Dickschat JS: Terpenoids are widespread in actinomycetes: a correlation of secondary metabolism and genome data. Chembiochem 2012, 13:202-214.
  • [11]Lykidis A, Mavromatis K, Ivanova N, Anderson I, Land M, DiBartolo G, Martinez M, Lapidus A, Lucas S, Copeland A, Richardson P, Wilson DB, Kyrpides N: Genome sequence and analysis of the soil cellulolytic actinomycete Thermobifida fusca YX. J Bacteriol 2007, 189:2477-2486.
  • [12]Deng Y, Fong SS: Metabolic engineering of Thermobifida fusca for direct aerobic bioconversion of untreated lignocellulosic biomass to 1-propanol. Metab Eng 2011, 13:570-577.
  • [13]Carere CR, Sparling R, Cicek N, Levin DB: Third generation biofuels via direct cellulose fermentation. Int J Mol Sci 2008, 9:1342-1360.
  • [14]Edwards JS, Covert M, Palsson B: Metabolic modelling of microbes: the flux-balance approach. Environ Microbiol 2002, 4:133-140.
  • [15]Orth JD, Thiele I, Palsson BO: What is flux balance analysis? Nat Biotechnol 2010, 28:245-248.
  • [16]Varma A, Palsson BO: Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotechnol 1994, 12:994-998.
  • [17]Segre D, Vitkup D, Church GM: Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 2002, 99:15112-15117.
  • [18]Shlomi T, Berkman O, Ruppin E: Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci U S A 2005, 102:7695-7700.
  • [19]Rapoport TA, Heinrich R, Jacobasch G, Rapoport S: A linear steady-state treatment of enzymatic chains. A mathematical model of glycolysis of human erythrocytes. Eur J Biochem 1974, 42:107-120.
  • [20]Durot M, Bourguignon P-Y, Schachter V: Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 2009, 33:164-190.
  • [21]Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27-30.
  • [22]Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006, 34:D354-D357.
  • [23]Schellenberger J, Park JO, Conrad TC, Palsson BØ: BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 2010, 11:213. BioMed Central Full Text
  • [24]Thorleifsson SG, Thiele I: rBioNet: A COBRA toolbox extension for reconstructing high-quality biochemical networks. Bioinformatics 2011, 27:2009-2010.
  • [25]UniProt C: Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res 2013, 41:D43-D47.
  • [26]Chagoyen M, Pazos F: MBRole: enrichment analysis of metabolomic data. Bioinformatics 2011, 27:730-731.
  • [27]Roberts SB, Gowen CM, Brooks JP, Fong SS: Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production.BMC Syst Biol 2010, 4:31-0509-0504-0531.
  • [28]Roberts SB, Robichaux JL, Chavali AK, Manque PA, Lee V, Lara AM, Papin JA, Buck GA: Proteomic and network analysis characterize stage-specific metabolism in Trypanosoma cruzi.BMC Syst Biol 2009, 3:52-0509-0503-0552.
  • [29]Vanee N, Roberts SB, Fong SS, Manque P, Buck GA: A genome-scale metabolic model of Cryptosporidium hominis. Chem Biodivers 2010, 7:1026-1039.
  • [30]Brooks JP, Burns WP, Fong SS, Gowen CM, Roberts SB: Gap detection for genome-scale constraint-based models. Adv Bioinformatics 2012, 2012:323472.
  • [31]Joyce AR, Palsson BÃ: Toward Whole Cell Modeling And Simulation: Comprehensive Functional Genomics Through The Constraint-Based Approach. Prog Drug Res 2007, 64:267-309.
  • [32]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.
  • [33]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.
  • [34]Yang L, Cluett WR, Mahadevan R: EMILiO: a fast algorithm for genome-scale strain design. Metab Eng 2011, 13:272-281.
  • [35]Palsson BÃ: Systems Biology: Properties Of Reconstructed Networks. Cambridge University Press, New York; 2007.
  • [36]Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, de Crecy-Lagard V, Diaz N, Disz T, Edwards R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata-Reuyl D, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V, et al.: The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 2005, 33:5691-5702.
  • [37]Wilson DB, Kostylev M: Cellulase processivity. Meth Mol Biol 2012, 908:93-99.
  • [38]Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL: High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 2010, 28:977-982.
  • [39]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.
  • [40]Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE: Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 2009, 5:e1000489.
  • [41]Lerman JA, Hyduke DR, Latif H, Portnoy VA, Lewis NE, Orth JD, Schrimpe-Rutledge AC, Smith RD, Adkins JN, Zengler K, Palsson BO: In silico method for modelling metabolism and gene product expression at genome scale. Nat Commun 2012, 3:929.
  • [42]Shlomi T, Cabili MN, Herrgard MJ, Palsson BÃ, Ruppin E: Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 2008, 26:1003-1010.
  • [43]Gowen CM, Fong SS: Genome-scale metabolic model integrated with RNAseq data to identify metabolic states of Clostridium thermocellum. Biotechnol J 2010, 5:759-767.
  • [44]Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BO: A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol Syst Biol 2011, 7:535.
  • [45]Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Jacob B, Huang J, Williams P, Huntemann M, Anderson I, Mavromatis K, Ivanova NN, Kyrpides NC: IMG: the Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res 2012, 40:D115-D122.
  • [46]Hyduke DR, Lewis NE, Palsson BO: Analysis of omics data with genome-scale models of metabolism. Mol Biosyst 2013, 9:167-174.
  • [47]Dwivedi RC, Spicer V, Harder M, Antonovici M, Ens W, Standing KG, Wilkins JA, Krokhin OV: Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics. Anal Chem 2008, 15;80(18):7036-42.
  • [48]McQueen P, Krokhin O: Optimal selection of 2D reversed-phase-reversed-phase HPLC separation techniques in bottom-up proteomics. Expert Rev Proteomics 2012, 9(2):125-8.
  • [49]Craig R, Beavis RC: TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004, 12;20(9):1466-7.
  • [50]Feist AM, Herrgard MJ, Thiele I, Reed JL, Palsson BO: Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 2009, 7:129-143.
  文献评价指标  
  下载次数:30次 浏览次数:48次