In silico reconstruction and experimental validation of Saccharopolyspora erythraea genome-scale metabolic model iZZ1342 that accounts for 1685 ORFs

Zhendong Zhuang , Mingzhi Huang , Ju Chu

Bioresources and Bioprocessing ›› 2018, Vol. 5 ›› Issue (1) : 26

PDF
Bioresources and Bioprocessing ›› 2018, Vol. 5 ›› Issue (1) : 26 DOI: 10.1186/s40643-018-0212-x
Research

In silico reconstruction and experimental validation of Saccharopolyspora erythraea genome-scale metabolic model iZZ1342 that accounts for 1685 ORFs

Author information +
History +
PDF

Abstract

Background

Saccharopolyspora erythraea (S. erythraea) is a Gram-positive erythromycin–producing filamentous bacterium. The lack of comprehensive S. erythraea genome-scale metabolic models (GEMs) hinders the efficiency of metabolic engineering as well as fermentation process optimization.

Results

In this study, the GEMs model of S. erythraea iZZ1342 was reconstructed according to the latest genome annotations, omics databases, and literatures. Compared with the previous S. erythraea model—GSMR, the new model iZZ1342 presented great improvements both on scope and coverage in the number of reactions, metabolites, and annotated genes. In detail, the number of unique reactions in iZZ1342 was increased from 1482 to 1684, the number of metabolites was increased from 1546 to 1614, and the number of unique genes was increased from 1272 to 1342. We also added 1441 gene-protein-reaction associations in iZZ1342 which lacks in the previous model to overcome the limitation in the application of strain designing. Compared with the transcriptomics data obtained from the published literature, 86.3% ORFs and 92.9% reactions in iZZ1342 can be verified. The results of the sensitivity analysis showed the similar trend in the E. coli GEMs. The prediction of growth on available 27 kinds of carbon sources and 33 kinds of nitrogen sources showed the accuracy rate was 77.8 and 87.9%, respectively. Compared with the physiological data obtained from chemostat cultivation, the simulation results showed good consistency. The correlation coefficient between the 13C-labeled experiment data and the flux simulation result was 0.97. All the above results showed that the iZZ1342 model has good performance. Furthermore, four genes are in the range of successful knockout by comparing these targets with the results which have been earlier published.

Conclusion

The new model iZZ1342 improved significantly in model size and prediction performance, which will lay a good foundation to study the systematic metabolic engineering of S. erythraea system in vivo.

Keywords

Saccharopolyspora erythraea / Genome-scale metabolic model / Multi-omics / Erythromycin

Cite this article

Download citation ▾
Zhendong Zhuang, Mingzhi Huang, Ju Chu. In silico reconstruction and experimental validation of Saccharopolyspora erythraea genome-scale metabolic model iZZ1342 that accounts for 1685 ORFs. Bioresources and Bioprocessing, 2018, 5(1): 26 DOI:10.1186/s40643-018-0212-x

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Alam MT, Merlo ME, Hodgson DA, Wellington EM, Takano E, Breitling R. Metabolic modeling and analysis of the metabolic switch in Streptomyces coelicolor. BMC Genomics, 2010, 11(1): 1-9.

[2]

Alam MT, Medema MH, Takano E, Breitling R. Comparative genome-scale metabolic modeling of actinomycetes: the topology of essential core metabolism. FEBS Lett, 2011, 585(14): 2389-2394.

[3]

Albert DB, Martens CS. Determination of low-molecular-weight organic acid concentrations in seawater and pore-water samples via HPLC. Mar Chem, 1997, 56(1–2): 27-37.

[4]

Bibb MJ. Regulation of secondary metabolism in streptomycetes. Curr Opin Microbiol, 2005, 8(2): 208-215.

[5]

Bordbar A, Monk JM, King ZA, Palsson BO. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet, 2014, 15(2): 107-120.

[6]

Borodina I, Krabben P, Nielsen J. Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism. Genome Res, 2005, 15(6): 820-829.

[7]

Bushell ME, Smitht J, Lynch HC. A physiological control model for erythromycin production in batch and cyclic fed batch culture. Microbiology, 1997, 143(2): 475-480.

[8]

Carata E, Peano C, Tredici SM, Ferrari F, Talà A, Corti G, Alifano P. Phenotypes and gene expression profiles of Saccharopolyspora erythraea rifampicin-resistant (rif) mutants affected in erythromycin production. Microb Cell Fact, 2009, 8(1): 18.

[9]

Carreras C, Frykman S, Ou S, Cadapan L, Zavala S, Woo E, Patel S. Saccharopolyspora erythraea-catalyzed bioconversion of 6-deoxyerythronolide B analogs for production of novel erythromycins. J Biotechnol, 2002, 92(3): 217-228.

[10]

Caspi R, Altman T, Dreher K, Fulcher CA, Subhraveti P, Keseler IM, Mueller LA. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res, 2012, 36: 623-631.

[11]

Chen C, Hong M, Chu J, Huang M, Ouyang L, Tian X, Zhuang Y. Blocking the flow of propionate into TCA cycle through a mutB knockout leads to a significant increase of erythromycin production by an industrial strain of Saccharopolyspora erythraea. Bioprocess Biosyst Eng, 2016, 40: 1-9.

[12]

Damiani AL, He QP, Jeffries TW, Wang J. Comprehensive evaluation of two genome-scale metabolic network models for Scheffersomyces stipitis. Biotechnol Bioeng, 2015, 112(6): 1250-1262.

[13]

Donachie WD, Begg KJ. Growth of the bacterial cell. Nature, 1970, 227(5264): 1220-1224.

[14]

El-Enshasy HA, Mohamed NA, Farid MA, El-Diwany AI. Improvement of erythromycin production by Saccharopolyspora erythraea in molasses based medium through cultivation medium optimization. Bioresour Technol, 2008, 99(10): 4263-4268.

[15]

Ellis DI, Goodacre R. Metabolomics-assisted synthetic biology. Curr Opin Biotechnol, 2012, 23(1): 22-28.

[16]

Feist AM, Al E. Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol, 2009, 7(2): 129-143.

[17]

Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Palsson . A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol, 2007, 3(1): 1-18.

[18]

Gehlenborg N, O’Donoghue SI, Baliga NS, Goesmann A, Hibbs MA, Kitano H, Tenenbaum D. Visualization of omics data for system biology. Nat Methods, 2010, 7(3): 56-68.

[19]

Ghojavand Bonakdarpour H, Heydarian B, Hamedi SM. The inter-relationship between inoculum concentration, morphology, rheology and erythromycin productivity in submerged cultivation of Saccharopolyspora erythraea. Braz J Chem Eng, 2011, 28(4): 565-574.

[20]

Hollywood K, Brison DR, Goodacre R. Metabolomics: current technologies and future trends. Proteomics, 2006, 6(17): 4716-4723.

[21]

Hong M, Huang M, Chu J, Zhuang Y, Zhang S. Impacts of proline on the central metabolism of an industrial erythromycin-producing strain Saccharopolyspora erythraea via 13C labeling experiments. J Biotechnol, 2016, 231: 1-8.

[22]

Kanehisa M, Goto S, Hattori M, Aokikinoshita KF, Itoh M, Kawashima S, Hirakawa M. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res, 2006, 34: 354-357.

[23]

Kim TY, Sohn SB, Kim YB, Kim WJ, Lee SY. Recent advances in reconstruction and applications of genome-scale metabolic models. Curr Opin Biotechnol, 2012, 23(4): 617-623.

[24]

Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Shoemaker BA. PubChem substance and compound databases. Nucleic Acids Res, 2016, 44: 1202-1213.

[25]

Kjeldsen KR, Nielsen J. In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network. Biotechnol Bioeng, 2009, 102(2): 583.

[26]

Kumar VS, Dasika MS, Maranas CD. Optimization based automated curation of metabolic reconstructions. BMC Bioinform, 2007, 8(1): 1-16.

[27]

Li YY, Xiao C, Yu WB, Hao L, Ye ZQ, Hui Y, Ye BC. Systems perspectives on erythromycin biosynthesis by comparative genomic and transcriptomic analyses of S. erythraea E3 and NRRL23338 strains. BMC Genomics, 2013, 14(1): 523.

[28]

Licona-Cassani C, Marcellin E, Quek LE, Jacob S, Nielsen LK. Reconstruction of the Saccharopolyspora erythraea genome-scale model and its use for enhancing erythromycin production. Antonie Van Leeuwenhoek, 2012, 102(3): 493-502.

[29]

Markowitz VM, Chen IM, Palaniappan K. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res, 2012, 40: 115-122.

[30]

Martin SM, Bushell ME. Effect of hyphal morphology on bioreactor performance of antibiotic-producing Saccharopolyspora erythraea cultures. Microbiology, 1996, 142(7): 1783-1788.

[31]

Mcdermott JF, Lethbridge G, Bushell ME. Estimation of the kinetic constants and elucidation of trends in growth and erythromycin production in batch and continuous cultures of Saccharopolyspora erythraea using curve-fitting techniques. Enzyme Microb Technol, 1993, 15(8): 657.

[32]

Medema MH, Trefzer A, Kovalchuk A, van den Berg M, Müller U, Heijne W, Nierman WC. The sequence of a 1.8-mb bacterial linear plasmid reveals a rich evolutionary reservoir of secondary metabolic pathways. Genome Biol Evol, 2010, 2(1): 212-224.

[33]

Minas W, Brünker P, Kallio PT, Bailey JE. Improved erythromycin production in a genetically engineered industrial strain of Saccharopolyspora erythraea. Biotechnol Prog, 1998, 14(4): 561-566.

[34]

Ming H, Han M, Liu X, Huang M, Ju C. 13C-assisted metabolomics analysis reveals the positive correlation between specific erythromycin production rate and intracellular propionyl-CoA pool size in Saccharopolyspora erythraea. Bioprocess Biosyst Eng, 2017, 40(4): 1-12.

[35]

Mironov VA, Sergienko OV, Nastasiak IN, Danilenko VN. Biogenesis and regulation of biosynthesis of erythromycins in Saccharopolyspora erythraea: a review. Prikl Biokhim Mikrobiol, 2004, 40(6): 613.

[36]

Mishra P, Park GY, Lakshmanan M, Lee HS, Lee H, Chang MW, Lee DY. Genome-scale metabolic modeling and in silico analysis of lipid accumulating yeast Candida tropicalis for dicarboxylic acid production. Biotechnol Bioeng, 2016, 113(9): 1993.

[37]

Nielsen J. It is all about metabolic fluxes. J Bacteriol, 2003, 185(24): 7031-7035.

[38]

O’Brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson . Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol, 2013, 9: 1-13.

[39]

Oliynyk M, Samborskyy M, Lester JB, Mironenko T, Scott N, Dickens S, Leadlay PF. Complete genome sequence of the erythromycin-producing bacterium Saccharopolyspora erythraea NRRL23338. Nat Biotechnol, 2007, 25(4): 447-453.

[40]

Orth JD, Thiele I, Palsson BO. What is flux balance analysis?. Nat Biotechnol, 2010, 28(3): 245-248.

[41]

Pan P, Qiang H. Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, Yarrowia lipolytica. PloS ONE, 2012, 7(12): 1-11.

[42]

Peano C, Talà A, Corti G, Pasanisi D, Durante M, Mita G, Alifano P. Comparative genomics and transcriptional profiles of Saccharopolyspora erythraea NRRL 2338 and a classically improved erythromycin over-producing strain. Microb Cell Fact, 2012, 11(1): 32.

[43]

Rostamza M, Noohi A, Hamedi J. Enhancement in production of erythromycin by Saccharopolyspora erythraea by the use of suitable industrial seeding media. Daru, 2008, 16(1): 13-17.

[44]

Saratram G, Maranas CD. 13C metabolic flux analysis at a genome-scale. Metab Eng, 2015, 32: 12-22.

[45]

Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Rahmanian S. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc, 2011, 6(9): 1290.

[46]

Stefanovic E, Fitzgerald G, Mcauliffe O. Advances in the genomics and metabolomics of dairy lactobacilli: a review. Food Microbiol, 2017, 61: 33-49.

[47]

Thiele I, Palsson . A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc, 2010, 5(1): 93-121.

[48]

Tomàs-Gamisans M, Ferrer P, Albiol J. Integration and validation of the genome-scale metabolic models of Pichia pastoris: a comprehensive update of protein glycosylation pathways, lipid and energy metabolism. PloS ONE, 2016, 11(1): e0148031.

[49]

Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 2009, 10: 57-63.

[50]

Weber JM, Wierman CK, Hutchinson CR. Genetic analysis of erythromycin production in Streptomyces erythreus. J Bacteriol, 1985, 164(1): 425-433.

[51]

Weber JM, Cernota WH, Gonzalez MC, Leach BI, Reeves AR, Wesley RK. An erythromycin process improvement using the diethyl methylmalonate responsive (Dmr) phenotype of the Saccharopolyspora erythraea mutB strain. Appl Microbiol Biotechnol, 2012, 93(4): 1575-1583.

[52]

Wentzel A, Sletta H, Consortium S, Ellingsen TE, Bruheim P. Intracellular metabolite pool changes in response to nutrient depletion induced metabolic switching in Streptomyces coelicolor. Metabolites, 2012, 2(1): 178-194.

[53]

Young JD. INCA: a computational platform for isotopically non-stationary metabolic flux analysis. Bioinformatics, 2014, 30(9): 1333-1335.

[54]

Zou X, Hang HF, Chu J, Zhuang YP, Zhang SL. Enhancement of erythromycin A production with feeding available nitrogen sources in erythromycin biosynthesis phase. Bioresour Technol, 2009, 100(13): 3358-3365.

Funding

The Major State Basic Research Development Program of China(No. 2012CB721006)

National Natural Science Foundation of China(No. 21276081)

the National Scientific and Technological Major Special Project(No. 2011ZX09203-001-03)

NWOMoST Joint Program(2013DFG32630)

AI Summary AI Mindmap
PDF

127

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/