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In silico model-driven cofactor engineering strategies for improving the overall NADP(H) turnover in microbial cell factories

  • Systems Biotechnology
  • Published:
Journal of Industrial Microbiology & Biotechnology

Abstract

Optimizing the overall NADPH turnover is one of the key challenges in various value-added biochemical syntheses. In this work, we first analyzed the NADPH regeneration potentials of common cell factories, including Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, and Pichia pastoris across multiple environmental conditions and determined E. coli and glycerol as the best microbial chassis and most suitable carbon source, respectively. In addition, we identified optimal cofactor specificity engineering (CSE) enzyme targets, whose cofactors when switched from NAD(H) to NADP(H) improve the overall NADP(H) turnover. Among several enzyme targets, glyceraldehyde-3-phosphate dehydrogenase was recognized as a global candidate since its CSE improved the NADP(H) regeneration under most of the conditions examined. Finally, by analyzing the protein structures of all CSE enzyme targets via homology modeling, we established that the replacement of conserved glutamate or aspartate with serine in the loop region could change the cofactor dependence from NAD(H) to NADP(H).

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References

  1. Ahn J, Chung BKS, Lee D-Y et al (2011) NADPH-dependent pgi-gene knockout Escherichia coli metabolism producing shikimate on different carbon sources. FEMS Microbiol Lett 324:10–16

    Article  CAS  PubMed  Google Scholar 

  2. Bastian S, Liu X, Meyerowitz JT et al (2011) Engineered ketol-acid reductoisomerase and alcohol dehydrogenase enable anaerobic 2-methylpropan-1-ol production at theoretical yield in Escherichia coli. Metab Eng 13:345–352

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Brinkmann-Chen S, Flock T, Cahn JKB et al (2013) General approach to reversing ketol-acid reductoisomerase cofactor dependence from NADPH to NADH. Proc Natl Acad Sci 110:10946–10951

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Chemler JA, Fowler ZL, McHugh KP, Koffas MAG (2010) Improving NADPH availability for natural product biosynthesis in Escherichia coli by metabolic engineering. Metab Eng 12:96–104

    Article  CAS  PubMed  Google Scholar 

  6. Chen Y, Nielsen J (2013) Advances in metabolic pathway and strain engineering paving the way for sustainable production of chemical building blocks. Curr Opin Biotechnol 24:965–972

    Article  CAS  PubMed  Google Scholar 

  7. Chin JW, Khankal R, Monroe CA et al (2009) Analysis of NADPH supply during xylitol production by engineered Escherichia coli. Biotechnol Bioeng 102:209–220

    Article  CAS  PubMed  Google Scholar 

  8. Christen S, Sauer U (2011) Intracellular characterization of aerobic glucose metabolism in seven yeast species by 13C flux analysis and metabolomics. FEMS Yeast Res 11:263–272

    Article  CAS  PubMed  Google Scholar 

  9. Chung BKS, Lee D-Y (2009) Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network. BMC Syst Biol 3:117

    Article  PubMed Central  PubMed  Google Scholar 

  10. Chung BKS, Selvarasu S, Andrea C et al (2010) Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement. Microb Cell Fact 9:50

    Article  PubMed Central  PubMed  Google Scholar 

  11. Chung BKS, Lakshmanan M, Klement M et al (2013) Genome-scale in silico modeling and analysis for designing synthetic terpenoid-producing microbial cell factories. Chem Eng Sci 103:100–108

    Article  CAS  Google Scholar 

  12. Durate NC, Palsson BØ, Fu P (2004) Integrated analysis of metabolic phenotypes in Saccharomyces cerevisiae. BMC Genom 5:63

    Article  Google Scholar 

  13. Feist AM, Palsson BØ (2011) The biomass objective function. Curr Opin Microbiol 13:344–349

    Article  Google Scholar 

  14. Fuhrer T, Sauer U (2009) Different biochemical mechanisms ensure network-wide balancing of reducing equivalents in microbial metabolism. J Bacteriol 191:2112–2121

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Ghosh A, Zhao H, Price ND (2011) Genome-scale consequences of cofactor balancing in engineered pentose utilization pathways in Saccharomyces cerevisiae. PLoS One 6:e27316

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Gruchattka E, Hädicke O, Klamt S et al (2013) In silico profiling of Escherichia coli and Saccharomyces cerevisiae as terpenoid factories. Microb Cell Fact 12:84

    Article  PubMed Central  PubMed  Google Scholar 

  17. Kim P-J, Lee D-Y, Kim TY et al (2007) Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc Natl Acad Sci 104:13638–13642

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. King ZA, Feist AM (2013) Optimizing cofactor specificity of oxidoreductase enzymes for the generation of microbial production strains—optswap. Ind Biotechnol 9:236–246

    Article  CAS  Google Scholar 

  19. King ZA, Feist AM (2014) Optimal cofactor swapping can increase the theoretical yield for chemical production in Escherichia coli and Saccharomyces cerevisiae. Metab Eng 24:117–128

    Article  CAS  PubMed  Google Scholar 

  20. Lakshmanan M, Chung BK-S, Liu C et al (2013) Cofactor modification analysis: a computational framework to identify cofactor specificity engineering targets for strain improvement. J Bioinform Comput Biol 11:1343006

    Article  PubMed  Google Scholar 

  21. Lakshmanan M, Koh G, Chung BKS, Lee D-Y (2014) Software applications for flux balance analysis. Brief Bioinform 15:108–122

    Article  PubMed  Google Scholar 

  22. Lee HC, Kim JS, Jang W, Kim SY (2010) High NADPH/NADP+ ratio improves thymidine production by a metabolically engineered Escherichia coli strain. J Biotechnol 149:24–32

    Article  CAS  PubMed  Google Scholar 

  23. Lee JW, Kim TY, Jang Y-S et al (2011) Systems metabolic engineering for chemicals and materials. Trends Biotechnol 29:370–378

    Article  CAS  PubMed  Google Scholar 

  24. Lee WH, Park JB, Park K et al (2007) Enhanced production of ε-caprolactone by overexpression of NADPH-regenerating glucose 6-phosphate dehydrogenase in recombinant Escherichia coli harboring cyclohexanone monooxygenase gene. Appl Microbiol Biotechnol 76:329–338

    Article  CAS  PubMed  Google Scholar 

  25. Lee WH, Kim MD, Jin YS, Seo JH (2013) Engineering of NADPH regenerators in Escherichia coli for enhanced biotransformation. Appl Microbiol Biotechnol 97:2761–2772

    Article  CAS  PubMed  Google Scholar 

  26. Lee SY, Lee D-Y, Kim TY (2005) Systems biotechnology for strain improvement. Trends Biotechnol 23:349–358

    Article  CAS  PubMed  Google Scholar 

  27. Lesk AM (1995) NAD-binding domains of dehydrogenases. Curr Opin Struct Biol 5:775–783

    Article  CAS  PubMed  Google Scholar 

  28. Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305

    PubMed Central  CAS  PubMed  Google Scholar 

  29. Li ZJ, Cai L, Wu Q, Chen GQ (2009) Overexpression of NAD kinase in recombinant Escherichia coli harboring the phbCAB operon improves poly(3-hydroxybutyrate) production. Appl Microbiol Biotechnol 83:939–947

    Article  CAS  PubMed  Google Scholar 

  30. Lim S-J, Jung Y-M, Shin H-D, Lee Y-H (2002) Amplification of the NADPH-related genes zwf and gnd for the oddball biosynthesis of PHB in an E. coli transformant harboring a cloned phbCAB operon. J Biosci Bioeng 93:543–549

    Article  CAS  PubMed  Google Scholar 

  31. Londesborough J, Penttilae M, Richard P, Verho R (2003) Fungal micro-organism having an increased ability to carryout biotechnological process(es). WO2003/038067 A1

  32. Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276

    Article  CAS  PubMed  Google Scholar 

  33. Martínez I, Zhu J, Lin H et al (2008) Replacing Escherichia coli NAD-dependent glyceraldehyde 3-phosphate dehydrogenase (GAPDH) with a NADP-dependent enzyme from Clostridium acetobutylicum facilitates NADPH dependent pathways. Metab Eng 10:352–359

    Article  PubMed  Google Scholar 

  34. Medema MH, van Raaphorst R, Takano E, Breitling R (2012) Computational tools for the synthetic design of biochemical pathways. Nat Rev Microbiol 10:191–202

    Article  CAS  PubMed  Google Scholar 

  35. Mo ML, Palsson BO, Herrgård MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 3:37

    Article  PubMed Central  PubMed  Google Scholar 

  36. Monk J, Nogales J, Palsson BO (2014) Optimizing genome-scale network reconstructions. Nat Biotechnol 32:447–452

    Article  CAS  PubMed  Google Scholar 

  37. Oh Y-K, Palsson BO, Park SM et al (2007) Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem 282:28791–28799

    Article  CAS  PubMed  Google Scholar 

  38. Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Price ND, Famili I, Beard DA, Palsson BØ (2002) Extreme pathways and Kirchhoff’s second law. Biophys J 83(5):2879–2882

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Reed JL, Vo TD, Schilling CH, Palsson BO (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4:R54

    Article  PubMed Central  PubMed  Google Scholar 

  41. Rossmann MG, Moras D, Olsen KW (1974) Chemical and biological evolution of nucleotide-binding protein. Nature 250:194–199

    Article  CAS  PubMed  Google Scholar 

  42. San K-Y, Bennett GN, Berríos-Rivera SJ et al (2002) Metabolic engineering through cofactor manipulation and its effects on metabolic flux redistribution in Escherichia coli. Metab Eng 4:182–192

    Article  CAS  PubMed  Google Scholar 

  43. Sauer U, Canonaco F, Heri S et al (2004) The soluble and membrane-bound transhydrogenases UdhA and PntAB have divergent functions in NADPH metabolism of Escherichia coli. J Biol Chem 279:6613–6619

    Article  CAS  PubMed  Google Scholar 

  44. Seo JH, Lee WH, Chin YW et al (2011) Enhanced production of GDP-L-fucose by overexpression of NADPH regenerator in recombinant Escherichia coli. Appl Microbiol Biotechnol 91:967–976

    Article  PubMed  Google Scholar 

  45. Sievers F, Wilm A, Dineen D et al (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539

    Article  PubMed Central  PubMed  Google Scholar 

  46. Söding J, Biegert A, Lupas AN (2005) The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res 33(Web Server issue):W244–W248

    Article  PubMed Central  PubMed  Google Scholar 

  47. Verho R, Londesborough J, Penttilä M, Richard P (2003) Engineering redox cofactor regeneration for improved pentose fermentation in Saccharomyces cerevisiae. Appl Environ Microbiol 69:5892–5897

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Wang Y, San KY, Bennett GN (2013) Cofactor engineering for advancing chemical biotechnology. Curr Opin Biotechnol 24:994–999

    Article  CAS  PubMed  Google Scholar 

  49. Zamboni N, Fischer E, Laudert D et al (2004) The Bacillus subtilis yqjI gene encodes the NADP+-dependent 6-P-gluconate dehydrogenase in the pentose phosphate pathway. J Bacteriol 186:4528–4534

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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Acknowledgments

This work was supported by the National University of Singapore, the National Research Foundation of Singapore (NRF2013-THE001-035), Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore, and a grant from the Next-Generation BioGreen 21 Program (SSAC, No. PJ01109405), Rural Development Administration, Republic of Korea.

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Authors and Affiliations

  1. Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore

    Meiyappan Lakshmanan & Dong-Yup Lee

  2. Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore

    Kai Yu, Lokanand Koduru & Dong-Yup Lee

  3. NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore

    Kai Yu & Dong-Yup Lee

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  1. Meiyappan Lakshmanan
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Correspondence to Dong-Yup Lee.

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No conflicts of interests declared. This study does not involve any human or animal subjects.

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Lakshmanan, M., Yu, K., Koduru, L. et al. In silico model-driven cofactor engineering strategies for improving the overall NADP(H) turnover in microbial cell factories. J Ind Microbiol Biotechnol 42, 1401–1414 (2015). https://doi.org/10.1007/s10295-015-1663-0

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