OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

Abstract

Constraint-based flux analysis has been widely used in metabolic engineering to predict genetic optimization strategies. These methods seek to find genetic manipulations that maximally couple the desired metabolites with the cellular growth objective. However, such framework does not work well for overproducing chemicals that are not closely correlated with biomass, for example non-native biochemical production by introducing synthetic pathways into heterologous host cells. Here, we present a computational method called OP-Synthetic, which can identify effective manipulations (upregulation, downregulation and deletion of reactions) and produce a step-by-step optimization strategy for the overproduction of indigenous and non-native chemicals. We compared OP-Synthetic with several state-of-the-art computational approaches on the problems of succinate overproduction and N-acetylneuraminic acid synthetic pathway optimization in Escherichia coli. OP-Synthetic showed its advantage for efficiently handling multiple steps optimization problems on genome wide metabolic networks. And more importantly, the optimization strategies predicted by OP-Synthetic have a better match with existing engineered strains, especially for the engineering of synthetic metabolic pathways for non-native chemical production. OP-Synthetic is freely available at:http://bioinfo.au.tsinghua.edu.cn/member/xwwang/OPSynthetic/.

References

  1. 1.

    Lee, J. W., Na, D., Park, J. M., Lee, J., Choi, S. and Lee, S. Y. (2012) Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nat. Chem. Biol., 8, 536–546

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Prather, K. L. J. and Martin, C. H. (2008) De novo biosynthetic pathways: rational design of microbial chemical factories. Curr. Opin. Biotechnol., 19, 468–474

    PubMed  Article  Google Scholar 

  3. 3.

    Alper, H., Miyaoku, K. and Stephanopoulos, G. (2005) Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nat. Biotechnol., 23, 612–616

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Park, J. H., Lee, K. H., Kim, T. Y. and Lee, S. Y. (2007) Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc. Natl. Acad. Sci. USA, 104, 7797–7802

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  5. 5.

    Ro, D.-K., Paradise, E. M., Ouellet, M., Fisher, K. J., Newman, K. L., Ndungu, J. M., Ho, K. A., Eachus, R. A., Ham, T. S., Kirby, J., et al. (2006) Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature, 440, 940–943

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Kang, J., Gu, P., Wang, Y., Li, Y., Yang, F., Wang, Q. and Qi, Q. (2012) Engineering of an N-acetylneuraminic acid synthetic pathway in Escherichia coli. Metab. Eng., 14, 623–629

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Steen, E. J., Kang, Y., Bokinsky, G., Hu, Z., Schirmer, A., McClure, A., Del Cardayre, S. B. and Keasling, J. D. (2010) Microbial production of fatty-acid-derived fuels and chemicals from plant biomass. Nature, 463, 559–562

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Lee, J. W., Kim, T. Y., Jang, Y.-S., Choi, S. and Lee, S. Y. (2011) Systems metabolic engineering for chemicals and materials. Trends Biotechnol., 29, 370–378

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Park, J. M., Kim, T. Y. and Lee, S. Y. (2009) Constraints-based genome-scale metabolic simulation for systems metabolic engineering. Biotechnol. Adv., 27, 979–988

    PubMed  Article  Google Scholar 

  10. 10.

    Cox, S. J., Shalel Levanon, S., Sanchez, A., Lin, H., Peercy, B., Bennett, G. N. and San, K.-Y. (2006) Development of a metabolic network design and optimization framework incorporating implementation constraints: a succinate production case study. Metab. Eng., 8, 46–57

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Lin, H., Bennett, G. N. and San, K.-Y. (2005) Metabolic engineering of aerobic succinate production systems in Escherichia coli to improve process productivity and achieve the maximum theoretical succinate yield. Metab. Eng., 7, 116–127

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Lee, K. H., Park, J. H., Kim, T. Y., Kim, H. U. and Lee, S. Y. (2007) Systems metabolic engineering of Escherichia coli for L-threonine production. Mol. Syst. Biol., 3, 149

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  13. 13.

    Alper, H., Jin, Y.-S., Moxley, J. F. and Stephanopoulos, G. (2005) Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng., 7, 155–164

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Bro, C., Regenberg, B., Förster, J. and Nielsen, J. (2006) In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab. Eng., 8, 102–111

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Kauffman, K. J., Prakash, P. and Edwards, J. S. (2003) Advances in flux balance analysis. Curr. Opin.Biotechnol., 14, 491–496

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Orth, J. D., Thiele, I. and Palsson, B. Ø. (2010) What is flux balance analysis? Nat. Biotechnol., 28, 245–248

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  17. 17.

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

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Segrè, D., Vitkup, D. and Church, G. M. (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA, 99, 15112–15117

    PubMed Central  PubMed  Article  Google Scholar 

  19. 19.

    Price, N. D., Reed, J. L. and Palsson, B. O. (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol., 2, 886–897

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Kim, J. and Reed, J. L. (2010) OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst. Biol., 4, 53

    PubMed Central  PubMed  Article  Google Scholar 

  21. 21.

    Yang, L., Cluett, W. R. and Mahadevan, R. (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab. Eng., 13, 272–281

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Burgard, A. P., Pharkya, P. and Maranas, C. D. (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng., 84, 647–657

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Pharkya, P. and Maranas, C. D. (2006) An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab. Eng., 8, 1–13

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Pharkya, P., Burgard, A. P. and Maranas, C. D. (2004) OptStrain: a computational framework for redesign of microbial production systems. Genome Res., 14, 2367–2376

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  25. 25.

    Rockwell, G., Guido, N. J., and Church, G. M. (2013) Redirector: designing cell factories by reconstructing the metabolic objective. PLoS Comput. Biol., 9, e1002882

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  26. 26.

    Choi, H. S., Lee, S. Y., Kim, T. Y. and Woo, H. M. (2010) In silico identification of gene amplification targets for improvement of lycopene production. Appl. Environ. Microbiol., 76, 3097–3105

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  27. 27.

    Park, J. M., Park, H. M., Kim, W. J., Kim, H. U., Kim, T. Y. and Lee, S. Y. (2012) Flux variability scanning based on enforced objective flux for identifying gene amplification targets. BMC Syst. Biol., 6, 106

    PubMed Central  PubMed  Article  Google Scholar 

  28. 28.

    Ranganathan, S., Suthers, P. F. and Maranas, C. D. (2010) OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput. Biol., 6, e1000744

    PubMed Central  PubMed  Article  Google Scholar 

  29. 29.

    Cotten, C. and Reed, J. L. (2013) Constraint-based strain design using continuous modifications (CosMos) of flux bounds finds new strategies for metabolic engineering. Biotechnol. J., 8, 595–604

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Lun, D. S., Rockwell, G., Guido, N. J., Baym, M., Kelner, J. A., Berger, B., Galagan, J. E. and Church, G. M. (2009) Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol., 5, 296

    PubMed Central  PubMed  Article  Google Scholar 

  31. 31.

    Egen, D. and Lun, D. S. (2012) Truncated branch and bound achieves efficient constraint-based genetic design. Bioinformatics, 28, 1619–1623

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Hatzimanikatis, V. and Palsson, B. Ø. (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol., 3, 121

    PubMed Central  PubMed  Article  Google Scholar 

  33. 33.

    Jantama, K., Haupt, M. J., Svoronos, S. A., Zhang, X., Moore, J. C., Shanmugam, K. T. and Ingram, L. O. (2008) Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate. Biotechnol. Bioeng., 99, 1140–1153

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Sánchez, A. M., Bennett, G. N. and San, K.-Y. (2006) Batch culture characterization and metabolic flux analysis of succinate-producing Escherichia coli strains. Metab. Eng., 8, 209–226

    PubMed  Article  Google Scholar 

  35. 35.

    Jensen, P. A. and Papin, J. A. (2011) Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics, 27, 541–547

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Ibarra, R. U., Edwards, J. S. and Palsson, B. O. (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature, 420, 186–189

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Sánchez, A. M., Bennett, G. N. and San, K. Y. (2005) Efficient succinic acid production from glucose through overexpression of pyruvate carboxylase in an Escherichia coli alcohol dehydrogenase and lactate dehydrogenase mutant. Biotechnol. Prog., 21, 358–365

    PubMed  Article  Google Scholar 

  38. 38.

    Sánchez, A. M., Bennett, G. N. and San, K.-Y. (2005) Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity. Metab. Eng., 7, 229–239

    PubMed  Article  Google Scholar 

  39. 39.

    Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc., 6, 1290–1307

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  40. 40.

    Schauer, R. (2000) Achievements and challenges of sialic acid research. Glycoconj. J., 17, 485–499

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Wang, B. (2009) Sialic acid is an essential nutrient for brain development and cognition. Annu. Rev. Nutr., 29, 177–222

    PubMed  Article  Google Scholar 

  42. 42.

    Ishikawa, M. and Koizumi, S. (2010) Microbial production of N-acetylneuraminic acid by genetically engineered Escherichia coli. Carbohydr. Res., 345, 2605–2609

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Tao, F., Zhang, Y., Ma, C. and Xu, P. (2011) One-pot bio-synthesis: N-acetyl-D-neuraminic acid production by a powerful engineered wholecell catalyst. Sci. Rep., 1, 142

    PubMed Central  PubMed  Article  Google Scholar 

  44. 44.

    Wang, H. H., Isaacs, F. J., Carr, P. A., Sun, Z. Z., Xu, G., Forest, C. R. and Church, G. M. (2009) Programming cells by multiplex genome engineering and accelerated evolution. Nature, 460, 894–898

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., Adkins, J. N., Schramm, G., Purvine, S. O., Lopez-Ferrer, D., et al. (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol., 6, 390

    PubMed Central  PubMed  Article  Google Scholar 

  46. 46.

    Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P. and Lim, W. A. (2013) Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell, 152, 1173–1183

    CAS  PubMed Central  PubMed  Article  Google Scholar 

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Correspondence to Xiaowo Wang.

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Liu, H., Li, Y. & Wang, X. OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites. Quant Biol 2, 100–109 (2014). https://doi.org/10.1007/s40484-014-0033-7

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Keywords

  • metabolic network
  • flux analysis
  • optimization