Advertisement

Quantitative Biology

, Volume 2, Issue 3, pp 100–109 | Cite as

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

  • Honglei Liu
  • Yanda Li
  • Xiaowo WangEmail author
Research Article

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/.

Keywords

metabolic network flux analysis optimization 

Supplementary material

40484_2014_33_MOESM1_ESM.pdf (669 kb)
Supplementary material, approximately 654 KB.

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, 536–546PubMedCrossRefGoogle 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, 468–474PubMedCrossRefGoogle 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, 612–616PubMedCrossRefGoogle 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, 7797–7802PubMedCentralPubMedCrossRefGoogle 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, 940–943PubMedCrossRefGoogle 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, 623–629PubMedCrossRefGoogle 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, 559–562PubMedCrossRefGoogle 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, 370–378PubMedCrossRefGoogle 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, 979–988PubMedCrossRefGoogle 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, 46–57PubMedCrossRefGoogle 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, 116–127PubMedCrossRefGoogle 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, 149PubMedCentralPubMedCrossRefGoogle 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, 155–164PubMedCrossRefGoogle 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, 102–111PubMedCrossRefGoogle Scholar
  15. 15.
    Kauffman, K. J., Prakash, P. and Edwards, J. S. (2003) Advances in flux balance analysis. Curr. Opin.Biotechnol., 14, 491–496PubMedCrossRefGoogle Scholar
  16. 16.
    Orth, J. D., Thiele, I. and Palsson, B. Ø. (2010) What is flux balance analysis? Nat. Biotechnol., 28, 245–248PubMedCentralPubMedCrossRefGoogle 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, 264–276PubMedCrossRefGoogle 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, 15112–15117PubMedCentralPubMedCrossRefGoogle 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, 886–897PubMedCrossRefGoogle 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, 53PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Yang, L., Cluett, W. R. and Mahadevan, R. (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab. Eng., 13, 272–281PubMedCrossRefGoogle 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, 647–657PubMedCrossRefGoogle 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, 1–13PubMedCrossRefGoogle 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, 2367–2376PubMedCentralPubMedCrossRefGoogle 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, e1002882PubMedCentralPubMedCrossRefGoogle 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, 3097–3105PubMedCentralPubMedCrossRefGoogle 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, 106PubMedCentralPubMedCrossRefGoogle 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, e1000744PubMedCentralPubMedCrossRefGoogle 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, 595–604PubMedCrossRefGoogle 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, 296PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Egen, D. and Lun, D. S. (2012) Truncated branch and bound achieves efficient constraint-based genetic design. Bioinformatics, 28, 1619–1623PubMedCrossRefGoogle 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, 121PubMedCentralPubMedCrossRefGoogle 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, 1140–1153PubMedCrossRefGoogle 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, 209–226PubMedCrossRefGoogle 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, 541–547PubMedCrossRefGoogle 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, 186–189PubMedCrossRefGoogle 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, 358–365PubMedCrossRefGoogle 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, 229–239PubMedCrossRefGoogle 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, 1290–1307PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Schauer, R. (2000) Achievements and challenges of sialic acid research. Glycoconj. J., 17, 485–499PubMedCrossRefGoogle Scholar
  41. 41.
    Wang, B. (2009) Sialic acid is an essential nutrient for brain development and cognition. Annu. Rev. Nutr., 29, 177–222PubMedCrossRefGoogle Scholar
  42. 42.
    Ishikawa, M. and Koizumi, S. (2010) Microbial production of N-acetylneuraminic acid by genetically engineered Escherichia coli. Carbohydr. Res., 345, 2605–2609PubMedCrossRefGoogle 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, 142PubMedCentralPubMedCrossRefGoogle 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, 894–898PubMedCrossRefGoogle 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, 390PubMedCentralPubMedCrossRefGoogle 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, 1173–1183PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH 2014

Authors and Affiliations

  1. 1.MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST/Department of AutomationTsinghua UniversityBeijingChina

Personalised recommendations