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Optimization of a blueprint for in vitro glycolysis by metabolic real-time analysis

Abstract

Recruiting complex metabolic reaction networks for chemical synthesis has attracted considerable attention but frequently requires optimization of network composition and dynamics to reach sufficient productivity. As a design framework to predict optimal levels for all enzymes in the network is currently not available, state-of-the-art pathway optimization relies on high-throughput phenotype screening. We present here the development and application of a new in vitro real-time analysis method for the comprehensive investigation and rational programming of enzyme networks for synthetic tasks. We used this first to rationally and rapidly derive an optimal blueprint for the production of the fine chemical building block dihydroxyacetone phosphate (DHAP) via Escherichia coli's highly evolved glycolysis. Second, the method guided the three-step genetic implementation of the blueprint, yielding a synthetic operon with the predicted 2.5-fold–increased glycolytic flux toward DHAP. The new analytical setup drastically accelerates rational optimization of synthetic multienzyme networks.

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Figure 1: Setup for MS-based quantitative real-time analysis.
Figure 2: Quantitative metabolic real-time analysis of in vitro multienzyme network dynamics for a glycolytic network engineered for the production of DHAP with integrated cofactor regeneration.
Figure 3: Identification of rate-limiting steps for DHAP production in the upper part of E. coli's engineered in vitro glycolysis (on the basis of CFX of W3110 Δamn tpiA:Kn).
Figure 4: Operon construction and optimization of in vivo expression levels for improved in vitro DHAP production in a cell-free system obtained from W3110 Δamn tpiAkn provided with plasmids as indicated.

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References

  1. Anderson, J.C., Clarke, E.J., Arkin, A.P. & Voigt, C.A. Environmentally controlled invasion of cancer cells by engineered bacteria. J. Mol. Biol. 355, 619–627 (2006).

    Article  CAS  Google Scholar 

  2. Yokobayashi, Y., Weiss, R. & Arnold, F.H. Directed evolution of a genetic circuit. Proc. Natl. Acad. Sci. USA 99, 16587–16591 (2002).

    Article  CAS  Google Scholar 

  3. Pfleger, B.F., Pitera, D.J., Smolke, C.D. & Keasling, J.D. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat. Biotechnol. 24, 1027–1032 (2006).

    Article  CAS  Google Scholar 

  4. Anthony, J.R. et al. Optimization of the mevalonate-based isoprenoid biosynthetic pathway in Escherichia coli for production of the anti-malarial drug precursor amorpha-4,11-diene. Metab. Eng. 11, 13–19 (2009).

    Article  CAS  Google Scholar 

  5. Salis, H.M., Mirsky, E.A. & Voigt, C.A. Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 27, 946–950 (2009).

    Article  CAS  Google Scholar 

  6. Alper, H., Fischer, C., Nevoigt, E. & Stephanopoulos, G. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA 102, 12678–12683 (2005).

    Article  CAS  Google Scholar 

  7. Hadlich, F., Noack, S. & Wiechert, W. Translating biochemical network models between different kinetic formats. Metab. Eng. 11, 87–100 (2009).

    Article  CAS  Google Scholar 

  8. Kotte, O., Zaugg, J.B. & Heinemann, M. Bacterial adaptation through distributed sensing of metabolic fluxes. Mol. Syst. Biol. 6, 355 (2010).

    Article  Google Scholar 

  9. Wang, H.H. et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898 (2009).

    Article  CAS  Google Scholar 

  10. Linshiz, G. et al. Recursive construction of perfect DNA molecules from imperfect oligonucleotides. Mol. Syst. Biol. 4, 191 (2008).

    Article  Google Scholar 

  11. Bennett, M.R. & Hasty, J. Microfluidic devices for measuring gene network dynamics in single cells. Nat. Rev. Genet. 10, 628–638 (2009).

    Article  CAS  Google Scholar 

  12. El Massaoudi, M., Spelthahn, J., Drysch, A., de Graaf, A. & Takors, R. Production process monitoring by serial mapping of microbial carbon flux distributions using a novel sensor reactor approach: I–Sensor reactor system. Metab. Eng. 5, 86–95 (2003).

    Article  CAS  Google Scholar 

  13. Buziol, S. et al. New bioreactor-coupled rapid stopped-flow sampling technique for measurements of metabolite dynamics on a subsecond time scale. Biotechnol. Bioeng. 80, 632–636 (2002).

    Article  CAS  Google Scholar 

  14. Büscher, J.M., Czernik, D., Ewald, J.C., Sauer, U. & Zamboni, N. Cross-platform comparison of methods for quantitative metabolomics of primary metabolism. Anal. Chem. 81, 2135–2143 (2009).

    Article  Google Scholar 

  15. Bennett, B.D. et al. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 5, 593–599 (2009).

    Article  CAS  Google Scholar 

  16. van den Brink, J. et al. Dynamics of glycolytic regulation during adaptation of Saccharomyces cerevisiae to fermentative metabolism. Appl. Environ. Microbiol. 74, 5710–5723 (2008).

    Article  CAS  Google Scholar 

  17. van Eunen, K. et al. Measuring enzyme activities under standardized in vivo-like conditions for systems biology. FEBS J. 277, 749–760 (2010).

    Article  CAS  Google Scholar 

  18. Woodward, J., Orr, M., Cordray, K. & Greenbaum, E. Biotechnology: Enzymatic production of biohydrogen. Nature 405, 1014–1015 (2000).

    Article  CAS  Google Scholar 

  19. Jewett, M.C., Calhoun, K.A., Voloshin, A., Wuu, J.J. & Swartz, J.R. An integrated cell-free metabolic platform for protein production and synthetic biology. Mol. Syst. Biol. 4, 220 (2008).

    Article  Google Scholar 

  20. Shimizu, Y. et al. Cell-free translation reconstituted with purified components. Nat. Biotechnol. 19, 751–755 (2001).

    Article  CAS  Google Scholar 

  21. Zhang, Y.H.P., Evans, B.R., Mielenz, J.R., Hopkins, R.C. & Adams, M.W.W. High-yield hydrogen production from starch and water by a synthetic enzymatic pathway. PLoS ONE 2, e456 (2007).

    Article  Google Scholar 

  22. Bujara, M., Schümperli, M., Billerbeck, S., Heinemann, M. & Panke, S. Exploiting cell-free systems: Implementation and debugging of a system of biotransformations. Biotechnol. Bioeng. 106, 376–389 (2010).

    CAS  PubMed  Google Scholar 

  23. Chen, H. & Zenobi, R. Neutral desorption sampling of biological surfaces for rapid chemical characterization by extractive electrospray ionization mass spectrometry. Nat. Protoc. 3, 1467–1475 (2008).

    Article  CAS  Google Scholar 

  24. Zhu, L. et al. Real-time, on-line monitoring of organic chemical reactions using extractive electrospray ionization tandem mass spectrometry. Rapid Commun. Mass Spectrom. 22, 2993–2998 (2008).

    Article  CAS  Google Scholar 

  25. Schümperli, M., Pellaux, R. & Panke, S. Chemical and enzymatic routes to dihydroxyacetone phosphate. Appl. Microbiol. Biotechnol. 75, 33–45 (2007).

    Article  Google Scholar 

  26. Ehlde, M. & Zacchi, G. Influence of experimental errors on the determination of flux control coefficients from transient metabolite concentrations. Biochem. J. 313, 721–727 (1996).

    Article  CAS  Google Scholar 

  27. Kummel, A., Panke, S. & Heinemann, M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2, 2006.0034 (2006).

    Article  Google Scholar 

  28. Jamshidi, N. & Palsson, B.A. Top-down analysis of temporal hierarchy in biochemical reaction networks. PLOS Comput. Biol. 4, e1000177 (2008).

    Article  Google Scholar 

  29. Chassagnole, C., Noisommit-Rizzi, N., Schmid, J.W., Mauch, K. & Reuss, M. Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol. Bioeng. 79, 53–73 (2002).

    Article  CAS  Google Scholar 

  30. Teusink, B., Walsh, M.C., van Dam, K. & Westerhoff, H.V. The danger of metabolic pathways with turbo design. Trends Biochem. Sci. 23, 162–169 (1998).

    Article  CAS  Google Scholar 

  31. Emmerling, M., Bailey, J.E. & Sauer, U. Altered regulation of pyruvate kinase or co-overexpression of phosphofructokinase increases glycolytic fluxes in resting Escherichia coli. Biotechnol. Bioeng. 67, 623–627 (2000).

    Article  CAS  Google Scholar 

  32. Meyer, D., Schneider-Fresenius, C., Horlacher, R., Peist, R. & Boos, W. Molecular characterization of glucokinase from Escherichia coli K-12. J. Bacteriol. 179, 1298–1306 (1997).

    Article  CAS  Google Scholar 

  33. Cho, B.-K. et al. The transcription unit architecture of the Escherichia coli genome. Nat. Biotechnol. 27, 1043–1049 (2009).

    Article  CAS  Google Scholar 

  34. Güell, M. et al. Transcriptome complexity in a genome-reduced bacterium. Science 326, 1268–1271 (2009).

    Article  Google Scholar 

  35. Sharma, C.M. et al. The primary transcriptome of the major human pathogen Helicobacter pylori. Nature 464, 250–255 (2010).

    Article  CAS  Google Scholar 

  36. Holtz, W.J. & Keasling, J.D. Engineering static and dynamic control of synthetic pathways. Cell 140, 19–23 (2010).

    Article  CAS  Google Scholar 

  37. Gibson, D.G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

    Article  CAS  Google Scholar 

  38. Fell, D. Understanding the Control of Metabolism (Portland Press, London, UK, 1997).

  39. Stephanopoulos, G., Aristidou, A.A. & Nielsen, J. Metabolic Engineering–Principles and Methodologies (Academic Press, London, UK, 1998).

  40. Delgado, J. & Liao, J.C. Determination of flux control coefficients from transient metabolite concentrations. Biochem. J. 282, 919–927 (1992).

    Article  CAS  Google Scholar 

  41. Delgado, J. & Liao, J.C. Metabolic control analysis using transient metabolite concentrations. Determination of metabolite concentration control coefficients. Biochem. J. 285, 965–972 (1992).

    Article  Google Scholar 

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Acknowledgements

We would like to thank N. Zamboni, U. Sauer, M. Oldiges and C. Wandrey for help with MS analyses. This work was supported by the EU-FP6 projects EUROBIOSYN and NANOMOT.

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Contributions

M.B. did the experiments and analyzed the data and wrote the manuscript with S.P., who also supervised the work. R.P. and M.S. helped to construct the setup, and M.H. supervised part of the work.

Corresponding author

Correspondence to Sven Panke.

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The authors declare no competing financial interests.

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Supplementary Methods, Supplementary Figures 1–11 and Supplementary Tables 1–5 (PDF 2301 kb)

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Bujara, M., Schümperli, M., Pellaux, R. et al. Optimization of a blueprint for in vitro glycolysis by metabolic real-time analysis. Nat Chem Biol 7, 271–277 (2011). https://doi.org/10.1038/nchembio.541

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