Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit

Nature Biotechnology volume 35, pages 273–279 (2017)Cite this article

Subjects

Abstract

Metabolic engineering of microorganisms to produce desirable products on an industrial scale can result in unbalanced cellular metabolic networks that reduce productivity and yield. Metabolic fluxes can be rebalanced using dynamic pathway regulation, but few broadly applicable tools are available to achieve this. We present a pathway-independent genetic control module that can be used to dynamically regulate the expression of target genes. We apply our module to identify the optimal point to redirect glycolytic flux into heterologous engineered pathways in Escherichia coli, resulting in titers of myo-inositol increased 5.5-fold and titers of glucaric acid increased from unmeasurable to >0.8 g/L, compared to the parent strains lacking dynamic flux control. Scaled-up production of these strains in benchtop bioreactors resulted in almost ten- and fivefold increases in specific titers of myo-inositol and glucaric acid, respectively. We also used our module to control flux into aromatic amino acid biosynthesis to increase titers of shikimate in E. coli from unmeasurable to >100 mg/L.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Characterization of a QS circuit to dynamically modulate a target gene of interest (GOI).
Figure 2: QS-based valve controlling Pfk-1 expression regulates cell growth and flux into central carbon metabolism.
Figure 3: Functionality of QS-based dynamic regulation in multiple culture media.
Figure 4: Glucaric acid production using the QS valve at the G6P branchpoint.
Figure 5: Shikimate production through the aromatic amino acid (AAA) biosynthesis pathway using the QS valve.

Similar content being viewed by others

References

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

    Article  CAS  Google Scholar 

  2. Biggs, B.W., De Paepe, B., Santos, C.N.S., De Mey, M. & Kumaran Ajikumar, P. Multivariate modular metabolic engineering for pathway and strain optimization. Curr. Opin. Biotechnol. 29, 156–162 (2014).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  4. Moon, T.S., Yoon, S.-H., Lanza, A.M., Roy-Mayhew, J.D. & Prather, K.L.J. Production of glucaric acid from a synthetic pathway in recombinant Escherichia coli. Appl. Environ. Microbiol. 75, 589–595 (2009).

    Article  CAS  Google Scholar 

  5. Rodrigues, A.L. et al. Systems metabolic engineering of Escherichia coli for production of the antitumor drugs violacein and deoxyviolacein. Metab. Eng. 20, 29–41 (2013).

    Article  CAS  Google Scholar 

  6. Martin, V.J.J., Pitera, D.J., Withers, S.T., Newman, J.D. & Keasling, J.D. Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat. Biotechnol. 21, 796–802 (2003).

    Article  CAS  Google Scholar 

  7. Zaslaver, A. et al. Just-in-time transcription program in metabolic pathways. Nat. Genet. 36, 486–491 (2004).

    Article  CAS  Google Scholar 

  8. Farmer, W.R. & Liao, J.C. Improving lycopene production in Escherichia coli by engineering metabolic control. Nat. Biotechnol. 18, 533–537 (2000).

    Article  CAS  Google Scholar 

  9. Dahl, R.H. et al. Engineering dynamic pathway regulation using stress-response promoters. Nat. Biotechnol. 31, 1039–1046 (2013).

    Article  CAS  Google Scholar 

  10. Zhou, L.-B. & Zeng, A.-P. Exploring lysine riboswitch for metabolic flux control and improvement of L-lysine synthesis in Corynebacterium glutamicum. ACS Synth. Biol. 4, 729–734 (2015).

    Article  CAS  Google Scholar 

  11. Cardinale, S. & Arkin, A.P. Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems. Biotechnol. J. 7, 856–866 (2012).

    Article  CAS  Google Scholar 

  12. Van Dien, S. From the first drop to the first truckload: commercialization of microbial processes for renewable chemicals. Curr. Opin. Biotechnol. 24, 1061–1068 (2013).

    Article  CAS  Google Scholar 

  13. Weber, W. & Fussenegger, M. Inducible product gene expression technology tailored to bioprocess engineering. Curr. Opin. Biotechnol. 18, 399–410 (2007).

    Article  CAS  Google Scholar 

  14. Soma, Y. & Hanai, T. Self-induced metabolic state switching by a tunable cell density sensor for microbial isopropanol production. Metab. Eng. 30, 7–15 (2015).

    Article  CAS  Google Scholar 

  15. Tsao, C.Y., Hooshangi, S., Wu, H.C., Valdes, J.J. & Bentley, W.E. Autonomous induction of recombinant proteins by minimally rewiring native quorum sensing regulon of E. coli. Metab. Eng. 12, 291–297 (2010).

    Article  CAS  Google Scholar 

  16. Solomon, K.V., Moon, T.S., Ma, B., Sanders, T.M. & Prather, K.L.J. Tuning primary metabolism for heterologous pathway productivity. ACS Synth. Biol. 2, 126–135 (2013).

    Article  CAS  Google Scholar 

  17. Brockman, I.M. & Prather, K.L.J. Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites. Metab. Eng. 28, 104–113 (2015).

    Article  CAS  Google Scholar 

  18. Juminaga, D. et al. Modular engineering of L-tyrosine production in Escherichia coli. Appl. Environ. Microbiol. 78, 89–98 (2012).

    Article  CAS  Google Scholar 

  19. Saeidi, N. et al. Engineering microbes to sense and eradicate Pseudomonas aeruginosa, a human pathogen. Mol. Syst. Biol. 7, 521 (2011).

    Article  Google Scholar 

  20. Balagaddé, F.K. et al. A synthetic Escherichia coli predator-prey ecosystem. Mol. Syst. Biol. 4, 187 (2008).

    Article  Google Scholar 

  21. Minogue, T.D., Wehland-von Trebra, M., Bernhard, F. & von Bodman, S.B. The autoregulatory role of EsaR, a quorum-sensing regulator in Pantoea stewartii ssp. stewartii: evidence for a repressor function. Mol. Microbiol. 44, 1625–1635 (2002).

    Article  CAS  Google Scholar 

  22. Mutalik, V.K. et al. Precise and reliable gene expression via standard transcription and translation initiation elements. Nat. Methods 10, 354–360 (2013).

    Article  CAS  Google Scholar 

  23. BioFAB. Biofab Data Access Client (BioFAB, 2012).

  24. Hansen, C.A., Dean, A.B., Draths, K.M. & Frost, J.W. Synthesis of 1,2,3,4-Tetrahydroxybenzene from D-glucose: exploiting myo-Inositol as a precursor to aromatic chemicals. J. Am. Chem. Soc. 121, 3799–3800 (1999).

    Article  CAS  Google Scholar 

  25. Werpy, T. et al. Top value added chemicals from biomass volume I—results of screening for potential candidates from sugars and synthesis gas (US Department of Energy, Washington, DC, 2004).

  26. Yamaoka, M., Osawa, S., Morinaga, T., Takenaka, S. & Yoshida, K. A cell factory of Bacillus subtilis engineered for the simple bioconversion of myo-inositol to scyllo-inositol, a potential therapeutic agent for Alzheimer's disease. Microb. Cell Fact. 10, 69 (2011).

    Article  CAS  Google Scholar 

  27. Moser, F. et al. Genetic circuit performance under conditions relevant for industrial bioreactors. ACS Synth. Biol. 1, 555–564 (2012).

    Article  CAS  Google Scholar 

  28. Reizman, I.M.B. et al. Improvement of glucaric acid production in E. coli via dynamic control of metabolic fluxes. Metab. Eng. Commun. 2, 109–116 (2015).

    Article  Google Scholar 

  29. Raman, S., Rogers, J.K., Taylor, N.D. & Church, G.M. Evolution-guided optimization of biosynthetic pathways. Proc. Natl. Acad. Sci. USA 111, 17803–17808 (2014).

    Article  CAS  Google Scholar 

  30. Draths, K.M., Knop, D.R. & Frost, J.W. Shikimic acid and quinic acid: replacing isolation from plant sources with recombinant microbial biocatalysis. J. Am. Chem. Soc. 121, 1603–1604 (1999).

    Article  CAS  Google Scholar 

  31. Kim, C.U. et al. Influenza neuraminidase inhibitors possessing a novel hydrophobic interaction in the enzyme active site: design, synthesis, and structural analysis of carbocyclic sialic acid analogues with potent anti-influenza activity. J. Am. Chem. Soc. 119, 681–690 (1997).

    Article  CAS  Google Scholar 

  32. Way, J.C. & Davis, J.H. Methods and molecules for yield improvement involving metabolic engineering. US Patent Application No. 13/322,383 (2010).

  33. Chen, K. et al. Deletion of the aroK gene is essential for high shikimic acid accumulation through the shikimate pathway in E. coli. Bioresour. Technol. 119, 141–147 (2012).

    Article  CAS  Google Scholar 

  34. Xu, P., Li, L., Zhang, F., Stephanopoulos, G. & Koffas, M. Improving fatty acids production by engineering dynamic pathway regulation and metabolic control. Proc. Natl. Acad. Sci. USA 111, 11299–11304 (2014).

    Article  CAS  Google Scholar 

  35. Pfleger, B.F., Pitera, D.J., Newman, J.D., Martin, V.J.J. & Keasling, J.D. Microbial sensors for small molecules: development of a mevalonate biosensor. Metab. Eng. 9, 30–38 (2007).

    Article  CAS  Google Scholar 

  36. Studier, F.W. Protein production by auto-induction in high density shaking cultures. Protein Expr. Purif. 41, 207–234 (2005).

    Article  CAS  Google Scholar 

  37. Wang, G. et al. Integration of microbial kinetics and fluid dynamics toward model-driven scale-up of industrial bioprocesses. Eng. Life Sci. 15, 20–29 (2015).

    Article  CAS  Google Scholar 

  38. Brockman, I.M. & Prather, K.L.J. Dynamic metabolic engineering: new strategies for developing responsive cell factories. Biotechnol. J. 10, 1360–1369 (2015).

    Article  CAS  Google Scholar 

  39. Venayak, N., Anesiadis, N., Cluett, W.R. & Mahadevan, R. Engineering metabolism through dynamic control. Curr. Opin. Biotechnol. 34, 142–152 (2015).

    Article  CAS  Google Scholar 

  40. McNerney, M.P., Watstein, D.M. & Styczynski, M.P. Precision metabolic engineering: the design of responsive, selective, and controllable metabolic systems. Metab. Eng. 31, 123–131 (2015).

    Article  CAS  Google Scholar 

  41. Shong, J., Huang, Y.-M., Bystroff, C. & Collins, C.H. Directed evolution of the quorum-sensing regulator EsaR for increased signal sensitivity. ACS Chem. Biol. 8, 789–795 (2013).

    Article  CAS  Google Scholar 

  42. St-Pierre, F. et al. One-step cloning and chromosomal integration of DNA. ACS Synth. Biol. 2, 537–541 (2013).

    Article  CAS  Google Scholar 

  43. Kuhlman, T.E. & Cox, E.C. Site-specific chromosomal integration of large synthetic constructs. Nucleic Acids Res. 38, e92 (2010).

    Article  Google Scholar 

  44. Shong, J. & Collins, C.H. Engineering the esaR promoter for tunable quorum sensing- dependent gene expression. ACS Synth. Biol. 2, 568–575 (2013).

    Article  CAS  Google Scholar 

  45. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

    Article  Google Scholar 

  46. Reisch, C.R. & Prather, K.L.J. The no-SCAR (Scarless Cas9 Assisted Recombineering) system for genome editing in Escherichia coli. Sci. Rep. 5, 15096 (2015).

    Article  CAS  Google Scholar 

  47. Salis, H.M. The ribosome binding site calculator. Methods Enzymol. 498, 19–42 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank C. Collins at Rensselaer Polytechnic Institute (Troy, New York, USA) for providing plasmids and parts for the Esa QS system. In addition, we thank S. Arussy for help in preparing strains for glucaric acid production, and m2p-labs for the loan of a BioLector unit. This work was supported by the US National Science Foundation through the CAREER program (I.M.B.R. and K.L.J.P., Grant No. CBET-0954986), the Graduate Research Fellowship program (A.G.), the Synthetic Biology Engineering Research Center (SynBERC; A.G. and K.L.J.P., Grant No. EEC-0540879), and the Division of Molecular and Cellular Biosciences (A.G. and K.L.J.P., Grant No. MCB-1517913); by the Biotechnology Training Program of the National Institutes of Health (I.M.B.R., Grant No. T32GM008334); and by the USDA National Institute of Food and Agriculture Postdoctoral Fellowship (C.R.R., Grant No. 2013-67012-21022).

Author information

Author notes

  1. Irene M Brockman Reizman & Christopher R Reisch

    Present address: Present addresses: Department of Chemical Engineering, Rose-Hulman Institute of Technology, Terre Haute, Indiana, USA (I.M.B.R.); Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida, USA (C.R.R.).,

Authors and Affiliations

  1. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Apoorv Gupta

  2. Synthetic Biology Engineering Research Center (SynBERC), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Apoorv Gupta & Kristala L J Prather

  3. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Irene M Brockman Reizman, Christopher R Reisch & Kristala L J Prather

Authors
  1. Apoorv Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Irene M Brockman Reizman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher R Reisch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kristala L J Prather
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.G., I.M.B.R., C.R.R. and K.L.J.P. designed and performed the experiments and analyzed the data. A.G., I.M.B.R. and K.L.J.P. wrote the manuscript.

Corresponding author

Correspondence to Kristala L J Prather.

Ethics declarations

Competing interests

A.G., I.M.B.R. and K.L.J.P. are co-inventors on a patent application that includes the reported methods.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–3, Supplementary Figures 1–14, Supplementary Tables 1, 3, 4, and 5 (PDF 1705 kb)

Supplementary Table 2

All strains and corresponding genotypes relevant in this study (PDF 309 kb)

Supplementary Table 6

Promoter, 5′UTR and degradation tag sequences for the expression cassettes used in this study (PDF 71 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, A., Reizman, I., Reisch, C. et al. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit. Nat Biotechnol 35, 273–279 (2017). https://doi.org/10.1038/nbt.3796

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.3796

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research