Pyruvate-responsive genetic circuits for dynamic control of central metabolism

Abstract

Dynamic regulation is a promising strategy for fine-tuning metabolic fluxes in microbial cell factories. However, few of these synthetic regulatory systems have been developed for central carbon metabolites. Here we created a set of programmable and bifunctional pyruvate-responsive genetic circuits for dynamic dual control (activation and inhibition) of central metabolism in Bacillus subtilis. We used these genetic circuits to design a feedback loop control system that relies on the intracellular concentration of pyruvate to fine-tune the target metabolic modules, leading to the glucaric acid titer increasing from 207 to 527 mg l−1. The designed logic gate-based circuits were enabled by the characterization of a new antisense transcription mechanism in B. subtilis. In addition, a further increase to 802 mg l−1 was achieved by blocking the formation of by-products. Here, the constructed pyruvate-responsive genetic circuits are presented as effective tools for the dynamic control of central metabolism of microbial cell factories.

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Fig. 1: Construction and characteristics of PdhR-based pyruvate-responsive genetic circuits.
Fig. 2: Construction of pyruvate-responsive gene circuits with different dynamic ranges and phase dependence.
Fig. 3: Characteristics of the regulation effect of antisense transcription in B. subtilis.
Fig. 4: Construction of pyruvate-inhibited gene circuits using antisense transcription.
Fig. 5: Dynamic regulation of glucaric acid synthesis in B. subtilis.
Fig. 6: Blocking the synthesis of by-product acetoin.

Data availability

All data produced or analyzed for this study are included in the published article, supplementary information files and source data files, or are available from the corresponding authors upon reasonable request. Sequence data in this article can be found in NCBI under the following accession numbers: pdhR (GenBank ID: 944827), yeeZ (GenBank ID: 946538), udh (GenBank ID: SPD80607.1), gene ino1 (GenBank ID: CAA89448.1). Source data are provided with this paper.

Code availability

The Python code used in this study can be found in Supplementary Note 1.

References

  1. 1.

    Choi, K. R. et al. Systems metabolic engineering strategies: integrating systems and synthetic biology with metabolic engineering. Trends Biotechnol. 37, 817–837 (2019).

    CAS  PubMed  Google Scholar 

  2. 2.

    Gu, Y. et al. Advances and prospects of Bacillus subtilis cellular factories: from rational design to industrial applications. Metab. Eng. 50, 109–121 (2018).

    CAS  PubMed  Google Scholar 

  3. 3.

    Liu, D. et al. Construction, model-based analysis, and characterization of a promoter library for fine-tuned gene expression in Bacillus subtilis. ACS Synth. Biol. 7, 1785–1797 (2018).

    CAS  PubMed  Google Scholar 

  4. 4.

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

    CAS  PubMed  Google Scholar 

  5. 5.

    Deng, C. et al. Synthetic repetitive extragenic palindromic (REP) sequence as an efficient mRNA stabilizer for protein production and metabolic engineering in prokaryotic cells. Biotechnol. Bioeng. 116, 5–18 (2019).

    CAS  PubMed  Google Scholar 

  6. 6.

    Trentini, D. B. et al. Arginine phosphorylation marks proteins for degradation by a Clp protease. Nature 539, 48–53 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

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

    CAS  PubMed  Google Scholar 

  8. 8.

    Phan, T. T., Tran, L. T., Schumann, W. & Nguyen, H. D. Development of Pgrac100-based expression vectors allowing high protein production levels in Bacillus subtilis and relatively low basal expression in Escherichia coli. Microb. Cell Fact. 14, 72 (2015).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Bhavsar, A. P., Zhao, X. & Brown, E. D. Development and characterization of a xylose-dependent system for expression of cloned genes in Bacillus subtilis: conditional complementation of a teichoic acid mutant. Appl. Environ. Microbiol. 67, 403–410 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Guzman, L. M., Belin, D., Carson, M. J. & Beckwith, J. Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. J. Bacteriol. 177, 4121–4130 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Xu, P. et al. Design and kinetic analysis of a hybrid promoter-regulator system for malonyl-CoA sensing in Escherichia coli. ACS Chem. Biol. 9, 451–458 (2014).

    CAS  PubMed  Google Scholar 

  12. 12.

    Peters, G. et al. Development of N-acetylneuraminic acid responsive biosensors based on the transcriptional regulator NanR. Biotechnol. Bioeng. 115, 1855–1865 (2018).

    CAS  PubMed  Google Scholar 

  13. 13.

    Lehning, C. E., Siedler, S., Ellabaan, M. M. H. & Sommer, M. O. A. Assessing glycolytic flux alterations resulting from genetic perturbations in E. coli using a biosensor. Metab. Eng. 42, 194–202 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wang, J. et al. Developing a pyruvate-driven metabolic scenario for growth-coupled microbial production. Metab. Eng. 55, 191–200 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Bothfeld, W., Kapov, G. & Tyo, K. E. J. A glucose-sensing toggle switch for autonomous, high productivity genetic control. ACS Synth. Biol. 6, 1296–1304 (2017).

    CAS  PubMed  Google Scholar 

  16. 16.

    Yang, Y. et al. Sensor-regulator and RNAi based bifunctional dynamic control network for engineered microbial synthesis. Nat. Commun. 9, 3043 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Liang, M. et al. A CRISPR–Cas12a-derived biosensing platform for the highly sensitive detection of diverse small molecules. Nat. Commun. 10, 3672 (2019).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Pelechano, V. & Steinmetz, L. M. Gene regulation by antisense transcription. Nat. Rev. Genet. 14, 880–893 (2013).

    CAS  PubMed  Google Scholar 

  19. 19.

    Steensels, J. & Verstrepen, K. J. Stop that noise and turn up the antisense transcription. Cell Rep. 15, 2575–2576 (2016).

    CAS  PubMed  Google Scholar 

  20. 20.

    Quereda, J. J. & Cossart, P. Regulating bacterial virulence with RNA. Annu. Rev. Microbiol. 71, 263–280 (2017).

    CAS  PubMed  Google Scholar 

  21. 21.

    Brophy, J. A. & Voigt, C. A. Antisense transcription as a tool to tune gene expression. Mol. Syst. Biol. 12, 854 (2016).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Ogasawara, H., Ishida, Y., Yamada, K., Yamamoto, K. & Ishihama, A. PdhR (pyruvate dehydrogenase complex regulator) controls the respiratory electron transport system in Escherichia coli. J. Bacteriol. 189, 5534–5541 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Ma, W. et al. Metabolic engineering of carbon overflow metabolism of Bacillus subtilis for improved N-acetyl-glucosamine production. Bioresour. Technol. 250, 642–649 (2018).

    CAS  PubMed  Google Scholar 

  24. 24.

    Panahi, R., Vasheghani-Farahani, E., Shojaosadati, S. A. & Bambai, B. Auto-inducible expression system based on the SigB-dependent ohrB promoter in Bacillus subtilis. Mol. Biol. 48, 970–976 (2014).

    CAS  Google Scholar 

  25. 25.

    Yang, S., Du, G., Chen, J. & Kang, Z. Characterization and application of endogenous phase-dependent promoters in Bacillus subtilis. Appl. Microbiol. Biotechnol. 101, 4151–4161 (2017).

    CAS  PubMed  Google Scholar 

  26. 26.

    Crampton, N., Bonass, W. A., Kirkham, J., Rivetti, C. & Thomson, N. H. Collision events between RNA polymerases in convergent transcription studied by atomic force microscopy. Nucleic Acids Res. 34, 5416–5425 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Sesto, N., Wurtzel, O., Archambaud, C., Sorek, R. & Cossart, P. The excludon: a new concept in bacterial antisense RNA-mediated gene regulation. Nat. Rev. Microbiol. 11, 75–82 (2013).

    CAS  PubMed  Google Scholar 

  28. 28.

    Sneppen, K. et al. A mathematical model for transcriptional interference by RNA polymerase traffic in Escherichia coli. J. Mol. Biol. 346, 399–409 (2005).

    CAS  PubMed  Google Scholar 

  29. 29.

    Palmer, A. C., Ahlgren-Berg, A., Egan, J. B., Dodd, I. B. & Shearwin, K. E. Potent transcriptional interference by pausing of RNA polymerases over a downstream promoter. Mol. Cell 34, 545–555 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Walaszek, Z., Szemraj, J., Hanausek, M., Adams, A. K. & Sherman, U. d-Glucaric acid content of various fruits and vegetables and cholesterol-lowering effects of dietary d-glucarate in the rat. Nutr. Res. 16, 673–681 (1996).

    CAS  Google Scholar 

  31. 31.

    Werpy, T. & Peterson, G. (eds) Top Value Added Chemicals from Biomass. Volume 1—Results of Screening for Potential Candidates from Sugars and Synthesis Gas (US Department of Energy, 2004).

  32. 32.

    Doong, S. J., Gupta, A. & Prather, K. L. J. Layered dynamic regulation for improving metabolic pathway productivity in Escherichia coli. Proc. Natl Acad. Sci. USA 115, 2964–2969 (2018).

    CAS  PubMed  Google Scholar 

  33. 33.

    Gupta, A., Reizman, I. M., Reisch, C. R. & Prather, K. L. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit. Nat. Biotechnol. 35, 273–279 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Gu, Y. et al. Synthetic redesign of central carbon and redox metabolism for high yield production of N-acetylglucosamine in Bacillus subtilis. Metab. Eng. 51, 59–69 (2019).

    PubMed  Google Scholar 

  35. 35.

    Ma, W. et al. Combinatorial pathway enzyme engineering and host engineering overcomes pyruvate overflow and enhances overproduction of N-acetylglucosamine in Bacillus subtilis. Microb. Cell Fact. 18, 1 (2019).

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Tan, S. Z. & Prather, K. L. Dynamic pathway regulation: recent advances and methods of construction. Curr. Opin. Chem. Biol. 41, 28–35 (2017).

    CAS  PubMed  Google Scholar 

  37. 37.

    San Martin, A. et al. Imaging mitochondrial flux in single cells with a FRET sensor for pyruvate. PLoS ONE 9, e85780 (2014).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Meyer, A. J., Segall-Shapiro, T. H., Glassey, E., Zhang, J. & Voigt, C. A. Escherichia coli “marionette” strains with 12 highly optimized small-molecule sensors. Nat. Chem. Biol. 15, 196–204 (2019).

    CAS  PubMed  Google Scholar 

  39. 39.

    Mutalik, V. K., Qi, L., Guimaraes, J. C., Lucks, J. B. & Arkin, A. P. Rationally designed families of orthogonal RNA regulators of translation. Nat. Chem. Biol. 8, 447–454 (2012).

    CAS  PubMed  Google Scholar 

  40. 40.

    Stazic, D., Lindell, D. & Steglich, C. Antisense RNA protects mRNA from RNase E degradation by RNA-RNA duplex formation during phage infection. Nucleic Acids Res. 39, 4890–4899 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Leistra, A. N., Curtis, N. C. & Contreras, L. M. Regulatory non-coding sRNAs in bacterial metabolic pathway engineering. Metab. Eng. 52, 190–214 (2019).

    CAS  PubMed  Google Scholar 

  42. 42.

    Hao, N., Palmer, A. C., Ahlgren-Berg, A., Shearwin, K. E. & Dodd, I. B. The role of repressor kinetics in relief of transcriptional interference between convergent promoters. Nucleic Acids Res. 44, 6625–6638 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Camargo, S., Valladares, A., Flores, E. & Herrero, A. Transcription activation by NtcA in the absence of consensus NtcA-binding sites in an Anabaena heterocyst differentiation gene promoter. J. Bacteriol. 194, 2939–2948 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Tinberg, C. E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Taylor, N. D. et al. Engineering an allosteric transcription factor to respond to new ligands. Nat. Methods 13, 177–183 (2016).

    CAS  PubMed  Google Scholar 

  46. 46.

    Zhao, E. M. et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature 555, 683–687 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    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).

    CAS  PubMed  Google Scholar 

  48. 48.

    Whiteley, M., Diggle, S. P. & Greenberg, E. P. Corrigendum: progress in and promise of bacterial quorum sensing research. Nature 555, 126 (2018).

    CAS  PubMed  Google Scholar 

  49. 49.

    Zong, Y. et al. Insulated transcriptional elements enable precise design of genetic circuits. Nat. Commun. 8, 52 (2017).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Moon, T. S., Lou, C., Tamsir, A., Stanton, B. C. & Voigt, C. A. Genetic programs constructed from layered logic gates in single cells. Nature 491, 249–253 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Zhang, X. Z., Cui, Z. L., Hong, Q. & Li, S. P. High-level expression and secretion of methyl parathion hydrolase in Bacillus subtilis WB800. Appl. Environ. Microbiol. 71, 4101–4103 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Zheng, S. et al. One-pot two-strain system based on glucaric acid biosensor for rapid screening of myo-inositol oxygenase mutations and glucaric acid production in recombinant cells. Metab. Eng. 49, 212–219 (2018).

    CAS  PubMed  Google Scholar 

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (grant nos 31870069, 31930085 and 21676119), the National Key Research and Development Program of China (grant nos 2018YFA0900300 and 2018YFA0900504), the Fundamental Research Funds for the Central Universities (grant no. JUSRP51713B) and by BBSRC (grant no. BB/R01602X/1).

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X.X., J.C. and L.L. conceived this project and designed the experiments. X.X. and Y.Z. performed experiments. X.L. performed model studies. X.X. wrote the manuscript. Y.L., J.L., G.D., R.L.-A. and L.L. edited the manuscript.

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Correspondence to Jian Chen or Long Liu.

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Xu, X., Li, X., Liu, Y. et al. Pyruvate-responsive genetic circuits for dynamic control of central metabolism. Nat Chem Biol 16, 1261–1268 (2020). https://doi.org/10.1038/s41589-020-0637-3

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