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Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors

Abstract

Cellular processes are carried out by many genes, and their study and optimization requires multiple levers by which they can be independently controlled. The most common method is via a genetically encoded sensor that responds to a small molecule. However, these sensors are often suboptimal, exhibiting high background expression and low dynamic range. Further, using multiple sensors in one cell is limited by cross-talk and the taxing of cellular resources. Here, we have developed a directed evolution strategy to simultaneously select for lower background, high dynamic range, increased sensitivity, and low cross-talk. This is applied to generate a set of 12 high-performance sensors that exhibit >100-fold induction with low background and cross-reactivity. These are combined to build a single “sensor array” in the genomes of E. coli MG1655 (wild-type), DH10B (cloning), and BL21 (protein expression). These “Marionette” strains allow for the independent control of gene expression using 12 small-molecule inducers.

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Fig. 1: A dual selection for sensor optimization.
Fig. 2: Improved sensor performance.
Fig. 3: Marionette-Wild performance.
Fig. 4: Marionette-Wild expression level optimization metabolic pathway balancing using Marionette-Wild.

Data availability

Additional data supporting this study are available from the corresponding author upon reasonable request. The sequences of the following plasmids and strains are provided in GenBank: pAJM.711 (PPhlF-YFP) MH101715; pAJM.712 (PCymRC-YFP) MH101716; pAJM.713 (PLuxB-YFP) MH101717; pAJM.714 (PVanCC-YFP) MH101718; pAJM.715 (PTac-YFP) MH101719; pAJM.717 (PTet*-YFP) MH101720; pAJM.716 (PBAD-YFP) MH101721; pAJM.718 (PBetI-YFP) MH101722; pAJM.719 (PTtg-YFP) MH101723; pAJM.1459 (P3B5C-YFP) MH101724; pAJM.721 (PSalTTC-YFP) MH101725; pAJM.944 (PCin-YFP) MH101726; pAJM.847 (PhlFAM + PPhlF-YFP) MH101727; pAJM.657 (CymRAM + PCymRC-YFP) MH101728; pAJM.474 (LuxR + PLuxB-YFP) MH101729; pAJM.773 (VanRAM + PVanCC-YFP) MH101730; pAJM.336 (LacIAM + PTac-YFP) MH101731; pAJM.011 (TetR + PTet*-YFP) MH101732; pAJM.677 (AraCAM + AraE + PBAD-YFP) MH101733; pAJM.683 (BetIAM + PBetI-YFP) MH101734; pAJM.661 (TtgRAM + PTtg-YFP) MH101735; pAJM.690 (PcaUAM + P3B5B-YFP) MH101736; pAJM.771 (NahRAM + PSalTTC-YFP) MH101737; pAJM.1642 (CinRAM + PCin-YFP) MH101738; pAJM.884 (AcuRAM + PAcu-YFP) MH101739; pAJM.969 (MphRAM + EryR + PMph-YFP) MH101740. The following plasmids and strains can be acquired from Addgene: pAJM.711 (PPhlF-YFP) 108512; pAJM.712 (PCymRC-YFP) 108513; pAJM.713 (PLuxB-YFP) 108514; pAJM.714 (PVanCC-YFP) 108515; pAJM.715 (PTac-YFP) 108516; pAJM.717 (PTet*-YFP) 108517; pAJM.716 (PBAD-YFP) 108518; pAJM.718 (PBetI-YFP) 108519; pAJM.719 (PTtg-YFP) 108520; pAJM.1459 (P3B5C-YFP) 108521; pAJM.721 (PSalTTC-YFP) 108522; pAJM.944 (PCin-YFP) 108523; pAJM.847 (PhlFAM + PPhlF-YFP) 108524; pAJM.657 (CymRAM + PCymRC-YFP) 108525; pAJM.474 (LuxR + PLuxB-YFP) 108526; pAJM.773 (VanRAM + PVanCC-YFP) 108527; pAJM.336 (LacIAM + PTac-YFP) 108528; pAJM.011 (TetR + PTet*-YFP) 108529; pAJM.677 (AraCAM + AraE + PBAD-YFP) 108530; pAJM.683 (BetIAM + PBetI-YFP) 108531; pAJM.661 (TtgRAM + PTtg-YFP) 108532; pAJM.690 (PcaUAM + P3B5B-YFP) 108533; pAJM.771 (NahRAM + PSalTTC-YFP) 108534; pAJM.1642 (CinRAM + PCin-YFP) 108535; pAJM.884 (AcuRAM + PAcu-YFP) 108536; pAJM.969 (MphRAM + EryR + PMph-YFP) 108537; sAJM.1504 (Marionette-Clo) 108251; sAJM.1505 (Marionette-Pro) 108253; sAJM.1506 (Marionette-Wild) 108254.

References

  1. 1.

    Zuo, J. & Chua, N. H. Chemical-inducible systems for regulated expression of plant genes. Curr. Opin. Biotechnol. 11, 146–151 (2000).

    CAS  Article  Google Scholar 

  2. 2.

    Keyes, W. M. & Mills, A. A. Inducible systems see the light. Trends Biotechnol. 21, 53–55 (2003).

    CAS  Article  Google Scholar 

  3. 3.

    Mijakovic, I., Petranovic, D. & Jensen, P. R. Tunable promoters in systems biology. Curr. Opin. Biotechnol. 16, 329–335 (2005).

    CAS  Article  Google Scholar 

  4. 4.

    de Boer, H. A., Comstock, L. J. & Vasser, M. The tac promoter: a functional hybrid derived from the trp and lac promoters. Proc. Natl. Acad. Sci. USA 80, 21–25 (1983).

    CAS  Article  Google Scholar 

  5. 5.

    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  Article  Google Scholar 

  6. 6.

    Skerra, A. Use of the tetracycline promoter for the tightly regulated production of a murine antibody fragment in Escherichia coli. Gene 151, 131–135 (1994).

    CAS  Article  Google Scholar 

  7. 7.

    Lutz, R. & Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Res. 25, 1203–1210 (1997).

    CAS  Article  Google Scholar 

  8. 8.

    Urban, J. H. & Vogel, J. Translational control and target recognition by Escherichia coli small RNAs in vivo. Nucleic Acids Res. 35, 1018–1037 (2007).

    CAS  Article  Google Scholar 

  9. 9.

    Cookson, N. A. et al. Queueing up for enzymatic processing: correlated signaling through coupled degradation. Mol. Syst. Biol. 7, 561 (2011).

    Article  Google Scholar 

  10. 10.

    Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000).

    CAS  Article  Google Scholar 

  11. 11.

    Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    CAS  Article  Google Scholar 

  12. 12.

    Golding, I. & Cox, E. C. RNA dynamics in live Escherichia coli cells. Proc. Natl. Acad. Sci. USA 101, 11310–11315 (2004).

    CAS  Article  Google Scholar 

  13. 13.

    Kalir, S. & Alon, U. Using a quantitative blueprint to reprogram the dynamics of the flagella gene network. Cell 117, 713–720 (2004).

    CAS  Article  Google Scholar 

  14. 14.

    Golding, I., Paulsson, J., Zawilski, S. M. & Cox, E. C. Real-time kinetics of gene activity in individual bacteria. Cell 123, 1025–1036 (2005).

    CAS  Article  Google Scholar 

  15. 15.

    Voigt, C. A. Genetic parts to program bacteria. Curr. Opin. Biotechnol. 17, 548–557 (2006).

    CAS  Article  Google Scholar 

  16. 16.

    Caliando, B. J. & Voigt, C. A. Targeted DNA degradation using a CRISPR device stably carried in the host genome. Nat. Commun. 6, 6989 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    Lee, S. K. et al. Directed evolution of AraC for improved compatibility of arabinose- and lactose-inducible promoters. Appl. Environ. Microbiol. 73, 5711–5715 (2007).

    CAS  Article  Google Scholar 

  18. 18.

    Scott, S. R. & Hasty, J. Quorum sensing communication modules for microbial consortia. ACS Synth. Biol. 5, 969–977 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Tashiro, Y. et al. Directed evolution of the autoinducer selectivity of Vibrio fischeri LuxR. J. Gen. Appl. Microbiol. 62, 240–247 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    Halleran, A. D. & Murray, R. M. Cell-free and in vivo characterization of Lux, Las, and Rpa quorum activation systems in E. coli. ACS Synth. Biol. 7, 752–755 (2018).

    CAS  Article  Google Scholar 

  21. 21.

    Gyorgy, A. et al. Isocost lines describe the cellular economy of genetic circuits. Biophys. J. 109, 639–646 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Callura, J. M., Cantor, C. R. & Collins, J. J. Genetic switchboard for synthetic biology applications. Proc. Natl. Acad. Sci. USA 109, 5850–5855 (2012).

    CAS  Article  Google Scholar 

  23. 23.

    Bintu, L. et al. Transcriptional regulation by the numbers: models. Curr. Opin. Genet. Dev. 15, 116–124 (2005).

    CAS  Article  Google Scholar 

  24. 24.

    Salis, H., Tamsir, A. & Voigt, C. Engineering bacterial signals and sensors. Contrib. Microbiol. 16, 194–225 (2009).

    CAS  Article  Google Scholar 

  25. 25.

    Daber, R., Sochor, M. A. & Lewis, M. Thermodynamic analysis of mutant lac repressors. J. Mol. Biol. 409, 76–87 (2011).

    CAS  Article  Google Scholar 

  26. 26.

    Gatti-Lafranconi, P., Dijkman, W. P., Devenish, S. R. & Hollfelder, F. A single mutation in the core domain of the lac repressor reduces leakiness. Microb. Cell. Fact. 12, 67 (2013).

    CAS  Article  Google Scholar 

  27. 27.

    Ike, K. et al. Evolutionary design of choline-inducible and -repressible T7-based induction systems. ACS Synth. Biol. 4, 1352–1360 (2015).

    CAS  Article  Google Scholar 

  28. 28.

    Ellefson, J. W., Ledbetter, M. P. & Ellington, A. D. Directed evolution of a synthetic phylogeny of programmable Trp repressors. Nat. Chem. Biol. 14, 361–367 (2018).

    CAS  Article  Google Scholar 

  29. 29.

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

    CAS  Article  Google Scholar 

  30. 30.

    Tang, S. Y., Fazelinia, H. & Cirino, P. C. AraC regulatory protein mutants with altered effector specificity. J. Am. Chem. Soc. 130, 5267–5271 (2008).

    CAS  Article  Google Scholar 

  31. 31.

    Tashiro, Y., Fukutomi, H., Terakubo, K., Saito, K. & Umeno, D. A nucleoside kinase as a dual selector for genetic switches and circuits. Nucleic Acids Res. 39, e12 (2011).

    Article  Google Scholar 

  32. 32.

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

    CAS  Article  Google Scholar 

  33. 33.

    Maranhao, A. C. & Ellington, A. D. Evolving orthogonal suppressor tRNAs to incorporate modified amino acids. ACS Synth. Biol. 6, 108–119 (2017).

    CAS  Article  Google Scholar 

  34. 34.

    Thyer, R., Filipovska, A. & Rackham, O. Engineered rRNA enhances the efficiency of selenocysteine incorporation during translation. J. Am. Chem. Soc. 135, 2–5 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Ellefson, J. W. et al. Directed evolution of genetic parts and circuits by compartmentalized partnered replication. Nat. Biotechnol. 32, 97–101 (2014).

    CAS  Article  Google Scholar 

  36. 36.

    Diaz Ricci, J. C. & Hernández, M. E. Plasmid effects on Escherichia coli metabolism. Crit. Rev. Biotechnol. 20, 79–108 (2000).

    CAS  Article  Google Scholar 

  37. 37.

    Kunjapur, A. M. & Prather, K. L. J. Development of a vanillate biosensor for the vanillin biosynthesis pathway in E. coli. https://doi.org/10.1101/375287 (2018).

  38. 38.

    Khlebnikov, A., Datsenko, K. A., Skaug, T., Wanner, B. L. & Keasling, J. D. Homogeneous expression of the P(BAD) promoter in Escherichia coli by constitutive expression of the low-affinity high-capacity AraE transporter. Microbiology 147, 3241–3247 (2001).

    CAS  Article  Google Scholar 

  39. 39.

    Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1102 (2010).

    CAS  Article  Google Scholar 

  40. 40.

    Lee, J. W. et al. Creating single-copy genetic circuits. Mol. Cell 63, 329–336 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Barrick, J. E. & Lenski, R. E. Genome dynamics during experimental evolution. Nat. Rev. Genet. 14, 827–839 (2013).

    CAS  Article  Google Scholar 

  42. 42.

    Lee, M. E., Aswani, A., Han, A. S., Tomlin, C. J. & Dueber, J. E. Expression-level optimization of a multi-enzyme pathway in the absence of a high-throughput assay. Nucleic Acids Res. 41, 10668–10678 (2013).

    CAS  Article  Google Scholar 

  43. 43.

    Zelcbuch, L. et al. Spanning high-dimensional expression space using ribosome-binding site combinatorics. Nucleic Acids Res. 41, e98 (2013).

    CAS  Article  Google Scholar 

  44. 44.

    Smanski, M. J. et al. Functional optimization of gene clusters by combinatorial design and assembly. Nat. Biotechnol. 32, 1241–1249 (2014).

    CAS  Article  Google Scholar 

  45. 45.

    Ghodasara, A. & Voigt, C. A. Balancing gene expression without library construction via a reusable sRNA pool. Nucleic Acids Res. 45, 8116–8127 (2017).

    CAS  Article  Google Scholar 

  46. 46.

    Yoon, S. H. et al. Engineering the lycopene synthetic pathway in E. coli by comparison of the carotenoid genes of Pantoea agglomerans and Pantoea ananatis. Appl. Microbiol. Biotechnol. 74, 131–139 (2007).

    CAS  Article  Google Scholar 

  47. 47.

    Alon, U., Surette, M. G., Barkai, N. & Leibler, S. Robustness in bacterial chemotaxis. Nature 397, 168–171 (1999).

    CAS  Article  Google Scholar 

  48. 48.

    Zhang, Y., Lara-Tejero, M., Bewersdorf, J. & Galán, J. E. Visualization and characterization of individual type III protein secretion machines in live bacteria. Proc. Natl. Acad. Sci. USA 114, 6098–6103 (2017).

    CAS  Article  Google Scholar 

  49. 49.

    Larson, M. H. et al. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat. Protoc. 8, 2180–2196 (2013).

    CAS  Article  Google Scholar 

  50. 50.

    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  Article  Google Scholar 

  51. 51.

    Blattner, F. R. et al. The complete genome sequence of Escherichia coli K-12. Science 277, 1453–1462 (1997).

    CAS  Article  Google Scholar 

  52. 52.

    Kittleson, J. T., Cheung, S. & Anderson, J. C. Rapid optimization of gene dosage in E. coli using DIAL strains. J. Biol. Eng. 5, 10 (2011).

    CAS  Article  Google Scholar 

  53. 53.

    Green, R. & Rogers, E. J. Transformation of chemically competent E. coli. Methods Enzymol. 529, 329–336 (2013).

    CAS  Article  Google Scholar 

  54. 54.

    Meyer, A. J., Ellefson, J. W. & Ellington, A. D. Library generation by gene shuffling. Curr. Protoc. Mol. Biol. 105, 15.12.1–15.12.7 (2014).

    Article  Google Scholar 

  55. 55.

    Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. USA 97, 6640–6645 (2000).

    CAS  Article  Google Scholar 

  56. 56.

    Thomason, L. C., Costantino, N. & Court, D. L. E. coli genome manipulation by P1 transduction. Curr. Protoc. Mol. Biol. 79, 1.17.1–1.17.8 (2007).

  57. 57.

    Li, G. W., Burkhardt, D., Gross, C. & Weissman, J. S. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635 (2014).

    CAS  Article  Google Scholar 

  58. 58.

    Volkmer, B. & Heinemann, M. Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling. PLoS One 6, e23126 (2011).

    CAS  Article  Google Scholar 

  59. 59.

    Reetz, M. T. & Carballeira, J. D. Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes. Nat. Protoc. 2, 891–903 (2007).

    CAS  Article  Google Scholar 

  60. 60.

    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).

    CAS  Article  Google Scholar 

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Acknowledgements

This work was supported by the US Office of Naval Research Multidisciplinary University Research Initiative grant #N00014-16-1-2388 (A.J.M., T.H.S.-S., E.G., J.Z., and C.A.V.). This work was supported in part by the Koch Institute Support (core) Grant P30-CA14051 from the National Cancer Institute. We would like to thank A.M. Kunjapur and K.L.J. Prather (Department of Chemical Engineering, Massachusetts Institute of Technology) for providing DNA templates for the amplification of PVan, P3B5, vanR, and pcaU and performing the initial characterization of VanR in E. coli. We would also like to thank S. Liu at the MIT-Broad Foundry for assisting in the RNA-seq and ribosome profiling sequencing run.

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A.J.M. and C.A.V. conceived the study and designed the experiments; E.G. performed the mass spectrometry experiments; J.Z. performed the RNA sequencing and ribosome profiling experiments; A.J.M. performed all other experiments. A.J.M., T.H.S.-S., E.G., and J.Z., analyzed the data; A.J.M. and C.A.V. wrote the manuscript with input from all the authors.

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Correspondence to Christopher A. Voigt.

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Supplementary Tables 1–12, Supplementary Figures 1–21, Supplementary Notes 1–4

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Meyer, A.J., Segall-Shapiro, T.H., Glassey, E. et al. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat Chem Biol 15, 196–204 (2019). https://doi.org/10.1038/s41589-018-0168-3

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