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Genomic mining of prokaryotic repressors for orthogonal logic gates

Nature Chemical Biology volume 10, pages 99–105 (2014)Cite this article

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Abstract

Genetic circuits perform computational operations based on interactions between freely diffusing molecules within a cell. When transcription factors are combined to build a circuit, unintended interactions can disrupt its function. Here, we apply 'part mining' to build a library of 73 TetR-family repressors gleaned from prokaryotic genomes. The operators of a subset were determined using an in vitro method, and this information was used to build synthetic promoters. The promoters and repressors were screened for cross-reactions. Of these, 16 were identified that both strongly repress their cognate promoter (5- to 207-fold) and exhibit minimal interactions with other promoters. Each repressor-promoter pair was converted to a NOT gate and characterized. Used as a set of 16 NOT/NOR gates, there are >1054 circuits that could be built by changing the pattern of input and output promoters. This represents a large set of compatible gates that can be used to construct user-defined circuits.

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Figure 1: A large repressor library is compiled using genome mining.
Figure 2: The identification of operator sequences using an in vitro array assay.
Figure 3: Design and screening of orthogonal promoters.
Figure 4: Response function measurement.
Figure 5: Construction and characterization of integrated circuits.

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References

  1. Weiss, R. & Knight, T. Jr DNA Computing Vol. 2054 (eds. Condon, A. & Rozenberg, G.) 1–16 (2001).

  2. Khalil, A.S. & Collins, J.J. Synthetic biology: applications come of age. Nat. Rev. Genet. 11, 367–379 (2010).

    Article  CAS  Google Scholar 

  3. Weber, W. & Fussenegger, M. Molecular diversity—the toolbox for synthetic gene switches and networks. Curr. Opin. Chem. Biol. 15, 414–420 (2011).

    Article  CAS  Google Scholar 

  4. Endy, D. Foundations for engineering biology. Nature 438, 449–453 (2005).

    Article  CAS  Google Scholar 

  5. Purnick, P.E. & Weiss, R. The second wave of synthetic biology: from modules to systems. Nat. Rev. Mol. Cell Biol. 10, 410–422 (2009).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  7. Thompson, K.E., Bashor, C.J., Lim, W.A. & Keating, A.E. SYNZIP protein interaction toolbox: in vitro and in vivo specifications of heterospecific coiled-coil interaction domains. ACS Synth. Biol. 1, 118–129 (2012).

    Article  CAS  Google Scholar 

  8. 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 

  9. Tabor, J.J., Levskaya, A. & Voigt, C.A. Multichromatic control of gene expression in Escherichia coli. J. Mol. Biol. 405, 315–324 (2011).

    Article  CAS  Google Scholar 

  10. Fu, G. et al. Female-specific flightless phenotype for mosquito control. Proc. Natl. Acad. Sci. USA 107, 4550–4554 (2010).

    Article  CAS  Google Scholar 

  11. Tamsir, A., Tabor, J.J. & Voigt, C.A. Robust multicellular computing using genetically encoded NOR gates and chemical 'wires'. Nature 469, 212–215 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Hurt, J.A., Thibodeau, S.A., Hirsh, A.S., Pabo, C.O. & Joung, J.K. Highly specific zinc finger proteins obtained by directed domain shuffling and cell-based selection. Proc. Natl. Acad. Sci. USA 100, 12271–12276 (2003).

    Article  CAS  Google Scholar 

  15. Boch, J. et al. Breaking the code of DNA binding specificity of TAL-type III effectors. Science 326, 1509–1512 (2009).

    Article  CAS  Google Scholar 

  16. Khalil, A.S. et al. A synthetic biology framework for programming eukaryotic transcription functions. Cell 150, 647–658 (2012).

    Article  CAS  Google Scholar 

  17. Garg, A., Lohmueller, J.J., Silver, P.A. & Armel, T.Z. Engineering synthetic TAL effectors with orthogonal target sites. Nucleic Acids Res. 40, 7584–7595 (2012).

    Article  CAS  Google Scholar 

  18. Zhang, F. et al. Efficient construction of sequence-specific TAL effectors for modulating mammalian transcription. Nat. Biotechnol. 29, 149–153 (2011).

    Article  Google Scholar 

  19. Politz, M.C., Copeland, M.F. & Pfleger, B.F. Artificial repressors for controlling gene expression in bacteria. Chem. Commun. (Camb.) 49, 4325–4327 (2013).

    Article  CAS  Google Scholar 

  20. Durai, S., Bosley, A., Abulencia, A.B., Chandrasegaran, S. & Ostermeier, M. A bacterial one-hybrid selection system for interrogating zinc finger–DNA interactions. Comb. Chem. High Throughput Screen. 9, 301–311 (2006).

    Article  CAS  Google Scholar 

  21. Qi, L.S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    Article  CAS  Google Scholar 

  22. Ramos, J.L. et al. The TetR family of transcriptional repressors. Microbiol. Mol. Biol. Rev. 69, 326–356 (2005).

    Article  CAS  Google Scholar 

  23. 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).

    Article  CAS  Google Scholar 

  24. Guss, A.M., Rother, M., Zhang, J.K., Kulkarni, G. & Metcalf, W.W. New methods for tightly regulated gene expression and highly efficient chromosomal integration of cloned genes for Methanosarcina species. Archaea 2, 193–203 (2008).

    Article  CAS  Google Scholar 

  25. Dingermann, T. et al. RNA polymerase III catalysed transcription can be regulated in Saccharomyces cerevisiae by the bacterial tetracycline repressor-operator system. EMBO J. 11, 1487–1492 (1992).

    Article  CAS  Google Scholar 

  26. Lycett, G.J., Kafatos, F.C. & Loukeris, T.G. Conditional expression in the malaria mosquito Anopheles stephensi with Tet-On and Tet-Off systems. Genetics 167, 1781–1790 (2004).

    Article  CAS  Google Scholar 

  27. Gatz, C. & Quail, P.H. Tn10-encoded tet repressor can regulate an operator-containing plant promoter. Proc. Natl. Acad. Sci. USA 85, 1394–1397 (1988).

    Article  CAS  Google Scholar 

  28. Gossen, M. & Bujard, H. Tight control of gene expression in mammalian cells by tetracycline-responsive promoters. Proc. Natl. Acad. Sci. USA 89, 5547–5551 (1992).

    Article  CAS  Google Scholar 

  29. Saez, E., No, D., West, A. & Evans, R.M. Inducible gene expression in mammalian cells and transgenic mice. Curr. Opin. Biotechnol. 8, 608–616 (1997).

    Article  CAS  Google Scholar 

  30. Weber, W. et al. A synthetic time-delay circuit in mammalian cells and mice. Proc. Natl. Acad. Sci. USA 104, 2643–2648 (2007).

    Article  CAS  Google Scholar 

  31. Orth, P., Schnappinger, D., Hillen, W., Saenger, W. & Hinrichs, W. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nat. Struct. Biol. 7, 215–219 (2000).

    Article  CAS  Google Scholar 

  32. Helbl, V., Tiebel, B. & Hillen, W. Stepwise selection of TetR variants recognizing tet operator 6C with high affinity and specificity. J. Mol. Biol. 276, 319–324 (1998).

    Article  CAS  Google Scholar 

  33. Krueger, M., Scholz, O., Wisshak, S. & Hillen, W. Engineered Tet repressors with recognition specificity for the tetO-4C5G operator variant. Gene 404, 93–100 (2007).

    Article  CAS  Google Scholar 

  34. Itzkovitz, S., Tlusty, T. & Alon, U. Coding limits on the number of transcription factors. BMC Genomics 7, 239 (2006).

    Article  Google Scholar 

  35. Hunter, S. et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 40, D306–D312 (2012).

    Article  CAS  Google Scholar 

  36. Warren, C.L. et al. Defining the sequence-recognition profile of DNA-binding molecules. Proc. Natl. Acad. Sci. USA 103, 867–872 (2006).

    Article  CAS  Google Scholar 

  37. Stanton, B.C. et al. Cognate Site Identifier analysis reveals novel binding properties of the Sex Inducer homeodomain proteins of Cryptococcus neoformans. Mol. Microbiol. 72, 1334–1347 (2009).

    Article  CAS  Google Scholar 

  38. Bailey, T.L., Williams, N., Misleh, C. & Li, W.W. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res. 34, W369–W373 (2006).

    Article  CAS  Google Scholar 

  39. Kelly, J.R. et al. Measuring the activity of BioBrick promoters using an in vivo reference standard. J. Biol. Eng. 3, 4 (2009).

    Article  Google Scholar 

  40. Grkovic, S., Brown, M.H., Schumacher, M.A., Brennan, R.G. & Skurray, R.A. The staphylococcal QacR multidrug regulator binds a correctly spaced operator as a pair of dimers. J. Bacteriol. 183, 7102–7109 (2001).

    Article  CAS  Google Scholar 

  41. Kittleson, J.T., Wu, G.C. & Anderson, J.C. Successes and failures in modular genetic engineering. Curr. Opin. Chem. Biol. 16, 329–336 (2012).

    Article  CAS  Google Scholar 

  42. Rudell, R.L. Mutiple-valued Logic Minimization for PLA Synthesis (Electronics Research Laboratory, College of Engineering, University of California, 1986).

  43. Brown, S.V.Z. Fundamentals of Digital Logic with Verilog Design 2nd edn. (McGraw-Hill, 2008).

  44. Lou, C., Stanton, B., Chen, Y.J., Munsky, B. & Voigt, C.A. Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nat. Biotechnol. 30, 1137–1142 (2012).

    Article  CAS  Google Scholar 

  45. Daniel, R., Rubens, J.R., Sarpeshkar, R. & Lu, T.K. Synthetic analog computation in living cells. Nature 497, 619–623 (2013).

    Article  CAS  Google Scholar 

  46. Chen, Y.J. et al. Characterization of 582 natural and synthetic terminators and quantification of their design constraints. Nat. Methods 10, 659–664 (2013).

    Article  CAS  Google Scholar 

  47. Sleight, S.C. & Sauro, H.M. Visualization of evolutionary stability dynamics and competitive fitness of Escherichia coli engineered with randomized multigene circuits. ACS Synth. Biol. 2, 519–528 (2013).

    Article  CAS  Google Scholar 

  48. Lee, T.H. & Maheshri, N. A regulatory role for repeated decoy transcription factor binding sites in target gene expression. Mol. Syst. Biol. 8, 576 (2012).

    Article  Google Scholar 

  49. Buchler, N.E. & Cross, F.R. Protein sequestration generates a flexible ultrasensitive response in a genetic network. Mol. Syst. Biol. 5, 272 (2009).

    Article  Google Scholar 

  50. Bintu, L. et al. Transcriptional regulation by the numbers: applications. Curr. Opin. Genet. Dev. 15, 125–135 (2005).

    Article  CAS  Google Scholar 

  51. Cormack, B.P., Valdivia, R.H. & Falkow, S. FACS-optimized mutants of the green fluorescent protein (GFP). Gene 173, 33–38 (1996).

    Article  CAS  Google Scholar 

  52. Fath, S. et al. Multiparameter RNA and codon optimization: a standardized tool to assess and enhance autologous mammalian gene expression. PLoS ONE 6, e17596 (2011).

    Article  CAS  Google Scholar 

  53. 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 

  54. Urbanowski, M.L., Lostroh, C.P. & Greenberg, E.P. Reversible acyl-homoserine lactone binding to purified Vibrio fischeri LuxR protein. J. Bacteriol. 186, 631–637 (2004).

    Article  CAS  Google Scholar 

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Acknowledgements

We would like to thank C. Warren of Illumavista Biosciences for aid with array design, array experiments, data analysis and Perl scripts that were used for array data extraction. Research for this publication was conducted with government support under FA9550-11-C-0028 and was awarded by the Department of Defense, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate Fellowship, 32 CFR 168a to A.A.K.N. C.A.V. is supported by Life Technologies, Defense Advanced Research Projects Agency Chronicle of Lineage Indicative of Origins (DARPA CLIO) (N66001-12-C-4016), Office of Naval Research (N00014-13-1-0074), US National Institutes of Health (GM095765) and the US National Science Foundation Synthetic Biology Engineering Research Center (SynBERC, SA5284-11210).

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Authors and Affiliations

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

    Brynne C Stanton, Alec A K Nielsen & Christopher A Voigt

  2. Department of Biochemistry and Biophysics, University of California–San Francisco, San Francisco, California, USA

    Alvin Tamsir

  3. Synthetic Biology R&D Unit, Life Technologies, Carlsbad, California, USA

    Kevin Clancy & Todd Peterson

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  1. Brynne C Stanton
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Contributions

C.A.V., B.C.S. and A.A.K.N. designed and performed experiments, analyzed data and wrote the manuscript. A.T. designed experiments. K.C. aided in project management. T.P. aided in project management.

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Correspondence to Christopher A Voigt.

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Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Figures 1–16 and Supplementary Tables 1–7. (PDF 2150 kb)

Supplementary Data Set 1

Supplementary Repressor Library Sequence Table (XLSX 64 kb)

Supplementary Data Set 2

Supplementary Repressor Motif Array Data (XLSX 62 kb)

Supplementary Data Set 3

Supplementary Promoter Library Cytometry Data (XLSX 2719 kb)

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Stanton, B., Nielsen, A., Tamsir, A. et al. Genomic mining of prokaryotic repressors for orthogonal logic gates. Nat Chem Biol 10, 99–105 (2014). https://doi.org/10.1038/nchembio.1411

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