Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays


Engineering cellular phenotypes often requires the regulation of many genes. When using CRISPR interference, coexpressing many single-guide RNAs (sgRNAs) triggers genetic instability and phenotype loss, due to the presence of repetitive DNA sequences. We stably coexpressed 22 sgRNAs within nonrepetitive extra-long sgRNA arrays (ELSAs) to simultaneously repress up to 13 genes by up to 3,500-fold. We applied biophysical modeling, biochemical characterization and machine learning to develop toolboxes of nonrepetitive genetic parts, including 28 sgRNA handles that bind Cas9. We designed ELSAs by combining nonrepetitive genetic parts according to algorithmic rules quantifying DNA synthesis complexity, sgRNA expression, sgRNA targeting and genetic stability. Using ELSAs, we created three highly selective phenotypes in Escherichia coli, including redirecting metabolism to increase succinic acid production by 150-fold, knocking down amino acid biosynthesis to create a multi-auxotrophic strain and repressing stress responses to reduce persister cell formation by 21-fold. ELSAs enable simultaneous and stable regulation of many genes for metabolic engineering and synthetic biology applications.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1
Fig. 2: Design and characterization of nonrepetitive sgRNA handles.
Fig. 3: Design, expression and application of ELSAs.
Fig. 4

Data availability

High-throughput sequencing data have been deposited in the NCBI Sequence Read Archive database (PRJNA504834). Sanger sequencing analysis is available as a Supplementary Note.

Code availability

A web interface to the ELSA Calculator is available at Python source code and a Dockerfile are available at


  1. 1.

    Dominguez, A. A., Lim, W. A. & Qi, L. S. Beyond editing: repurposing CRISPR–Cas9 for precision genome regulation and interrogation. Nat. Rev. Mol. Cell Biol. 17, 5–15 (2016).

    CAS  Google Scholar 

  2. 2.

    Barrangou, R. & Horvath, P. A decade of discovery: CRISPR functions and applications. Nat. Microbiol. 2, 17092 (2017).

    CAS  Google Scholar 

  3. 3.

    Halperin, S. O. et al. CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window. Nature 560, 248–252 (2018).

    CAS  Google Scholar 

  4. 4.

    Peters, J. M. et al. Enabling genetic analysis of diverse bacteria with Mobile-CRISPRi. Nat. Microbiol. 4, 244–250 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Hess, G. T., Tycko, J., Yao, D. & Bassik, M. C. Methods and applications of CRISPR-mediated base editing in eukaryotic genomes. Mol. Cell 68, 26–43 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Klann, T. S., Black, J. B. & Gersbach, C. A. CRISPR-based methods for high-throughput annotation of regulatory DNA. Curr. Opin. Biotechnol. 52, 32–41 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e1821 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Swiech, L. et al. In vivo interrogation of gene function in the mammalian brain using CRISPR–Cas9. Nat. Biotechnol. 33, 102–106 (2015).

    CAS  Google Scholar 

  10. 10.

    Yao, L., Cengic, I., Anfelt, J. & Hudson, E. P. Multiple gene repression in cyanobacteria using CRISPRi. ACS Synth. Biol. 5, 207–212 (2015).

    Google Scholar 

  11. 11.

    Zhao, Y. et al. CRISPR/dCas9‐mediated multiplex gene repression in Streptomyces. Biotechnol. J. 13, 1800121 (2018).

    Google Scholar 

  12. 12.

    Kim, S. K., Seong, W., Han, G. H., Lee, D.-H. & Lee, S.-G. CRISPR interference-guided multiplex repression of endogenous competing pathway genes for redirecting metabolic flux in Escherichia coli. Microb. Cell Fact. 16, 188 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Ordon, J. et al. Generation of chromosomal deletions in dicotyledonous plants employing a user‐friendly genome editing toolkit. Plant J. 89, 155–168 (2017).

    CAS  Google Scholar 

  14. 14.

    Hughes, R. A. & Ellington, A. D. Synthetic DNA synthesis and assembly: putting the synthetic in synthetic biology. Cold Spring Harb. Perspect. Biol. 9, a023812 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Stapley, J., Feulner, P. G., Johnston, S. E., Santure, A. W. & Smadja, C. M. Variation in recombination frequency and distribution across eukaryotes: patterns and processes. Phil. Trans. R. Soc. B 372, 20160455 (2017).

    Google Scholar 

  16. 16.

    Vos, M. & Didelot, X. A comparison of homologous recombination rates in bacteria and archaea. ISME J. 3, 199 (2009).

    CAS  Google Scholar 

  17. 17.

    Casini, A. et al. A pressure test to make 10 molecules in 90 days: external evaluation of methods to engineer biology. J. Am. Chem. Soc. 140, 4302–4316 (2018).

    CAS  Google Scholar 

  18. 18.

    Najm, F. J. et al. Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36, 179–189 (2018).

    CAS  Google Scholar 

  19. 19.

    Jack, B. R. et al. Predicting the genetic stability of engineered DNA sequences with the EFM calculator. ACS Synth. Biol. 4, 939–943 (2015).

    CAS  Google Scholar 

  20. 20.

    Brophy, J. A. & Voigt, C. A. Principles of genetic circuit design. Nat. Methods 11, 508–520 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Lovett, S. T. Encoded errors: mutations and rearrangements mediated by misalignment at repetitive DNA sequences. Mol. Microbiol. 52, 1243–1253 (2004).

    CAS  Google Scholar 

  22. 22.

    Shen, P. & Huang, H. V. Homologous recombination in Escherichia coli: dependence on substrate length and homology. Genetics 112, 441–457 (1986).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Tang, N. C. & Chilkoti, A. Combinatorial codon scrambling enables scalable gene synthesis and amplification of repetitive proteins. Nat. Mater. 15, 419–424 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

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

    CAS  Google Scholar 

  25. 25.

    Casini, A. et al. R2oDNA designer: computational design of biologically neutral synthetic DNA sequences. ACS Synth. Biol. 3, 525–528 (2014).

    CAS  Google Scholar 

  26. 26.

    Lorenz, R. et al. ViennaRNA package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Jiang, F. et al. Structures of a CRISPR–Cas9 R-loop complex primed for DNA cleavage. Science 351, 867–871 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Jinek, M. et al. Structures of Cas9 endonucleases reveal RNA-mediated conformational activation. Science 343, 1247997 (2014).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Briner, A. E. et al. Guide RNA functional modules direct Cas9 activity and orthogonality. Mol. Cell 56, 333–339 (2014).

    CAS  Google Scholar 

  30. 30.

    Nishimasu, H. et al. Crystal structure of Cas9 in complex with guide RNA and target DNA. Cell 156, 935–949 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Nielsen, A. A. & Voigt, C. A. Multi‐input CRISPR/Cas genetic circuits that interface host regulatory networks. Mol. Syst. Biol. 10, 763 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Dagdas, Y. S., Chen, J. S., Sternberg, S. H., Doudna, J. A. & Yildiz, A. A conformational checkpoint between DNA binding and cleavage by CRISPR–Cas9. Sci. Adv. 3, eaao0027 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Anders, C., Niewoehner, O., Duerst, A. & Jinek, M. Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease. Nature 513, 569–573 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Farasat, I. & Salis, H. M. A biophysical model of CRISPR/Cas9 activity for rational design of genome editing and gene regulation. PLoS Comput. Biol. 12, e1004724 (2016).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    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 

  36. 36.

    Nyerges, Á. et al. A highly precise and portable genome engineering method allows comparison of mutational effects across bacterial species. Proc. Natl Acad. Sci. USA 113, 2502–2507 (2016).

    CAS  Google Scholar 

  37. 37.

    Lin, H., Bennett, G. N. & San, K. Y. Genetic reconstruction of the aerobic central metabolism in Escherichia coli for the absolute aerobic production of succinate. Biotechnol. Bioeng. 89, 148–156 (2005).

    CAS  Google Scholar 

  38. 38.

    Li, X.-t et al. tCRISPRi: tunable and reversible, one-step control of gene expression. Sci. Rep. 6, 39076 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Chen, P.-Y., Qian, Y. & Del Vecchio, D. A model for resource competition in CRISPR-mediated gene repression. Preprint at bioRxiv (2018).

  40. 40.

    Farasat, I. et al. Efficient search, mapping, and optimization of multi‐protein genetic systems in diverse bacteria. Mol. Syst. Biol. 10, 731 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    de Kok, S. et al. Rapid and reliable DNA assembly via ligase cycling reaction. ACS Synth. Biol. 3, 97–106 (2014).

    CAS  Google Scholar 

  43. 43.

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

    CAS  Google Scholar 

  44. 44.

    Ledoit, O. & Wolf, M. A well-conditioned estimator for large-dimensional covariance matrices. J. Multivar. Anal. 88, 365–411 (2004).

    Google Scholar 

  45. 45.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Der, B. S. et al. DNAplotlib: programmable visualization of genetic designs and associated data. ACS Synth. Biol. 6, 1115–1119 (2016).

    Google Scholar 

  48. 48.

    Keseler, I. M. et al. The EcoCyc database: reflecting new knowledge about Escherichia coli K-12. Nucleic Acids Res. 45, D543–D550 (2016).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Gama-Castro, S. et al. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res. 44, D133–D143 (2015).

    PubMed  PubMed Central  Google Scholar 

Download references


We thank the Synthetic Biology Application Support Team at Integrated DNA Technologies (IDT) for providing insights into the gene synthesis process, the Penn State Proteomics and Mass Spectrometry Core Facility for the LC-MS analysis, the CSL Behring Fermentation Facility for use of their HPLC/RI and C. Praul and the Penn State Genomics Core Facility for technical support. This project was supported by funds from the Air Force Office of Scientific Research (grant no. FA9550-14-1-0089), an NSF Career Award to H.M.S. (grant no. CBET-1253641), the Defense Advanced Research Projects Agency (grant no. FA8750-17-C-0254) and the Department of Energy (grant no. DE-SC0019090).

Author information




H.M.S., A.C.R. and S.M.H. conceived the study, designed the experiments and wrote the manuscript. A.C.R., S.M.H., P.R.C., A.H., D.P.C. and G.E.V. carried out experiments. A.C.R., S.M.H. and A.H. developed algorithms and performed data analysis.

Corresponding author

Correspondence to Howard M. Salis.

Ethics declarations

Competing interests

H.M.S. is the founder of De Novo DNA, which received funds from the Defense Advanced Research Projects Agency to commercialize this technology (grant no. D17PC00133).

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–14, Tables 1–4 and Note.

Reporting Summary

Supplementary Data 1

Genetic part sequences, measurements and calculations.

Supplementary Data 2

ELSA compositions, sequences, measurements and calculations.

Supplementary Data 3

ELSA-Stress: RNA-seq results and analysis.

Supplementary Data 4

ELSA-MultiAux: RNA-seq results and analysis.

Supplementary Data 5

MIQE: minimum information for RT–qPCR experiments.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Reis, A.C., Halper, S.M., Vezeau, G.E. et al. Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays. Nat Biotechnol 37, 1294–1301 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing