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

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Abstract

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.

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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 https://salislab.net/software. Python source code and a Dockerfile are available at https://github.com/hsalis/SalisLabCode.

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Acknowledgements

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.

Correspondence to Howard M. Salis.

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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).

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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.

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