Expanding the flexibility of base editing for high-throughput genetic screens in bacteria.

Genome-wide screens have become powerful tools for elucidating genotype-to-phenotype relationships in bacteria. Of the varying techniques to achieve knockout and knockdown, CRISPR base editors are emerging as promising options. However, the limited number of available, efficient target sites hampers their use for high-throughput screening. Here, we make multiple advances to enable flexible base editing as part of high-throughput genetic screening in bacteria. We first co-opt the Streptococcus canis Cas9 that exhibits more flexible protospacer-adjacent motif recognition than the traditional Streptococcus pyogenes Cas9. We then expand beyond introducing premature stop codons by mutating start codons. Next, we derive guide design rules by applying machine learning to an essentiality screen conducted in Escherichia coli. Finally, we rescue poorly edited sites by combining base editing with Cas9-induced cleavage of unedited cells, thereby enriching for intended edits. The efficiency of this dual system was validated through a conditional essentiality screen based on growth in minimal media. Overall, expanding the scope of genome-wide knockout screens with base editors could further facilitate the investigation of new gene functions and interactions in bacteria.

Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration.

CRISPR interference (CRISPRi) is the leading technique to silence gene expression in bacteria; however, design rules remain poorly defined. We develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in genome-wide essentiality screens, with the surprising discovery that gene-specific features substantially impact prediction. We develop a mixed-effect random forest regression model that provides better estimates of guide efficiency. We further apply methods from explainable AI to extract interpretable design rules from the model. This study provides a blueprint for predictive models for CRISPR technologies where only indirect measurements of guide activity are available.

Shortened CRISPR-Cas9 arrays enable multiplexed gene targeting in bacteria from a smaller DNA footprint.

CRISPR technologies comprising a Cas nuclease and a guide RNA (gRNA) can utilize multiple gRNAs to enact multi-site editing or regulation in the same cell. Nature devised a highly compact means of encoding gRNAs in the form of CRISPR arrays composed of conserved repeats separated by targeting spacers. However, the capacity to acquire new spacers keeps the arrays longer than necessary for CRISPR technologies. Here, we show that CRISPR arrays utilized by the Cas9 nuclease can be shortened without compromising and sometimes even enhancing targeting activity. Using multiplexed gene repression in E. coli, we found that each region could be systematically shortened to varying degrees before severely compromising targeting activity. Surprisingly, shortening some spacers yielded enhanced targeting activity, which was linked to folding of the transcribed array prior to processing. Overall, shortened CRISPR-Cas9 arrays can facilitate multiplexed editing and gene regulation from a smaller DNA footprint across many bacterial applications of CRISPR technologies.