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. 2020 Jun 19;48(11):e64.
doi: 10.1093/nar/gkaa294.

On-target activity predictions enable improved CRISPR-dCas9 screens in bacteria

Alicia Calvo-Villamañán  1   2 , Jérome Wong Ng  1 , Rémi Planel  3 , Hervé Ménager  3 , Arthur Chen  1 , Lun Cui  1 , David Bikard  1
Affiliations

On-target activity predictions enable improved CRISPR-dCas9 screens in bacteria

Alicia Calvo-Villamañán et al. Nucleic Acids Res. .

Abstract

The ability to block gene expression in bacteria with the catalytically inactive mutant of Cas9, known as dCas9, is quickly becoming a standard methodology to probe gene function, perform high-throughput screens, and engineer cells for desired purposes. Yet, we still lack a good understanding of the design rules that determine on-target activity for dCas9. Taking advantage of high-throughput screening data, we fit a model to predict the ability of dCas9 to block the RNA polymerase based on the target sequence, and validate its performance on independently generated datasets. We further design a novel genome wide guide RNA library for E. coli MG1655, EcoWG1, using our model to choose guides with high activity while avoiding guides which might be toxic or have off-target effects. A screen performed using the EcoWG1 library during growth in rich medium improved upon previously published screens, demonstrating that very good performances can be attained using only a small number of well designed guides. Being able to design effective, smaller libraries will help make CRISPRi screens even easier to perform and more cost-effective. Our model and materials are available to the community through crispr.pasteur.fr and Addgene.

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Figures

Figure 1.
Figure 1.
A linear model trained on screening data predicts guide activity. (A) High variability in the effect of guides (log2FC) targeting the essential gene secA. The log2FC of guides is plotted along the position on the chromosome of E. coli MG1655 (NC_000913.3). (B) A linear (L1) model was trained to predict the activity of guides based on the target sequence. The sequence logo reflects the coefficient of each base in the model, drawn using logomaker (29). Positive values indicate a positive effect of the base on dCas9 activity. Note that the GG of the PAM are not fitted by the model and are displayed with an arbitrary size for ease of reading. Positions 15–20 refer to the last six bases of the target sequence. Positions +1 to +16 refer to positions after the PAM. (C) The activity of 32 guides targeting lacZ was measured in a Miller assay. The log10 of the repression fold is plotted versus the predicted guide activity. (D, E) The activity of 33 guides targeting sfGFP was measured through FACS-seq by Hawkins et al. (16). The measured guide activity is plotted against the activity predicted by the model. The R2 and Pearson R values are indicated on the plots.
Figure 2.
Figure 2.
Performance of the EcoWG1 library. (A) Experimental setup for the screen performed with the genome-wide EcoWG1 library in strain LC-E75 (MG1655 with dCas9 controlled by a Ptet promoter integrated at the 186 attB site). (B) Distribution of the depletion scores of non-essential and essential genes at the different time points of the experiment. The gene depletion score is computed as the median log2FC of the guides targeting the gene. (C) AUC of gene essentiality prediction using increasing numbers of randomly picked guides per gene in each dataset. Three random draws are shown for each dataset.
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