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

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

1 Biologie de Synthèse - Synthetic biology
2 Université Paris Diderot, Sorbonne Paris Cité, Paris, France
3 Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB

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.

Domaines: Bio-informatique [q-bio.QM]; Biochimie, Biologie Moléculaire

Alicia Calvo-Villamañán, Jérôme Wong Ng, Rémi Planel, Hervé Ménager, Arthur Chen, et al.. On-target activity predictions enable improved CRISPR-dCas9 screens in bacteria. Nucleic Acids Research, 2020, gkaa294, ⟨10.1093/nar/gkaa294⟩. ⟨pasteur-02773823⟩