PAM adenine methylation and flanking sequence regulate SaCas9 activity in bacteria

Abstract

Cas9 nucleases are the effectors of the class 2 type II CRISPR system in bacteria and function to restrict invading DNA. They can also be used with single guide RNAs (sgRNAs) as antimicrobials and genome engineering tools in bacteria, yet applications are hindered by an incomplete understanding of Cas9-target interactions. Here, we generate large-scale SaCas9/sgRNA in vivo bacterial activity datasets and train a machine learning model (crisprHAL) to predict SaCas9 activity. The highest predictive performance was found when downstream sequence flanking the canonical NNGRRN PAM motif at positions [+1] and [+2] was included in model training, correlating with high in vivo activity on sites that included T-rich di-nucleotides in the [+1] and [+2] flanking positions. Strikingly, model predictions and experimentally determined activity in pooled sgRNA experiments in Escherichia coli and Citrobacter rodentium showed an [~]10-fold reduced SaCas9 activity at sites with 5'-NNGGAT[C]-3' PAM [+1] sequences. Cleavage assays using plasmid DNA isolated from E. coli inactivated for DNA adenine methyltransferase (DAM) and SaCas9/sgRNA combinations targeting sites with NNGGAT[C] PAM sequences confirmed that adenine methylation impacts SaCas9 cleavage. Moreover, ablation of a GATC DAM site in a PAM sequence enhanced SaCas9 in vitro activity, whereas creation of a DAM site reduced activity, providing a mechanistic link between adenine methylation and SaCas9 activity. Our results show that a general purpose machine learning architecture can provide biologically relevant insights into SaCas9-PAM interactions that can better inform activity predictions for bacterial applications. Avoidance of adenine methylated PAM sites by SaCas9 may be a mechanism of self versus non-self discrimination or reflect an evolutionary adaptation to counter methylation as an anti-restriction strategy by phage or plasmids.