Mobile element warfare via CRISPR and anti-CRISPR in Pseudomonas aeruginosa.

Bacteria deploy multiple defenses to prevent mobile genetic element (MGEs) invasion. CRISPR-Cas immune systems use RNA-guided nucleases to target MGEs, which counter with anti-CRISPR (Acr) proteins. Our understanding of the biology and co-evolutionary dynamics of the common Type I-C CRISPR-Cas subtype has lagged because it lacks an in vivo phage-host model system. Here, we show the anti-phage function of a Pseudomonas aeruginosa Type I-C CRISPR-Cas system encoded on a conjugative pKLC102 island, and its Acr-mediated inhibition by distinct MGEs. Seven genes with anti-Type I-C function (acrIC genes) were identified, many with highly acidic amino acid content, including previously described DNA mimic AcrIF2. Four of the acr genes were broad spectrum, also inhibiting I-E or I-F P. aeruginosa CRISPR-Cas subtypes. Dual inhibition comes at a cost, however, as simultaneous expression of Type I-C and I-F systems renders phages expressing the dual inhibitor AcrIF2 more sensitive to targeting. Mutagenesis of numerous acidic residues in AcrIF2 did not impair anti-I-C or anti-I-F function per se but did exacerbate inhibition defects during competition, suggesting that excess negative charge may buffer DNA mimics against competition. Like AcrIF2, five of the Acr proteins block Cascade from binding DNA, while two function downstream, likely preventing Cas3 recruitment or activity. One such inhibitor, AcrIC3, is found in an 'anti-Cas3' cluster within conjugative elements, encoded alongside bona fide Cas3 inhibitors AcrIF3 and AcrIE1. Our findings demonstrate an active battle between an MGE-encoded CRISPR-Cas system and its diverse MGE targets.

Machine-learning approach expands the repertoire of anti-CRISPR protein families.

The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the development of powerful genome editing and engineering tools. In the incessant host-parasite arms race, viruses evolved multiple anti-defense mechanisms including diverse anti-CRISPR proteins (Acrs) that specifically inhibit CRISPR-Cas and therefore have enormous potential for application as modulators of genome editing tools. Most Acrs are small and highly variable proteins which makes their bioinformatic prediction a formidable task. We present a machine-learning approach for comprehensive Acr prediction. The model shows high predictive power when tested against an unseen test set and was employed to predict 2,500 candidate Acr families. Experimental validation of top candidates revealed two unknown Acrs (AcrIC9, IC10) and three other top candidates were coincidentally identified and found to possess anti-CRISPR activity. These results substantially expand the repertoire of predicted Acrs and provide a resource for experimental Acr discovery.