Computational pipeline for targeted integration and variable payload expression for bacteriophage engineering

Abstract

AbstractBacteriophages offer a promising alternative to conventional antimicrobial treatments, particularly in cases where such treatments have proven ineffective. While naturally occurring phages serve as viable options for phage therapy, advances in synthetic biology and genome engineering enable precise modifications to phages to enhance their therapeutic potential. One such approach is the introduction of antimicrobial genetic payloads into the phage genome. Conventional practice is to integrate such payloads behind genes expressed at very high levels late in the infection cycle, such as the major capsid gene (cps). Nevertheless, phages engineered to contain antimicrobial payloads are often difficult to obtain. For instance, the high expression of toxic payloads can prematurely halt host metabolism, leading to the failure in assembling viable phage progeny. To potentially expand the range of genes viable as genetic payloads, we developed a method to identify intergenic loci with favorable expression levels. We used the machine learning (ML)-based promoter prediction algorithm PhagePromoter to identify these loci. We then used this information to design a computationally-assisted engineering pipeline for the integration of genomic payloads at these locations. We validated our approach experimentally, engineering phages with bioluminescent reporter payloads at various predicted loci. We used the well characterized, strictly lytic,Staphylococcus aureus-infecting bacteriophage, K, as an engineering scaffold and employed homologous recombination to engineer three recombinant phages containing the reporter payload at different predicted loci throughout the genome. The recombinant phages exhibited expression levels consistent with our computational predictions and showed temporal expression patterns corresponding to their genomic locations in early, middle, or late gene clusters. Our study underscores the potential of integrating computational tools with classical sequence analysis to streamline the phage engineering process. This approach not only facilitates the rational design of phages with targeted payload insertions but also paves the way for high-throughput, automated phage engineering, fostering a new era of personalized phage therapy.