Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information

Predicting bacteriophage infection of specific bacterial strains promises advancements in phage therapy and microbial ecology. Whether the dynamics of well-established phage–host model systems generalize to the wide diversity of microbes is currently unknown. Here we show that we could accurately predict the outcomes of phage–bacteria interactions at the strain level in natural isolates from the genus Escherichia using only genomic data (area under the receiver operating characteristic curve (AUROC) of 86%). We experimentally established a dataset of interactions between 403 diverse Escherichia strains and 96 phages. Most interactions are explained by adsorption factors as opposed to antiphage systems which play a marginal role. We trained predictive algorithms and pinpoint poorly predicted interactions to direct future research efforts. Finally, we established a pipeline to recommend tailored phage cocktails, demonstrating efficiency on 100 pathogenic E. coli isolates. This work provides quantitative insights into phage–host specificity and supports the use of predictive algorithms in phage therapy. Phage–host interactions are computationally predicted using only genomic information, highlighting future research directions and enabling generation of custom phage cocktails.