Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary – and largely uncharacterized – genetics of adsorption, injection, cell take-over and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage l strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. We found that the most effective approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, accurately predicting 86% of interactions while reducing the relative error in t..., , , # Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics The manuscript *\"Lucia-Sanz A, Peng S, Leung CYJ, Gupta A, Meyer JR, Weitz JS. Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics\"* has been accepted for publication in Virus Evolution (11/20/2024). ## Supplementary data We use experimental data from Gupta et al., 2022 where *E. coli* B strain REL606 and phage λ strain cI26 were co-cultured for a 37-day period. Samples were taken on checkpoint days for sequencing and pairwise quantitative plaque assays as described in (Gupta et al., 2022). * **Data_S1.csv** is a table that contains the list of genome wide changes (rows) of 50 bacterial host (descended from E. coli B strain REL606) strains (columns) found during the 37-day coevolutionary experiment. Additional columns contain information about each mutation. * **Data_S2.csv** is a table that...