Predicting phage-bacteria interactions at the strain level from genomes

Abstract

Predicting how phages can selectively infect specific bacterial strains holds promise for developing novel approaches to combat bacterial infections and better understanding microbial ecology. Experimental studies on phage-bacteria interactions have been mostly focusing on a few model organisms to understand the molecular mechanisms which makes a particular bacterial strain susceptible to a given phage. However, both bacteria and phages are extremely diverse in natural contexts. How well the concepts learned from well-established experimental models generalize to a broad diversity of what is encountered in the wild is currently unknown. Recent advances in genomics allow to identify traits involved in phage-host specificity, implying that these traits could be utilized for the prediction of such interactions. Here, we show that we could predict outcomes of most phage-bacteria interactions at the strain level inEscherichianatural isolates based solely on genomic data. First, we established a dataset of experimental outcomes of phage-bacteria interactions of 403 natural, phylogenetically diverse,Escherichiastrains to 96 bacteriophages matched with fully sequenced and genomically characterized strains and phages. To predict these interactions, we set out to define genomic traits with predictive power. We show that most interactions in our dataset can be explained by adsorption factors as opposed to antiphage systems which play a marginal role. We then trained predictive algorithms to pinpoint which interactions could be accurately predicted and where future research should focus on. Finally, we show the application of such predictions by establishing a pipeline to recommend tailored phage cocktails to target pathogenic strains from their genomes only and show higher efficiency of tailored cocktails on a collection of 100 pathogenicE. coliisolates. Altogether, this work provides quantitative insights into understanding phage–host specificity at the strain level and paves the way for the use of predictive algorithms in phage therapy.

“This training is bound for glory” : selection by experimental evolution of a bacteriophage with expanded host-range and increased virulence

“…Previous results have shown that adaptive mutations impacted the host-interaction subunits proteins, such as capsides or spikes proteins for microvirid bacteriophages (Sackman et al, 2017), ΦX174 (B X174 (Bull et al, 1997;Crill et al, 2000;Pepin et al, 2008) and receptor binding proteins in the RNA virus ΦX174 (B 6 (Duffy et al, 2006;Ferris et al, 2007) as well as almost hundred Caudoviricetes (Gaborieau et al, 2023). Interestingly, we didn't observe any adaptive mutation in the gene coding for pb5 the major receptor binding proteins (RBP), that commits the phage to infection, triggering cell wall perforation and DNA ejection through a non-reversible interaction with…”

The branched receptor binding complex ofAckermannviridaephages promotes adaptative host recognition

“…Bacterial receptors are the major determinant of phage susceptibility, even more than internal phage resistance mechanisms 1 . Hence, phages have evolved different strategies to overcome the diversity of phage receptors.…”

“…When infecting a bacterial host, the phage receptor binding proteins recognize the surface receptor of the bacterial host. The specificity of this initial phage-host interaction ensures the infection of proper hosts, and receptors have been suggested as the major determinant of the host range of phages 1 . On the phage side, most characterized phages typically use a single receptor binding protein to recognize their bacterial host.…”

A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases.

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