High-throughput mapping of the phage resistance landscape inE. coli

Abstract

Bacteriophages (phages) are critical players in the dynamics and function of microbial communities and drive processes as diverse as global biogeochemical cycles and human health. Phages tend to be predators finely tuned to attack specific hosts, even down to the strain level, which in turn defend themselves using an array of mechanisms. However, to date, efforts to rapidly and comprehensively identify bacterial host factors important in phage infection and resistance have yet to be fully realized. Here, we globally map the host genetic determinants involved in resistance to 14 phylogenetically diverse double-stranded DNA phages using two model Escherichia coli strains (K-12 and BL21) with known sequence divergence to demonstrate strain-specific differences. Using genome-wide loss-of-function and gain-of-function genetic technologies, we are able to confirm previously described phage receptors as well as uncover a number of previously unknown host factors that confer resistance to one or more of these phages. We uncover differences in resistance factors that strongly align with the susceptibility of K-12 and BL21 to specific phage. We also identify both phage specific mechanisms, such as the unexpected role of cyclic-di-GMP in host sensitivity to phage N4, and more generic defenses, such as the overproduction of colanic acid capsular polysaccharide that defends against a wide array of phages. Our results indicate that host responses to phages can occur via diverse cellular mechanisms. Our systematic and highthroughput genetic workflow to characterize phage-host interaction determinants can be extended to diverse bacteria to generate datasets that allow predictive models of how phagemediated selection will shape bacterial phenotype and evolution. The results of this study and future efforts to map the phage resistance landscape will lead to new insights into the coevolution of hosts and their phage, which can ultimately be used to design better phage therapeutic treatments and tools for precision microbiome engineering.3 Introduction:

The Genetic Basis of phage susceptibility, cross-resistance and host-range in Salmonella

“…In other words, a gene denoted with a high-fitness score is a host-side gene critical for phage infection. As observed in our earlier work 26 , we observed very strong phage selection pressures during our experiments, so we limited analysis to positive fitness scores.…”

“…For each phage, we challenged our mutant library in liquid culture, where fitness scores reflected competition between mutant strains. Akin to Mutalik et al 26 , we also performed non-competitive experiments where we plated the library shortly after phage exposure (thus noncompetitively reflecting strain fitness against phage). For each gDNA sample (collected before and at the end of each experiment), we performed deep sequencing on the 20 base pair barcodes (ie BarSeq) associated with each transposon mutant.…”

“…Nonetheless, it is likely that these mutants primarily impact motility, thus reducing Chi infectivity (see Protein Receptors and Regulation) 40 ; additionally, homologs of rfaH, rfaP, rfaQ, rfaY, and rfaG are among the least motile mutants in experiments for E. coli BW25113 27 . Since LPS disruptions induce stress responses, this pleiotropy may be stress-induced downregulation of outer membrane proteins and flagellar activity 26 .…”

“…Metagenomics and isolation studies from these complex samples have identified numerous Salmonella phages, but they often lack substantial characterization 13,[20][21][22] . Metabolic, stress-response, and regulatory hostfactor requirements beyond receptors are known for individual phages [23][24][25][26] , but have not yet entered the conversation for therapeutic design.…”

“…Recent papers from our group and others demonstrated substantial increases in both the pace and resolution that we can infer bacteriophage-host interactions by use of pooled genetic screens (such as randomly barcoded transposon sequencing (RB-TnSeq)). Because these screens rely on sequencing of short barcodes as a proxy for genetic variants, they can be efficiently and economically scaled to assess the fitness benefits of thousands of genes across hundreds of conditions including bacteriophage predation [26][27][28][29] . However, the scale of these screens has not yet been applied to many phages in model pathogens such as S. typhimurium.…”