Phage-Seq phenotyping of the Pseudomonas aeruginosa surface-ome

Yale University, New Haven CT

Investigators

Abstract

Pseudomonas aeruginosa is an opportunistic pathogen that is intrinsically resistant to many antibiotics, metabolically plastic and capable of secreting molecules that manipulate prokaryotic and eukaryotic competitors and predators. When it establishes prolonged chronic infections in human hosts, it further evolves to survive antimicrobial treatment and evade host defenses. Many such adaptive changes occur on the P. aeruginosa cell surface, but our technologies for directly identifying such adaptations are limited. To address this unmet need, we have combined phage display of VHH recognition domains derived from camelid heavy chain-only immunoglobulins (“nanobodies”) with high-throughput DNA sequencing (HTS) to create a high throughput, highly multiplexed technology for surveying bacterial cell surfaces that we call “Phage-seq”. We performed phage display panning on hundreds of pairs of bacterial genotypes and used sequencing data to analyze the dynamics of the phage display selection process. Our published data demonstrate that these datasets capture important biological information about the surfaces of the cells under study. Phage-seq has also enabled the discovery of dozens of nanobodies to date that recognize key P. aeruginosa virulence factors, including determinants of antimicrobial resistance, in their native conformations on live cells. These recombinantly expressed nanobodies have numerous potential applications in diagnostics and therapeutics. We propose that “Phage-seq” enables a new paradigm for studying the bacterial cell surface by identifying and profiling many surface features in parallel. Importantly, Phage-seq does not require antigens to be known in advance and decouples profiling from antigen identification. In this application we extend the reach of Phage- seq to a broad range of genetically diverse P. aeruginosa isolates. The Phage-seq datasets that result will be compared to both genomic data and phenotypes measured via other experimental approaches, allowing us to assess how this method contributes to understanding of bacterial cell surface biology.