Defining Proteomic Signatures to Predict Multidrug Persistence in Pseudomonas aeruginosa

Phage Paride can kill dormant, antibiotic-tolerant cells of Pseudomonas aeruginosa by direct lytic replication

Bacteriophages are ubiquitous viral predators that have primarily been studied using fast-growing laboratory cultures of their bacterial hosts. However, microbial life in nature is mostly in a slow- or non-growing, dormant state. Here, we show that diverse phages can infect deep-dormant bacteria and suspend their replication until the host resuscitates ("hibernation"). However, a newly isolated Pseudomonas aeruginosa phage, named Paride, can directly replicate and induce the lysis of deep-dormant hosts. While non-growing bacteria are notoriously tolerant to antibiotic drugs, the combination with Paride enables the carbapenem meropenem to eradicate deep-dormant cultures in vitro and to reduce a resilient bacterial infection of a tissue cage implant in mice. Our work might inspire new treatments for persistent bacterial infections and, more broadly, highlights two viral strategies to infect dormant bacteria (hibernation and direct replication) that will guide future studies on phage-host interactions.

Combining CRISPRi and metabolomics for functional annotation of compound libraries

Molecular profiling of small-molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small molecule direct targets and secondary effects. However, current profiling methods are either limited in the number of measurable parameters or throughput. Here, we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from (CRISPR) interference with 352 genes in all major essential biological processes. Next, based on the comparison of genetic with 1342 drug-induced metabolic changes we made de novo predictions of compound functionality and revealed antibacterials with unconventional Modes of Action. We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality, from bacteria to human cell lines.