Genome-Scale Identification of Resistance Functions in Pseudomonas aeruginosa Using Tn-seq

We describe a deep-sequencing procedure for tracking large numbers of transposon mutants of Pseudomonas aeruginosa. The procedure employs a new Tn-seq methodology based on the generation and amplification of single-strand circles carrying transposon junction sequences (the Tn-seq circle method), a method which can be used with virtually any transposon. The procedure reliably identified more than 100,000 transposon insertions in a single experiment, providing near-saturation coverage of the genome. To test the effectiveness of the procedure for mutant identification, we screened for mutations reducing intrinsic resistance to the aminoglycoside antibiotic tobramycin. Intrinsic tobramycin resistance had been previously analyzed at genome scale using mutant-by-mutant screening and thus provided a benchmark for evaluating the new method. The new Tn-seq procedure identified 117 tobramycin resistance genes, the majority of which were then verified with individual mutants. The group of genes with the strongest mutant phenotypes included nearly all (13 of 14) of those with strong mutant phenotypes identified in the previous screening, as well as a nearly equal number of new genes. The results thus show the effectiveness of the Tn-seq method in defining the genetic basis of a complex resistance trait of P. aeruginosa and indicate that it can be used to analyze a variety of growth-related processes. IMPORTANCE Research progress in microbiology is technology limited in the sense that the analytical methods available dictate how questions are experimentally addressed and, to some extent, what questions are asked. This report describes a new transposon tracking procedure for defining the genetic basis of growth-related processes in bacteria. The method employs next-generation sequencing to monitor the makeup of mutant populations (Tn-seq) and has several potential advantages over other Tn-seq methodologies. The new method was validated through the analysis of a clinically relevant antibiotic resistance trait in Pseudomonas aeruginosa, an important bacterial pathogen.