Parallel Evolution of Pseudomonas aeruginosa during a Prolonged ICU-Infection Outbreak

Abstract

Most knowledge about Pseudomonas aeruginosa pathoadaptation is derived from studies on airway colonization in cystic fibrosis; little is known about adaptation in acute settings. P. aeruginosa frequently affects burned patients and the burn wound niche has distinct properties that likely influence pathoadaptation. This study aimed to genetically and phenotypically characterize P. aeruginosa isolates collected during an outbreak of infection in a burn intensive care unit (ICU). Sequencing reads from 58 isolates of ST1076 P. aeruginosa taken from 23 patients were independently mapped to a complete reference genome for the lineage (H25338); genetic differences were identified and were used to define the population structure. Comparative genomic analysis at single-nucleotide resolution identified pathoadaptive genes that evolved multiple, independent mutations. Three key phenotypic assays (growth performance, motility, carbapenem resistance) were performed to complement the genetic analysis for 47 unique isolates. Population structure for the ST1076 lineage revealed 11 evolutionary sublineages. Fifteen pathoadaptive genes evolved mutations in at least two sublineages. The most prominent functional classes affected were transcription/two-component regulatory systems, and chemotaxis/motility and attachment. The most frequently mutated gene was oprD, which codes for outer membrane porin involved in uptake of carbapenems. Reduced growth performance and motility were found to be adaptive phenotypic traits, as was high level of carbapenem resistance, which correlated with higher carbapenem consumption during the outbreak. Multiple prominent linages evolved each of the three traits in parallel providing evidence that they afford a fitness advantage for P. aeruginosa in the context of human burn infection. IMPORTANCE Pseudomonas aeruginosa is a Gram-negative pathogen causing infections in acutely burned patients. The precise mechanisms required for the establishment of infection in the burn setting, and adaptive traits underpinning prolonged outbreaks are not known. We have assessed genotypic data from 58 independent P. aeruginosa isolates taken from a single lineage that was responsible for an outbreak of infection in a burn ICU that lasted for almost 2.5 years and affected 23 patients. We identified a core set of 15 genes that we predict to control pathoadaptive traits in the burn infection based on the frequency with which independent mutations evolved. We combined the genotypic data with phenotypic data (growth performance, motility, antibiotic resistance) and clinical data (antibiotic consumption) to identify adaptive phenotypes that emerged in parallel. High-level carbapenem resistance evolved rapidly, and frequently, in response to high clinical demand for this antibiotic class during the outbreak.

Keywords: adaptative evolution; antibiotic resistance; carbapenems; oprD; outer membrane porin D.

FIG 1

Population structure of P. aeruginosa isolates from an ICU outbreak of infection. (A) P. aeruginosa isolates from a lineage of ST1076 were collected from 23 infected patients during the outbreak in the burn ICU (2010 to 2012). Patient 01 (P01) was not included as the ST1076 isolate was only distantly related to those from the outbreak. Each white circle represents a single isolate. Bars indicate patient time in the ICU (adapted from reference 25). (B) Population structure was inferred from whole-genome sequencing data (Table S2) to create a minimum spanning tree. Sequence reads were mapped to the complete genome of H25883 (in bold font). A common ancestor was inferred when subbranches had a shared mutation. The color of each circle corresponds to the patient shown in panel A.

FIG 2

Fifteen frequently mutated “pathoadaptive genes.” Pathoadaptive genes were defined as those with at least two independent mutations across the isolate collection. CDS, coding DNA sequence; LPS, lipopolysaccharide; TCRS, two-component regulatory system.

FIG 3

Reduced growth rate is an adaptive trait for P. aeruginosa lineages evolving during the outbreak in the burn ICU. (A) Temporal in vitro growth dynamics of P. aeruginosa from the burn ICU. An isolate at the core of the population structure, H26076, is presented in bold text. (B) Bimodal distribution of isolates based on the change in optical density at 600 nm (OD₆₀₀) per hour during the exponential phase of growth. (C) Change in OD₆₀₀ per hour in the context of population structure for the P. aeruginosa lineage.

FIG 4

Motility is an adaptive trait for P. aeruginosa in the burn infection setting. (A) Each of the 108 mutated genes was functionally classified using PseudoCAP. A percentage of genes’ mutated measure was determined by relating the number of mutated genes to the total number of genes within each functional category. (B) Each unique P. aeruginosa isolate was stab inoculated into 0.3% LB agar plates. Plates were imaged after 24 h of incubation at 37°C. Images of each plate was then plotted in the context of population structure. PTM, posttranslational modification; LPS, lipopolysaccharide; RMR, recombination, modification, repair; TCRS, two-component regulatory system.

FIG 5

OprD mutation is associated with carbapenem use in the ICU and the emergence of carbapenem resistance. (A) Schematic representation of the impact of oprD mutation to predicted OprD amino acid sequence. oprD_WT is the “wild-type” unmutated gene (translates to a sequence 444 amino acids in length) and oprD_1 through to oprD_10 are mutations that emerged during the infection outbreak. Regions in green show 100% identity with oprD_WT sequence. Regions in gray are different to oprD_WT (disparate region). (B) Defined daily dose (DDD) of antibiotics from different classes used before and during the ICU infection outbreak (adapted from reference 31). (C) Distribution of meropenem MICs across tested isolates (n = 47) as determined by Etest (the highest concentration in the meropenem Etest is 32 μg/mL). The color corresponds to clinical breakpoints defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST). (D) Meropenem MIC plotted in the context of population structure. Each green number corresponds to the OprD schematic from panel A.

FIG 6

Parallel evolution of adaptive traits for P. aeruginosa sublineages isolated during an ICU infection outbreak. (A) Eleven distinct sublineages were identified based on population structure. (B) TwoStep cluster analysis was used to identify four distinct phenotypic clusters (clusters A to D). (C) Each phenotypic cluster contained representative isolates from a range of sublineages. Conversely, most sublineages contained isolates from multiple phenotypic clusters. GR, growth rate (GR), as inferred from ΔOD₆₀₀ per hour; MER MIC, meropenem MIC.