(23f) Comparative Genomics and Phenomics Reveal Genetic and Functional Diversity and Fitness within Pseudomonas Aeruginosa clinical Isolates

Pseudomonas aeruginosa is the leading cause of nosocomial infections and well-known for infecting patients with cystic fibrosis. These infections can be complicated by a variety of virulence mechanisms and a robust metabolic functionality repertoire. However, how key components of the environment modulate these metabolic pathways and virulence mechanisms during infection are poorly understood.

We hypothesize that mucin-driven modulations of the metabolism of P. aeruginosa are dependent on a complex combination of host and pathogen-specific factors which can be delineated using a combination of genomic, transcriptomic, and phenomic study. With that goal, we obtained 971 clinical isolates of P. aeruginosa from 590 patients from the UVA Health System Clinical Microbiology Laboratory, which collects samples from a range of outpatient and inpatient settings in the region. For each isolate, we recorded associated patient metadata, bacterial morphological phenotypes, and antimicrobial susceptibility profiles.

We selected a set of 25 phenotypically representative isolates from the entire isolate collection through stratified random sampling while guaranteeing the robustness of original phenotypic characteristics during repeat cultures. These 25 isolates were then cultured in LB medium with 5% Fetal Bovine Serum for whole genome sequencing. The genome sequence data was used for comparative genomic analysis using the PA14 strain as the reference genome. A dissimilarity matrix was enumerated from the output of multiple local alignment searches and was used to cluster the isolates according to their sequence similarities. The genotypic clustering was contrasted with the phenotypic clustering generated from a multi-parametric analysis to assess the genotype-phenotype correlation. In addition, we enumerated the fitness profile of the isolates across a vast library of Carbon substrates in defined media conditions. The fitness profile, combined with the genotypic variation of the isolates, explain their universal pattern of genetic diversity as well as the key metabolic and regulatory identifiers for phenotypic diversity. Overall, this study demonstrates the value in surveying clinical data in terms of pathogen isolate sources, patient metadata, morphological phenotypes, and correlating that data with genome sequence data and phenotype fitness to answer biologically relevant questions about genotype-phenotype relationships.