Application of a quantitative framework to improve the accuracy of a bacterial infection model

Abstract

Laboratory models are critical to basic and translational microbiology research. Models serve multiple purposes, from providing tractable systems to study cell biology to allowing the investigation of inaccessible clinical and environmental ecosystems. Although there is a recognized need for improved model systems, there is a gap in rational approaches to accomplish this goal. We recently developed a framework for assessing the accuracy of microbial models by quantifying how closely each gene is expressed in the natural environment and in various models. The accuracy of the model is defined as the percentage of genes that are similarly expressed in the natural environment and the model. Here, we leverage this framework to develop and validate two generalizable approaches for improving model accuracy, and as proof of concept, we apply these approaches to improve models of Pseudomonas aeruginosa infecting the cystic fibrosis (CF) lung. First, we identify two models, an in vitro synthetic CF sputum medium model (SCFM2) and an epithelial cell model, that accurately recapitulate different gene sets. By combining these models, we developed the epithelial cell-SCFM2 model which improves the accuracy of over 500 genes. Second, to improve the accuracy of specific genes, we mined publicly available transcriptome data, which identified zinc limitation as a cue present in the CF lung and absent in SCFM2. Induction of zinc limitation in SCFM2 resulted in accurate expression of 90% of P. aeruginosa genes. These approaches provide generalizable, quantitative frameworks for microbiological model improvement that can be applied to any system of interest.

Pseudomonas aeruginosa senses and responds to epithelial potassium flux via Kdp operon to promote biofilm

“…Utilizing an existing data set of P . aeruginosa transcriptional profiles in expectorated sputum [ 64 ], we found that all of the kdp genes are expressed during infection in people with CF ( Fig 7A ). We went on to determine whether these genes are conserved in clinical isolates of P .…”

“…Kdp gene expression was assessed using previously reported data set of RNAseq data from recently expectorated P . aeruginosa from CF patients [ 64 ]. Multisequence alignment was performed on all of the Kdp proteins’ amino acid sequences using genomes for PAO1, PA14, and 8 clinical isolates from patients with CF acquired from the NCBI Genome Database.…”

“…To assess the importance of the Kdp operon during infection in people with CF, we investigated the expression of the kdp genes in clinical samples. Utilizing an existing data set of P. aeruginosa transcriptional profiles in expectorated sputum [64], we found that all of the kdp genes are expressed during infection in people with CF (Fig 7A). We went on to determine whether these genes are conserved in clinical isolates of P. aeruginosa.…”

Mucus polymer concentration and in vivo adaptation converge to define the antibiotic response of Pseudomonas aeruginosa during chronic lung infection

“…Ultracentrifugation eliminated microbial contaminants, insoluble aggregated mucin polymers, and other debris, while preserving intact soluble mucins. Dialysis serves to equilibrate salt and metal concentrations, which have been previously reported to affect P. aeruginosa behavior and antimicrobial efficacy ( 54 ).…”

“…Previous studies investigating the effect of airway mucus polymers on bacterial behaviors have traditionally relied on: laboratory media, focused on the presence or absence of mucus polymers ( 56 ), and/or utilized non-biological polymers, such as polyethylene glycol ( 57 ) or agar gels ( 58 ). Using synthetic CF sputum medium (SCFM2), a well-characterized and chemically defined in vitro CF mucus simulant ( 50 – 52 , 54 ), as a starting point, we investigated the contribution of increasing concentrations of mucin and eDNA to antipseudomonal antibiotic efficacy.…”