2020
DOI: 10.1128/mbio.03042-19
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Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case

Abstract: Laboratory models are a cornerstone of modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, researchers often choose models based on intuition or incomplete data. We propose a general quantitative framework to assess model accuracy from RNA sequencing data and use this framework to evaluate models of Pseudomonas aeruginosa cystic fibrosis (CF) lung infection. We found that an in vitro synthetic CF sputum medium model and a CF airway epithelial cell model had… Show more

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Cited by 107 publications
(142 citation statements)
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References 50 publications
(45 reference statements)
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“…Given the potential for context-dependent PDE signaling ( 36 ), we sought to determine which PDEs were required for biotic biofilm dispersal triggered by NO. We used existing transcriptomic data from our model ( 40 ) and tested dispersal of deletion strains for the most highly expressed putative PDEs/DGCs, as well as deletions of two other published PDEs (listed in Table S2 ) in a PAO1 background. LapDG was excluded, as it is likely an effector ( 26 ).…”
Section: Resultsmentioning
confidence: 99%
“…Given the potential for context-dependent PDE signaling ( 36 ), we sought to determine which PDEs were required for biotic biofilm dispersal triggered by NO. We used existing transcriptomic data from our model ( 40 ) and tested dispersal of deletion strains for the most highly expressed putative PDEs/DGCs, as well as deletions of two other published PDEs (listed in Table S2 ) in a PAO1 background. LapDG was excluded, as it is likely an effector ( 26 ).…”
Section: Resultsmentioning
confidence: 99%
“…As suggested by Soukarieh et al [ 48 ], two important drawbacks for the further clinical development of QSIs are uncertainty in regard to anti-virulence profiles and efficacy towards P. aeruginosa clinical isolates, as well as the lack of standardization of methods used to assess the compounds. A recent study by Cornforth et al [ 49 ] further highlights the need of proper evaluation and evidence-based selection of infection models. The authors propose a quantitative framework for model evaluation based on comparing transcriptome data acquired from a target system, such as clinical samples, resulting in an accuracy score [ 49 ].…”
Section: Discussionmentioning
confidence: 99%
“…A recent study by Cornforth et al [ 49 ] further highlights the need of proper evaluation and evidence-based selection of infection models. The authors propose a quantitative framework for model evaluation based on comparing transcriptome data acquired from a target system, such as clinical samples, resulting in an accuracy score [ 49 ].…”
Section: Discussionmentioning
confidence: 99%
“…The study of microbiota such as those in soil ( 1 ), diseases ( 2 ), fermented food ( 3 ), and sites of human activity ( 4 , 5 ) has paid greater attention to various temporal dimensions, such as seasonal ( 6 ) and age ( 7 ) differences. Comparing microbial abundances between different samples in various temporal dimensions is important for expanding the breadth and depth of the research.…”
Section: Introductionmentioning
confidence: 99%