An RNA-Seq based framework to evaluate human infection models

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. Researchers at the Georgia Institute of Technology 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. They found that an in vitro synthetic CF sputum medium model and a CF airway epithelial cell model had the highest genome-wide accuracy but underperformed on distinct functional categories, including porins and polyamine biosynthesis for the synthetic sputum medium and protein synthesis for the epithelial cell model. The researchers identified 211 “elusive” genes that were not mimicked in a reference strain grown in any laboratory model but found that many were captured by using a clinical isolate. These methods provide researchers with an evidence-based foundation to select and improve laboratory models.



P. aeruginosa transcriptomes from human CF sputum cluster distinctly from in vitro and mouse acute lung transcriptomes using principal-component analysis. The analysis is based on 2,606 genes that had at least one read mapping to them in all samples. 

Cornforth DM, Diggle FL, Melvin JA, Bomberger JM, Whiteley M. (2020) Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case. mBio 11(1). [article]

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