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Pseudomonas aeruginosa transcriptome during human infection

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Laboratory experiments have uncovered many basic aspects of bacterial physiology and behavior. After the past century of mostly in vitro experiments, we now have detailed knowledge of bacterial behavior in standard laboratory conditions, but only a superficial understanding of bacterial functions and behaviors during human infection. It is well-known that the growth and behavior of bacteria are largely dictated by their environment, but how bacterial physiology differs in laboratory models compared with human infections is not known. To address this question, we compared the transcriptome of Pseudomonas aeruginosa during human infection to that of P. aeruginosa in a variety of laboratory conditions. Several pathways, including the bacterium's primary quorum sensing system, had significantly lower expression in human infections than in many laboratory conditions. On the other hand, multiple genes known to confer antibiotic resistance had substantially higher expression in human infection than in laboratory conditions, potentially explaining why antibiotic resistance assays in the clinical laboratory frequently underestimate resistance in patients. Using a standard machine learning technique known as support vector machines, we identified a set of genes whose expression reliably distinguished in vitro conditions from human infections. Finally, we used these support vector machines with binary classification to force P. aeruginosa mouse infection transcriptomes to be classified as human or in vitro. Determining what differentiates our current models from clinical infections is important to better understand bacterial infections and will be necessary to create model systems that more accurately capture the biology of infection.

Original languageEnglish
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number22
Pages (from-to)E5125-E5134
ISSN0027-8424
DOIs
Publication statusPublished - 29 May 2018

    Research areas

  • Animals, Biofilms, Cystic Fibrosis, Disease Models, Animal, Drug Resistance, Bacterial, Gene Expression Regulation, Bacterial/genetics, Genes, Bacterial, Humans, Machine Learning, Mice, Pseudomonas Infections/metabolism, Pseudomonas aeruginosa/genetics, Quorum Sensing/genetics, Support Vector Machine, Surgical Wound Infection/metabolism, Transcriptome/genetics

ID: 56255849