Year: 2019
Type: article
Abstract: Although bacteria have been studied in infection for over 100 years, the majority of these studies have utilized laboratory and animal models that often have unknown relevance to the human infections they are meant to represent. A primary challenge has been to assess bacterial physiology in the human host. To address this challenge, we performed transcriptomics of S. aureus during human cystic fibrosis (CF) lung infection. Using a machine learning framework, we defined a “human CF lung transcriptome signature” that primarily included genes involved in metabolism and virulence. In addition, we were able to apply our findings to improve an in vitro model of CF infection. Understanding bacterial gene expression within human infection is a critical step toward the development of improved laboratory models and new therapeutics.
Source: mBio
Authors: Carolyn B. Ibberson, Marvin Whiteley
Institution: Georgia Institute of Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,900
OpenAlex ID: W2987587883