The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung Infection

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 huma...

Full description

Bibliographic Details
Main Authors: Carolyn B. Ibberson, Marvin Whiteley
Format: Article
Language:English
Published: American Society for Microbiology 2019-11-01
Series:mBio
Subjects:
Online Access:https://doi.org/10.1128/mBio.02774-19
id doaj-7d4b6c880a1b48b2b1692e7364ea0d7a
record_format Article
spelling doaj-7d4b6c880a1b48b2b1692e7364ea0d7a2021-07-02T11:42:20ZengAmerican Society for MicrobiologymBio2150-75112019-11-01106e02774-1910.1128/mBio.02774-19The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung InfectionCarolyn B. IbbersonMarvin WhiteleyAlthough 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.Laboratory models have been invaluable for the field of microbiology for over 100 years and have provided key insights into core aspects of bacterial physiology such as regulation and metabolism. However, it is important to identify the extent to which these models recapitulate bacterial physiology within a human infection environment. Here, we performed transcriptomics (RNA-seq), focusing on the physiology of the prominent pathogen Staphylococcus aureusin situ in human cystic fibrosis (CF) infection. Through principal-component and hierarchal clustering analyses, we found remarkable conservation in S. aureus gene expression in the CF lung despite differences in the patient clinic, clinical status, age, and therapeutic regimen. We used a machine learning approach to identify an S. aureus transcriptomic signature of 32 genes that can reliably distinguish between S. aureus transcriptomes in the CF lung and in vitro. The majority of these genes were involved in virulence and metabolism and were used to improve a common CF infection model. Collectively, these results advance our knowledge of S. aureus physiology during human CF lung infection and demonstrate how in vitro models can be improved to better capture bacterial physiology in infection.https://doi.org/10.1128/mBio.02774-19staphylococcus aureusrna-seqtranscriptomicsmachine learningvirulencecystic fibrosishuman infectionvirulence factors
collection DOAJ
language English
format Article
sources DOAJ
author Carolyn B. Ibberson
Marvin Whiteley
spellingShingle Carolyn B. Ibberson
Marvin Whiteley
The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung Infection
mBio
staphylococcus aureus
rna-seq
transcriptomics
machine learning
virulence
cystic fibrosis
human infection
virulence factors
author_facet Carolyn B. Ibberson
Marvin Whiteley
author_sort Carolyn B. Ibberson
title The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung Infection
title_short The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung Infection
title_full The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung Infection
title_fullStr The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung Infection
title_full_unstemmed The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung Infection
title_sort staphylococcus aureus transcriptome during cystic fibrosis lung infection
publisher American Society for Microbiology
series mBio
issn 2150-7511
publishDate 2019-11-01
description 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.Laboratory models have been invaluable for the field of microbiology for over 100 years and have provided key insights into core aspects of bacterial physiology such as regulation and metabolism. However, it is important to identify the extent to which these models recapitulate bacterial physiology within a human infection environment. Here, we performed transcriptomics (RNA-seq), focusing on the physiology of the prominent pathogen Staphylococcus aureusin situ in human cystic fibrosis (CF) infection. Through principal-component and hierarchal clustering analyses, we found remarkable conservation in S. aureus gene expression in the CF lung despite differences in the patient clinic, clinical status, age, and therapeutic regimen. We used a machine learning approach to identify an S. aureus transcriptomic signature of 32 genes that can reliably distinguish between S. aureus transcriptomes in the CF lung and in vitro. The majority of these genes were involved in virulence and metabolism and were used to improve a common CF infection model. Collectively, these results advance our knowledge of S. aureus physiology during human CF lung infection and demonstrate how in vitro models can be improved to better capture bacterial physiology in infection.
topic staphylococcus aureus
rna-seq
transcriptomics
machine learning
virulence
cystic fibrosis
human infection
virulence factors
url https://doi.org/10.1128/mBio.02774-19
work_keys_str_mv AT carolynbibberson thestaphylococcusaureustranscriptomeduringcysticfibrosislunginfection
AT marvinwhiteley thestaphylococcusaureustranscriptomeduringcysticfibrosislunginfection
AT carolynbibberson staphylococcusaureustranscriptomeduringcysticfibrosislunginfection
AT marvinwhiteley staphylococcusaureustranscriptomeduringcysticfibrosislunginfection
_version_ 1721330885934972928