Dataset Information

Refining the Application of Microbial Lipids as Tracers of Staphylococcus aureus Growth Rates in Cystic Fibrosis Sputum.

ABSTRACT: Chronic lung infections in cystic fibrosis (CF) could be treated more effectively if the effects of antimicrobials on pathogens in situ were known. Here, we compared changes in the microbial community composition and pathogen growth rates in longitudinal studies of seven pediatric CF patients undergoing intravenous antibiotic administration during pulmonary exacerbations. The microbial community composition was determined by counting rRNA with NanoString DNA analysis, and growth rates were obtained by incubating CF sputum with heavy water and tracing incorporation of deuterium into two branched-chain ("anteiso") fatty acids (a-C15:0 and a-C17:0) using gas chromatography-mass spectrometry (GC/MS). Prior to this study, both lipids were thought to be specific for Staphylococcaceae; hence, their isotopic enrichment was interpreted as a growth proxy for Staphylococcus aureus Our experiments revealed, however, that Prevotella is also a relevant microbial producer of a-C17:0 fatty acid in some CF patients; thus, deuterium incorporation into these lipids is better interpreted as a more general pathogen growth rate proxy. Even accounting for a small nonmicrobial background source detected in some patient samples, a-C15:0 fatty acid still appears to be a relatively robust proxy for CF pathogens, revealing a median generation time of ?1.5 days, similar to prior observations. Contrary to our expectation, pathogen growth rates remained relatively stable throughout exacerbation treatment. We suggest two straightforward "best practices" for application of stable-isotope probing to CF sputum metabolites: (i) parallel determination of microbial community composition in CF sputum using culture-independent tools and (ii) assessing background levels of the diagnostic metabolite.IMPORTANCE In chronic lung infections, populations of microbial pathogens change and mature in ways that are often unknown, which makes it challenging to identify appropriate treatment options. A promising tool to better understand the physiology of microorganisms in a patient is stable-isotope probing, which we previously developed to estimate the growth rates of S. aureus in cystic fibrosis (CF) sputum. Here, we tracked microbial communities in a cohort of CF patients and found that anteiso fatty acids can also originate from other sources in CF sputum. This awareness led us to develop a new workflow for the application of stable-isotope probing in this context, improving our ability to estimate pathogen generation times in clinical samples.

SUBMITTER: Neubauer C

PROVIDER: S-EPMC6256016 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Refining the Application of Microbial Lipids as Tracers of Staphylococcus aureus Growth Rates in Cystic Fibrosis Sputum.

Neubauer Cajetan C Kasi Ajay S AS Grahl Nora N Sessions Alex L AL Kopf Sebastian H SH Kato Roberta R Hogan Deborah A DA Newman Dianne K DK

Journal of bacteriology 20181126 24

Chronic lung infections in cystic fibrosis (CF) could be treated more effectively if the effects of antimicrobials on pathogens <i>in situ</i> were known. Here, we compared changes in the microbial community composition and pathogen growth rates in longitudinal studies of seven pediatric CF patients undergoing intravenous antibiotic administration during pulmonary exacerbations. The microbial community composition was determined by counting rRNA with NanoString DNA analysis, and growth rates wer ...[more]

PMID: 30249710

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Project description: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 aureus in 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.IMPORTANCE 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.

Project description:Hallmarks of cystic fibrosis (CF) are increased viscosity of mucus and impaired mucociliary clearance within the airways due to mutations of the cystic fibrosis conductance regulator gene. This facilitates the colonization of the lung by microbial pathogens and the concomitant establishment of chronic infections leading to tissue damage, reduced lung function, and decreased life expectancy. Although the interplay between key CF pathogens plays a major role during disease progression, the pathophysiology of the microbial community in CF lungs remains poorly understood. Particular challenges in the analysis of the microbial population present in CF sputum is (I) the inhomogeneous, viscous, and slimy consistence of CF sputum, and (II) the high number of human proteins masking comparably low abundant microbial proteins. To address these challenges, we used 21 CF sputum samples to develop a reliable, reproducible and widely applicable protocol for sputum processing, microbial enrichment, cell disruption, protein extraction and subsequent metaproteomic analyses. As a proof of concept, we selected three sputum samples for detailed metaproteome analyses and complemented and validated metaproteome data by 16S sequencing, metabolomic as well as microscopic analyses. Applying our protocol, the number of bacterial proteins/protein groups increased from 199-425 to 392-868 in enriched samples compared to nonenriched controls. These early microbial metaproteome data suggest that the arginine deiminase pathway and multiple proteases and peptidases identified from various bacterial genera could so far be underappreciated in their contribution to the CF pathophysiology. By providing a standardized and effective protocol for sputum processing and microbial enrichment, our study represents an important basis for future studies investigating the physiology of microbial pathogens in CF in vivo - an important prerequisite for the development of novel antimicrobial therapies to combat chronic recurrent airway infection in CF.

Project description:BackgroundCystic fibrosis (CF) is an autosomal recessive disease characterized by recurrent lung infections. Studies of the lung microbiome have shown an association between decreasing diversity and progressive disease. 454 pyrosequencing has frequently been used to study the lung microbiome in CF, but will no longer be supported. We sought to identify the benefits and drawbacks of using two state-of-the-art next generation sequencing (NGS) platforms, MiSeq and PacBio RSII, to characterize the CF lung microbiome. Each has its advantages and limitations.MethodsTwelve samples of extracted bacterial DNA were sequenced on both MiSeq and PacBio NGS platforms. DNA was amplified for the V4 region of the 16S rRNA gene and libraries were sequenced on the MiSeq sequencing platform, while the full 16S rRNA gene was sequenced on the PacBio RSII sequencing platform. Raw FASTQ files generated by the MiSeq and PacBio platforms were processed in mothur v1.35.1.ResultsThere was extreme discordance in alpha-diversity of the CF lung microbiome when using the two platforms. Because of its depth of coverage, sequencing of the 16S rRNA V4 gene region using MiSeq allowed for the observation of many more operational taxonomic units (OTUs) and higher Chao1 and Shannon indices than the PacBio RSII. Interestingly, several patients in our cohort had Escherichia, an unusual pathogen in CF. Also, likely because of its coverage of the complete 16S rRNA gene, only PacBio RSII was able to identify Burkholderia, an important CF pathogen.ConclusionWhen comparing microbiome diversity in clinical samples from CF patients using 16S sequences, MiSeq and PacBio NGS platforms may generate different results in microbial community composition and structure. It may be necessary to use different platforms when trying to correctly identify dominant pathogens versus measuring alpha-diversity estimates, and it would be important to use the same platform for comparisons to minimize errors in interpretation.

Dataset Information

Refining the Application of Microbial Lipids as Tracers of Staphylococcus aureus Growth Rates in Cystic Fibrosis Sputum.

Publications

Refining the Application of Microbial Lipids as Tracers of Staphylococcus aureus Growth Rates in Cystic Fibrosis Sputum.

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