Dataset Information

The metabolic footprint of the airway bacterial community in cystic fibrosis.

ABSTRACT: Progressive, chronic bacterial infection of the airways is a leading cause of death in cystic fibrosis (CF). Culture-independent methods based on sequencing of the bacterial 16S rRNA gene describe a distinct microbial community that decreases in richness and diversity with disease progression. Understanding the functional characteristics of the microbial community may aid in identifying potential therapies and may assist in management, but current methods are cumbersome. Here, we demonstrate the use of an oxidative metabolic assay as a complement to sequencing methods to describe the microbiome in the airways of patients with CF.Expectorated sputum was collected from 16 CF subjects and 8 control subjects. The Biolog Gen III Microplate was used in a community-level physiological profiling (CLPP)-based assay to examine oxidative metabolic activity. 16S rRNA V4 amplicon sequencing was used to characterize the taxonomy and diversity of the samples. Correlations were then identified among the oxidative activity and taxonomy data. In an additional paired analysis, sputum from seven CF subjects were collected at two separate clinic visits and compared for oxidative activity, taxonomy, and diversity.Significant differences in oxidative metabolic activity, microbial taxonomy, and diversity were found between the CF and control sputum samples. Oxidative activity correlated positively with total genera but not with other measures of diversity or taxonomy, demonstrating that the metabolic assay complements the structural aspects of the microbiome. As expected, Pseudomonas was significantly enriched in CF samples, while Streptococcus and Prevotella were similarly abundant in both CF and control samples. Paired analysis of CF samples at separate clinic visits revealed comparable oxidative activity that correlated with similar stability in taxonomy and diversity.The CLPP assay used in this study complements existing sequencing methods to delineate the oxidative metabolic footprint of the CF airway bacterial community. This method may be useful to study the CF microbial community over time and with changes in disease state.

SUBMITTER: Narayanamurthy V

PROVIDER: S-EPMC5493850 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The metabolic footprint of the airway bacterial community in cystic fibrosis.

Narayanamurthy Vaishnavi V Sweetnam John M JM Denner Darcy R DR Chen Lena W LW Naureckas Edward T ET Laxman Bharathi B White Steven R SR

Microbiome 20170630 1

<h4>Background</h4>Progressive, chronic bacterial infection of the airways is a leading cause of death in cystic fibrosis (CF). Culture-independent methods based on sequencing of the bacterial 16S rRNA gene describe a distinct microbial community that decreases in richness and diversity with disease progression. Understanding the functional characteristics of the microbial community may aid in identifying potential therapies and may assist in management, but current methods are cumbersome. Here, ...[more]

PMID: 28666467

Similar Datasets

Project description:BACKGROUND: Given the polymicrobial nature of pulmonary infections in patients with cystic fibrosis (CF), it is essential to enhance our knowledge on the composition of the microbial community to improve patient management. In this study, we developed a pyrosequencing approach to extensively explore the diversity and dynamics of fungal and prokaryotic populations in CF lower airways. METHODOLOGY AND PRINCIPAL FINDINGS: Fungi and bacteria diversity in eight sputum samples collected from four adult CF patients was investigated using conventional microbiological culturing and high-throughput pyrosequencing approach targeting the ITS2 locus and the 16S rDNA gene. The unveiled microbial community structure was compared to the clinical profile of the CF patients. Pyrosequencing confirmed recently reported bacterial diversity and observed complex fungal communities, in which more than 60% of the species or genera were not detected by cultures. Strikingly, the diversity and species richness of fungal and bacterial communities was significantly lower in patients with decreased lung function and poor clinical status. Values of Chao1 richness estimator were statistically correlated with values of the Shwachman-Kulczycki score, body mass index, forced vital capacity, and forced expiratory volume in 1 s (p = 0.046, 0.047, 0.004, and 0.001, respectively for fungal Chao1 indices, and p = 0.010, 0.047, 0.002, and 0.0003, respectively for bacterial Chao1 values). Phylogenetic analysis showed high molecular diversities at the sub-species level for the main fungal and bacterial taxa identified in the present study. Anaerobes were isolated with Pseudomonas aeruginosa, which was more likely to be observed in association with Candida albicans than with Aspergillus fumigatus. CONCLUSIONS: In light of the recent concept of CF lung microbiota, we viewed the microbial community as a unique pathogenic entity. We thus interpreted our results to highlight the potential interactions between microorganisms and the role of fungi in the context of improving survival in CF.

Project description:Microbial communities in the airways of persons with CF (pwCF) are variable, may include genera that are not typically associated with CF, and their composition can be difficult to correlate with long-term disease outcomes. Leveraging two large data sets characterizing sputum communities of 167 pwCF and associated metadata, we identified five bacterial community types. These communities explain 24% of the variability in lung function in this cohort, far more than single factors like Simpson diversity, which explains only 4%. Subjects with Pseudomonas-dominated communities tended to be older and have reduced percent predicted FEV1 (ppFEV1) compared to subjects with Streptococcus-dominated communities, consistent with previous findings. To assess the predictive power of these five communities in a longitudinal setting, we used random forests to classify 346 additional samples from 24 subjects observed 8 years on average in a range of clinical states. Subjects with mild disease were more likely to be observed at baseline, that is, not in the context of a pulmonary exacerbation, and community structure in these subjects was more self-similar over time, as measured by Bray-Curtis distance. Interestingly, we found that subjects with mild disease were more likely to remain in a mixed Pseudomonas community, providing some support for the climax-attack model of the CF airway. In contrast, patients with worse outcomes were more likely to show shifts among community types. Our results suggest that bacterial community instability may be a risk factor for lung function decline and indicates the need to understand factors that drive shifts in community composition. IMPORTANCE While much research supports a polymicrobial view of the CF airway, one in which the community is seen as the pathogenic unit, only controlled experiments using model bacterial communities can unravel the mechanistic role played by different communities. This report uses a large data set to identify a small number of communities as a starting point in the development of tractable model systems. We describe a set of five communities that explain 24% of the variability in lung function in our data set, far more than single factors like Simpson diversity, which explained only 4%. In addition, we report that patients with severe disease experienced more shifts among community types, suggesting that bacterial community instability may be a risk factor for lung function decline. Together, these findings provide a proof of principle for selecting bacterial community model systems.

Dataset Information

The metabolic footprint of the airway bacterial community in cystic fibrosis.

Publications

The metabolic footprint of the airway bacterial community in cystic fibrosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets