Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis.

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis.

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed bacterial communities in 880 CF sputum samples and developed microbiome descriptors to model community reorganization prior to and during 18 PEx. We identified two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx showed hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx displayed stochasticity and increased diversity. A simulation of antimicrobial treatment predicted better efficacy for hierarchically organized communities. This link between PEx type, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis.

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease (COPD). Intermittent pulmonary exacerbations (PEx) in these conditions are associated with lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed 880 near-daily CF sputum samples and developed non-standard microbiome descriptors to model community reorganization prior and during 18 PEx. We identified two communal microbial regimes with opposing ecology and dynamics. Whereas pathogen-governed dysbiosis showed hierarchical community organization and reduced diversity, anaerobic bloom dysbiosis displayed stochasticity and increased diversity. Microbiome organization modulated the relevance of pathogens and a simulation of antimicrobial treatment predicted better efficacy for hierarchically organized microbiota. This causal link between PEx, microbiome organization, and treatment success advances the development of personalized dysbiosis management in CF and, potentially, other obstructive lung diseases.

Airway bacterial community composition in persons with advanced cystic fibrosis lung disease.

BACKGROUND: The progression of lung disease in people with cystic fibrosis (pwCF) has been associated with a decrease in the diversity of airway bacterial communities. How often low diversity communities occur in advanced CF lung disease and how they may be associated with clinical outcomes is not clear, however. METHODS: We sequenced a region of the bacterial 16S ribosomal RNA gene to characterize bacterial communities in sputum from 190 pwCF with advanced lung disease (FEV1≤40% predicted), with particular attention to the prevalence and relative abundance of dominant genera. We evaluated relationships between community diversity and clinical outcomes. RESULTS: Although most of the 190 pwCF with advanced lung disease had airway bacterial communities characterized by low diversity with a dominant genus, a considerable minority (40%) did not. The absence of a dominant genus, presence of methicillin-susceptible Staphylococcus aureus, and greater bacterial richness positively correlated with lung function. Higher relative abundance of the dominant genus and greater antimicrobial use negatively correlated with lung function. PwCF with a low diversity community and dominant genus had reduced lung transplant-free survival compared to those without (median survival of 1.6 vs 2.9 years). CONCLUSIONS: A considerable proportion of pwCF with advanced lung disease do not have airway bacterial communities characterized by low diversity and a dominant genus and these individuals had better survival. An understanding of the antecedents of low diversity airway communities- and the impact these may have on lung disease trajectory - may provide avenues for improved management strategies.

Changes in airway bacterial communities occur soon after initiation of antibiotic treatment of pulmonary exacerbations in cystic fibrosis.

Chronic polymicrobial airway infections are a hallmark of cystic fibrosis (CF) lung disease. Antibiotic therapy is a primary treatment of CF pulmonary exacerbations (PEx); however, the impact of episodic antibiotic treatment on airway bacterial communities has not been well described. We analyzed sputum samples from adults with CF obtained immediately before and during antibiotic treatment of PEx. Sequencing of the V4 region of the bacterial 16S ribosomal RNA gene was used to assess changes in bacterial community structure during antibiotic treatment. The peak impact of antibiotic treatment was observed by day four or five of treatment. These findings advance our understanding of bacterial community dynamics during antibiotic treatment of PEx and complement recent and ongoing studies evaluating the optimal duration of antibiotic therapy for PEx.

Lung Microbiota and Metabolites Collectively Associate with Clinical Outcomes in Milder Stage Chronic Obstructive Pulmonary Disease.

Rationale: Chronic obstructive pulmonary disease (COPD) is variable in its development. Lung microbiota and metabolites collectively may impact COPD pathophysiology, but relationships to clinical outcomes in milder disease are unclear. Objectives: Identify components of the lung microbiome and metabolome collectively associated with clinical markers in milder stage COPD. Methods: We analyzed paired microbiome and metabolomic data previously characterized from bronchoalveolar lavage fluid in 137 participants in the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), or (GOLD [Global Initiative for Chronic Obstructive Lung Disease Stage 0-2). Datasets used included 1) bacterial 16S rRNA gene sequencing; 2) untargeted metabolomics of the hydrophobic fraction, largely comprising lipids; and 3) targeted metabolomics for a panel of hydrophilic compounds previously implicated in mucoinflammation. We applied an integrative approach to select features and model 14 individual clinical variables representative of known associations with COPD trajectory (lung function, symptoms, and exacerbations). Measurements and Main Results: The majority of clinical measures associated with the lung microbiome and metabolome collectively in overall models (classification accuracies, >50%, P < 0.05 vs. chance). Lower lung function, COPD diagnosis, and greater symptoms associated positively with Streptococcus, Neisseria, and Veillonella, together with compounds from several classes (glycosphingolipids, glycerophospholipids, polyamines and xanthine, an adenosine metabolite). In contrast, several Prevotella members, together with adenosine, 5'-methylthioadenosine, sialic acid, tyrosine, and glutathione, associated with better lung function, absence of COPD, or less symptoms. Significant correlations were observed between specific metabolites and bacteria (Padj < 0.05). Conclusions: Components of the lung microbiome and metabolome in combination relate to outcome measures in milder COPD, highlighting their potential collaborative roles in disease pathogenesis.

Sputum Metabolites Associated with Nontuberculous Mycobacterial Infection in Cystic Fibrosis.

Nontuberculous mycobacterial (NTM) pulmonary infections in people with cystic fibrosis (CF) are associated with significant morbidity and mortality and are increasing in prevalence. Host risk factors for NTM infection in CF are largely unknown. We hypothesize that the airway microbiota represents a host risk factor for NTM infection. In this study, 69 sputum samples were collected from 59 people with CF; 42 samples from 32 subjects with NTM infection (14 samples collected before incident NTM infection and 28 samples collected following incident NTM infection) were compared to 27 samples from 27 subjects without NTM infection. Sputum samples were analyzed with 16S rRNA gene sequencing and metabolomics. A supervised classification and correlation analysis framework (sparse partial least-squares discriminant analysis [sPLS-DA]) was used to identify correlations between the microbial and metabolomic profiles of the NTM cases compared to the NTM-negative controls. Several metabolites significantly differed in the NTM cases compared to controls, including decreased levels of tryptophan-associated and branched-chain amino acid metabolites, while compounds involved in phospholipid metabolism displayed increased levels. When the metabolome and microbiome data were integrated by sPLS-DA, the models and component ordinations showed separation between the NTM and control samples. While this study could not determine if the observed differences in sputum metabolites between the cohorts reflect metabolic changes that occurred as a result of the NTM infection or metabolic features that contributed to NTM acquisition, it is hypothesis generating for future work to investigate host and bacterial community factors that may contribute to NTM infection risk in CF. IMPORTANCE Host risk factors for nontuberculous mycobacterial (NTM) infection in people with cystic fibrosis (CF) are largely unclear. The goal of this study was to help identify potential host and bacterial community risk factors for NTM infection in people with CF, using microbiome and metabolome data from CF sputum samples. The data obtained in this study identified several metabolic profile differences in sputum associated with NTM infection in CF, including 2-methylcitrate/homocitrate and selected ceramides. These findings represent potential risk factors and therapeutic targets for preventing and/or treating NTM infections in people with CF.

Cystic fibrosis airway microbiota associated with outcomes of nontuberculous mycobacterial infection.

RATIONALE: Pulmonary infections with nontuberculous mycobacteria (NTM) are increasingly prevalent in people with cystic fibrosis (CF). Clinical outcomes following NTM acquisition are highly variable, ranging from transient self-resolving infection to NTM pulmonary disease associated with significant morbidity. Relationships between airway microbiota and variability of NTM outcomes in CF are unclear. OBJECTIVE: To identify features of CF airway microbiota associated with outcomes of NTM infection. METHODS: 188 sputum samples, obtained from 24 subjects with CF, each with three or more samples collected from 3.5 years prior to, and up to 6 months following incident NTM infection, were selected from a sample repository. Sputum DNA underwent bacterial 16S rRNA gene sequencing. Airway microbiota were compared based on the primary outcome, a diagnosis of NTM pulmonary disease, using Wilcoxon rank-sum testing, autoregressive integrated moving average modelling and network analyses. MEASUREMENTS AND MAIN RESULTS: Subjects with and without NTM pulmonary disease were similar in clinical characteristics, including age and lung function at the time of incident NTM infection. Time-series analyses of sputum samples prior to incident NTM infection identified positive correlations between Pseudomonas, Streptococcus, Veillonella, Prevotella and Rothia with diagnosis of NTM pulmonary disease and with persistent NTM infection. Network analyses identified differences in clustering of taxa between subjects with and without NTM pulmonary disease, and between subjects with persistent versus transient NTM infection. CONCLUSIONS: CF airway microbiota prior to incident NTM infection are associated with subsequent outcomes, including diagnosis of NTM pulmonary disease, and persistence of NTM infection. Associations between airway microbiota and NTM outcomes represent targets for validation as predictive markers and for future therapies.

Parallel Analysis of Cystic Fibrosis Sputum and Saliva Reveals Overlapping Communities and an Opportunity for Sample Decontamination.

Culture-independent studies of the cystic fibrosis (CF) airway microbiome typically rely on expectorated sputum to assess the microbial makeup of lower airways. These studies have revealed rich bacterial communities. There is often considerable overlap between taxa observed in sputum and those observed in saliva, raising questions about the reliability of expectorated sputum as a sample representing lower airway microbiota. These concerns prompted us to compare pairs of sputum and saliva samples from 10 persons with CF. Using 16S rRNA gene sequencing and droplet digital PCR (ddPCR), we analyzed 37 pairs of sputum and saliva samples, each collected from the same person on the same day. We developed an in silico postsequencing decontamination procedure to remove from sputum the fraction of DNA reads estimated to have been contributed by saliva during expectoration. We demonstrate that while there was often sizeable overlap in community membership between sample types, expectorated sputum typically contains a higher bacterial load and a less diverse community compared to saliva. The differences in diversity between sputum and saliva were more pronounced in advanced disease stage, owing to increased relative abundance of the dominant taxa in sputum. Our effort to model saliva contamination of sputum in silico revealed generally minor effects on community structure after removal of contaminating reads. Despite considerable overlap in taxa observed between expectorated sputum and saliva samples, the impact of saliva contamination on measures of lower airway bacterial community composition in CF using expectorated sputum appears to be minimal.IMPORTANCE Cystic fibrosis is an inherited disease characterized by chronic respiratory tract infection and progressive lung disease. Studies of cystic fibrosis lung microbiology often rely on expectorated sputum to reflect the microbiota present in the lower airways. Passage of sputum through the oropharynx during collection, however, contributes microbes present in saliva to the sample, which could confound interpretation of results. Using culture-independent DNA sequencing-based analyses, we characterized the bacterial communities in pairs of expectorated sputum and saliva samples to generate a model for "decontaminating" sputum in silico Our results demonstrate that salivary contamination of expectorated sputum does not have a large effect on most sputum samples and that observations of high bacterial diversity likely accurately reflect taxa present in cystic fibrosis lower airways.

Measures of Cystic Fibrosis Airway Microbiota during Periods of Clinical Stability.

Rationale: Differences in cystic fibrosis (CF) airway microbiota between periods of clinical stability and exacerbation of respiratory symptoms have been investigated in efforts to better understand microbial triggers of CF exacerbations. Prior studies have often relied on a single sample or a limited number of samples to represent airway microbiota. However, the variability in airway microbiota during periods of clinical stability is not well known.Objectives: To determine the temporal variability of measures of airway microbiota during periods of clinical stability, and to identify factors associated with this variability.Methods: Sputum samples (N = 527), obtained daily from six adults with CF during 10 periods of clinical stability, underwent sequencing of the V4 region of the bacterial 16S ribosomal RNA gene. The variability in airway microbiota among samples within each period of clinical stability was calculated as the average of the Bray-Curtis similarity measures of each sample to every other sample within the same period. Outlier samples were defined as samples outside 1.5 times the interquartile range within a baseline period with respect to the average Bray-Curtis similarity. Total bacterial load was measured with droplet digital polymerase chain reaction.Results: The variation in Bray-Curtis similarity and total bacterial load among samples within the same baseline period was greater than the variation observed in technical replicate control samples. Overall, 6% of samples were identified as outliers. Within baseline periods, changes in bacterial community structure occurred coincident with changes in maintenance antibiotics (P < 0.05, analysis of molecular variance). Within subjects, bacterial community structure changed between baseline periods (P < 0.01, analysis of molecular variance). Sample-to-sample similarity within baseline periods was greater with fewer interval days between sampling.Conclusions: During periods of clinical stability, airway bacterial community structure and bacterial load vary among daily sputum samples from adults with CF. This day-to-day variation has bearing on study design and interpretation of results, particularly in analyses that rely on single samples to represent periods of interest (e.g., clinical stability vs. pulmonary exacerbation). These data also emphasize the importance of accounting for maintenance antibiotic use and granularity of sample collection in studies designed to assess the dynamics of CF airway microbiota relative to changes in clinical state.