Metabolomics analysis of bronchoalveolar lavage fluid predicts unique features of the lower airway in pediatric cystic fibrosis.

BACKGROUND: Progressive, obstructive lung disease resulting from chronic infection and inflammation is the leading cause of morbidity and mortality in persons with cystic fibrosis (PWCF). Metabolomics and next -generation sequencing (NGS) of airway secretions can allow for better understanding of cystic fibrosis (CF) pathophysiology. In this study, global metabolomic profiling on bronchoalveolar lavage fluid (BALF) obtained from pediatric PWCF and disease controls (DCs) was performed and compared to lower airway microbiota, inflammation, and lung function. METHODS: BALF was collected from children undergoing flexible bronchoscopies for clinical indications. Metabolomic profiling was performed using a platform developed by Metabolon Inc. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR), and bacterial communities were characterized using 16S ribosomal RNA (rRNA) sequencing. Random Forest Analysis (RFA), principal component analysis (PCA), and hierarchical clustering analysis (HCA) were performed. RESULTS: One hundred ninety-five BALF samples were analyzed, 142 (73 %) from PWCF. Most metabolites (425/665) and summed categories (7/9) were significantly increased in PWCF. PCA of the metabolomic data revealed CF BALF exhibited more dispersed clustering compared to DC BALF. Higher metabolite concentrations correlated with increased inflammation, increased abundance of Staphylococcus, and decreased lung function. CONCLUSIONS: The lower airway metabolome of PWCF was defined by a complex expansion of metabolomic activity. These findings could be attributed to heightened inflammation in PWCF and aspects of the CF airway polymicrobial ecology. CF-specific metabolomic features are associated with the unique underlying biology of the CF airway.

Network Analysis to Identify Multi-Omic Correlations in the Lower Airways of Children With Cystic Fibrosis.

The leading cause of morbidity and mortality in cystic fibrosis (CF) is progressive lung disease secondary to chronic airway infection and inflammation; however, what drives CF airway infection and inflammation is not well understood. By providing a physiological snapshot of the airway, metabolomics can provide insight into these processes. Linking metabolomic data with microbiome data and phenotypic measures can reveal complex relationships between metabolites, lower airway bacterial communities, and disease outcomes. In this study, we characterize the airway metabolome in bronchoalveolar lavage fluid (BALF) samples from persons with CF (PWCF) and disease control (DC) subjects and use multi-omic network analysis to identify correlations with the airway microbiome. The Biocrates targeted liquid chromatography mass spectrometry (LC-MS) platform was used to measure 409 metabolomic features in BALF obtained during clinically indicated bronchoscopy. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR). The Qiagen EZ1 Advanced automated extraction platform was used to extract DNA, and bacterial profiling was performed using 16S sequencing. Differences in metabolomic features across disease groups were assessed univariately using Wilcoxon rank sum tests, and Random forest (RF) was used to identify features that discriminated across the groups. Features were compared to TBL and markers of inflammation, including white blood cell count (WBC) and percent neutrophils. Sparse supervised canonical correlation network analysis (SsCCNet) was used to assess multi-omic correlations. The CF metabolome was characterized by increased amino acids and decreased acylcarnitines. Amino acids and acylcarnitines were also among the features most strongly correlated with inflammation and bacterial burden. RF identified strong metabolomic predictors of CF status, including L-methionine-S-oxide. SsCCNet identified correlations between the metabolome and the microbiome, including correlations between a traditional CF pathogen, Staphylococcus, a group of nontraditional taxa, including Prevotella, and a subnetwork of specific metabolomic markers. In conclusion, our work identified metabolomic characteristics unique to the CF airway and uncovered multi-omic correlations that merit additional study.

Divergence of bacterial communities in the lower airways of CF patients in early childhood.

RATIONALE: Chronic airway infection and inflammation resulting in progressive, obstructive lung disease is the leading cause of morbidity and mortality in cystic fibrosis. Understanding the lower airway microbiota across the ages can provide valuable insight and potential therapeutic targets. OBJECTIVES: To characterize and compare the lower airway microbiota in cystic fibrosis and disease control subjects across the pediatric age spectrum. METHODS: Bronchoalveolar lavage fluid samples from 191 subjects (63 with cystic fibrosis) aged 0 to 21 years were collected along with relevant clinical data. We measured total bacterial load using quantitative polymerase chain reaction and performed 16S rRNA gene sequencing to characterize bacterial communities with species-level sensitivity for select genera. Clinical comparisons were investigated. MEASUREMENTS AND MAIN RESULTS: Cystic fibrosis samples had higher total bacterial load and lower microbial diversity, with a divergence from disease controls around 2-5 years of age, as well as higher neutrophilic inflammation relative to bacterial burden. Cystic fibrosis samples had increased abundance of traditional cystic fibrosis pathogens and decreased abundance of the Streptococcus mitis species group in older subjects. Interestingly, increased diversity in the heterogeneous disease controls was independent of diagnosis and indication. Sequencing was more sensitive than culture, and antibiotic exposure was more common in disease controls, which showed a negative relationship with load and neutrophilic inflammation. CONCLUSIONS: Analysis of lower airway samples from people with cystic fibrosis and disease controls across the ages revealed key differences in airway microbiota and inflammation. The divergence in subjects during early childhood may represent a window of opportunity for intervention and additional study.