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.

Novel Application of Aptamer Proteomic Analysis in Cystic Fibrosis Bronchoalveolar Lavage Fluid.

PURPOSE: Biomarkers are needed in cystic fibrosis (CF) to understand disease progression, assess response to therapy, and enrich enrollment for clinical trials. Aptamer-based proteomics have proven useful in blood samples. The aim is to evaluate proteins in bronchoalveolar lavage fluid (BALF) in CF children compared to controls and identify endotypes during CF exacerbations. EXPERIMENTAL DESIGN: BALF is collected clinically from 50 patients with CF and nine disease controls, processed, and stored per protocol. BALF supernatants are analyzed for 1129 proteins by aptamer approach (SOMAscan proteomics platform). Proteins are compared across groups and used for pathway analysis. Endotypes are identified within the CF group. RESULTS: CF BALF has increased concentrations of neutrophil elastase, myeloperoxidase, and decreased concentration of protein folding and host defense proteins. Pathways that distinguished CF subjects included interferon gamma signaling, membrane trafficking, and phospholipid metabolism. In the CF group, unbiased analysis of proteins identified two distinct endotypes that differed based on BALF white blood cell and neutrophil counts and detection of CF pathogens. CONCLUSIONS AND CLINICAL RELEVANCE: Proteomic analysis of the CF airway demonstrates a complex environment of proteins and pathways. This work provides evidence that aptamer-based proteomics can differentiate between groups and can determine endotypes within CF.