Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society · 2024
Metabolomics analysis of bronchoalveolar lavage fluid predicts unique features of the lower airway in pediatric cystic fibrosis.
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O'Connor John B, Mottlowitz Madison, Wagner Brandie D, Harris J Kirk, Laguna Theresa A
Аннотация
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.
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.