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

Abstract

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.

Identification of Exhaled Metabolites in Children with Cystic Fibrosis

“…These results are consistent with previous studies that also found decreased levels of acylcarnitines in CF [58][59][60]. O'Connor et al, reported decreased levels of acylcarnitines that correlated with inflammation and the amount of bacteria in patients with CF, as a major finding of their recent untargeted metabolomic study in BALF samples [58]. Acylcarnitines are important for cellular energy production, mainly for the β-oxidation of fatty acids, as they are used as intermediates to transport acyl groups into the mitochondria [61].…”

“…Figure 3 A–D shows box plots of carnitine, acetylcarnitine, propionylcarnitine and butyrylcarnitine. These results are consistent with previous studies that also found decreased levels of acylcarnitines in CF [ 58 , 59 , 60 ]. O’Connor et al, reported decreased levels of acylcarnitines that correlated with inflammation and the amount of bacteria in patients with CF, as a major finding of their recent untargeted metabolomic study in BALF samples [ 58 ].…”

An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples

“…The uid also contains endogenous peptides generated as products of protease activity on larger proteins, some of which have biological activity and can serve as biomarkers (11,12). Small molecule metabolites, acted upon by enzymes, are also detectable (13)(14)(15). Finally, extracellular vesicles, packed with nucleic acids, proteins and other molecule types have also been well-described (16,17).…”

Prevotella copri exhausts host’s intrinsic indol-3-pyruvid acid to promote breast cancer development: Inactivation of AMPK via UHRF1-mediated negative regulation

“…Prevotella spp. is one of the dominant commensals that inhabits in multiple body locations of human and animal, such as gut [18], airways [19], and oral cavity [20]. Increasing studies revealed that dysbiosis of Prevotella genus is related to various diseases, including cystic brosis [19], gout [21], and obesity [22], although direct causation of disease remains uncertain.…”

“…is one of the dominant commensals that inhabits in multiple body locations of human and animal, such as gut [18], airways [19], and oral cavity [20]. Increasing studies revealed that dysbiosis of Prevotella genus is related to various diseases, including cystic brosis [19], gout [21], and obesity [22], although direct causation of disease remains uncertain. In the present study, we found that Prevotella genus in gut microbiota was enriched in breast cancer patients, which is accordant with the report by Zhu and colleagues [8].…”