Microbial, host and xenobiotic diversity in the cystic fibrosis sputum metabolome

Abstract

Cystic fibrosis (CF) lungs are filled with thick mucus that obstructs airways and facilitates chronic infections. Pseudomonas aeruginosa is a significant pathogen of this disease that produces a variety of toxic small molecules. We used molecular networking-based metabolomics to investigate the chemistry of CF sputa and assess how the microbial molecules detected reflect the microbiome and clinical culture history of the patients. Metabolites detected included xenobiotics, P. aeruginosa specialized metabolites and host sphingolipids. The clinical culture and microbiome profiles did not correspond to the detection of P. aeruginosa metabolites in the same samples. The P. aeruginosa molecules that were detected in sputum did not match those from laboratory cultures. The pseudomonas quinolone signal (PQS) was readily detectable from cultured strains, but absent from sputum, even when its precursor molecules were present. The lack of PQS production in vivo is potentially due to the chemical nature of the CF lung environment, indicating that culture-based studies of this pathogen may not explain its behavior in the lung. The most differentially abundant molecules between CF and non-CF sputum were sphingolipids, including sphingomyelins, ceramides and lactosylceramide. As these highly abundant molecules contain the inflammatory mediator ceramide, they may have a significant role in CF hyperinflammation. This study demonstrates that the chemical makeup of CF sputum is a complex milieu of microbial, host and xenobiotic molecules. Detection of a bacterium by clinical culturing and 16S rRNA gene profiling do not necessarily reflect the active production of metabolites from that bacterium in a sputum sample.

Figure 1

Molecular network of CF sputa and bacterial isolate HPLC-MS/MS metabolomic data generated on GNPS. The network was visualized using the Cytoscape software. Each node represents a unique spectrum that was detected at least twice in the data set and is colored by its sample of origin according to the legend. Nodes that were detected in multiple bacteria as well as a sputum sample are considered together regardless of which bacteria were represented. Bacterial-only nodes are colored gray and were not considered for subsequent sputum statistical analysis and ISP2 media blank nodes are colored black to be ignored as a background. Nodes that hit to the GNPS database based on the molecular networking algorithm (Watrous et al., 2012) are shaped as a ‘V' and circular nodes are those that are not known in GNPS. Molecular families and identified molecules are highlighted. *Singleton nodes are those that did not have any molecular relatives and are not shown in the network.

Figure 2

(a) Bar chart of the distribution of the detected molecules based on the network mapping. Molecules are assigned to a particular bacterium when a spectrum was detected in sputum and that bacterium only. Shared molecules between multiple bacteria are also shown. (b) Clinical culture results and 16S rRNA microbiome profiles of the seven sequenced CF sputum samples in this study. Microbiome profiles were generated using 16S rRNA gene amplicon sequencing and operational taxonomic unit clustering using Mothur, 19 of those most abundant are shown here while the rest are clustered into ‘other' as they were of very low abundance.

Figure 3

Molecular clusters of P. aeruginosa quinolones, phenazines and rhamnolipids. The metabolomes of CF9 and CF6 were networked separately with PAnmFLR01 to explore the specific chemistry of these molecules in the samples that they were detected in. The rhamnolipid cluster is from the network in Figure 1, because they were detected in multiple samples. Nodes are colored and shaped by their sample origin as indicated in the legend. The name and chemical structure of each detected P. aeruginosa metabolite in sputum is shown.

Figure 4

Molecular network highlighting xenobiotic metabolites detected through GNPS library searching in CF samples (green nodes). Successfully annotated xenobiotic molecular families and associated chemical structures are highlighted including nodes representing particular transformations and breakdown products.

Figure 5

(a) The molecular network cluster of sphingolipids and related molecules in sputum data. Node size was scaled according to their abundances using the Cytoscape software. Nodes identified in the random forests VIP are shown as squares, and round nodes were not identified by random forests. Annotation of sphingomyelin (d18:1/16:0) was verified with a purchased standard and its chemical structure is shown. Putative annotations of other nodes in the cluster are according to the LipidMaps database and guided by MS/MS fragmentation spectra. Abundances of the metabolites in the cluster are shown using boxplots from CF and non-CF area under the curve data with attention to the most abundant molecules. (b) Area under the curve abundances of ceramide (Cer), sphingomyelin (SM) and C18 phosphocholine (PC) in CF and non-CF sputa. Statistically significant differences were determined using a Wilcoxon rank-sum test.