Abstract
The identification of 16S rDNA biomarkers from respiratory samples to describe the continuum of clinical disease states within persons having cystic fibrosis (CF) has remained elusive. We sought to combine 16S, metagenomics, and metabolomics data to describe multiple transitions between clinical disease states in 14 samples collected over a 12-month period in a single person with CF. We hypothesized that each clinical disease state would have a unique combination of bacterial genera and volatile metabolites as a potential signature that could be utilized as a biomarker of clinical disease state. Taxonomy identified by 16S sequencing corroborated clinical culture results, with the majority of the 109 PCR amplicons belonging to the bacteria grown in clinical cultures (Escherichia coli and Staphylococcus aureus). While alpha diversity measures fluctuated across disease states, no significant trends were present. Principle coordinates analysis showed that treatment samples trended toward a different community composition than baseline and exacerbation samples. This was driven by the phylum Bacteroidetes (less abundant in treatment, log2 fold difference −3.29, p = 0.015) and the genus Stenotrophomonas (more abundant in treatment, log2 fold difference 6.26, p = 0.003). Across all sputum samples, 466 distinct volatile metabolites were identified with total intensity varying across clinical disease state. Baseline and exacerbation samples were rather uniform in chemical composition and similar to one another, while treatment samples were highly variable and differed from the other two disease states. When utilizing a combination of the microbiome and metabolome data, we observed associations between samples dominated Staphylococcus and Escherichia and higher relative abundances of alcohols, while samples dominated by Achromobacter correlated with a metabolomics shift toward more oxidized volatiles. However, the microbiome and metabolome data were not tightly correlated; examining both the metagenomics and metabolomics allows for more context to examine changes across clinical disease states. In our study, combining the sputum microbiome and metabolome data revealed stability in the sputum composition through the first exacerbation and treatment episode, and into the second exacerbation. However, the second treatment ushered in a prolonged period of instability, which after three additional exacerbations and treatments culminated in a new lung microbiome and metabolome.
Highlights
Cystic fibrosis (CF) is a genetic disease affecting more than 30,000 people in the United States, associated with both intermittent and chronic suppurative lung infections (Wagener et al, 2013; MacKenzie et al, 2014)
Since our hypothesis for this study was that the volatile metabolomes would change with clinical disease state, we reduced our list of variables to those in the top 50th percentile for variance across samples, yielding 229 volatile metabolites for subsequent analyses with a median FOO of 8 specimens
We found no statistically-significant correlations, whether we binned all metabolites that are known to be associated with E. coli or S. aureus, the two beststudied bacterial volatile metabolomes represented in this sample set (Allardyce et al, 2006; Filipiak et al, 2012; Tait et al, 2014; Baptista et al, 2019; Jenkins and Bean, 2019), or treated metabolites independently, e.g., correlated E. coli relative abundance to indole absolute or relative abundance
Summary
Cystic fibrosis (CF) is a genetic disease affecting more than 30,000 people in the United States, associated with both intermittent and chronic suppurative lung infections (Wagener et al, 2013; MacKenzie et al, 2014). Current clinical practice relies on culturing pathogens from sputum samples, culture-independent approaches such as the 16S rDNA amplicon sequencing described above have revealed that CF airways are dominated by bacteria from two main populations: slow-growing opportunistic pathogens (e.g., Pseudomonas aeruginosa, Stenotrophomonas maltophilia, Burkholderia cepacia and others), and anaerobes common to the oral cavity that migrate to the airways in the context of poor mucociliary clearance (e.g., Streptococcus spp., Rothia mucilaginosa and others). Metabolomics offers another culture independent approach to profile clinical samples. The volatile metabolome may be able to complement genomics as an additional culture-independent method for identifying exacerbation onset and recovery, and/or understanding the root causes of these events
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