High-Resolution Longitudinal Dynamics of the Cystic Fibrosis Sputum Microbiome and Metabolome through Antibiotic Therapy

Abstract

Microbial diversity in the cystic fibrosis (CF) lung decreases over decades as pathogenic bacteria such as Pseudomonas aeruginosa take over. The dynamics of the CF microbiome and metabolome over shorter time frames, however, remain poorly studied. Here, we analyze paired microbiome and metabolome data from 594 sputum samples collected over 401 days from six adult CF subjects (subject mean = 179 days) through periods of clinical stability and 11 CF pulmonary exacerbations (CFPE). While microbiome profiles were personalized (permutational multivariate analysis of variance [PERMANOVA] r2 = 0.79, P < 0.001), we observed significant intraindividual temporal variation that was highest during clinical stability (linear mixed-effects [LME] model, P = 0.002). This included periods where the microbiomes of different subjects became highly similar (UniFrac distance, <0.05). There was a linear increase in the microbiome alpha-diversity and in the log ratio of anaerobes to pathogens with time (n = 14 days) during the development of a CFPE (LME P = 0.0045 and P = 0.029, respectively). Collectively, comparing samples across disease states showed there was a reduction of these two measures during antibiotic treatment (LME P = 0.0096 and P = 0.014, respectively), but the stability data and CFPE data were not significantly different from each other. Metabolome alpha-diversity was higher during CFPE than during stability (LME P = 0.0085), but no consistent metabolite signatures of CFPE across subjects were identified. Virulence-associated metabolites from P. aeruginosa were temporally dynamic but were not associated with any disease state. One subject died during the collection period, enabling a detailed look at changes in the 194 days prior to death. This subject had over 90% Pseudomonas in the microbiome at the beginning of sampling, and that level gradually increased to over 99% prior to death. This study revealed that the CF microbiome and metabolome of some subjects are dynamic through time. Future work is needed to understand what drives these temporal dynamics and if reduction of anaerobes correlate to clinical response to CFPE therapy. IMPORTANCE Subjects with cystic fibrosis battle polymicrobial lung infections throughout their lifetime. Although antibiotic therapy is a principal treatment for CF lung disease, we have little understanding of how antibiotics affect the CF lung microbiome and metabolome and how much the community changes on daily timescales. By analyzing 594 longitudinal CF sputum samples from six adult subjects, we show that the sputum microbiome and metabolome are dynamic. Significant changes occur during times of stability and also through pulmonary exacerbations (CFPEs). Microbiome alpha-diversity increased as a CFPE developed and then decreased during treatment in a manner corresponding to the reduction in the log ratio of anaerobic bacteria to classic pathogens. Levels of metabolites from the pathogen P. aeruginosa were also highly variable through time and were negatively associated with anaerobes. The microbial dynamics observed in this study may have a significant impact on the outcome of antibiotic therapy for CFPEs and overall subject health.

Mining Public Mass Spectrometry Data to Characterize the Diversity and Ubiquity of P. aeruginosa Specialized Metabolites

“…Supporting its presence in feces is the finding of pyochelin, a phenazine and rhamnolipids in the same datasets. This result indicates that LC-MS/MS based detection of these compounds may be a useful method for screening fecal samples for P. aeruginosa infection, much as it has been for CF mucus samples [6,20,21], as this can be done rapidly within hours of sample collection [22]. There were few instances where a molecular family of P. aeruginosa metabolites was found in only a single sample type, indicating that these compounds generally are produced together across a wide variety of infection sites and environments.…”

Dual oxic-anoxic co-culture enables direct study of host-anaerobe interactions at the airway epithelial interface

“…To demonstrate the utility of our model we used a polymicrobial consortium representative of anaerobic bacterial community signatures observed in acute and chronic airway disease (8, 11, 39, 58). Dominated by Streptococcus, Prevotella , and Veillonella spp., this consortium (among other bacterial taxa) is thought to seed the airways through microaspiration from the oral cavity and is recognized as a key risk factor in the development of COPD, CF, pneumonia, sinusitis and other diseases.…”

“…To address this knowledge gap, here we optimized and characterized a model system that facilitates co-culture of anaerobes and polarized airway epithelial cells. To demonstrate the utility of our model we used a polymicrobial consortium representative of anaerobic bacterial community signatures observed in acute and chronic airway disease (8,11,39,58).…”

“…Data in C were compared using a non-parametric Kruskal-Wallace test (p<0.0001) with multiple comparisons.Pairwise comparisons in panel D were performed using were compared using an unpaired t-test with Welch's correction (*p <0.05, ***p<0001).DISCUSSIONThe recent surge in culture-independent sequencing of human microbiota has spawned renewed interest in the importance of anaerobic bacteria in disease etiologies. However, despite anaerobes comprising a significant component of airway bacterial communities, studies on their contributions to disease pathophysiology have reported seemingly contradictory results(7,19,(36)(37)(38)(39)(40)(41), calling into question their role in patient morbidity. Proposed pathogenic mechanisms are supported by compelling in vitro data(15-17, 42, 43), but their relevance is unresolved due to a lack of compatible models with which to test…”

Lung Microbiome in Cystic Fibrosis

“…This has important clinical implications, since, at least in infants, it makes the upper respiratory tract niches inadequate for the determination of lung microbial diversity and, most importantly, for the identification of all pathogens. Furthermore, especially under disease conditions, the microbiome could be different between the left and right lungs [ 20 ] and in the different areas of the thick mucus layers characteristic of the CF patients [ 21 ], reinforcing the idea that analyzing the microbiomes from patients’ BALFs would make diagnosis and therapies more accurate. As described earlier, in young CF patients, the microbiome still seems to preserve some diversity, with Streptococcus being the predominant taxon; the common CF pathogens ( Staphylococcus and P. aeruginosa ) comprise about 50% of the total microbial community.…”