Longitudinal sampling of the lung microbiota in individuals with cystic fibrosis

Abstract

Cystic fibrosis (CF) manifests in the lungs resulting in chronic microbial infection. Most morbidity and mortality in CF is due to cycles of pulmonary exacerbations-episodes of acute inflammation in response to the lung microbiome-which are difficult to prevent and treat because their cause is not well understood. We hypothesized that longitudinal analyses of the bacterial component of the CF lung microbiome may elucidate causative agents within this community for pulmonary exacerbations. In this study, 6 participants were sampled thrice-weekly for up to one year. During sampling, sputum, and data (antibiotic usage, spirometry, and symptom scores) were collected. Time points were categorized based on relation to exacerbation as Stable, Intermediate, and Treatment. Retrospectively, a subset of were interrogated via 16S rRNA gene sequencing. When samples were examined categorically, a significant difference between the lung microbiota in Stable, Intermediate, and Treatment samples was observed in a subset of participants. However, when samples were examined longitudinally, no correlations between microbial composition and collected data (antibiotic usage, spirometry, and symptom scores) were observed upon exacerbation onset. In this study, we identified no universal indicator within the lung microbiome of exacerbation onset but instead showed that changes to the CF lung microbiome occur outside of acute pulmonary episodes and are patient-specific.

Fig 1. Outline of sputum collection and samples chosen for sequencing.

Participants self-collected sputum 3 times a week while simultaneously recording clinical symptoms. On occasion, sputum could not or was not collected yet participant information was recorded (gray dots). Samples were retroactively chosen for microbiome analysis based on the sample type, aiming to follow Treatment more closely (1 sample/per 2–3 days) then Intermediate (1 sample per 1 week) and Stable (1 sample per 1 month) samples. All but one participant, C, experienced an exacerbation during the study period. Exact dates and length of sample collection for each participant is provided in S1 Table.

Fig 2. The CF lung microbiome is distinguished by individual.

A. PCoA plots of all participants illustrate the clustering of participant samples, indicated as significant by PERMANOVA (p = 0.001). Health state within participants, as defined as Stable, Intermediate (<1 month pre- or post-Treatment), and Treatment was significant (PERMANOVA, p = 0.016), but was highly confounded by the participant (p = 0.042 of Participant:Health interaction term). B. UPGMA phylogeny depicting the Bray-Curtis dissimilarity between samples. It is apparent that the principle driver of similarity between samples are inter-individual microbial lung composition due to the almost complete separation of participant samples. PC = Principal Coordinate.

Fig 3. The effects of exacerbation on the lung microbiome are not consistently seen at the community level.

A. Taxonomic summaries of all samples sequenced. These summaries indicate that changes to the lung microbiome upon exacerbation are not often obvious when examining the community-wide taxa composition. Taxa present at <2% are summarized in the gray bar. Participant E experienced 4 exacerbations during the study period which are indicated with black lines. B. Heatmaps indicate the Bray-Curtis dissimilarity between each sample. Here, we can see that samples taken during some exacerbations are more dissimilar to those collected during stability; however, this is not true for every exacerbation. These observations are qualified by statistical measures (S2 Table) and were independent of FEV1 (S3 Table).

Fig 4. Diversity within the lung community does not consistently decrease with exacerbation.

A longitudinal representation of the evenness and richness of the CF lung microbiota across study participants indicates patient-specific levels of within-patient diversity.

Fig 5. Longitudinal dynamics of two select participants (C and E).

Two participants who were the outliers in terms of the number of pulmonary exacerbations experienced over the course of the study period were chosen for closer examination. A. Sample collection for participant C is shown in relation to, antibiotic use, FEV1, and symptom scores. B. Correlations between collected data, diversity metrics, and OTU relative abundance were calculated and significant correlations were reported (S4 Table); a subset of these significant correlations are plotted. C. Sample collection for participant E in relation to antibiotic use, FEV1, and symptom scores. D. Correlations between these collected data and the OTUs present within the microbiome were calculated and significant correlations were reported (S5 Table); a subset of these significant correlations are plotted.

Fig 6. Examples of stability and variability in the CF lung microbial communities of two select participants (C and E).

A. Visualization of the stability of participant C's lung microbial community over the study period. Each OTU is presented as a terminal node on the phylogeny; its presence in each sample evaluated using 16S rRNA gene sequencing is shown extending outwardly from the inner phylogeny in chronological order. The density of the color indicates the relative abundance of the OTU; when the OTU is not identified, the space is left blank. B. Participant E, who experienced 4 exacerbations over the course of the year, has a much more variable lung microbiota than participant C. Similar to Fig 5c, OTUs are represented as nodes in the phylogeny whose relative abundance is indicated with varying color density. Rings in the phylogeny are colored to indicate the sample type (Treatment red, Intermediate blue, Stable green). Density of the color indicates relative abundance of the OTU and time periods are colored according to the health state.