Decade-long bacterial community dynamics in cystic fibrosis airways - PubMed (original) (raw)

. 2012 Apr 10;109(15):5809-14.

doi: 10.1073/pnas.1120577109. Epub 2012 Mar 26.

Patrick D Schloss, Linda M Kalikin, Lisa A Carmody, Bridget K Foster, Joseph F Petrosino, James D Cavalcoli, Donald R VanDevanter, Susan Murray, Jun Z Li, Vincent B Young, John J LiPuma

Affiliations

Decade-long bacterial community dynamics in cystic fibrosis airways

Jiangchao Zhao et al. Proc Natl Acad Sci U S A. 2012.

Abstract

The structure and dynamics of bacterial communities in the airways of persons with cystic fibrosis (CF) remain largely unknown. We characterized the bacterial communities in 126 sputum samples representing serial collections spanning 8-9 y from six age-matched male CF patients. Sputum DNA was analyzed by bar-coded pyrosequencing of the V3-V5 hypervariable region of the 16S rRNA gene, defining 662 operational taxonomic units (OTUs) from >633,000 sequences. Bacterial community diversity decreased significantly over time in patients with typically progressive lung disease but remained relatively stable in patients with a mild lung disease phenotype. Antibiotic use, rather than patient age or lung function, was the primary driver of decreasing diversity. Interpatient variability in community structure exceeded intrapatient variability in serial samples. Antibiotic treatment was associated with pronounced shifts in community structure, but communities showed both short- and long-term resilience after antibiotic perturbation. There was a positive correlation between OTU occurrence and relative abundance, with a small number of persistent OTUs accounting for the greatest abundance. Significant changes in community structure, diversity, or total bacterial density at the time of pulmonary exacerbation were not observed. Despite decreasing community diversity in patients with progressive disease, total bacterial density remained relatively stable over time. These findings show the critical relationship between airway bacterial community structure, disease stage, and clinical state at the time of sample collection. These features are the key parameters with which to assess the complex ecology of the CF airway.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

Changes in lung function and community diversity. (A) Percent predicted FEV1 values (y axis) are plotted against patient age (x axis). Patients S1, S2, and S3 have relatively stable disease; patients P1, P2, and P3 have progressive disease. Solid circles indicate the ages when the first and last sputum samples were obtained. (B) Temporal changes in community diversity in stable (Upper Row) and progressing (Lower Row) patients. In each plot, community diversity measured by Shannon index is plotted against patient age when sputum was collected. (C) Boxplot comparison of microbial diversity in sputum samples obtained during different BETR clinical states. The top and bottom boundaries of each box indicate 75th and 25th quartile values, respectively, and black lines inside each box represent 50th quartile (median) values. Ends of the whiskers mark the lowest and highest diversity index in each BETR category. The letters above the boxes indicate comparison results in group means; groups with the same letter do not have significant differences in means, but those indicated by different letters are significantly different. (See

SI Results

for details.)

Fig. 2.

Fig. 2.

PCoA of bacterial community structures using BC distances. (A) Community structures of samples from three stable patients (blue) and three progressing patients (red). Distances between symbols on the ordination plot reflect relative similarities in community structures. PC1, principal coordinate 1; PC2, principal coordinate 2. (B) Community structure dynamics in serial samples highlighting the patient's clinical state on day of collection. The scale and percent of variation explained by PC1 and PC2 for each plot are the same as shown in A. Upper row shows stable patients (S1, S2, S3); lower row shows progressing patients (P1, P2, P3). Circles represent individual sputum samples and are color coded to reflect BETR category (B, blue; E, red; T, green; R, yellow; U, gray); lines connect serial samples. Upright and inverted triangles indicate first and last samples, respectively, in each serial set. Samples 10–13 from patient P1 and sample 12 from patient P3 are indicated by black arrows and are described in the text.

Fig. 3.

Fig. 3.

Relative abundance of OTUs in serial sputum samples from patient P2. Relative abundances of OTUs, each accounting for >1% of the total bacterial community, are shown in each sample. Relative abundances of all remaining OTUs, each representing ≤1% of the community, are shown in gray at the top of each bar. Top line indicates patient age at time of sample collection. Letters beneath each bar indicate BETR category (Table 2). Heat map indicates antibiotic load (Materials and Methods) at the time of sputum sample collection. Circles indicate total bacterial density (16S rRNA copies/mL sputum) based on quantitative PCR. Similar graphs for patients S1, S2, S3, P1, and P3 are shown in

Fig. S4

. _Pseudomonas_-1 represents Pseudomonas aeruginosa. _Streptococcus_-1 represents species within the Streptococcus mitis group. _Streptococcus_-2 represents species within the Streptococcus milleri group.

References

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