Partitioning core and satellite taxa from within cystic fibrosis lung bacterial communities

Abstract

Cystic fibrosis (CF) patients suffer from chronic bacterial lung infections that lead to death in the majority of cases. The need to maintain lung function in these patients means that characterising these infections is vital. Increasingly, culture-independent analyses are expanding the number of bacterial species associated with CF respiratory samples; however, the potential significance of these species is not known. Here, we applied ecological statistical tools to such culture-independent data, in a novel manner, to partition taxa within the metacommunity into core and satellite species. Sputa and clinical data were obtained from 14 clinically stable adult CF patients. Fourteen rRNA gene libraries were constructed with 35 genera and 82 taxa, identified in 2139 bacterial clones. Shannon-Wiener and taxa-richness analyses confirmed no undersampling of bacterial diversity. By decomposing the distribution using the ratio of variance to the mean taxon abundance, we partitioned objectively the species abundance distribution into core and satellite species. The satellite group comprised 67 bacterial taxa from 33 genera and the core group, 15 taxa from 7 genera (including Pseudomonas (1 taxon), Streptococcus (2), Neisseria (2), Catonella (1), Porphyromonas (1), Prevotella (5) and Veillonella (3)], the last four being anaerobes). The core group was dominated by Pseudomonas aeruginosa. Other recognised CF pathogens were rare. Mantel and partial Mantel tests assessed which clinical factors influenced the composition observed. CF transmembrane conductance regulator genotype and antibiotic treatment correlated with all core taxa. Lung function correlated with richness. The clinical significance of these core and satellite species findings in the CF lung is discussed. GenBank accession numbers: FM995625–FM997761

Figure 1

Distribution and dispersal of bacterial taxa across patients. (a) The number of patients for whom each bacterial taxon was observed, plotted against the mean abundance (log₁₀ scale) across all patients (r²=0.44; F_1,80=62.2; P<0.0001). (b) Random and non-random dispersal through space visualised by decomposing the overall distribution using an index of dispersion based on the ratio of variance to the mean abundance for each bacterial taxon from the 14 patients sampled. The line depicts the 2.5% confidence limit for the χ² distribution. The 97.5% confidence limit was not plotted, as no taxon fell below that line.

Figure 2

The abundance distributions for the bacterial metacommunity for (a) all taxa, (b) core taxa best predicted by the log-normal model (χ²(4)=2.71; P=0.607) and (c) satellite taxa best predicted by the log-series model (χ²(4)=4.61; P=0.330). The frequency of each log₂ abundance class predicted by the log-normal and -series models is shown as a dot.

Figure 3

The richness and abundance of (a) aerobic and anaerobic bacteria and (b) oral microbiota within the whole metacommunity, the core group and the satellite group. Also given are the actual values of taxa richness and abundance (number of clones). Shaded areas represent percentage abundance of Pseudomonas aeruginosa within the whole metacommunity and the core group of bacterial taxa.

Figure 4

Dendrograms of bacterial community composition in the 14 patients for (a) all taxa, (b) the core and (c) satellite taxa groups. Patient taxa profiles were compared using the Bray–Curtis quantitative index of similarity and average linkage clustering.

Figure 5

The relationships between bacterial taxa richness and lung function (FEV₁ in litres) for (a) the whole metacommunity, (b) the core group and (c) the satellite group. In each case linear regression lines have been fitted. For the whole metacommunity, r²=0.43, F_1,12=9.2, P<0.01; core group, r²=0.40, F_1,12=7.82, P<0.02; and satellite group, r²=0.42, F_1,12=8.67, P<0.01.