Lung function and microbiota diversity in cystic fibrosis

Abstract

Background: Chronic infection and concomitant airway inflammation is the leading cause of morbidity and mortality for people living with cystic fibrosis (CF). Although chronic infection in CF is undeniably polymicrobial, involving a lung microbiota, infection surveillance and control approaches remain underpinned by classical aerobic culture-based microbiology. How to use microbiomics to direct clinical management of CF airway infections remains a crucial challenge. A pivotal step towards leveraging microbiome approaches in CF clinical care is to understand the ecology of the CF lung microbiome and identify ecological patterns of CF microbiota across a wide spectrum of lung disease. Assessing sputum samples from 299 patients attending 13 CF centres in Europe and the USA, we determined whether the emerging relationship of decreasing microbiota diversity with worsening lung function could be considered a generalised pattern of CF lung microbiota and explored its potential as an informative indicator of lung disease state in CF.

Results: We tested and found decreasing microbiota diversity with a reduction in lung function to be a significant ecological pattern. Moreover, the loss of diversity was accompanied by an increase in microbiota dominance. Subsequently, we stratified patients into lung disease categories of increasing disease severity to further investigate relationships between microbiota characteristics and lung function, and the factors contributing to microbiota variance. Core taxa group composition became highly conserved within the severe disease category, while the rarer satellite taxa underpinned the high variability observed in the microbiota diversity. Further, the lung microbiota of individual patient were increasingly dominated by recognised CF pathogens as lung function decreased. Conversely, other bacteria, especially obligate anaerobes, increasingly dominated in those with better lung function. Ordination analyses revealed lung function and antibiotics to be main explanators of compositional variance in the microbiota and the core and satellite taxa. Biogeography was found to influence acquisition of the rarer satellite taxa.

Conclusions: Our findings demonstrate that microbiota diversity and dominance, as well as the identity of the dominant bacterial species, in combination with measures of lung function, can be used as informative indicators of disease state in CF. Video Abstract.

Keywords: Antibiotics; Biogeography; Cystic fibrosis; Disease severity; Ecological patterns; Lung function; Lung microbiome; Lung microbiota; Microbial surveillance.

Fig. 1

Relationships between microbiota diversity, dominance and lung function. a Fisher’s alpha index of diversity plotted against percent predicted forced expiratory volume in 1 s (%FEV₁). b Berger-Parker dominance index and %FEV₁. c Berger-Parker dominance index plotted against Fisher’s alpha index of diversity. In each case linear regression lines have been fitted: (a) r² = 0.11, F_1,295 = 36.7, P < 0.0001; (b) r² = 0.10, F_1,295 = 31.2, P < 0.0001 and (c) r² = 0.41, F_1,295 = 202.6, P < 0.0001

Fig. 2

Distribution and abundance of bacterial taxa across patients in worsening lung disease categories. a Mild/normal. b Moderate. c Severe categories. Given is the percentage number of patient respiratory samples each bacterial taxon was observed to be distributed across, plotted against the mean percentage abundance across those samples. Core taxa are defined as those that fall within the upper quartile of distribution (orange circles), and satellite taxa (grey circles) defined as those that do not. Recognised pathogens are marked as follows: Pseudomonas aeruginosa, purple circle; Staphylococcus aureus, light green diamond; Stenotrophomonas maltophilia, blue diamond; Burkholderia cepacia complex, green square; Haemophilus influenzae, light blue triangle and Achromobacter xylosoxidans, black triangle. Distribution-abundance relationship regression statistics: (a) r² = 0.64, F_1,514 = 927.3, P < 0.0001; (b) r² = 0.62, F_1,581 = 961.9, P < 0.0001; (c) r² = 0.75, F_1,527 = 1549.1, P < 0.0001. Common taxa are listed Table S1

Fig. 3

Comparison of microbiota diversity, dominance and composition when stratified by lung disease category. In each instance, relationships within the microbiota, core taxa and satellite taxa are given. Changes in (a) Fisher’s alpha index of diversity and (b) Berger-Parker dominance index with lung disease category (%FEV₁). Boxplots show 25–75th interquartile (IQR) range with whiskers showing 1.5 times IQR. Black circles indicate individual patients and cross symbol represents the mean. Asterisks denote significant differences in diversity or dominance between two lung disease categories following both Kruskal-Wallis tests and Hedges’ d effect size analysis.(c) Variation in microbiota composition within (columns) and between (circles) lung disease categories using the Bray-Curtis index of similarity. Error bars represent standard deviation of the mean. Asterisks denote significant differences in composition between lung disease categories following one-way PERMANOVA tests with Bonferroni correction. Summary statistics for Kruskal-Wallis and PERMANOVA analyses are provided in supplementary Tables S2, S3 and S4. Hedges’ d effect size analyses are provided in Figure S1

Fig. 4

Dominant bacterial taxa across lung disease categories. Percent frequency of dominance for (a) recognised CF pathogens and (b) other bacterial taxa in each lung disease category. Dominant taxon is defined as the most abundant taxon by relative abundance within a given lung microbiota sample