Longitudinal development of the airway microbiota in infants with cystic fibrosis

Abstract

The pathogenesis of airway infection in cystic fibrosis (CF) is poorly understood. We performed a longitudinal study coupling clinical information with frequent sampling of the microbiota to identify changes in the airway microbiota in infancy that could underpin deterioration and potentially be targeted therapeutically. Thirty infants with CF diagnosed on newborn screening (NBS) were followed for up to two years. Two hundred and forty one throat swabs were collected as a surrogate for lower airway microbiota (median 35 days between study visits) in the largest longitudinal study of the CF oropharyngeal microbiota. Quantitative PCR and Illumina sequencing of the 16S rRNA bacterial gene were performed. Data analyses were conducted in QIIME and Phyloseq in R. Streptococcus spp. and Haemophilus spp. were the most common genera (55% and 12.5% of reads respectively) and were inversely related. Only beta (between sample) diversity changed with age (Bray Curtis r² = 0.15, P = 0.03). Staphylococcus and Pseudomonas were rarely detected. These results suggest that Streptococcus spp. and Haemophilus spp., may play an important role in early CF. Whether they are protective against infection with more typical CF micro-organisms, or pathogenic and thus meriting treatment needs to be determined.

Figure 1

Changes in the relative abundance of the five most common genera with age in infants with CF. Figure shows the mean relative abundance of Streptococcus spp., Haemophilus spp., Neisseria spp., Veillonella spp. and Granulicatella spp. using a proportional scale where all samples have been rarefied to 600 reads. The error bars represent the standard deviation. An inverse relationship in the relative abundance of Streptococcus spp. and Haemophilus spp. in the first two years of life is observed.

Figure 2

Example individual patient barplots illustrating changes with age (in months) in relative abundance of genera, bacterial load (16S rRNA copies per swab), alpha diversity changes (measured by Inverse Simpson’s) and clinical variables. Clinical variables illustrated include: antibiotic administration at the time of sample collection (Intraveous [IV], oral and nebulised [nebs]); bacterial culture results, and presence of respiratory tract symptoms at the time of sample collection. All infants were on prophylactic antibiotics at the time of sample collection. Four individual patient barplots are shown. Little change was seen in bacterial load, the Inverse Simpson’s diversity index or community structure (shown by the barplot) with changes in symptoms, growth of P.aeruginosa or antibiotic treatment.

Figure 3

Changes in diversity with age. (a) Boxplot illustrating changes in alpha diversity measured by species richness for each age group. Using a non-linear mixed effects model with a negative binomial distribution, there was no significant association between richness and age (P > 0.05). (b) Change in beta diversity with age measured using the Bray Curtis dissimilarity score. Mean and standard deviation in Bray Curtis dissimilarity score shown for each age group. This demonstrates an increase in dissimilarity by age (PERMANOVA, r² = 0.15, P = 0.03).