Serial analysis of the gut and respiratory microbiome in cystic fibrosis in infancy: interaction between intestinal and respiratory tracts and impact of nutritional exposures

Abstract

Pulmonary damage caused by chronic colonization of the cystic fibrosis (CF) lung by microbial communities is the proximal cause of respiratory failure. While there has been an effort to document the microbiome of the CF lung in pediatric and adult patients, little is known regarding the developing microflora in infants. We examined the respiratory and intestinal microbiota development in infants with CF from birth to 21 months. Distinct genera dominated in the gut compared to those in the respiratory tract, yet some bacteria overlapped, demonstrating a core microbiota dominated by Veillonella and Streptococcus. Bacterial diversity increased significantly over time, with evidence of more rapidly acquired diversity in the respiratory tract. There was a high degree of concordance between the bacteria that were increasing or decreasing over time in both compartments; in particular, a significant proportion (14/16 genera) increasing in the gut were also increasing in the respiratory tract. For 7 genera, gut colonization presages their appearance in the respiratory tract. Clustering analysis of respiratory samples indicated profiles of bacteria associated with breast-feeding, and for gut samples, introduction of solid foods even after adjustment for the time at which the sample was collected. Furthermore, changes in diet also result in altered respiratory microflora, suggesting a link between nutrition and development of microbial communities in the respiratory tract. Our findings suggest that nutritional factors and gut colonization patterns are determinants of the microbial development of respiratory tract microbiota in infants with CF and present opportunities for early intervention in CF with altered dietary or probiotic strategies.

Importance: While efforts have been focused on assessing the microbiome of pediatric and adult cystic fibrosis (CF) patients to understand how chronic colonization by these microbes contributes to pulmonary damage, little is known regarding the earliest development of respiratory and gut microflora in infants with CF. Our findings suggest that colonization of the respiratory tract by microbes is presaged by colonization of the gut and demonstrated a role of nutrition in development of the respiratory microflora. Thus, targeted dietary or probiotic strategies may be an effective means to change the course of the colonization of the CF lung and thereby improve patient outcomes.

FIG 1

Bacterial normalized abundance of the respiratory and intestinal microbiomes. (a) Stacked bar plot representing bacterial genera from serial respiratory microbiome samples from the 7 subjects. The y axis represents the number of reads for particular bacterial genera; the x axis represents subject numbers (1 to 7) and serial samples at progressive months of life. (b) Pie chart of the eight most abundant genera within the respiratory microbiome. (c) Stacked bar plot representing bacterial genera from serial intestinal microbiome samples. The y axis represents the number of reads for particular bacterial genera; the x axis represents subject numbers (1 to 7) and serial samples at progressive months of life. (d) Pie chart of the eight most abundant genera within the intestinal microbiome.

FIG 2

Heat map and SDI of the intestinal and respiratory microbiome samples. Heat map based on the hierarchical clustering solution (Euclidean distance metric and average linkage) of the 59 intestinal and respiratory microbiome samples. Rows represent samples (subjects and respective time points at which the samples were collected), columns represent genera, and the values in the heat map represent the log₁₀-normalized numbers of sequencing reads, with increasing grades of purple representing greater relative abundance. Box 1, core microbiome crossing respiratory and intestinal samples across all time points (P < 0.05). Boxes 2 and 4, microbes more abundantly represented in the respiratory microbiome. Box 3, microbes highlighted as having increased normalized abundance in the intestines early in life (inset a) and in the respiratory tract later in life (inset b), highlighted by a dark-green outline. Box 5, genera more highly represented in the intestinal samples. Pvclust (http://bioinformatics.oxfordjournals.org/content/22/12/1540.full), a method for assessing the uncertainty in hierarchical cluster analysis via multiscale bootstrap resampling, was used to determine the significance of identified clusters.

FIG 3

Changes in bacterial relative normalized abundance over time in the CF infant respiratory tract. (a) y axis is the coefficient (Coef) estimate representing the change in normalized log₁₀ abundance over time. Values below the line represent a decrease in normalized abundance of the indicated bacterial genera over time (highlighted with red font if a statistically significant change with P < 0.05); points above the line represent bacteria that increase in normalized abundance over time. Blue dots highlight the bacteria at the genus level that are also present in the intestinal tract (P < 0.05). (b) y axis is normalized log₁₀ abundance plotted versus time in months (x axis) for the 7 bacterial genera indicated. The intestinal relative normalized abundance is shown in blue, and the data for the respiratory samples are red.

FIG 4

Trajectories of microbial diversity for the intestinal and respiratory microbiomes. SDIs were arc sine square root transformed (55) to satisfy normality conditions. (a) Subject-specific and estimated trajectories of the microbial diversity of the respiratory tract as a function of time. (b) Subject-specific and estimated trajectories of the microbial diversity of the intestinal tract as a function of time. (c) Summarized results of the linear mixed-effects model. (d, e) Trajectory of microbial diversity for breast milk versus formula (d) and for pre- and post-introduction of solid foods (e).

FIG 5

Heat map based on RPMM solution. The resulting clusters from model-based clustering of intestinal microbiome samples are shown. Rows represent samples, and columns represent microbes at the genus level, depicting the four clusters that were estimated from the model-based clustering solution. The actual values represent the mean (within cluster) log₁₀-normalized abundances. Clusters, significantly associated with solid food (P = 0.006), adjusting for the time at which the sample was collected.