Early nasal microbiota and subsequent respiratory tract infections in infants with cystic fibrosis

Abstract

Background: Respiratory tract infections (RTIs) drive lung function decline in children with cystic fibrosis (CF). While the respiratory microbiota is clearly associated with RTI pathogenesis in infants without CF, data on infants with CF is scarce. We compared nasal microbiota development between infants with CF and controls and assessed associations between early-life nasal microbiota, RTIs, and antibiotic treatment in infants with CF.

Methods: We included 50 infants with CF and 30 controls from two prospective birth cohorts followed throughout the first year of life. We collected 1511 biweekly nasal swabs and analyzed the microbiota after amplifying the V3-V4 region of the 16S rRNA gene. We conducted structured weekly interviews to assess respiratory symptoms and antibiotic treatment. We calculated generalized additive mixed models and permutational analysis of variance.

Results: Here, we show that the nasal microbiota is already altered before the first RTI or antibiotic treatment in infants with CF. Microbiota diversity differs between infants with CF and controls following RTIs and/or antibiotic treatment. CF infants with lower α-diversity have a higher number of subsequent RTIs.

Conclusions: Early nasal microbiota alterations may reflect predisposition or predispose to RTIs in infants with CF, and further change after RTIs and antibiotic treatment. This highlights the potential of targeting the nasal microbiota in CF-related RTI management, while also questioning current practices in the era of novel modulator therapies.

Fig. 1. Collection of biweekly nasal swabs and longitudinal event courses.

This figure displays two example longitudinal courses of events in infants. In the upper part (green arrow), the rare case of antibiotic treatment before the first RTI is displayed (e.g., due to Otitis media). The lower part (ochre arrow) shows an infant with two RTIs in the first year of life, one of which was treated with antibiotics. To disentangle the interrelations between nasal microbiota, respiratory tract infections (RTIs), and antibiotic treatment, we calculated statistics overall and before/after the first antibiotic treatment or before/after the first RTI in the first year of life. Created with Biorender.com.

Fig. 2. Comparison of within-subject dissimilarity in infants with CF and controls.

a The median within-subject dissimilarities were calculated using the Bray-Curtis dissimilarity matrix for each two consecutive swabs from all infants. These dissimilarities were analyzed with a generalized additive mixed model, adjusting for confounders. The corrected within-subject dissimilarity values are shown as points on the y-axis, while the x-axis represents the age of the infants in weeks. Trendlines (loess function) are depicted in red for infants with CF (707 consecutive swabs from 50 infants) and in green for controls (442 consecutive swabs from 30 infants). Shaded areas indicate the 95% confidence interval for the trendlines. b The same analysis was conducted for consecutive swabs taken before the first antibiotic treatment. Data for infants with CF (397 consecutive swabs from 42 infants) are shown in red, and data for controls (426 consecutive swabs from 30 infants) are shown in green.

Fig. 3. Comparison of differential abundance of bacterial families between infants with CF and controls in first year of life.

a Relative abundance of the 9 most abundant families and “others” are displayed in alphabetical order and by age (months) for infants with CF (933 swabs from 50 infants) and controls (578 swabs from 30 infants). b Swabs of infants with CF (787 swabs from 42 infants) and controls (409 swabs from 21 infants) who experienced a RTI in first year of life were divided in swabs taken before and after the first RTI. c, d Results obtained from differential abundance analysis with MaAslin2 are plotted with effect size on the x-axes and FDR adjusted p-values (q-values) on the y-axes. The dashed horizontal line shows significance threshold (q = 0.05). We display differential abundance analysis (c) for all swabs (933 swabs from 50 infants with CF and 578 from 30 controls) and (d) for swabs only before the first RTI occurred (435 swabs from 48 infants with CF and 357 swabs from 30 controls).

Fig. 4. α-diversity in infants with CF with higher and lower number of RTIs.

Shannon-diversity index (SDI) was compared between infants with CF with higher (300 swabs from 15 infants) and lower number (487 swabs from 27 infants) of RTIs in a generalized additive mixed model. Points display the model fitted SDI values on the y-axis, and (a) time to first RTI, or (b) time to first antibiotic treatment on the x-axis. Swabs taken before the first RTI (a), or antibiotic treatment (b), are displayed in red, and swabs taken after in green. The magenta trendline (loess function) reflects infants with a lower number of RTIs and the blue trendline displays infants with a higher number of RTIs in first year of life. The gray shadings show the 95% confidence intervals.

Fig. 5. α-diversity before and after the first antibiotic treatment in infants with CF.

Shannon-diversity index (SDI) was compared in a generalized additive mixed model before and after the first antibiotic treatment (635 swabs from 33 infants). Displayed are the model fitted SDI values on the y-axis and the time to first antibiotic treatment on the x-axis. Swabs taken before the first antibiotic treatment (n = 241) are displayed in red and after in green (n = 394). The trendlines (loess function) are displayed with 95% confidence intervals (gray shadings).