Background: The role of the airway microbiome in the development of recurrent wheezing and asthma remains uncertain, particularly in the high-risk group of infants hospitalized for bronchiolitis. Objective: We sought to examine the relation of the nasal microbiota at bronchiolitis-related hospitalization and 3 later points to the risk of recurrent wheezing by age 3 years. Methods: In 17 US centers researchers collected clinical data and nasal swabs from infants hospitalized for bronchiolitis. Trained parents collected nasal swabs 3 weeks after hospitalization and, when healthy, during the summer and 1 year after hospitalization. We applied 16S rRNA gene sequencing to all nasal swabs. We used joint modeling to examine the relation of longitudinal nasal microbiota abundances to the risk of recurrent wheezing. Results: Among 842 infants hospitalized for bronchiolitis, there was 88% follow-up at 3 years, and 31% had recurrent wheezing. The median age at enrollment was 3.2 months (interquartile range, 1.7-5.8 months). In joint modeling analyses adjusting for 16 covariates, including viral cause, a 10% increase in relative abundance of Moraxella or Streptococcus species 3 weeks after day 1 of hospitalization was associated with an increased risk of recurrent wheezing (hazard ratio [HR] of 1.38 and 95% highdensity interval [HDI] of 1.11-1.85 and HR of 1.76 and 95% HDI of 1.13-3.19, respectively). Increased Streptococcus species abundance the summer after hospitalization was also associated with a greater risk of recurrent wheezing (HR, 1.76; 95% HDI,. Conclusions: Enrichment of Moraxella or Streptococcus species after bronchiolitis hospitalization was associated with recurrent wheezing by age 3 years, possibly providing new avenues to ameliorate the long-term respiratory outcomes of infants with severe bronchiolitis. (J Allergy Clin Immunol 2020;145:518-27.)

BackgroundThe airway microbiome is a subject of great interest for the study of respiratory disease. Anterior nare samples are more accessible than samples from deeper within the nasopharynx. However, the correlation between the microbiota found in the anterior nares and the microbiota found within the nasopharynx is unknown. We assessed the anterior nares and nasopharyngeal microbiota to determine (1) the relation of the microbiota from these two upper airway sites and (2) if associations were maintained between the microbiota from these two sites and two bronchiolitis severity outcomes.ResultsAmong 815 infants hospitalized at 17 US centers for bronchiolitis with optimal 16S rRNA gene sequence reads from both nasal swab and nasopharyngeal aspirate samples, there were strong intra-individual correlations in the microbial communities between the two sample types, especially relating to Haemophilus and Moraxella genera. By contrast, we found a high abundance of Staphylococcus genus in the nasal swabs—a pattern not found in the nasopharyngeal samples and not informative when predicting the dominant nasopharyngeal genera. While these disparities may have been due to sample processing differences (i.e., nasal swabs were mailed at ambient temperature to emulate processing of future parent collected swabs while nasopharyngeal aspirates were mailed on dry ice), a previously reported association between Haemophilus-dominant nasopharyngeal microbiota and the increased severity of bronchiolitis was replicated utilizing the nasal swab microbiota and the same outcome measures: intensive care use (adjusted OR 6.43; 95% CI 2.25–20.51; P < 0.001) and hospital length-of-stay (adjusted OR 4.31; 95% CI, 1.73–11.11; P = 0.002). Additionally, Moraxella-dominant nasopharyngeal microbiota was previously identified as protective against intensive care use, a result that was replicated when analyzing the nasal swab microbiota (adjusted OR 0.30; 95% CI, 0.11–0.64; P = 0.01).ConclusionsWhile the microbiota of the anterior nares and the nasopharynx are distinct, there is considerable overlap between the bacterial community compositions from these two anatomic sites. Despite processing differences between the samples, these results indicate that microbiota severity associations from the nasopharynx are recapitulated in the anterior nares, suggesting that nasal swab samples not only are effective sample types, but also can be used to detect microbial risk markers.Electronic supplementary materialThe online version of this article (10.1186/s40168-017-0385-0) contains supplementary material, which is available to authorized users.

The short and long-term impact of birth mode on the developing gut microbiome in neonates has potential implications for the health of infants. In term infants, the microbiome immediately following birth across multiple body sites corresponds to birth mode, with increased Bacteroides in vaginally delivered infants. We aimed to determine the impact of birth mode of the preterm gut microbiome over the first 100 days of life and following neonatal intensive care unit (NICU) discharge. In total, 867 stool samples from 46 preterm infants (21 cesarean and 25 vaginal), median gestational age 27 weeks, were sequenced (V4 region 16S rRNA gene, Illumina MiSeq). Of these, 776 samples passed quality filtering and were included in the analysis. The overall longitudinal alpha-diversity and within infant beta-diversity was comparable between cesarean and vaginally delivered infants. Vaginally delivered infants kept significantly more OTUs from 2 months of life and following NICU discharge, but OTUs lost, gained, and regained were not different based on birth mode. Furthermore, the temporal progression of dominant genera was comparable between birth modes and no significant difference was found for any genera following adjustment for covariates. Lastly, preterm gut community types (PGCTs) showed some moderate differences in very early life, but progressed toward a comparable pattern by week 5. No PGCT was significantly associated with cesarean or vaginal birth. Unlike term infants, birth mode was not significantly associated with changes in microbial diversity, composition, specific taxa, or overall microbial development in preterm infants. This may result from the dominating effects of NICU exposures including the universal use of antibiotics immediately following birth and/or the lack of Bacteroides colonizing preterm infants.

Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.