Nasopharyngeal Microbiome Diversity Changes over Time in Children with Asthma

<div><p>Background</p><p>The nasopharynx is a reservoir for pathogens associated with respiratory illnesses such as asthma. Next-generation sequencing (NGS) has been used to characterize the nasopharyngeal microbiome of infants and adults during health and disease; less is known, however, about the composition and temporal dynamics (i.e., longitudinal variation) of microbiotas from children and adolescents. Here we use NGS technology to characterize the nasopharyngeal microbiomes of asthmatic children and adolescents (6 to 18 years) and determine their stability over time.</p><p>Methods</p><p>Two nasopharyngeal washes collected 5.5 to 6.5 months apart were taken from 40 children and adolescents with asthma living in the Washington D.C. area. Sequence data from the 16S-V4 rRNA gene region (~250 bp) were collected from the samples using the MiSeq platform. Raw data were processed in mothur (SILVA123 reference database) and Operational Taxonomic Units (OTU)-based alpha- and beta-diversity metrics were estimated. Relatedness among samples was assessed using PCoA ordination and Procrustes analyses. Differences in microbial diversity and taxon mean relative proportions were assessed using linear mixed effects models. Core microbiome analyses were also performed to identify stable and consistent microbes of the nasopharynx.</p><p>Results and Discussion</p><p>A total of 2,096,584 clean 16S sequences corresponding to an average of 167 OTUs per sample were generated. Representatives of <i>Moraxella*</i>, <i>Staphylococcus*</i>, <i>Dolosigranulum</i>, <i>Corynebacterium</i>, <i>Prevotella</i>, <i>Streptococcus*</i>, <i>Haemophilus*</i>, <i>Fusobacterium*</i> and a Neisseriaceae genus accounted for 86% of the total reads. These nine genera have been previously found in the nasopharynxes of both infants and adults, but in different proportions. OTUs from the five genera highlighted (<i>*</i>) above defined the nasopharyngeal core microbiome at the 95% level. No significant differences in alpha- and beta-diversity were observed between seasons, but bacterial mean relative proportions of <i>Haemophilus</i>, <i>Moraxella</i>, <i>Staphylococcus</i> and <i>Corynebacterium</i> varied significantly between summer-fall and age groups (inter-patient variation). Additionally, OTUs varied significantly within patients between time points in 35 of the 40 patients analyzed. Future cross-sectional studies should be mindful of the temporal dynamics of the nasopharyngeal microbiota.</p></div

Procrustes analysis of 40 nasopharyngeal sample pairs collected 5.5 to 6.5 months apart.

<p>Sample pairs (red dots) collected from the same patient are connected by a yellow bar. The length of the bar is proportional to the dissimilarity between sample pairs.</p

3D Principal Coordinates Analyses of weighted (A) and unweighted (B) unifrac distances between 80 nasopharyngeal microbiomes colored by season.

<p>3D Principal Coordinates Analyses of weighted (A) and unweighted (B) unifrac distances between 80 nasopharyngeal microbiomes colored by season.</p

Taxonomic profiles of 80 nasopharyngeal microbiomes from 40 asthmatic children.

<p>Only OTUs with a minimum total observation count of 0.1% are shown. W1 = nasal microbiome from wash 1; W2 = nasal microbiome from wash 2 collected 5.5 to 6.5 months later. Sample pairs are alternatively colored in red and black to facilitate their visualization.</p

Summary of studies of the nasopharyngeal microbiome in non-asthmatic patients.

<p>Number of samples (Ns), age group by month (m) and year (yr), sequenced 16S region, patient condition and genus mean relative proportions (with SD estimates for our study) are indicated. Studies (included ours) are ordered by patient age group.</p

Linear mixed effects (LME) models analysis.

<p>Significance (P-values) of the LME analysis of mean relative proportions of most abundant genera (>1% of reads) and clinical and demographic variables. All four seasons were compared, but only the lowest P-values are reported.</p

Dual Transcriptomic Profiling of Host and Microbiota during Health and Disease in Pediatric Asthma

<div><p>Background</p><p>High-throughput sequencing (HTS) analysis of microbial communities from the respiratory airways has heavily relied on the 16S rRNA gene. Given the intrinsic limitations of this approach, airway microbiome research has focused on assessing bacterial composition during health and disease, and its variation in relation to clinical and environmental factors, or other microbiomes. Consequently, very little effort has been dedicated to describing the functional characteristics of the airway microbiota and even less to explore the microbe-host interactions. Here we present a simultaneous assessment of microbiome and host functional diversity and host-microbe interactions from the same RNA-seq experiment, while accounting for variation in clinical metadata.</p><p>Methods</p><p>Transcriptomic (host) and metatranscriptomic (microbiota) sequences from the nasal epithelium of 8 asthmatics and 6 healthy controls were separated <i>in silico</i> and mapped to available human and NCBI-NR protein reference databases. Human genes differentially expressed in asthmatics and controls were then used to infer upstream regulators involved in immune and inflammatory responses. Concomitantly, microbial genes were mapped to metabolic databases (COG, SEED, and KEGG) to infer microbial functions differentially expressed in asthmatics and controls. Finally, multivariate analysis was applied to find associations between microbiome characteristics and host upstream regulators while accounting for clinical variation.</p><p>Results and Discussion</p><p>Our study showed significant differences in the metabolism of microbiomes from asthmatic and non-asthmatic children for up to 25% of the functional properties tested. Enrichment analysis of 499 differentially expressed host genes for inflammatory and immune responses revealed 43 upstream regulators differentially activated in asthma. Microbial adhesion (virulence) and Proteobacteria abundance were significantly associated with variation in the expression of the upstream regulator IL1A; suggesting that microbiome characteristics modulate host inflammatory and immune systems during asthma.</p></div

Principal Component Analysis (PCA) plot comparing functional profiles of asthmatics (AS) and controls (CL).

<p>PCA plots for COG category 2 (A), SEED category 2 (B) and KEGG category 3 (C).</p

Ingenuity pathway enrichment analyses to detect upstream regulators associated with the inflammatory and immune responses during asthma.

<p>The activation <i>z</i>-score depicts the degree of activation or suppression of a given regulator.</p

MaAsLin scattergrams showing associations between the microbial functional property of Adhesion (A) and proportions of Proteobacteria (B) and change in expression of the host gene regulator <i>IL1A</i>.

<p><i>P</i> and <i>q</i> value tests for significance and correlation coefficients are shown.</p