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

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

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

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

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

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

Glucocorticoids modulate gastrointestinal microbiome in a wild bird

Physiological and biometric databas

31395_WetzerEtAl_Sphaeromatidae_2018.pdf

<div> <div> <div> <div> <p>Relationships of the Sphaeromatidae genera (Peracarida: Isopoda) inferred from 18S rDNA and 16S rDNA genes<br></p><p><br></p><p>Abstract. The Sphaeromatidae has 100 genera and close to 700 species with a worldwide distribution. Most are abundant primarily in shallow (< 200 m) marine communities, but extend to 1.400 m, and are occasionally present in permanent freshwater habitats. They play an important role as prey for epibenthic shes and are commensals and scavengers. Sphaeromatids’ impressive exploitation of diverse habitats, in combination with diversity in female life history strategies and elaborate male combat structures, has resulted in extraordinary levels of homoplasy. We sequenced specimens from 39 genera for nuclear 18S rDNA and mitochondrial 16S rDNA genes, comprehensively reviewed the effects of alignments on tree topology, and performed Garli and MrBayes analyses. These data consistently retrieved clades (genus groups), Sphaeroma, Exosphaeroma, Cymodoce, Ischyromene, Cerceis, and Dynamenella and the monogeneric clade of Gnorimo- sphaeroma. We de ne the major clades using morphological characters, attribute sampled taxa to consistently and strongly supported ones and suggest placement of unsampled genera based on their morphological characteristics. Within each clade, we also highlight unresolved and poorly sampled genera. We point out taxonomic problems in hopes of encouraging further phylogenetic exploration. Although we identify clades containing consistent generic groups and are con dent that some groups will prove stable and reliable, we feel our sampling is insuf cient to propose nomenclatural changes at this time.<br></p> </div> </div> </div> </div

Supplemetary Materia from Glucocorticoids modulate gastrointestinal microbiome in a wild bird

Additional methodological details, statistical analyses and graph

Table S2 from Glucocorticoids modulate gastrointestinal microbiome in a wild bird

Summary of DESeq2 analyses for the effect of sex on differential OTUs abundanc