Altered Middle Ear Microbiome in Children With Chronic Otitis Media With Effusion and Respiratory Illnesses

Abstract

Chronic otitis media with effusion (COME) is a common childhood disease characterized by an accumulation of fluid behind the eardrum. COME often requires surgical intervention and can also lead to significant hearing loss and subsequent learning disabilities. Recent characterization of the middle ear fluid (MEF) microbiome in pediatric patients has led to an improved understanding of the microbiota present in the middle ear during COME. However, it is not currently known how the MEF microbiome might vary due to other conditions, particularly respiratory disorders. Here, we apply an amplicon sequence variant (ASV) pipeline to MEF 16S rRNA high-throughput sequencing data from 50 children with COME (ages 3–176 months) undergoing tube placement. We achieve a more detailed taxonomic resolution than previously reported, including species and genus level resolution. Additionally, we provide the first report of the functional roles of the MEF microbiome and demonstrate that despite high taxonomic diversity, the functional capacity of the MEF microbiome remains uniform between patients. Furthermore, we analyze microbiome differences between children with COME with and without a history of lower airway disease (i.e., asthma or bronchiolitis). The MEF microbiome was less diverse in participants with lower airway disease than in patients without, and phylogenetic β-diversity (weighted UniFrac) was significantly different based on lower airway disease status. Differential abundance between patients with lower airway disease and those without was observed for the genera Haemophilus, Moraxella, Staphylococcus, Alloiococcus, and Turicella. These findings support previous suggestions of a link between COME and respiratory illnesses and emphasize the need for future study of the middle ear and respiratory tract microbiomes in diseases such as asthma and bronchiolitis.

Niche- and Gender-Dependent Immune Reactions in Relation to the Microbiota Profile in Pediatric Patients with Otitis Media with Effusion

“…Among the identified species, Alloiococcus , S. epidermidis , and Turicella predominated in the MEE and EEC lavage samples compared to the NPH samples. This is in line with results of previous studies ( 8 , 28 ). MEEs also displayed a higher relative abundance of Haemophilus compared to that in EEC lavages.…”

Study on the Salivary Microbial Alteration of Men With Head and Neck Cancer and Its Relationship With Symptoms in Southwest China

“…Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) was used to infer saliva microbial functions in association with HNC ( Douglas et al, 2020 ). After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway abundances derived from the predicted KEGG ORTHOLOGY (KO) abundances were performed with MinPath and the “–no_regroup” option (pathway_pipeline.py) ( Ye and Doak, 2009 ; Douglas, 2017 ; Douglas, 2019 ; Kim et al, 2019 ; Kolbe et al, 2019 ). Significant differences in KEGG categories between the patients and controls were determined using the non-parametric Mann-Whitney U test.…”

Review of otitis media microbiome studies: What do they tell us?

“…The MEE microbiome was found to be less diverse in participants with concurrent lower airway disease (asthma or bronchiolitis) than in patients without, and phylogenetic β-diversity (weighted UniFrac) was significantly different based on lower airway disease status. 11 Differential abundance in patients with lower airway disease was observed for the genera Haemophilus, Moraxella, Staphylococcus, Alloiococcus, and Turicella. These findings suggest a link between COME and respiratory illnesses, perhaps reflecting a postulated association of COME with atopic disease.…”

“…9,10 More recent studies have shown that the same data can be classified using amplicon sequence variants (ASV) yielding greater genus and species level classification rates. 11 With the eventual implementation of full length 16 seconds RNA sequencing, subspecies level classification will be also possible. 12 Despite improved detection and reliability with 16 seconds rRNA sequencing, a metanalysis of recent publications has highlighted sampling and methodological inconsistencies which precludes deriving overarching conclusions about the OM microbiome.…”