Introduction: The oral cavity harbors an abundant and diverse microbial community (i.e. the microbiome), whose composition and roles in health and disease have been the focus of intense research. Down syndrome (DS) is associated with particular characteristics in the oral cavity, and with a lower incidence of caries and higher incidence of periodontitis and gingivitis compared to control populations. However, the overall composition of the oral microbiome in DS and how it varies with diverse factors like host age or the pH within the mouth are still poorly understood. Methods: Using a Citizen-Science approach in collaboration with DS associations in Spain, we performed 16S rRNA metabarcoding and high-throughput sequencing, combined with culture and proteomics-based identification of fungi to survey the bacterial and fungal oral microbiome in 27 DS persons (age range 7-55) and control samples matched by geographical distribution, age range, and gender. Results: We found that DS is associated with low salivary pH and less diverse oral microbiomes, which were characterized by lower levels of Alloprevotella, Atopobium, Candidatus Saccharimonas, and higher amounts of Kingella, Staphylococcus, Gemella, Cardiobacterium, Rothia, Actinobacillus, and greater prevalence of Candida. Conclusion: Altogether, our study provides a first global snapshot of the oral microbiome in DS. Future studies are required to establish whether the observed differences are related to differential pathology in the oral cavity in DS.
Pubmed ID: 33456723 RIS Download
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High quality ribosomal RNA databases providing comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya). Supplementary services include a rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches. Alignment tool, SINA, is available for download as well as available for use online.
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