Core of the saliva microbiome: an analysis of the MG-RAST data

Abstract

Background Oral microbiota is considered as the second most complex in the human body and its dysbiosis can be responsible for oral diseases. Interactions between the microorganism communities and the host allow establishing the microbiological proles. Identifying the core microbiome is essential to predicting diseases and changes in environmental behavior from microorganisms. Methods Projects containing the term “SALIVA”, deposited between 2014 and 2019 were recovered on the MG-RAST portal. Quality ( Failed ), taxonomic prediction ( Unknown and Predicted ), species richness ( Rarefaction ), and species diversity ( Alpha ) were analyzed according to sequencing approaches (Amplicon sequencing and Shotgun metagenomics). All data were checked for normality and homoscedasticity. Metagenomic projects were compared using the Mann–Whitney U test and Spearman s correlation. Microbiome cores were inferred by Principal Component Analysis. For all statistical tests, p \u2009<\u20090.05 was used. Results The study was performed with 3 projects, involving 245 Amplicon and 164 Shotgun metagenome datasets. All comparisons of variables, according to the type of sequencing, showed significant differences, except for the Predicted . In Shotgun metagenomics datasets the highest correlation was between Rarefaction and Failed (r\u2009=\u2009\u2009−\u20090.78) and the lowest between Alpha and Unknown (r\u2009=\u2009\u2009−\u20090.12). In Amplicon sequencing datasets, the variables Rarefaction and Unknown (r\u2009=\u20090.63) had the highest correlation and the lowest was between Alpha and Predicted (r\u2009=\u2009\u2009−\u20090.03). Shotgun metagenomics datasets showed a greater number of genera than Amplicon. Propionibacterium , Lactobacillus , and Prevotella were the most representative genera in Amplicon sequencing. In Shotgun metagenomics, the most representative genera were Escherichia , Chitinophaga, and Acinetobacter . Conclusions Core of the salivary microbiome and genera diversity are dependent on the sequencing approaches. Available data suggest that Shotgun metagenomics and Amplicon sequencing have similar sensitivities to detect the taxonomic level investigated, although Shotgun metagenomics allows a deeper analysis of the microorganism diversity. Microbiome studies must consider characteristics and limitations of the sequencing approaches. Were identified 20 genera in the core of saliva microbiome, regardless of the health condition of the host. Some bacteria of the core need further study to better understand their role in the oral cavity.