A 16S rRNA Gene and Draft Genome Database for the Murine Oral Bacterial Community

A curated murine oral microbiome database to be used as a reference for mouse-based studies has been constructed using a combination of bacterial culture, 16S rRNA gene amplicon, and whole-genome sequencing. The database comprises a collection of nearly full-length 16S rRNA gene sequences from cultured isolates and draft genomes from representative taxa collected from a range of sources, including specific-pathogen-free laboratory mice, wild Mus musculus domesticus mice, and formerly wild wood mouse Apodemus sylvaticus At present, it comprises 103 mouse oral taxa (MOT) spanning four phyla-Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes-including 12 novel undescribed species-level taxa. The key observations from this study are (i) the low diversity and predominantly culturable nature of the laboratory mouse oral microbiome and (ii) the identification of three major murine-specific oral bacterial lineages, namely, Streptococcus danieliae (MOT10), Lactobacillus murinus (MOT93), and Gemella species 2 (MOT43), which is one of the novel, still-unnamed taxa. Of these, S. danieliae is of particular interest, since it is a major component of the oral microbiome from all strains of healthy and periodontally diseased laboratory mice, as well as being present in wild mice. It is expected that this well-characterized database should be a useful resource for in vitro experimentation and mouse model studies in the field of oral microbiology.IMPORTANCE Mouse model studies are frequently used in oral microbiome research, particularly to investigate diseases such as periodontitis and caries, as well as other related systemic diseases. We have reported here the details of the development of a curated reference database to characterize the oral microbial community in laboratory and some wild mice. The genomic information and findings reported here can help improve the outcomes and accuracy of host-microbe experimental studies that use murine models to understand health and disease. Work is also under way to make the reference data sets publicly available on a web server to enable easy access and downloading for researchers across the world.