Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Joseph S

PROVIDER: S-EPMC7883545 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalizing syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterize taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth CS) to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from survey metadata. Specifically, we modeled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on both the shape of landscape scale pH-abundance responses, and pH optima (pH at which OTU abundance is maximal). We identify that most of the soil OTUs examined exhibited a non-flat relationship with soil pH. Further, the pH optima could not be generalized by broad taxonomy, highlighting the need for tools and databases synthesizing ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER/), and flat files are made available for use in bioinformatic pipelines. The further development of advanced informatics infrastructures incorporating modeled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.

Project description:BackgroundAccurate identification of bacterial communities is crucial for research applications, diagnostics, and clinical interventions. Although 16S ribosomal RNA (rRNA) gene sequencing is a widely employed technique for bacterial taxonomic classification, it often results in misclassified or unclassified bacterial taxa. This study sought to refine the full-length 16S rRNA gene sequencing protocol using the MinION sequencer, focusing on the V1-V9 regions. Our methodological enquiry examined several factors, including the number of PCR amplification cycles, choice of primers and Taq polymerase, and specific sequence databases and workflows employed. We used a microbial standard comprising eight bacterial strains (five gram-positive and three gram-negative) in known proportions as a validation control.ResultsBased on the MinION protocol, we employed the microbial standard as the DNA template for the 16S rRNA gene amplicon sequencing procedure. Our analysis showed that an elevated number of PCR amplification cycles introduced PCR bias, and the selection of Taq polymerase and primer sets significantly affected the subsequent analysis. Bacterial identification at genus level demonstrated Pearson correlation coefficients ranging from 0.73 to 0.79 when assessed using BugSeq, Kraken-Silva and EPI2ME-16S workflows. Notably, the EPI2ME-16S workflow exhibited the highest Pearson correlation with the microbial standard, minimised misclassification, and increased alignment accuracy. At the species taxonomic level, the BugSeq workflow was superior, with a Pearson correlation coefficient of 0.92.ConclusionsThese findings emphasise the importance of careful selection of PCR settings and a well-structured analytical framework for 16S rRNA full-length gene sequencing. The results showed a robust correlation between the predicted and observed bacterial abundances at both the genus and species taxonomic levels, making these findings applicable across diverse research contexts and with clinical utility for reliable pathogen identification.

Dataset Information

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

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