Dataset Information

The Supragingival Biofilm in Early Childhood Caries: Clinical and Laboratory Protocols and Bioinformatics Pipelines Supporting Metagenomics, Metatranscriptomics, and Metabolomics Studies of the Oral Microbiome.

ABSTRACT: Early childhood caries (ECC) is a biofilm-mediated disease. Social, environmental, and behavioral determinants as well as innate susceptibility are major influences on its incidence; however, from a pathogenetic standpoint, the disease is defined and driven by oral dysbiosis. In other words, the disease occurs when the natural equilibrium between the host and its oral microbiome shifts toward states that promote demineralization at the biofilm-tooth surface interface. Thus, a comprehensive understanding of dental caries as a disease requires the characterization of both the composition and the function or metabolic activity of the supragingival biofilm according to well-defined clinical statuses. However, taxonomic and functional information of the supragingival biofilm is rarely available in clinical cohorts, and its collection presents unique challenges among very young children. This paper presents a protocol and pipelines available for the conduct of supragingival biofilm microbiome studies among children in the primary dentition, that has been designed in the context of a large-scale population-based genetic epidemiologic study of ECC. The protocol is being developed for the collection of two supragingival biofilm samples from the maxillary primary dentition, enabling downstream taxonomic (e.g., metagenomics) and functional (e.g., transcriptomics and metabolomics) analyses. The protocol is being implemented in the assembly of a pediatric precision medicine cohort comprising over 6000 participants to date, contributing social, environmental, behavioral, clinical, and biological data informing ECC and other oral health outcomes.

SUBMITTER: Divaris K

PROVIDER: S-EPMC6628198 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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The Supragingival Biofilm in Early Childhood Caries: Clinical and Laboratory Protocols and Bioinformatics Pipelines Supporting Metagenomics, Metatranscriptomics, and Metabolomics Studies of the Oral Microbiome.

Divaris Kimon K Shungin Dmitry D Rodríguez-Cortés Adaris A Basta Patricia V PV Roach Jeff J Cho Hunyong H Wu Di D Ferreira Zandoná Andrea G AG Ginnis Jeannie J Ramamoorthy Sivapriya S Kinchen Jason M JM Kwintkiewicz Jakub J Butz Natasha N Ribeiro Apoena A AA Azcarate-Peril M Andrea MA

Methods in molecular biology (Clifton, N.J.) 20190101

Early childhood caries (ECC) is a biofilm-mediated disease. Social, environmental, and behavioral determinants as well as innate susceptibility are major influences on its incidence; however, from a pathogenetic standpoint, the disease is defined and driven by oral dysbiosis. In other words, the disease occurs when the natural equilibrium between the host and its oral microbiome shifts toward states that promote demineralization at the biofilm-tooth surface interface. Thus, a comprehensive under ...[more]

PMID: 30838598

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Project description:BackgroundIdentifying the microbial species in caries lesions is instrumental to determine the etiology of dental caries. However, a significant proportion of bacteria in carious lesions have not been cultured, and the use of molecular methods has been limited to DNA-based approaches, which detect both active and inactive or dead microorganisms.ObjectiveTo identify the RNA-based, metabolically active bacterial composition of caries lesions at different stages of disease progression in order to provide a list of potential etiological agents of tooth decay.DesignNon-cavitated enamel caries lesions (n=15) and dentin caries lesions samples (n=12) were collected from 13 individuals. RNA was extracted and cDNA was constructed, which was used to amplify the 16S rRNA gene. The resulting 780 bp polymerase chain reaction products were pyrosequenced using Titanium-plus chemistry, and the sequences obtained were used to determine the bacterial composition.ResultsA mean of 4,900 sequences of the 16S rRNA gene with an average read length of 661 bp was obtained per sample, giving a comprehensive view of the active bacterial communities in caries lesions. Estimates of bacterial diversity indicate that the microbiota of cavities is highly complex, each sample containing between 70 and 400 metabolically active species. The composition of these bacterial consortia varied among individuals and between caries lesions of the same individuals. In addition, enamel and dentin lesions had a different bacterial makeup. Lactobacilli were found almost exclusively in dentin cavities. Streptococci accounted for 40% of the total active community in enamel caries, and 20% in dentin caries. However, Streptococcus mutans represented only 0.02-0.73% of the total bacterial community.ConclusionsThe data indicate that the etiology of dental caries is tissue dependent and that the disease has a clear polymicrobial origin. The low proportion of mutans streptococci detected confirms that they are a minority and questions its importance as the main etiological agent of tooth decay. Future experimental work should be performed to confirm the cariogenicity of the identified bacteria.

Project description:BACKGROUND: Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts. RESULTS: To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (e.g., for biomolecular sequences, alignments, structures) and functionality (e.g., to parse/write standard file formats). CONCLUSIONS: PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at http://muralab.org/PaPy, and includes extensive documentation and annotated usage examples.

Dataset Information

The Supragingival Biofilm in Early Childhood Caries: Clinical and Laboratory Protocols and Bioinformatics Pipelines Supporting Metagenomics, Metatranscriptomics, and Metabolomics Studies of the Oral Microbiome.

Publications

The Supragingival Biofilm in Early Childhood Caries: Clinical and Laboratory Protocols and Bioinformatics Pipelines Supporting Metagenomics, Metatranscriptomics, and Metabolomics Studies of the Oral Microbiome.

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