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A Systematic Review of the Root Canal Microbiota Associated with Apical Periodontitis: Lessons from Next‐Generation Sequencing
Author(s) -
Manoil Daniel,
AlManei Khaled,
Belibasakis Georgios N.
Publication year - 2020
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201900060
Subject(s) - periodontitis , metagenomics , root canal , biology , dentistry , periapical periodontitis , microbiome , dna sequencing , medicine , bioinformatics , genetics , gene
Next‐generation sequencing (NGS) has now been applied for a decade to characterize the microbiota composition of infected dental root canals associated with apical periodontitis. Here, the study aims at systematically and critically reviewing these reports within the outcome of interest selected; the microbiota composition in different endodontic infection types. Standard methodological guidelines as stated by the PRISMA and the Joanna Briggs Institute are followed, including a risk of bias assessment. A literature search is conducted using the PubMed Advanced‐Search Builder on April 8, 2019; only original research articles that investigated the microbiota of infected root‐canals by means of NGS are screened. Among the 26 articles initially identified, 18 are included and evaluated for the following parameters; sampling protocol, sequencing strategy, and microbiota composition. The endodontic infections include primary apical periodontitis (PAP), secondary apical periodontitis (SAP), and apical abscess (AA). All infection types are associated with a highly diverse microbiota. Although some taxa appear differentially abundant between PAPs, SAPs, and AAs, no evident clustering of the microbiota by infection type is observed. These studies collectively formulate a comprehensive map of the taxa associated with endodontic infections and provide evidence of compositionally unspecific, yet abundance differentiates, community profiles according to clinical diagnosis.