{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["18(1)"],"submitter":["Xu T"],"pubmed_abstract":["<h4>Background</h4>Periodontitis progression is accompanied by a succession of the oral microbiome. However, the dynamic microbial transitions that link different disease stages and contribute to disease progression remain incompletely understood.<h4>Objective</h4>This study aims to identify microbial taxa that may serve as potential drivers underlying increasing severity of periodontitis.<h4>Design</h4>Subgingival sample 16S rRNA gene sequencing data were reprocessed for quality control and taxonomic annotation. MaAsLin2 was used to identify microbial differences between groups, and co-occurrence networks were built based on the differential taxa. NetMoss algorithm was applied to identify key microbes driving the transition from health to periodontitis. Correlation and mediation analyses "],"journal":["Journal of oral microbiology"],"pagination":["2609452"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12777760"],"repository":["biostudies-literature"],"pubmed_title":["Unveiling potential driver taxa in subgingival plaque and their roles in mediating periodontitis progression."],"pmcid":["PMC12777760"],"pubmed_authors":["Zeng Q","Xu T","Wei Z","Zhi M","Feng Q"],"additional_accession":[]},"is_claimable":false,"name":"Unveiling potential driver taxa in subgingival plaque and their roles in mediating periodontitis progression.","description":"<h4>Background</h4>Periodontitis progression is accompanied by a succession of the oral microbiome. However, the dynamic microbial transitions that link different disease stages and contribute to disease progression remain incompletely understood.<h4>Objective</h4>This study aims to identify microbial taxa that may serve as potential drivers underlying increasing severity of periodontitis.<h4>Design</h4>Subgingival sample 16S rRNA gene sequencing data were reprocessed for quality control and taxonomic annotation. MaAsLin2 was used to identify microbial differences between groups, and co-occurrence networks were built based on the differential taxa. NetMoss algorithm was applied to identify key microbes driving the transition from health to periodontitis. Correlation and mediation analyses ","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026","modification":"2026-06-09T05:29:04.385Z","creation":"2026-06-09T03:07:22.454Z"},"accession":"S-EPMC12777760","cross_references":{"pubmed":["41509038"],"doi":["10.1080/20002297.2025.2609452"]}}