Exploring Relationships Within the Microbiome of Root Canal Infections and the Influence of Associated Clinical Parameters.

Abstract

Aim: To identify relationships among bacterial species in endodontic infections, to determine their core microbiome, and associated clinical characteristics.

Methodology: 206 patients with endodontic infections and apical periodontitis were assessed for clinical parameters (periapical lesion, symptomatology, sinus tract). Samples from the apical third of roots were obtained before cleaning and shaping root canals, and microbial composition was analysed using 16S rRNA sequencing and HOMINGS. Correlation Network Analysis (CNA) was performed using the R package igraph, and networks were visualised in Cytoscape with centralities determined by CytoHubba. The core microbiome was identified using the R package Microbiome, listing species comprising at least 1% of samples in over 50% of cases.

Results: The endodontic core microbiome included Parvimonas micra, Streptococcus sanguinis, Enterococcus faecalis, Porphyromonas endodontalis, Prevotella nigrescens and Fusobacterium nucleatum. Symptomatic cases had a core microbiome of Bacteroidaceae.G.1. sp oral taxon 272 and Haemophilus parainfluenzae. Sinus cases had a core microbiome of Bacteroidaceae.G.1. sp oral taxon 272, Mogibacterium timidum, Peptostreptococcus stomatis, Pseudoramibacter alactolyticus and Rothia dentocariosa. Lesion cases had a core microbiome of Atopobium rimae, Eubacterium.11.G.1. infirmum, Mogibacterium timidum and Pseudoramibacter alactolyticus. Certain taxa like Bacillus clausii and Eubacterium limosum were never detected.

Conclusions: Despite critical gaps in root canal treatment clinical effectiveness, standardisation and understanding of pathogen complexity, our study, utilising next-generation 16S rRNA Sequencing and HOMINGS, provides valuable insights into core microbiome members involved in endodontic infections and their associations with distinct clinical signs and symptoms, offering insights that may guide more precise diagnosis and targeted treatment.