Bioinformatic Resistome Profiling of Metal Tolerance Mechanisms in Endodontic Infections: Implications for Antimicrobial Nanoparticle-Based Biomaterials.

BACKGROUND

Metallic and metal oxide nanoparticles are increasingly explored as antimicrobial biomaterials in endodontics due to their multi-target mechanisms of action, largely mediated by metal ion release (e.g., Ag+, Cu+). However, bacterial metal resistance systems, particularly efflux-related proteins, may influence their antimicrobial performance. This study aimed to analyze the prevalence and distribution of metal resistance-associated proteins in bacteria involved in endodontic infections using a bioinformatic approach.

METHODS

An in silico, cross-sectional bioinformatic analysis was conducted using publicly available genomes from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC). Bacterial species associated with acute apical abscess (AAA), symptomatic apical periodontitis (SAP), asymptomatic apical periodontitis (AAP), and post-treatment apical periodontitis (PTAP) were included. The presence of selected metal resistance-related proteins (CutC, CopA, CzcA, CusA, SilA, P-type ATPase, and PA3920) was assessed using a binary presence/absence framework. Prevalence, group comparisons (Fisher's exact test), and co-occurrence patterns (Phi coefficient) were analyzed.

RESULTS

Metal resistance-associated proteins were widely distributed across all infection types, with prevalence ranging from 70.0% to 82.9% and no significant differences between groups (p > 0.05). CutC was the most prevalent protein, followed by CopA and CzcA, whereas SilA and PA3920 were not detected. Correlation analysis revealed consistent co-occurrence patterns among key taxa, including Porphyromonas gingivalis, Fusobacterium nucleatum, and Prevotella spp.

CONCLUSIONS

Metal resistance-related proteins are broadly distributed in endodontic microbiota, indicating a conserved genetic capacity for metal tolerance. These findings suggest that microbial resistance determinants may influence, but do not directly determine, the antimicrobial performance of nanoparticle-based biomaterials. This study provides a hypothesis-generating, bioinformatic framework to support the design and optimization of antimicrobial biomaterials, highlighting the need for experimental validation and integration of phenotypic and biofilm-based analyses.