Dental Plaque Microbial Resistomes of Periodontal Health and Disease and Their Changes after Scaling and Root Planing Therapy

Abstract

The human oral microbial community has been considered a reservoir of antibiotic resistance. Currently, the effects of periodontitis and the scaling and root planing (SRP) treatment on the performance of antibiotic-resistant genes (ARGs) and metal-resistant genes (MRGs) in the dental plaque microbiota are not well characterized. To explore this issue, we selected 48 healthy-state (HS), 40 periodontitis-state (PS; before treatment), and 24 resolved-state (RS; after SRP treatment) metagenomic data of dental plaque samples from the Sequence Read Archive (SRA) database. NetShift analysis identified Fretibacterium fastidiosum, Tannerella forsythia, and Campylobacter rectus as key drivers during dental plaque microbiota alteration in the progression of periodontitis. Periodontitis and SRP treatment resulted in an increase in the number of ARGs and MRGs in dental plaque and significantly altered the composition of ARG and MRG profiles. Bacitracin, beta-lactam, macrolide-lincosamide-streptogramin (MLS), tetracycline, and multidrug resistance genes were the main classes of ARGs with high relative abundance, whereas multimetal, iron, chromium, and copper resistance genes were the primary types of MRGs in dental plaque microbiota. The cooccurrence of ARGs, MRGs, and mobile genetic elements (MGEs) indicated that a coselection phenomenon exists in the resistomes of dental plaque microbiota. Overall, our data provide new insights into the standing of the distribution of ARGs and MRGs in oral microbiota of periodontitis patients, and it was possible to contribute to the understanding of the complicated correlations among microorganisms, resistomes, and MGEs. IMPORTANCE The emergence and development of resistance to antibiotics in periodontal pathogens have affected the success rate of treatment for periodontitis. The development of new antibacterial strategies is urgently needed to help control and treat periodontal disease, and dental plaque microbiome studies offer a promising new angle of attack. In this study, we investigated the dental plaque microbiota and resistomes in periodontal health and disease states and their changes after SRP therapy. This is the first analysis of the profile of the microbial community and antibiotic and metal resistance genes in dental plaque by the metagenomic approach, to the best of our knowledge. Monitoring the profile of these resistomes has huge potential to provide reference levels for proper antibiotics use and the development of new antimicrobial strategies in periodontitis therapy and thereby improve actual efficacy of the treatment regimens.

Keywords: antibiotic resistance genes; metagenomic analysis; metal resistance genes; microbial community; mobile genetic elements; periodontitis.

FIG 1

Microbial community in the dental plaque among the HS group, PS group, and RS group. (a) Box plots of the Shannon-Wiener indexes of the microbial community at the species level in the HS group, PS group, and RS group. (b) PCoA of Bray-Curtis distance between the groups. (c) Relative abundance of the top 45 most different species across the groups. Species at a P value of <0.01 are marked with a pink star, a P value of <0.05 with a green star, and a P value of ≥0.05 with a blue star. The red typeface denotes the three members of the red complex; the orange typeface denotes the four members of the red complex. (d) We treated the HS and RS (after treatment) as the control and individuals with the PS (before treatment) as the case. Driver species are represented by bigger red nodes with a higher NESH score. Edge (line) is assigned between the nodes; green edges, association present only in the control; red edges, association present only in the case; and blue, association present in both control and case.

FIG 2

Antibiotic resistome differences among the HS group, PS group, and RS group. (a) Broad-spectrum quantitative profile of the ARG types (copy/cell) in 112 dental plaque samples. (b) Box plots of the number of ARG subtypes in HS group, PS group, and RS group. (c) PCoA of Bray-Curtis distance between groups. (d) Box plots showing Bray-Curtis distance between sample types. (e) Venn diagram depicting the number of shared and unique ARG subtypes among HS group, PS group, and RS group. (f) The relative abundance (%) of shared ARGs. (g) Procrustes analysis of the correlations between ARGs and microbial community structure based on Bray-Curtis dissimilarity matrix.

FIG 3

Abundance of the 134 shared ARGs by the 112 samples (copy/cell).

FIG 4

Metal resistome differences among HS group, PS group, and RS group. (a) Broad-spectrum quantitative profile of the MRG types (copies/cell) in 112 dental plaque samples. (b) Box plot comparing the number of MRG subtypes among the HS group, PS group, and RS group. (c) PCoA plot showing the similarities of the MRG subtype composition among the 112 dental plaque samples in three groups. (d) Box plots showing Bray-Curtis distance between sample types. (e) Venn diagram depicting the number of shared and unique MRG subtypes among the HS group, PS group, and RS group. (f) The relative abundance (%) of shared MRGs. (g) Procrustes analysis of the correlation between ARGs and microbial community based on the PCoA (Bray-Curtis) results of MRG subtype abundances and microbial communities abundances.

FIG 5

Abundance of the 160 shared MRGs by the 112 samples (copy/cell).

FIG 6

Composition and correlation analysis of MGEs. (a) Broad-spectrum quantitative profile of the MGE types (copies/cell) in 112 dental plaque samples. (b) Comparison of abundances of different MGE types in the 112 samples. (c and d) The total abundance of MGEs significantly correlated with the total abundance of detected ARGs (c) and the total abundance of detected MRGs (d) based on Pearson’s correlation. (e and d) pRDA differentiating the effect of microbial communities (at the phylum level) and MGEs on the profile of ARGs (e) and MRGs (f).

FIG 7

Network analysis showing the cooccurrence pattern between ARGs, MRGs, and MGEs based on Pearson’s correlation analysis. A connection represents a strong (Spearman’s r, >0.6) and significant (P < 0.01) correlation. The size of each node is proportional to the number of connections. (a) The nodes were colored according to ARGs, MRGs, and MGEs. (b) The nodes were colored according to modularity class.