Pyrosequencing of the subgingival microbiome in peri-implantitis after non-surgical mechanical debridement therapy

Microbiome of periodontitis and peri-implantitis before and after therapy: Long-read 16S rRNA gene amplicon sequencing

AIMS

The microbial profiles of peri-implantitis and periodontitis (PT) are inconclusive. The controversies mainly arise from the differences in sampling sites, targeted gene fragment, and microbiome analysis techniques. The objective of this study was to explore the microbiomes of peri-implantitis (PI), control implants (CI), PT and control teeth (CT), and the microbial change of PI after nonsurgical treatment (PIAT).

METHODS

Twenty-two patients diagnosed with both PT and peri-implantitis were recruited. Clinical periodontal parameters and radiographic bone levels were recorded. In each patient, the subgingival and submucosal plaque samples were collected from sites with PI, CI, PT, CT, and PIAT. Microbiome diversity was analyzed by high-throughput amplicon sequencing using full-length of 16S rRNA gene by next generation sequencing.

RESULTS

The 16S rRNA gene sequencing analysis revealed 512 OTUs in oral microbiome and 377 OTUs reached strain levels. The PI and PT groups possessed their own unique core microbiome. Treponema denticola was predominant in PI with probing depth of 8-10 mm. Interestingly, Thermovirga lienii DSM 17291 and Dialister invisus DSM 15470 were found to associate with PI. Nonsurgical treatment for peri-implantitis did not significantly alter the microbiome, except Rothia aeria.

CONCLUSION

Our study suggests Treponemas species may play a pivotal role in peri-implantitis. Nonsurgical treatment did not exert a major influence on the peri-implantitis microbiome in short-term follow-up. PT and peri-implantitis possess the unique microbiome profiles, and different therapeutic strategies may be suggested in the future.

Microbiological profile of peri-implantitis: Analyses of peri-implant microbiome

PURPOSE

To characterize the microbiome composition in peri-implant pocket of peri-implantitis and peri-implant sulcus controls using 16S rRNA gene sequencing.

MATERIALS AND METHODS

In this controlled clinical cross-sectional study, 23 subjects with control implants (n = 14) and diseased implants (peri-implantitis, n = 21) were included. The peri-implant pocket/sulcus was sampled and used to extract DNA and amplify the 16S rRNA gene using universal primers targeting the V3-V4 regions. The resulting 16S PCR amplicons were sequenced on Illumina MiSeq, and the sequences were processed using DADA2 and the Human Oral Microbiome Database (HOMD) as references. Alpha and Beta diversity, as well as core microbiome and differential abundance analyses, were performed using the MicrobiomeAnalyst workflow.

RESULTS

There were no significant differences in microbial diversity between control implants and implants with peri-implantitis (Shannon p = 0.82). Overall bacterial community structure assessed through beta diversity analysis was also not significantly different between the two groups (p = 0.18). However, high levels of Gram-negative bacteria were detected in peri-implant pockets compared to the control sulcus. Abundant species in peri-implantitis were Capnocytophaga leadbetteri, Treponema maltophilum, Peptostreptococcus, Neisseria, P. gingivalis, and Porphyromonas endodontali, Lactococcus lactis and Filifactor alocis (p < 0.05). Gram-positive bacteria such as Streptococcus salivaris, Prevotella melaninogenica, L. wadei, and Actinomyces spp. serve were more abundant in peri-implant control sulcus.

CONCLUSIONS

Peri-implant sulcus in control implants harbors predominantly Gram-positive bacteria, whereas pockets of implants with peri-implantitis harbor predominantly Gram-negative bacteria.

Favorable subgingival plaque microbiome shifts are associated with clinical treatment for peri-implant diseases

We performed a longitudinal shotgun metagenomic investigation of the plaque microbiome associated with peri-implant diseases in a cohort of 91 subjects with 320 quality-controlled metagenomes. Through recently improved taxonomic profiling methods, we identified the most discriminative species between healthy and diseased subjects at baseline, evaluated their change over time, and provided evidence that clinical treatment had a positive effect on plaque microbiome composition in patients affected by mucositis and peri-implantitis.

The Microbiome of Peri-Implantitis: A Systematic Review of Next-Generation Sequencing Studies

(1) Introduction: Current evidence shows that mechanical debridement augmented with systemic and topical antibiotics may be beneficial for the treatment of peri-implantitis. The microbial profile of peri-implantitis plays a key role in identifying the most suitable antibiotics to be used for the treatment and prevention of peri-implantitis. This systematic review aimed to summarize and critically analyze the methodology and findings of studies which have utilized sequencing techniques to elucidate the microbial profiles of peri-implantitis. (2) Results: Fusobacterium, Treponema, and Porphyromonas sp. are associated with peri-implantitis. Veillonella sp. are associated with healthy implant sites and exhibit a reduced prevalence in deeper pockets and with greater severity of disease progression. Streptococcus sp. have been identified both in diseased and healthy sites. Neisseria sp. have been associated with healthy implants and negatively correlate with the probing depth. Methanogens and AAGPRs were also detected in peri-implantitis sites. (3) Methods: The study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42023459266). The PRISMA criteria were used to select articles retrieved from a systematic search of the Scopus, Cochrane, and Medline databases until 1 August 2023. Title and abstract screening was followed by a full-text review of the included articles. Thirty-two articles were included in the final qualitative analysis. (4) Conclusions: A distinct microbial profile could not be identified from studies employing sequencing techniques to identify the microbiome. Further studies are needed with more standardization to allow a comparison of findings. A universal clinical parameter for the diagnosis of peri-implantitis should be implemented in all future studies to minimize confounding factors. The subject pool should also be more diverse and larger to compensate for individual differences, and perhaps a distinct microbial profile can be seen with a larger sample size.

Mucosal bleeding correlates with submucosal microbial dysbiosis in peri-implant mucositis of patients with periodontitis

OBJECTIVES

This study aimed to investigate the relationship between microbial communities and the severity of peri-implant mucosal bleeding in peri-implant mucositis.

MATERIALS AND METHODS

Submucosal plaque samples were collected from 54 implants divided into the healthy implant (HI) group, peri-implant mucositis (PM) group, and peri-implantitis (PI) group. Sequencing of 16S rRNA was performed using the Illumina MiSeq platform. Alpha diversity (i.e., Shannon and Chao index) and beta diversity were used to measure microbial diversity within and between microbial communities, respectively. Differences in microbial taxa between groups were assessed via linear discriminate analysis effect size. Correlation between the modified sulcus bleeding index (mSBI) and microbial dysbiosis index (MDI) was examined using Spearman correlation analysis and linear models.

RESULTS

The submucosal bacterial richness (Chao index) was positively correlated with the mean mSBI in the PM group. As the mean mSBI increased in the PM group, the beta diversity became closer to that of the PI group. In the PM group, the abundances of 47 genera were significantly correlated with the mean mSBI, and the MDI was positively associated with the mean mSBI. Fourteen of the forty-seven genera were discriminative taxa between the HI and PI groups, and the abundances of these biomarkers became closer to those in the PI group in the progression of peri-implant disease.

CONCLUSIONS

A higher mSBI value corresponded to a higher risk of microbial dysbiosis in peri-implant mucositis. The biomarkers identified may be useful for monitoring the progression of peri-implant disease.

Shift in the submucosal microbiome of diseased peri-implant sites after non-surgical mechanical debridement treatment

Objectives

The object of this prospective study was to assess the submucosal microbiome shifts in diseased peri-implant sites after non-surgical mechanical debridement therapy.

Materials and methods

Submucosal plaques were collected from 14 healthy implants and 42 diseased implants before and eight weeks after treatment in this prospective study. Mechanical debridement was performed using titanium curettes, followed by irrigation with 0.2% (w/v) chlorhexidine. Subsequently, 16S rRNA gene sequencing was used to analyze the changes in the submucosal microbiome before and after the non-surgical treatment.

Results

Clinical parameters and the submucosal microbiome were statistically comparable before and after mechanical debridement. The Alpha diversity decreased significantly after mechanical debridement. However, the microbial richness varied between the post-treatment and healthy groups. In network analysis, the post-treatment increased the complexity of the network compared to pre-treatment. The relative abundances of some pathogenic species, such as Porphyromonas gingivalis, Tannerella forsythia, Peptostreptococcaceae XIG-6 nodatum, Filifactor alocis, Porphyromonas endodontalis, TM7 sp., and Desulfobulbus sp. HMT 041, decreased significantly following the non-surgical treatment.

Conclusions

Non-surgical treatment for peri-implant diseases using mechanical debridement could provide clinical and microbiological benefits. The microbial community profile tended to shift towards a healthy profile, and submucosal dysbiosis was relieved following mechanical debridement.

Smoking by altering the peri-implant microbial community structure compromises the responsiveness to treatment

Smoking is an essential risk factor for peri-implant diseases. It also hampers the clinical outcomes of peri-implant therapies. Nonetheless, the effect of smoking can go undetected until the emergence of clinical signs. Bacterial-induced inflammation is responsible for the initiation and progression of peri-implant diseases. We hypothesize that smoking impacts the peri-implant microbiome even in status of clinical health, putting it into a sub-healthy condition that responds poorly to peri-implant treatments. To validate this, peri-implant plaque samples from 18 participants including 10 smokers (S) and 8 non-smokers (NS), who had received implant prostheses were analyzed using metagenomic shotgun sequencing. The results showed that in addition to taxonomical and functional differences, the local stability in the S group was also shown to be much higher than that in the NS group, indicating greater stubbornness of the peri-implant microbiome associated with smoking. Besides, the topological structures were also distinct between the two groups. The highly connected species interacted more preferentially with each other in the S group (eigenvector centralization, 0.0273 in S and 0.0183 in NS), resulting in a greater tendency of forming small-world modules (modularity, 0.714 in S and 0.582 in NS). While in the NS group, inter-species correlations were more evenly distributed (clustering coefficient, 0.532 in S and 0.666 in NS). These alterations overall explained the greater stubbornness of the peri-implant microbiome associated with smoking, which may cause poor responsiveness to peri-implant therapies. From a microbial perspective, this may be a potential reason why smoking impacts negatively on the outcome of peri-implant treatments.

Salivary Microbial Dysbiosis Is Associated With Peri-Implantitis: A Case-Control Study in a Brazilian Population

Background and Objectives

The aim of this study was to examine the salivary microbiome in healthy peri-implant sites and those with peri-implantitis.

Methods

Saliva samples were collected from 21 participants with healthy peri-implant sites and 21 participants with peri-implantitis. The V4 hypervariable region of the 16S rRNA gene was sequenced using the Ion Torrent PGM System (Ion 318™ Chip v2 400). The NGS analysis and composition of the salivary microbiome were determined by taxonomy assignment. Downstream bioinformatic analyses were performed in QIIME (v 1.9.1).

Results

Clinical differences according to peri-implant condition status were found. Alpha diversity metrics revealed that the bacterial communities of participants with healthy peri-implant sites tended to have a richer microbial composition than individuals with peri-implantitis. In terms of beta diversity, bleeding on probing (BoP) may influence the microbial diversity. However, no clear partitioning was noted between the salivary microbiome of volunteers with healthy peri-implant sites or volunteers with peri-implantitis. The highest relative abundance of Stenotrophomonas, Enterococcus and Leuconostoc genus, and Faecalibacterium prausnitzii, Haemophilus parainfluenzae, Prevotella copri, Bacteroides vulgatus, and Bacteroides stercoris bacterial species was found in participants with peri-implantitis when compared with those with healthy peri-implant sites.

Conclusion

Differences in salivary microbiome composition were observed between patients with healthy peri-implant sites and those with peri-implantitis. BoP could affect the diversity (beta diversity) of the salivary microbiome.

Non-surgical treatment for periodontitis and peri-implantitis: longitudinal clinical and bacteriological findings-A case report with a 7-year follow-up evaluation

The aim of this report is to show that periodontitis and peri-implantitis with horizontal bone resorption in a 68-year-old male patient were successfully treated by non-surgical treatment. Scaling with an ultrasonic device was performed for moderate periodontitis around the mandibular left first premolar and moderate peri-implantitis around the maxillary right molar implants. Root planing with a metal curette was performed for the periodontal site, and debridement with a plastic curette was performed for the peri-implant site. A month after treatment, probing depth decreased from 5 to 2 mm at the periodontal site and 8 to 3 mm at the peri-implant site. The investigation of bacterial composition by sequencing the 16S rRNA gene amplicons showed that the composition similarly changed at both sites, 5 years after treatment; the change reflected the typical recovery of periodontitis. The clinical condition was maintained for 7 years after treatment at both sites. This was a successful case of non-surgical treatment for peri-implantitis with horizontal bone resorption, promoting recovery of the microbiota from dysbiotic shift.

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes

Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system.

Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution.

Results: Unsupervised clustering identified risk groups with distinct immune profiles, microbial colonization dynamics, and regenerative outcomes. Low-risk patients exhibited elevated M1/M2-like macrophage ratios and lower B-cell infiltration. The low-risk immune profile was characterized by enhanced complement signaling and higher levels of Th1 and Th17 cytokines. Fusobacterium nucleatum and Prevotella intermedia were significantly enriched in high-risk individuals. Although surgery reduced microbial burden at the peri-implant interface in all groups, only low-risk individuals exhibited suppression of keystone pathogen re-colonization.

Conclusion: Peri-implant immune microenvironment shapes microbial composition and the course of regeneration. Immune signatures show untapped potential in improving the risk-grading for peri-implantitis.