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.

Microbiological profile of peri-implantitis: Analyses of microbiome within dental implants.

PURPOSE: To characterize the microbiome composition within dental implants of peri-implantitis subjects and healthy controls using 16S rRNA gene sequencing. MATERIALS AND METHODS: Twenty-three subjects with healthy (n = 11 implants) and diseased (peri-implantitis, n = 21) implants were included in this controlled clinical cross-sectional study. Samples were obtained from internal surfaces of dental implants using sterile paper points for microbiological analysis. DNA was extracted, and the16S rRNA gene was amplified using universal primers targeting the V3-V4 regions. The resulting 16S polymerize chain reaction 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 then performed using the MicrobiomeAnalyst workflow. RESULTS: A significant increase in microbial diversity was observed in the internal implant surface of healthy implants compared with the internal surfaces of peri-implantitis (Shannon p = 0.02). Bacterial community structure was significantly different among groups (p = 0.012). High levels of Gram-positive bacteria were detected inside implants with peri-implantitis compared to healthy implants, especially Enterococci. CONCLUSIONS: There is a shift in bacterial diversity inside implants with peri-implantitis from the healthy control. The microbial colonization within that space might contribute to the etiology of peri-implant disease.

Microbiological Profile and Human Immune Response Associated with Peri-Implantitis: A Systematic Review.

PURPOSE: To evaluate and synthesize the existing evidence on the microbiological and human immune response associated with peri-implantitis in comparison to healthy implants. MATERIALS AND METHODS: Three electronic databases (MEDLINE, Embase, and Cochrane Library) were searched in October 2019 to identify clinical studies evaluating the microbiota and the immune response associated with peri-implantitis. Two reviewers independently screened the studies and used the full text to extract the data. A qualitative synthesis was performed on the extracted data and summary tables were prepared. Due to clinical and methodological heterogeneity among included studies, no meta-analysis was performed. RESULTS: Forty studies were included in this review. Of these, 20 studies compared the microbiological profile of peri-implantitis with healthy implants. Nineteen studies focused on the immune response associated with peri-implantitis in comparison to healthy implants. Three studies focus on gene polymorphism associated with peri-implantitis. The most commonly reported bacteria associated with peri-implantitis were obligate anaerobe Gram-negative bacteria (OAGNB), asaccharolytic anaerobic Gram-positive rods (AAGPRs), and other Gram-positive species. In regard to immune response, the most frequently reported pro-inflammatory mediators associated with peri-implantitis were IL-1ß, IL-6, IL-17, TNF-α. Osteolytic mediator, e.g., RANK, RANKL, Wnt5a and proteinase enzymes, MMP-2, MMP-9, and Cathepsin-K were also expressed at higher level in peri-implantitis sites compared to control. CONCLUSIONS: Peri-implantitis is associated with complex and different microbiota than healthy implants including bacteria, archaea, fungi, and virus. This difference in the microbiota could provoke higher inflammatory response and osteolytic activity. All of this could contribute to the physiopathology of peri-implantitis.

A Longitudinal Metagenomic Comparative Analysis of Oral Microbiome Shifts in Patients Receiving Proton Radiation versus Photon Radiation for Head and Neck Cancer.

Introduction: Due to the radiation-sparing effects on salivary gland acini, changes in the composition of the oral microbiome may be a driver for improved outcomes in patients receiving proton radiation, with potentially worse outcomes in patients exposed to photon radiation therapy. To date, a head-to-head comparison of oral microbiome changes at a metagenomic level with longitudinal sampling has yet to be performed in these patient cohorts. Methods and Materials: To comparatively analyze oral microbiome shifts during head and neck radiation therapy, a prospective pilot cohort study was performed at the Maryland Proton Treatment Center and the University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center. A longitudinal metagenomic comparative analysis of oral microbiome shifts was performed at three time points (pre-radiation, during radiation, and immediately post-radiation). Head and neck cancer patients receiving proton radiation (n = 4) were compared to photon radiation (n = 4). Additional control groups included healthy age- and sex-matched controls (n = 5), head and neck cancer patients who never received radiation therapy (n = 8), and patients with oral inflammatory disease (n = 3). Results: Photon therapy patients presented with lower microbial alpha diversity at all timepoints, and there was a trend towards reduced species richness as compared with proton therapy. Healthy controls and proton patients exhibited overall higher and similar diversity. A more dysbiotic state was observed in patients receiving photon therapy as compared to proton therapy, in which oral microbial homeostasis was maintained. Mucositis was observed in 3/4 photon patients and was not observed in any proton patients during radiation therapy. The bacterial de novo pyrimidine biosynthesis pathway and the nitrate reduction V pathway were comparatively higher following photon exposure. These functional changes in bacterial metabolism may suggest that photon exposure produces a more permissive environment for the proliferation of pathogenic bacteria. Conclusion: Oral microbiome dysbiosis in patients receiving photon radiation may be associated with increased mucositis occurrence. Proton radiation therapy for head and neck cancer demonstrates a safer side effect profile in terms of oral complications, oral microbiome dysbiosis, and functional metabolic status.

How we are assessing the developing antibacterial resin-based dental materials? A scoping review.

OBJECTIVES: To identify antibacterial additives and screening/assessment approaches used to evaluate the antibacterial activity of resin-based restorative dental materials containing these additives. DATA: In vitro studies that compared the antibacterial effects of resin-based restorative dental materials with and without antibacterial additives were screened. Risk bias was assessed, and the following data were extracted: antibacterial additive, parental dental material, curing mode, bacterial growth outcome assessment, samples used as a substrate for bacterial growth, inoculum complexity, and culture time as an indicator of biofilm maturity. SOURCE: Arksey and O'Malley's five stages framework using Medline (OVID), EMBASE, and Scopus (Elsevier) databases guided this review. STUDY SELECTION: From 6503 studies initially identified, 348 studies were considered eligible for full-text screening, and 153 were included for data extraction. Almost all studies have a high sampling bias related to both sample size and blindness. Quaternary ammonium monomers were the most investigated additive (45 %), and the most prevailing parental material was resin composite (49 %). There was extensive methodological heterogeneity among the studies for outcome assessment with the majority using resin composite disks (78 %), mono-species Streptococcus mutans as the inoculum (54 %), and a relatively short period of biofilm growth (≤24 h). CONCLUSION: The findings herein present the urgent need for improved biological efficacy studies in this important and exciting field. There is a need for efforts to improve study designs to mimic the oral environment in vivo and to develop standardized methods to help understand and optimize these materials. CLINICAL SIGNIFICANCE: Most studies that incorporate antibacterial additives into resin-based materials claim promising results by bacterial reduction. However, these results should be interpreted with caution due to significant variation in the methods applied for quantifying bacterial growth, the frequent lack of complexity in the biofilms, and the often-short duration of biofilm growth.