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Periodontitis is an inflammatory disease involving complex interactions between oral microorganisms and the host immune response. Understanding the structure of the microbiota community associated with periodontitis is essential for improving classifications and diagnoses of various types of periodontal diseases and will facilitate clinical decision-making. In this study, we used a 16S rRNA metagenomics approach to investigate and compare the compositions of the microbiota communities from 76 subgingival plagues samples, including 26 from healthy individuals and 50 from patients with periodontitis. Furthermore, we propose a novel feature selection algorithm for selecting features with more information from many variables with a combination of these features and machine learning methods were used to construct prediction models for predicting the health status of patients with periodontal disease. We identified a total of 12 phyla, 124 genera, and 355 species and observed differences between health- and periodontitis-associated bacterial communities at all phylogenetic levels. We discovered that the generaPorphyromonas,Treponema,Tannerella,Filifactor, andAggregatibacterwere more abundant in patients with periodontal disease, whereasStreptococcus,Haemophilus,Capnocytophaga,Gemella,Campylobacter, andGranulicatellawere found at higher levels in healthy controls. Using our feature selection algorithm, random forests performed better in terms of predictive power than other methods and consumed the least amount of computational time.

Background and objective To examine the microbial diversity associated with implants with or without peri-implantitis and to identify differences between the bacterial communities in these clinical conditions. Material and methods Twenty subjects were assigned to a Control group consisting of subjects with healthy implants and a Test group consisting of subjects with peri-implantitis sites, as well as a healthy implant site (n = 10/group). In the Test group, subgingival biofilm samples were taken from the deepest sites of the diseased implant. In both groups, samples were collected from one site around a healthy implant. DNA was extracted and the 16S rRNA gene was amplified and sequenced, and the phylotypes were identified by comparison with known 16S rRNA sequences. Results The phylogenetic identity of 1387 16S rRNA gene clones was determined. Healthy implants demonstrated higher proportions of Actinomyces, Atopobium, Gemella, Kingella and Rothia and lower levels of Campylobacter, Desulfobulbus, Dialister, Eubacterium, Filifactor, Mitsukella, Porphyromonas and Pseudoramibacter (Mann–Whitney U-test; P

The entire microbial population and predominant microflora of root canals (RCs) and adjacent periodontal pockets (PPs) from teeth with combined periodontal-endodontic lesions were determined and compared.Pooled RC and PP samples were collected from the molars of 20 patients diagnosed with combined periodontal-endodontic lesions. DNA was extracted for polymerase chain reaction-based denaturing gradient gel electrophoresis (PCR-DGGE), cloning, and sequence analysis. A coefficient of similarity (Cs) was used to determine the similarity of the bacterial profiles from RCs and PPs.Significantly fewer bands were produced by PCR-DGGE from RCs (5.9 ± 1.7) than from PPs (8.0 ± 1.8) (P0.001). The average Cs of the RC and PP samples was 93.81% ± 10.26%. Overall, 60 genera/species were identified by sequencing. Of these, the predominant genera in RCs were Porphyromonas sp. (13.9%), Filifactor sp. (12.5%), and Parvimonas sp. (11.1%), similar to the genera obtained from PP samples. In total, 43 genera/species were common to the RC and PP samples. The most prevalent bacteria in both the RC and PP samples were (in descending order) Filifactor alocis, Parvimonas micra, Porphyromonas gingivalis, and Tannerella forsythia.The high similarity in the sets of organisms present in both RC and PP samples in this study suggests that the pocket could be a source of RC infection. The data also demonstrate that combined periodontal-endodontic lesions consist of a diverse and complex microbial community.

By using NGS-sequencing libraries of DNA from periodontal swabs with primers specific to V6 region of 16S rDNA prevalence of bacterial genera and species in periodontal microbiota of patients with aggressive periodontitis and healthy donors was analyzed. Six genera of putative periodontal protectors and eight periodontal pathogens were identified with respect to aggressive (but not chronic) periodontitis. Statistically relevant over-colonization by general Porphyromonas, Treponema, Synergistes, Tannerella, Filifactor, Ruminococcus, Parvimonas and Mycoplasma was found to be associated with the condition. From these, only three genera Porphyromonas, Treponema and Tannerella are traditionally considered as periodontal pathogens. Statistically confidential over-colonization by genus Veillonella was found in healthy patients. This genus should be considered as a relevant marker of a healthy periodontium. Genera Streptococcus, Bergeyella, Granulicatella, Kingella and Corynebacterium may be considered as putative periodontal protectors. Comparison of data of NGS-sequencing and real-time PCR demonstrated a good agreement if different PCR efficiency using independent primer pairs is taken into account.В работе с применением метода NGS-секвенирования банков суммарной ДНК пародонтальных смывов с праймерами на область V6 16S рДНК проанализирована представленность видов и родов бактерий в микрофлоре пародонта пациентов с агрессивным пародонтитом и лиц со здоровым пародонтом. В результате выявлено 6 родов потенциальных пародонтопротекторов и 8 родов потенциальных пародонтопатогенов, имеющих отношение к риску возникновения агрессивного (но не хронического) пародонтита. Выявлено статистически достоверное повышение представленности у больных агрессивным пародонтитом родов Porphyromonas, Treponema, Synergistes, Tannerella, Filifactor, Ruminococcus, Parvimonas, Mycoplasma, из которых только три Porphyromonas, Treponema и Tannerella традиционно рассматриваются как пародонтопатогены. У лиц со здоровым пародонтом было впервые выявлено статистически достоверное доминирование рода Veillonella, которое, таким образом, должно рассматриваться в качестве важного критерия здоровья пародонта. Роды Streptococcus, Bergeyella, Granulicatella, Kingella и Corynebacterium могут рассматриваться в качестве кандидатных пародонтопротекторов. Проведено сопоставление данных NGS-секвенирования и ПЦР в реальном времени. Показана хорошая воспроизводимость результатов при условии учета различной эффективности реакции ПЦР при использовании независимых пар праймеров.