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Objective: To describe the subgingival microbiome of individuals with Down syndrome (DS). Methods: We conducted a cross-sectional observational study that obtained bacterial DNA samples from 50 patients with DS, 25 with periodontitis (PDS) and 25 with a healthy periodontal condition (HDS). The samples were analyzed by sequencing the 16S rRNA gene V3&ndash, V4 hypervariable region using the MiSeq System. Taxonomic affiliations were assigned using the na&iuml, ve Bayesian classifier integrated in QIIME2 plugins. We evaluated the difference in bacteria abundance between the sample groups using Wilcoxon and Kruskal&ndash, Wallis tests. We evaluated the alpha diversity of the identified species using the Observed, Chao1metric, ACE and Shannon indices and evaluated beta diversity with principal coordinate analysis (registration code: 2018/510). Results: Twenty-one genera and 39 bacterial species showed a significantly different abundance between the study groups. Among the genera, Porphyromonas, Treponema, Tannerella and Aggregatibacter were more abundant in the PDS group than in the HDS group, as were the less commonly studied Filifactor, Fretibacterium and Desulfobulbus genera. Among the species, Porphyromonas spp. and Tannerella spp. were the most abundant in the PDS group, the most abundant species in the HDS group were Pseudomonas spp., Granulicatella spp. and Gemella spp. Conclusion: Well-recognized periodontal pathogens and newly proposed pathogenic taxa were associated with periodontitis in patients with DS.

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.

The etiology of periodontitis has traditionally been associated to a consortium of three bacterial species-the so-called "red-complex" of periodontal disease-which has been the target for most diagnostic and therapeutic strategies. However, other species have also been found to correlate with disease severity. In addition, the influence of smoking on periodontal microbiota is poorly understood. In the current manuscript, the composition of the subgingival microbiota in healthy individuals vs. patients with chronic periodontitis has been investigated using 16S pyrosequencing and the influence of smoking on periodontal composition has been examined. Subgingival bacterial communities were sampled from 82 patients: 22 non-smoking healthy controls, 28 non-smoking periodontal patients, and 32 smoking periodontal patients. Bacterial diversity was higher in periodontal patients than in healthy subjects, which could be interpreted as the consequence of a nutritionally richer environment or a reduced immune competence. Periodontal patients showed a significantly higher prevalence/relative abundance of "established" periopathogens but also other taxa whose role is not well-established and that should be targets for future research. These include Anaeroglobus, Bulleidia, Desulfobulbus, Filifactor, Mogibacterium, Phocaeicola, Schwartzia or TM7. The microbial community of smoking-associated periodontitis is less diverse and distinct from that of non-smokers, indicating that smoking has an influence on periodontal ecology. Interestingly, the high sequencing coverage allowed the detection at low proportions of periodontal pathogens in all healthy individuals, indicating that chronic periodontitis cannot be strictly considered an infectious disease but the outcome of a polymicrobial dysbiosis, where changes in the proportions of microbial consortia trigger the inflammatory and tissue-degradation responses of the host. © 2015 Camelo-Castillo, Mira, Pico, Nibali, Henderson, Donos and Tomás.

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.