Microbiota present in combined endodontic-periodontal diseases and its risks for endocarditis.

INTRODUCTION: Infective endocarditis (IE) is an inflammatory disease usually caused by bacteria that enter the bloodstream and establish infections in the inner linings or valves of the heart, including blood vessels. Despite the availability of modern antimicrobial and surgical treatments, IE continues to cause substantial morbidity and mortality. Oral microbiota is considered one of the most significant risk factors for IE. The objective of this study was to evaluate the microbiota present in root canal (RC) and periodontal pocket (PP) clinical samples in cases with combined endo-periodontal lesions (EPL) to detect species related to IE using NGS. METHODS: Microbial samples were collected from 15 RCs and their associated PPs, also from 05 RCs with vital pulp tissues (negative control, NC). Genomic studies associated with bioinformatics, combined with structuring of a database (genetic sequences of bacteria reported for infective endocarditis), allowed for the assessment of the microbial community at both sites. Functional prediction was conducted using PICRUSt2. RESULTS: Parvimonas, Streptococcus, and Enterococcus were the major genera detected in the RCs and PPs. A total of 79, 96, and 11 species were identified in the RCs, PPs, and NCs, respectively. From them, a total of 34 species from RCs, 53 from PPs, and 2 from NCs were related to IE. Functional inference demonstrated that CR and PP microbiological profiles may not be the only risk factors for IE but may also be associated with systemic diseases, including myocarditis, human cytomegalovirus infection, bacterial invasion of epithelial cells, Huntington's disease, amyotrophic lateral sclerosis, and hypertrophic cardiomyopathy. Additionally, it was possible to predict antimicrobial resistance variants for broad-spectrum drugs, including ampicillin, tetracycline, and macrolides. CONCLUSION: Microorganisms present in the combined EPL may not be the only risk factor for IE but also for systemic diseases. Antimicrobial resistance variants for broad-spectrum drugs were inferred based on PICRUSt-2. State-of-the-art sequencing combined with bioinformatics has proven to be a powerful tool for conducting studies on microbial communities and could considerably assist in the diagnosis of serious infections. CLINICAL RELEVANCE: Few studies have investigated the microbiota in teeth compromised by combined endo-periodontal lesions (EPL), but none have correlated the microbiological findings to any systemic condition, particularly IE, using NGS techniques. In such cases, the presence of apical periodontitis and periodontal disease can increase IE risk in susceptible patients.

Chemomechanical preparation influences the microbial community and the levels of LPS, LTA and cytokines in combined endodontic-periodontal lesions: A clinical study.

BACKGROUND: This study was conducted to compare the microbiomes, the levels of lipopolysaccharides (LPS), lipoteichoic acid (LTA), and cytokines (interleukin [IL]-1ß and tumor necrosis factor-alpha [TNF-α]), before and after chemomechanical preparation (CMP) of the root canals (RC) and their associated periodontal pockets (PP) in teeth with combined EPL. MATERIALS: Samples were taken from 10 RC and PP, before and after CMP. The microbiomes (next-generation sequencing, V3-V4 hypervariable region of the 16S rRNA gene), microbiome diversity (bioinformatics analyses), LPS (limulus amebocyte lysate), LTA, IL-1ß, and TNF-α (ELISA) were evaluated. A statistical analysis was performed with significance level set at 5%. RESULTS: The most abundant phyla in both sites were Firmicutes and Proteobacteria. Comparative studies of bacterial genera species revealed that some increased and others decreased after CMP at both sites. A 3% reduction in Gram-negative bacteria (RC) and a 4% increase in Gram-positive bacteria (PP) were detected. LPS levels were 4.4 times higher in PP than in the RC. LTA was detected in all samples investigated. Higher levels of IL-1ß and TNF-α were detected in both sites at baseline. After CMP, LPS, LTA, IL-1ß and TNF-α were reduced in both sites. CONCLUSION: The microbial community in the RC and PP in teeth with combined EPL indicated a similarity between both sites. CMP effectively reduced the microbial load and the LPS levels from teeth with EPL, and consequently diminished the cytokine levels. The reduction in LTA levels in the RC and PP proved challenging.

Association between metabolic syndrome and periodontitis: The role of lipids, inflammatory cytokines, altered host response, and the microbiome.

Periodontitis has been associated with many systemic diseases and conditions, including metabolic syndrome. Metabolic syndrome is a cluster of conditions that occur concomitantly and together they increase the risk of cardiovascular disease and double the risk of type 2 diabetes. In this review, we focus on the association between metabolic syndrome and periodontitis; however, we also include information on diabetes mellitus and cardiovascular disease, since these two conditions are significantly intertwined with metabolic syndrome. With regard to periodontitis and metabolic syndrome, to date, the vast majority of studies point to an association between these two conditions and also demonstrate that periodontitis can contribute to the development of, or can worsen, metabolic syndrome. Evaluating the effect of metabolic syndrome on the salivary microbiome, data presented herein support the hypothesis that the salivary bacterial profile is altered in metabolic syndrome patients compared with healthy patients. Considering periodontitis and these three conditions, the vast majority of human and animal studies point to an association between periodontitis and metabolic syndrome, diabetes, and cardiovascular disease. Moreover, there is evidence to suggest that metabolic syndrome and diabetes can alter the oral microbiome. However, more studies are needed to fully understand the influence these conditions have on each other.

Resolvin E1 Reverses Experimental Periodontitis and Dysbiosis.

Periodontitis is a biofilm-induced inflammatory disease characterized by dysbiosis of the commensal periodontal microbiota. It is unclear how natural regulation of inflammation affects the periodontal biofilm. Promoters of active resolution of inflammation, including resolvin E1 (RvE1), effectively treat inflammatory periodontitis in animal models. The goals of this study were 1) to compare periodontal tissue gene expression in different clinical conditions, 2) to determine the impact of local inflammation on the composition of subgingival bacteria, and 3) to understand how inflammation impacts these changes. Two clinically relevant experiments were performed in rats: prevention and treatment of ligature-induced periodontitis with RvE1 topical treatment. The gingival transcriptome was evaluated by RNA sequencing of mRNA. The composition of the subgingival microbiota was characterized by 16S rDNA sequencing. Periodontitis was assessed by bone morphometric measurements and histomorphometry of block sections. H&E and tartrate-resistant acid phosphatase staining were used to characterize and quantify inflammatory changes. RvE1 treatment prevented bone loss in ligature-induced periodontitis. Osteoclast density and inflammatory cell infiltration in the RvE1 groups were lower than those in the placebo group. RvE1 treatment reduced expression of inflammation-related genes, returning the expression profile to one more similar to health. Treatment of established periodontitis with RvE1 reversed bone loss, reversed inflammatory gene expression, and reduced osteoclast density. Assessment of the rat subgingival microbiota after RvE1 treatment revealed marked changes in both prevention and treatment experiments. The data suggest that modulation of local inflammation has a major role in shaping the composition of the subgingival microbiota.

Subgingival microbial profiles in pre- and postmenopausal women: Associations with serum estradiol levels.

BACKGROUND: Subgingival dental plaque is an ecosystem playing a key role in supporting both oral health and systemic health. Menopause-related changes have the potential to disrupt its balance, which is crucial to postmenopausal well-being. Our study explored how circulating estradiol levels correlate with subgingival microbial composition using checkerboard DNA-DNA hybridization in premenopausal and postmenopausal women. We also demonstrated that combining this method with 16S ribosomal RNA (rRNA) sequencing insights remains valuable for examining subgingival ecology. METHODS: We assessed 40 bacterial species in 77 premenopausal and 81 postmenopausal women using checkerboard DNA-DNA hybridization and measured serum estradiol with enzyme-linked immunosorbent assay (ELISA). Women were categorized by subgingival dysbiosis severity using a modified Subgingival Microbial Dysbiosis Index (mSMDI). Six women from each normobiotic and dysbiotic subgroup across premenopausal and postmenopausal women underwent 16S rRNA sequencing analysis. RESULTS: DNA checkerboard analysis revealed that most observed variability in individual bacterial proportions is associated with periodontitis. Two species, Leptotrichia buccalis and Streptococcus constellatus, exhibited differences related to estradiol levels within the premenopausal group (p = 0.055 and p = 0.009, respectively). 16S rRNA sequencing confirmed the mSMDI's validity in categorizing normobiotic and dysbiotic states. Menopausal status was not associated with a dysbiotic shift in the subgingival microbiome despite significantly more attachment loss in postmenopausal compared to premenopausal women. CONCLUSIONS: Our results indicate that decreased estradiol levels or increased attachment loss during menopause are not associated with changes in species abundance or dysbiotic shifts in women. The mSMDI may be a useful tool for classifying subgingival ecology based on its normobiotic or dysbiotic inclination.

The effect of dental material type and masticatory forces on periodontitis-derived subgingival microbiomes.

Restorative dental materials can frequently extend below the gingival margin, serving as a potential haven for microbial colonization, and altering the local oral microbiome to ignite infection. However, the contribution of dental materials on driving changes of the composition of the subgingival microbiome is under-investigated. This study evaluated the microbiome-modulating properties of three biomaterials, namely resin dental composites (COM), antimicrobial piezoelectric composites (BTO), and hydroxyapatite (HA), using an optimized in vitro subgingival microbiome model derived from patients with periodontal disease. Dental materials were subjected to static or cyclic loading (mastication forces) during biofilm growth. Microbiome composition was assessed by 16S rRNA gene sequencing. Dysbiosis was measured in terms of subgingival microbial dysbiosis index (SMDI). Biomaterials subjected to cyclic masticatory loads were associated with enhanced biofilm viability except on the antibacterial composite. Biomaterials held static were associated with increased biofilm biomass, especially on HA surfaces. Overall, the microbiome richness (Chao index) was similar for all the biomaterials and loading conditions. However, the microbiome diversity (Shannon index) for the HA beams was significantly different than both composites. In addition, beta diversity analysis revealed significant differences between composites and HA biomaterials, and between both loading conditions (static and cyclic). Under static conditions, microbiomes formed over HA surfaces resulted in increased dysbiosis compared to composites through the enrichment of periopathogens, including Porphyromonas gingivalis, Porphyromonas endodontalis, and Fretibacterium spp., and depletion of commensals such as Granulicatella and Streptococcus spp. Interestingly, cyclic loading reversed the dysbiosis of microbiomes formed over HA (depletion of periopathogenes) but increased the dysbiosis of microbiomes formed over composites (enrichment of Porphyromonas gingivalis and Fusobacterim nucleatum). Comparison of species formed on both composites (control and antibacterial) showed some differences. Commercial composites enriched Selenomonas spp. and depleted Campylobacter concisus. Piezoelectric composites effectively controlled the microbiome viability without significantly impacting the species abundance. Findings of this work open new understandings of the effects of different biomaterials on the modulation of oral biofilms and the relationship with oral subgingival infections.

Quantifying periodontitis-associated oral dysbiosis in tongue and saliva microbiomes-An integrated data analysis.

BACKGROUND: Periodontitis is primarily driven by subgingival biofilm dysbiosis. However, the quantification and impact of this periodontal dysbiosis on other oral microbial niches remain unclear. This study seeks to quantify the dysbiotic changes in tongue and salivary microbiomes resulting from periodontitis by applying a clinically relevant dysbiosis index to an integrated data analysis. METHODS: The National Center for Biotechnology Information (NCBI) database was searched to identify BioProjects with published studies on salivary and tongue microbiomes of healthy and periodontitis subjects. Raw sequence datasets were processed using a standardized bioinformatic pipeline and categorized by their ecological niche and periodontal status. The subgingival microbial dysbiosis index (SMDI), a dysbiosis index originally developed using the subgingival microbiome, was computed at species and genus levels and customized for each niche. Its diagnostic accuracy for periodontitis was evaluated using receiver operating characteristic curves. RESULTS: Four studies, contributing 328 microbiome samples, were included. At both species and genus levels, periodontitis samples had a higher SMDI, but the differences were only significant for subgingival biofilm and saliva (p < 0.001). However, SMDI showed good diagnostic accuracy for periodontitis status for all three niches (area under curve ranging from 0.76 to 0.90, p < 0.05). The dysbiosis index of subgingival biofilm was positively correlated with saliva consistently (p < 0.001) and with the tongue at the genus level (p = 0.036). CONCLUSIONS: While the impact on the tongue microbiome requires further investigation, periodontitis-associated dysbiosis affects the salivary microbiome and is quantifiable using the dysbiosis index. The diagnostic potential of salivary microbial dysbiosis as a convenient periodontal biomarker for assessing periodontal status has potential public health and clinical applications. PLAIN LANGUAGE SUMMARY: Periodontitis, a severe inflammation of the gums which causes bone loss, is a disease caused by an imbalance of good and bad bacteria under the gums. However, it is unclear how this bacterial imbalance in the gums affects the bacterial balance of other distinct parts of the mouth, such as the saliva and tongue. This study uses bacteria datasets of four previously published studies, contributing a total of 328 bacterial samples. The data were processed using a uniform data analysis workflow, and a bacterial score, the subgingival microbial dysbiosis index (SMDI), previously shown to capture periodontitis-associated bacteria imbalance, was calculated separately for samples from under the gums, the saliva, and the tongue. The SMDI was able to distinguish between health and periodontitis within each oral location, and in general, the scores were higher for periodontitis samples, though this difference was significant only for bacteria under the gums and in saliva. Saliva scores were also consistently correlated with bacteria under the gums. This study shows that periodontitis-associated bacterial imbalances are observed in oral locations beyond just under the gums, particularly the saliva. Thus, saliva bacteria may be used as a convenient biomarker for assessing gum disease, allowing for potential public health and clinical applications.

The nucleoid-associated protein HUß affects global gene expression in Porphyromonas gingivalis.

HU is a non-sequence-specific DNA-binding protein and one of the most abundant nucleoid-associated proteins in the bacterial cell. Like Escherichia coli, the genome of Porphyromonas gingivalis is predicted to encode both the HUα (PG1258) and the HUß (PG0121) subunit. We have previously reported that PG0121 encodes a non-specific DNA-binding protein and that PG0121 is co-transcribed with the K-antigen capsule synthesis operon. We also reported that deletion of PG0121 resulted in downregulation of capsule operon expression and produced a P. gingivalis strain that is phenotypically deficient in surface polysaccharide production. Here, we show through complementation experiments in an E. coli MG1655 hupAB double mutant strain that PG0121 encodes a functional HU homologue. Microarray and quantitative RT-PCR analysis were used to further investigate global transcriptional regulation by HUß using comparative expression profiling of the PG0121 (HUß) mutant strain to the parent strain, W83. Our analysis determined that expression of genes encoding proteins involved in a variety of biological functions, including iron acquisition, cell division and translation, as well as a number of predicted nucleoid associated proteins were altered in the PG0121 mutant. Phenotypic and quantitative real-time-PCR (qRT-PCR) analyses determined that under iron-limiting growth conditions, cell division and viability were defective in the PG0121 mutant. Collectively, our studies show that PG0121 does indeed encode a functional HU homologue, and HUß has global regulatory functions in P. gingivalis; it affects not only production of capsular polysaccharides but also expression of genes involved in basic functions, such as cell wall synthesis, cell division and iron uptake.

Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis.

BACKGROUND: Porphyromonas gingivalis (hereafter "Pg") is an oral pathogen that has been hypothesized to act as a keystone driver of inflammation and periodontal disease. Although Pg is most readily recovered from individuals with actively progressing periodontal disease, healthy individuals and those with stable non-progressing disease are also colonized by Pg. Insights into the factors shaping the striking strain-level variation in Pg, and its variable associations with disease, are needed to achieve a more mechanistic understanding of periodontal disease and its progression. One of the key forces often shaping strain-level diversity in microbial communities is infection of bacteria by their viral (phage) predators and symbionts. Surprisingly, although Pg has been the subject of study for over 40 years, essentially nothing is known of its phages, and the prevailing paradigm is that phages are not important in the ecology of Pg. RESULTS: Here we systematically addressed the question of whether Pg are infected by phages-and we found that they are. We found that prophages are common in Pg, they are genomically diverse, and they encode genes that have the potential to alter Pg physiology and interactions. We found that phages represent unrecognized targets of the prevalent CRISPR-Cas defense systems in Pg, and that Pg strains encode numerous additional mechanistically diverse candidate anti-phage defense systems. We also found that phages and candidate anti-phage defense system elements together are major contributors to strain-level diversity and the species pangenome of this oral pathogen. Finally, we demonstrate that prophages harbored by a model Pg strain are active in culture, producing extracellular viral particles in broth cultures. CONCLUSION: This work definitively establishes that phages are a major unrecognized force shaping the ecology and intra-species strain-level diversity of the well-studied oral pathogen Pg. The foundational phage sequence datasets and model systems that we establish here add to the rich context of all that is already known about Pg, and point to numerous avenues of future inquiry that promise to shed new light on fundamental features of phage impacts on human health and disease broadly. Video Abstract.

Bacteriome analysis of Aggregatibacter actinomycetemcomitans-JP2 genotype-associated Grade C periodontitis in Moroccan adolescents.

Background: Grade C (previously aggressive) periodontitis (GCP) in adolescents is prevalent in certain parts of Africa where it is associated with JP2 genotype, a highly virulent strain of Aggregatibacter actinomycetemcomitans. The aim of this study was to characterize the subgingival bacteriome in Moroccan subjects with GCP positive to A. actinomycetemcomitans JP2 genotype. Methods: Subgingival plaque samples were collected from shallow and deep pockets of 8 subjects with GCP (17.2 ± 1.5 years) and from gingival sulci of 13 controls with no periodontitis (14.6 ± 1.1 years). Identification and genotyping of A. actinomycetemcomitans was performed using PCR analysis of the ltx operon, while bacteriome profiling was done by 16S rRNA gene sequencing (V1-V3 region). Groups were compared in terms of microbial diversity, abundances, and dysbiosis. Results: The shallow and deep pocket sites from GCP cases had a significantly altered microbial composition compared to controls. Species associated with health included Haemophilus parainfluenzae, Lautropia mirabilis, Streptococcus spp., Gemella spp., and Rothia spp. While known periodontal pathogens, including Porphyromonas gingivalis, Tannerella forsythia, Treponema spp. and Fretibacterium spp., were significantly enriched in GCP, non-conventional taxa, including Pseudomonas oral taxon C61 and Enterobacter cloacae were more abundant and showed stronger association with the disease. Less significant differences in abundances of individual taxa were observed between shallow and deep pockets. Overall dysbiosis measured in terms of Subgingival Microbial Dysbiosis Index (SMDI) differentiated between GCP and no-periodontitis with 95% accuracy. Conclusions: The results suggest that several periodontal pathogens involved in the adult-type periodontitis also play a role in JP2 genotype-associated GCP. The potential role of non-conventional taxa in the pathogenesis of GCP warrants further investigation.

Longitudinal changes in subgingival biofilm composition following periodontal treatment.

BACKGROUND: Current periodontal treatment involves instrumentation using hand and/or ultrasonic instruments, which are used either alone or in combination based on patient and clinician preference, with comparable clinical outcomes. This study sought to investigate early and later changes in the subgingival biofilm following periodontal treatment, to identify whether these changes were associated with treatment outcomes, and to investigate whether the biofilm responded differently to hand compared with ultrasonic instruments. METHODS: This was a secondary-outcome analysis of a randomized-controlled trial. Thirty-eight periodontitis patients received full-mouth subgingival instrumentation using hand (n = 20) or ultrasonic instrumentation (n = 18). Subgingival plaque was sampled at baseline and 1, 7, and 90 days following treatment. Bacterial DNA was analyzed using 16S rRNA sequencing. Periodontal clinical parameters were evaluated before and after treatment. RESULTS: Biofilm composition was comparable in both (hand and ultrasonics) treatment groups at all time points (all genera and species; p[adjusted] > 0.05). Large-scale changes were observed within groups across time points. At days 1 and 7, taxonomic diversity and dysbiosis were reduced, with an increase in health-associated genera including Streptococcus and Rothia equating to 30% to 40% of the relative abundance. When reassessed at day 90 a subset of samples reformed a microbiome more comparable with baseline, which was independent of instrumentation choice and residual disease. CONCLUSIONS: Hand and ultrasonic instruments induced comparable impacts on the subgingival plaque microbiome. There were marked early changes in the subgingival biofilm composition, although there was limited evidence that community shifts associated with treatment outcomes.

Comprehensive transcriptome analysis of the periodontopathogenic bacterium Porphyromonas gingivalis W83.

High-density tiling microarray and RNA sequencing technologies were used to analyze the transcriptome of the periodontopathogenic bacterium Porphyromonas gingivalis. The compiled P. gingivalis transcriptome profiles were based on total RNA samples isolated from three different laboratory culturing conditions, and the strand-specific transcription profiles generated covered the entire genome, including both protein coding and noncoding regions. The transcription profiles revealed various operon structures, 5'- and 3'-end untranslated regions (UTRs), differential expression patterns, and many novel, not-yet-annotated transcripts within intergenic and antisense regions. Further transcriptome analysis identified the majority of the genes as being expressed within operons and most 5' and 3' ends to be protruding UTRs, of which several 3' UTRs were extended to overlap genes carried on the opposite/antisense strand. Extensive antisense RNAs were detected opposite most insertion sequence (IS) elements. Pairwise comparative analyses were also performed among transcriptome profiles of the three culture conditions, and differentially expressed genes and metabolic pathways were identified. With the growing realization that noncoding RNAs play important biological functions, the discovery of novel RNAs and the comprehensive transcriptome profiles compiled in this study may provide a foundation to further understand the gene regulation and virulence mechanisms in P. gingivalis. The transcriptome profiles can be viewed at and downloaded from the Microbial Transcriptome Database website, http://bioinformatics.forsyth.org/mtd.

Prolonged mask wearing does not alter the oral microbiome, salivary flow rate or gingival health status - A pilot study.

The COVID-19 pandemic has resulted in the widespread use of N95 respirators and surgical masks, with anecdotal reports among healthcare providers and the public of xerostomia, halitosis, and gingivitis, a consortium of symptoms colloquially termed "mask mouth". However, this has not been scientifically verified. The aim of this study was to assess changes in salivary flow rate, gingival health status and oral microbiome associated with prolonged mask use. A total of 25 dental students (mean age = 26.36 ± 1.58) were included in the study and evaluated at three time points: T1, at the end of at least 2 months of full-day mask wear (7.26 ± 1.56 hours/day); T2, at the end of a period of minimal mask use (1.13 ± 1.13 hours/day); and T3, at the end of 2-3 weeks of resuming full-day mask wear (6.93 ± 1.80 hours/day). Unstimulated whole saliva (UWS) flow rate, xerostomia (on a quantitative scale of 10), gingival index (GI) and plaque index (PI) were assessed at each time point. The salivary microbiome was characterized using 16S rRNA gene sequencing. Overall, UWS flow rates were normal (mean of 0.679 ml/min) and xerostomia, PI and GI scores were low (Mean of 3.11, 0.33 and 0.69, respectively) with no significant differences as a result of prolonged mask wearing. Similarly, there were no significant microbial changes at a false discovery rate (FDR) ≤ 0.05. However, some trends were identified using a nominal p-value cut-off of ≤ 0.01, namely Gemella sanguinis, Streptococcus sp. Oral taxon 066 and Oral taxon 058 were associated with prolonged mask wear. Trends were also seen by gender, race and age, for example an increase in P. gingivalis and P. intermedia with age. In conclusion, we found no evidence that prolonged mask wear adversely affects oral health. The findings support that the oral microbiome of healthy individuals is resilient.

Multimodal Data Integration Reveals Mode of Delivery and Snack Consumption Outrank Salivary Microbiome in Association With Caries Outcome in Thai Children.

Early childhood caries (ECC) is not only the most common chronic childhood disease but also disproportionately affects underserved populations. Of those, children living in Thailand have been found to have high rates of ECC and severe ECC. Frequently, the cause of ECC is blamed on a handful of cariogenic organisms, such as Streptococcus mutans and Streptococcus sobrinus. However, ECC is a multifactorial disease that results from an ecological shift in the oral cavity from a neutral pH (~7.5) to an acidic pH (<5.5) environment influenced by the host individual's biological, socio-behavioral, and lifestyle factors. Currently, there is a lack of understanding of how risk factors at various levels influence the oral health of children at risk. We applied a statistical machine learning approach for multimodal data integration (parallel and hierarchical) to identify caries-related multiplatform factors in a large cohort of mother-child dyads living in Chiang Mai, Thailand (N=177). Whole saliva (1 mL) was collected from each individual for DNA extraction and 16S rRNA sequencing. A set of maternal and early childhood factors were included in the data analysis. Significantly, vaginal delivery, preterm birth, and frequent sugary snacking were found to increase the risk for ECC. The salivary microbial diversity was significantly different in children with ECC or without ECC. Results of linear discriminant analysis effect size (LEfSe) analysis of the microbial community demonstrated that S. mutans, Prevotella histicola, and Leptotrichia hongkongensis were significantly enriched in ECC children. Whereas Fusobacterium periodonticum was less abundant among caries-free children, suggesting its potential to be a candidate biomarker for good oral health. Based on the multimodal data integration and statistical machine learning models, the study revealed that the mode of delivery and snack consumption outrank salivary microbiome in predicting ECC in Thai children. The biological and behavioral factors may play significant roles in the microbial pathobiology of ECC and warrant further investigation.

Strand-specific transcriptome profiling with directly labeled RNA on genomic tiling microarrays.

BACKGROUND: With lower manufacturing cost, high spot density, and flexible probe design, genomic tiling microarrays are ideal for comprehensive transcriptome studies. Typically, transcriptome profiling using microarrays involves reverse transcription, which converts RNA to cDNA. The cDNA is then labeled and hybridized to the probes on the arrays, thus the RNA signals are detected indirectly. Reverse transcription is known to generate artifactual cDNA, in particular the synthesis of second-strand cDNA, leading to false discovery of antisense RNA. To address this issue, we have developed an effective method using RNA that is directly labeled, thus by-passing the cDNA generation. This paper describes this method and its application to the mapping of transcriptome profiles. RESULTS: RNA extracted from laboratory cultures of Porphyromonas gingivalis was fluorescently labeled with an alkylation reagent and hybridized directly to probes on genomic tiling microarrays specifically designed for this periodontal pathogen. The generated transcriptome profile was strand-specific and produced signals close to background level in most antisense regions of the genome. In contrast, high levels of signal were detected in the antisense regions when the hybridization was done with cDNA. Five antisense areas were tested with independent strand-specific RT-PCR and none to negligible amplification was detected, indicating that the strong antisense cDNA signals were experimental artifacts. CONCLUSIONS: An efficient method was developed for mapping transcriptome profiles specific to both coding strands of a bacterial genome. This method chemically labels and uses extracted RNA directly in microarray hybridization. The generated transcriptome profile was free of cDNA artifactual signals. In addition, this method requires fewer processing steps and is potentially more sensitive in detecting small amount of RNA compared to conventional end-labeling methods due to the incorporation of more fluorescent molecules per RNA fragment.

A hidden Markov support vector machine framework incorporating profile geometry learning for identifying microbial RNA in tiling array data.

MOTIVATION: RNA expression signals detected by high-density genomic tiling microarrays contain comprehensive transcriptomic information of the target organism. Current methods for determining the RNA transcription units are still computation intense and lack the discriminative power. This article describes an efficient and accurate methodology to reveal complicated transcriptional architecture, including small regulatory RNAs, in microbial transcriptome profiles. RESULTS: Normalized microarray data were first subject to support vector regression to estimate the profile tendency by reducing noise interruption. A hybrid supervised machine learning algorithm, hidden Markov support vector machines, was then used to classify the underlying state of each probe to 'expression' or 'silence' with the assumption that the consecutive state sequence was a heterogeneous Markov chain. For model construction, we introduced a profile geometry learning method to construct the feature vectors, which considered both intensity profiles and changes of intensities over the probe spacing. Also, a robust strategy was used to dynamically evaluate and select the training set based only on prior computer gene annotation. The algorithm performed better than other methods in accuracy on simulated data, especially for small expressed regions with lower (<1) SNR (signal-to-noise ratio), hence more sensitive for detecting small RNAs. AVAILABILITY AND IMPLEMENTATION: Detail implementation steps of the algorithm and the complete result of the transcriptome analysis for a microbial genome Porphyromonas gingivalis W83 can be viewed at http://bioinformatics.forsyth.org/mtd.

Glycemic fluctuation exacerbates inflammation and bone loss and alters microbiota profile around implants in diabetic mice with experimental peri-implantitis.

BACKGROUND: The impact of glycemic fluctuation under diabetic condition on peri-implantitis in diabetic patients remains unclear. We hypothesized that glycemic fluctuation has greater adverse effect on experimental peri-implantitis, compared with sustained high blood glucose in diabetes. RESULTS: Maxillary left first and second molars of diabetic db/db mice were extracted and were replaced with one dental implant in the healed edentulous space. Glycemic control or fluctuation were managed by constant or interrupted oral administration of rosiglitazone to these mice. Meanwhile, experimental peri-implantitis was induced by ligation around implants. After 14 weeks, inflammatory responses, and peri-implant bone loss, together with oral microbiota profile were analyzed. Diabetic mice with glycemic fluctuation showed greater peri-implant bone loss, inflammatory cell infiltration, and osteoclastogenesis, compared with mice with sustained hyperglycemia. Compared to sustained hyperglycemia, glycemic fluctuation led to further increase in IL-1ß, TNFα, RANKL, TLR2/4, IRAK1, and TRAF6 mRNA expression in peri-implant gingival tissues. Both rosiglitazone-induced glycemic control and glycemic fluctuation caused microbiota profile change in diabetic mice compared to that in uncontrolled hyperglycemic mice. CONCLUSIONS: This study suggests that glycemic fluctuation may aggravate peri-implantitis inflammation and bone loss, which may be associated with a shift in peri-implant microbial profile towards dysbiotic changes and the activation of TLR2/4-IRAK1-TRAF6 signaling.

Interrelationship between the Microbial Communities of the Root Canals and Periodontal Pockets in Combined Endodontic-Periodontal Diseases.

Periodontal and Endodontic diseases are biofilm-related diseases. The presence of microorganisms in root canals (RCs) and the complex microbiota of periodontal pockets (PPs) contribute to the development of endodontic-periodontal diseases. This study performed a systemic analysis using state-of-the-art sequence data to assess the microbial composition of infected RCs and PPs to further assess the microbiota and verify the possibility of cross-infection between these sites. The microbiomes of these combined diseases were examined with a focus on the V3-V4 hypervariable region of the 16S rRNA gene. The number of species in PP was higher than in RC, and there was a predominance of obligate anaerobes and gram-negative bacteria. In the RCs, the genera Enterococcus, Parvimonas, Stomatobaculum predominated, in contrast, the PPs revealed a predominance of Enterococcus, Parvimonas, Stomatobaculum, Peptostreptococcus and Mogibacterium. The RC and PP microbiome was not similar with regards to the sharing of OTUs for phyla and genera (8 and 67, respectively). The evaluation of molecular markers revealed a large number of markers for resistance to antibiotics of the carbapenem and beta-lactam type (broad spectrum). Another relevant finding of this study was the markers related to systemic diseases related to cardiac muscle and rheumatology, among others. In conclusion, the RC microbiota was less complex and diverse than PP. Interactions between microbial communities were present. The shared genus can signal communication between the endodontic and periodontal microbiomes.

Tongue microbiome in children with autism spectrum disorder.

Background: A few recent studies have characterized the salivary microbiome in association with Autism Spectrum Disorder (ASD). Here, we sought to assess if there is an association between the tongue microbiome and ASD. Methods: Tongue scrapping samples were obtained from 25 children with ASD and 38 neurotypical controls. The samples were sequenced for the 16S rRNA gene (V1-V3) and the resultant high-quality reads were assigned to the species-level using our previously described BLASTn-based algorithm. Downstream analyses of microbial profiles were conducted using QIIME, LEfSe, and R. Results: Independent of grouping, Prevotella, Streptococcus, Leptotrichia, Veillonella, Haemophilus and Rothia accounted for > 60% of the average microbiome. Haemophilus parainfluenzae, Rothia mucilaginosa, Prevotella melaninogenica and Neisseria flavescens/subflava were the most abundant species. Species richness and diversity did not significantly differ between the study groups. Thirteen species and three genera were differentially abundant between the two groups, e.g. enrichment of Actinomyces odontolyticus and Actinomyces lingnae and depletion of Campylobacter concisus and Streptococcus vestibularis in the ASD group. However, none of them withstood adjustment for multiple comparisons. Conclusion: The tongue microbiome of children with ASD was not significantly different from that of healthy control children, which is largely consistent with results from the literature.

The bacterial microbiome and metabolome in caries progression and arrest.

Aim: This in vivo experimental study investigated bacterial microbiome and metabolome longitudinal changes associated with enamel caries lesion progression and arrest. Methods: We induced natural caries activity in three caries-free volunteers prior to four premolar extractions for orthodontic reasons. The experimental model included placement of a modified orthodontic band on smooth surfaces and a mesh on occlusal surfaces. We applied the caries-inducing protocol for 4- and 6-weeks, and subsequently promoted caries lesion arrest via a 2-week toothbrushing period. Lesions were verified clinically and quantitated via micro-CT enamel density measurements. The biofilm microbial composition was determined via 16S rRNA gene Illumina sequencing and NMR spectrometry was used for metabolomics. Results: Biofilm maturation and caries lesion progression were characterized by an increase in Gram-negative anaerobes, including Veillonella and Prevotella. Streptococcus was associated caries lesion progression, while a more equal distribution of Streptococcus, Bifidobacterium, Atopobium, Prevotella, Veillonella, and Saccharibacteria (TM7) characterized arrest. Lactate, acetate, pyruvate, alanine, valine, and sugars were more abundant in mature biofilms compared to newly formed biofilms. Conclusions: These longitudinal bacterial microbiome and metabolome results provide novel mechanistic insights into the role of the biofilm in caries progression and arrest and offer promising candidate biomarkers for validation in future studies.