Omics for deciphering oral microecology

The human oral microbiome harbors one of the most diverse microbial communities in the human body, playing critical roles in oral and systemic health. Recent technological innovations are propelling the characterization and manipulation of oral microbiota. High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes. New long-read platforms improve genome assembly from complex samples. Single-cell genomics provides insights into uncultured taxa. Advanced imaging modalities including fluorescence, mass spectrometry, and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution. Fluorescence techniques link phylogenetic identity with localization. Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification. Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches. Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly, gene expression, metabolites, microenvironments, virulence mechanisms, and microbe-host interfaces in the context of health and disease. However, significant knowledge gaps persist regarding community origins, developmental trajectories, homeostasis versus dysbiosis triggers, functional biomarkers, and strategies to deliberately reshape the oral microbiome for therapeutic benefit. The convergence of sequencing, imaging, cultureomics, synthetic systems, and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict, prevent, diagnose, and treat associated oral diseases.

Multiplatform Metabolomics Characterization Reveals Novel Metabolites and Phospholipid Compositional Rules of Haemophilus influenzae Rd KW20

Haemophilus influenzae is a gram-negative bacterium of relevant clinical interest. H. influenzae Rd KW20 was the first organism to be sequenced and for which a genome-scale metabolic model (GEM) was developed. However, current H. influenzae GEMs are unable to capture several aspects of metabolome nature related to metabolite pools. To directly and comprehensively characterize the endometabolome of H. influenzae Rd KW20, we performed a multiplatform MS-based metabolomics approach combining LC-MS, GC-MS and CE-MS. We obtained direct evidence of 15-20% of the endometabolome present in current H. influenzae GEMs and showed that polar metabolite pools are interconnected through correlating metabolite islands. Notably, we obtained high-quality evidence of 18 metabolites not previously included in H. influenzae GEMs, including the antimicrobial metabolite cyclo(Leu-Pro). Additionally, we comprehensively characterized and evaluated the quantitative composition of the phospholipidome of H. influenzae, revealing that the fatty acyl chain composition is largely independent of the lipid class, as well as that the probability distribution of phospholipids is mostly related to the conditional probability distribution of individual acyl chains. This finding enabled us to provide a rationale for the observed phospholipid profiles and estimate the abundance of low-level species, permitting the expansion of the phospholipidome characterization through predictive probabilistic modelling.

In silico evaluation and selection of the best 16S rRNA gene primers for use in next-generation sequencing to detect oral bacteria and archaea

BACKGROUND

Sequencing has been widely used to study the composition of the oral microbiome present in various health conditions. The extent of the coverage of the 16S rRNA gene primers employed for this purpose has not, however, been evaluated in silico using oral-specific databases. This paper analyses these primers using two databases containing 16S rRNA sequences from bacteria and archaea found in the human mouth and describes some of the best primers for each domain.

RESULTS

A total of 369 distinct individual primers were identified from sequencing studies of the oral microbiome and other ecosystems. These were evaluated against a database reported in the literature of 16S rRNA sequences obtained from oral bacteria, which was modified by our group, and a self-created oral archaea database. Both databases contained the genomic variants detected for each included species. Primers were evaluated at the variant and species levels, and those with a species coverage (SC) ≥75.00% were selected for the pair analyses. All possible combinations of the forward and reverse primers were identified, with the resulting 4638 primer pairs also evaluated using the two databases. The best bacteria-specific pairs targeted the 3-4, 4-7, and 3-7 16S rRNA gene regions, with SC levels of 98.83-97.14%; meanwhile, the optimum archaea-specific primer pairs amplified regions 5-6, 3-6, and 3-6, with SC estimates of 95.88%. Finally, the best pairs for detecting both domains targeted regions 4-5, 3-5, and 5-9, and produced SC values of 95.71-94.54% and 99.48-96.91% for bacteria and archaea, respectively.

CONCLUSIONS

Given the three amplicon length categories (100-300, 301-600, and >600 base pairs), the primer pairs with the best coverage values for detecting oral bacteria were as follows: KP_F048-OP_R043 (region 3-4; primer pair position for Escherichia coli J01859.1: 342-529), KP_F051-OP_R030 (4-7; 514-1079), and KP_F048-OP_R030 (3-7; 342-1079). For detecting oral archaea, these were as follows: OP_F066-KP_R013 (5-6; 784-undefined), KP_F020-KP_R013 (3-6; 518-undefined), and OP_F114-KP_R013 (3-6; 340-undefined). Lastly, for detecting both domains jointly they were KP_F020-KP_R032 (4-5; 518-801), OP_F114-KP_R031 (3-5; 340-801), and OP_F066-OP_R121 (5-9; 784-1405). The primer pairs with the best coverage identified herein are not among those described most widely in the oral microbiome literature. Video Abstract.

Qualitative and Quantitative Mass Spectrometry in Salivary Metabolomics and Proteomics

The metabolomics and proteomics analysis of saliva, an excellent biofluid that is a rich source of biological compounds, allows for the safe and frequent screening of drugs, their metabolites, and molecular biomarkers of various diseases. One of the most frequently used analytical methods in saliva analysis is liquid chromatography coupled with mass spectrometry (LC-MS) and tandem mass spectrometry. The low ionisation efficiency of some compounds and a complex matrix makes their identification by MS difficult. Furthermore, quantitative analysis by LC-MS frequently cannot be performed without isotopically labelled standards, which usually have to be specially synthesised. This review presented reports on qualitative and quantitative approaches in salivary metabolomics and proteomics. The purpose of this manuscript was to present the challenges, advances, and future prospects of mass spectrometry, both in the analysis of salivary metabolites and proteins. The presented review should appeal to those interested in the recent advances and trends in qualitative and quantitative mass spectrometry in salivary metabolomics and proteomics, which may facilitate a diagnostic accuracy, the evaluation of treatment efficacy, the early diagnosis of disease, and a forensic investigation of some unapproved drugs for any medical or dietary administration.

Probiotic Bifidobacterium longum BB68S Improves Cognitive Functions in Healthy Older Adults: A Randomized, Double-Blind, Placebo-Controlled Trial

Probiotics could improve cognitive functions in patients with neurological disorders such as Alzheimer’s disease, but the effects on cognitive function in healthy older adults without cognitive impairment need further study. The purpose of this study was to investigate the effect of Bifidobacterium longum BB68S (BB68S) on cognitive functions among healthy older adults without cognitive impairment. A randomized, double-blind, placebo-controlled trial was conducted with 60 healthy older adults without cognitive impairment who were divided into probiotic or placebo groups and required to consume either a sachet of probiotic (BB68S, 5 × 1010 CFU/sachet) or placebo once daily for 8 weeks. The Montreal Cognitive Assessment (MoCA) was used as an inclusion screening tool to screen elderly participants with healthy cognitive function in our study, and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was used to assess cognitive function in subjects before and after intervention as an assessment tool. BB68S significantly improved subjects’ cognitive functions (total RBANS score increased by 18.89 points after intervention, p < 0.0001), especially immediate memory, visuospatial/constructional, attention, and delayed memory domains. BB68S intervention increased the relative abundances of beneficial bacteria Lachnospira, Bifidobacterium, Dorea, and Cellulosilyticum, while decreasing those of bacteria related to cognition impairment, such as Collinsella, Parabacteroides, Tyzzerella, Bilophila, unclassified_c_Negativicutes, Epulopiscium, Porphyromonas, and Granulicatella. In conclusion, BB68S could improve cognitive functions in healthy elderly adults without cognitive impairment, along with having beneficial regulatory effects on their gut microbiota. This study supports probiotics as a strategy to promote healthy aging and advances cognitive aging research.

Periodontal disease metabolomics signatures from different biofluids: a systematic review

BACKGROUND

Periodontitis is resulted from a complex interaction between genetics and epigenetics, microbial factors, and the host response. Metabolomics analyses reflect both the steady-state physiological equilibrium of cells or organisms as well as their dynamic metabolic responses to environmental stimuli.

AIM OF REVIEW

This systematic review of the literature aimed to assess which low molecular weight metabolites are more often found in biological fluids of individuals with periodontitis compared to individuals with gingivitis or periodontal health.

KEY SCIENTIFIC CONCEPTS OF REVIEW

All the included studies employed untargeted analysis. One or more biological fluids were analyzed, including saliva (n = 14), gingival crevicular fluid (n = 6), mouthwash (n = 1), serum (n = 3) and plasma (n = 1). Fifty-six main metabolites related to periodontitis have been identified in at least two independent studies by NMR spectroscopy or MS-based metabolomics. Saliva was the main biological fluid sampled. It is noteworthy that 14 metabolites of the 56 detected were identified as main metabolites in all studies that sampled the saliva. The majority of metabolites found consistently among studies were amino acids, organic acids and derivates: acetate, alanine, butyrate, formate, GABA, lactate, propionate, phenylalanine and valine. They were either up- or down-regulated in the studies or this information was not mentioned. The main metabolic pathway was related to phenylalanine, tyrosine and tryptophan biosynthesis. Metabolites more frequently found in individuals with periodontitis were related to both the host and to microorganism responses. Future studies are needed, and they should follow some methodological standards to facilitate their comparison.

Systems Biology in Periodontitis

Systems biology is a promising scientific discipline that allows an integrated investigation of host factors, microbial composition, biomarkers, immune response and inflammatory mediators in many conditions such as chronic diseases, cancer, neurological disorders, and periodontitis. This concept utilizes genetic decoding, bioinformatic, flux-balance analysis in a comprehensive approach. The aim of this review is to better understand the current literature on systems biology and identify a clear applicability of it to periodontitis. We will mostly focus on the association between this condition and topics such as genomics, transcriptomics, proteomics, metabolomics, as well as contextualize delivery systems for periodontitis treatment, biomarker detection in oral fluids and associated systemic conditions.

Streptococcus mutans and Candida albicans Biofilm Inhibitors Produced by Lactiplantibacillus plantarum CCFM8724

Lactiplantibacillus plantarum CCFM8724 inhibits the growth of Streptococcus mutans and Candida albicans in mixed-species biofilm formation. In this study, bioactive compound including cyclo (leu-pro), cyclo (phe-pro), and some organic acids, such as 3-phenyllactic acid, hydrocinnamic acid, and palmitic acid, were identified through GC-MS analysis. At 50 μg·mL^-1, cyclo (leu-pro) reduced biofilm mass (OD₆₀₀) from 3.00 to 2.00, and hydrocinnamic acid at 25 μg·mL^-1 reduced biofilm mass (OD₆₀₀) from 3.00 to 1.00. The expression of ALS3 and HWP1 was downregulated by cyclo (leu-pro). Furthermore, a mixture of cyclo (leu-pro), cyclo (phe-pro), 3-phenyllactic acid, hydrocinnamic acid, and palmitic acid, had anti-biofilm activity. Overall, the results provide promising baseline information for the potential use of this probiotic and its components in preventing biofilm formation.

Salivary Metabolomics for Diagnosis and Monitoring Diseases: Challenges and Possibilities

Saliva is a useful biological fluid and a valuable source of biological information. Saliva contains many of the same components that can be found in blood or serum, but the components of interest tend to be at a lower concentration in saliva, and their analysis demands more sensitive techniques. Metabolomics is starting to emerge as a viable method for assessing the salivary metabolites which are generated by the biochemical processes in elucidating the pathways underlying different oral and systemic diseases. In oral diseases, salivary metabolomics has concentrated on periodontitis and oral cancer. Salivary metabolites of systemic diseases have been investigated mostly in the early diagnosis of different cancer, but also neurodegenerative diseases. This mini-review article aims to highlight the challenges and possibilities of salivary metabolomics from a clinical viewpoint. Furthermore, applications of the salivary metabolic profile in diagnosis and prognosis, monitoring the treatment success, and planning of personalized treatment of oral and systemic diseases are discussed.

The prevalence of novel periodontal pathogens and bacterial complexes in Stage II generalized periodontitis based on 16S rRNA next generation sequencing

Objective:

To define the subgingival microbial profile associated with Stage II generalized periodontitis using next-generation sequencing and to determine the relative abundance of novel periodontal pathogens and bacterial complexes.

Methodology:

Subgingival biofilm samples were collected from 80 subjects diagnosed with Stage II generalized periodontitis. Bacterial DNA was extracted, and 16S rRNA-based bacterial profiling via next-generation sequencing was carried out. The bacterial composition and diversity of microbial communities based on the age and sex of the patients were analyzed. The bacterial species were organized into groups: bacterial complexes (red, orange, purple, yellow, and green), novel periodontal pathogens, periodontal health-related species, and unclassified periodontal species. The results were analyzed and statistically evaluated.

Results:

The highest number of bacteria belonged to the phylum Bacteroidetes and Firmicutes. In terms of relative abundance, the orange complex represented 18.99%, novel bacterial species (Fretibacterium spp. and Saccharibacteria spp.) comprised 17.34%, periodontal health-related species accounted for 16.75% and unclassified periodontal species represented (Leptotrichia spp. and Selenomonas spp.) 15.61%. Novel periodontal pathogens had outweighed the periodontal disease-related red complex (5.3%). The one-sample z-test performed was statistically significant at p<0.05. The Beta diversity based on the unweighted UniFrac distance at the species level demonstrated a total variance of 15.77% based on age and 39.19% on sex, which was not statistically significant.

Conclusion:

The bacterial species corresponding to the disease-related orange complex and novel periodontal pathogens are predominant in Stage II generalized periodontitis.

Salivary metabolomics for the diagnosis of periodontal diseases: a systematic review with methodological quality assessment

INTRODUCTION

Early diagnosis of periodontitis by means of a rapid, accurate and non-invasive method is highly desirable to reduce the individual and epidemiological burden of this largely prevalent disease.

OBJECTIVES

The aims of the present systematic review were to examine potential salivary metabolic biomarkers and pathways associated to periodontitis, and to assess the accuracy of salivary untargeted metabolomics for the diagnosis of periodontal diseases.

METHODS

Relevant studies identified from MEDLINE (PubMed), Embase and Scopus databases were systematically examined for analytical protocols, metabolic biomarkers and results from the multivariate analysis (MVA). Pathway analysis was performed using the MetaboAnalyst online software and quality assessment by means of a modified version of the QUADOMICS tool.

RESULTS

Twelve studies met the inclusion criteria, with sample sizes ranging from 19 to 130 subjects. Compared to periodontally healthy individuals, valine, phenylalanine, isoleucine, tyrosine and butyrate were found upregulated in periodontitis patients in most studies; while lactate, pyruvate and N-acetyl groups were the most significantly expressed in healthy individuals. Metabolic pathways that resulted dysregulated are mainly implicated in inflammation, oxidative stress, immune activation and bacterial energetic metabolism. The findings from MVA revealed that periodontitis is characterized by a specific metabolic signature in saliva, with coefficients of determination ranging from 0.52 to 0.99.

CONCLUSIONS

This systematic review summarizes candidate metabolic biomarkers and pathways related to periodontitis, which may provide opportunities for the validation of diagnostic or predictive models and the discovery of novel targets for monitoring and treating such a disease (PROSPERO CRD42020188482).

Dysbiosis of Oral Microbiota Associated with Palmoplantar Pustulosis

BACKGROUND

Dysbiosis of oral microbiota is implicated not only in oral inflammatory lesions, but also in a variety of extraoral diseases. The etiology of palmoplantar pustulosis (PPP) remains unclear; however, it has been suggested that chronic inflammation caused by periodontopathic bacterial infection may play a role.

OBJECTIVES/METHODS

To determine whether patients with PPP have altered diversity and composition of oral microbiota, we conducted the 16S rDNA analysis using saliva samples collected from 21 outpatients with PPP and 10 healthy individuals.

RESULTS

We found that the proportion of bacteria in the phylum Proteobacteria was significantly lower in PPP patients (p = 0.025). At the genus level, patients with PPP had a significantly lower abundance of Neisseria (p = 0.014), which best accounted for the observed decrease in Proteobacteria. We also identified multiple minor genera and species that were represented at a significantly higher level in the PPP group, several of which have been associated with periodontal diseases.

CONCLUSION

Our results suggest a possible link between PPP and dysbiosis of oral microbiota, particularly the lower abundance of Neisseria, the most predominant genus of Proteobacteria in healthy oral microbiota. Probiotics that improves oral dysbiosis may be beneficial for patients with PPP as an adjunctive therapy.

Metaproteome and metabolome of oral microbial communities

The emergence of high-throughput technologies for the comprehensive measurement of biomolecules, also referred to as "omics" technologies, has helped us gather "big data" and characterize microbial communities. In this article, we focus on metaproteomic and metabolomic approaches that support hypothesis-driven investigations on various oral biologic samples. Proteomics reveals the working units of the oral milieu and metabolomics unveils the reactions taking place; and so these complementary techniques can unravel the functionality and underlying regulatory processes within various oral microbial communities. Current knowledge of the proteomic interplay and metabolic interactions of microorganisms within oral biofilm and salivary microbiome communities is presented and discussed, from both clinical and basic research perspectives. Communities indicative of, or from, health, caries, periodontal diseases, and endodontic lesions are represented. Challenges, future prospects, and examples of best practice are given.

Investigating the demographic history of Japan using ancient oral microbiota

While microbial communities in the human body (microbiota) are now commonly associated with health and disease in industrialised populations, we know very little about how these communities co-evolved and changed with humans throughout history and deep prehistory. We can now examine these communities by sequencing ancient DNA preserved within calcified dental plaque (calculus), providing insights into the origins of disease and their links to human history. Here, we examine ancient DNA preserved within dental calculus samples and their associations with two major cultural periods in Japan: the Jomon period hunter–gatherers approximately 3000 years before present (BP) and the Edo period agriculturalists 400–150 BP. We investigate how human oral microbiomes have changed in Japan through time and explore the presence of microorganisms associated with oral diseases (e.g. periodontal disease, dental caries) in ancient Japanese populations. Finally, we explore oral microbial strain diversity and its potential links to ancient demography in ancient Japan by performing phylogenomic analysis of a widely conserved oral species—Anaerolineaceae oral taxon 439. This research represents, to our knowledge, the first study of ancient oral microbiomes from Japan and demonstrates that the analysis of ancient dental calculus can provide key information about the origin of non-infectious disease and its deep roots with human demography.

This article is part of the theme issue ‘Insights into health and disease from ancient biomolecules’.

Potential Associations Among Alteration of Salivary miRNAs, Saliva Microbiome Structure, and Cognitive Impairments in Autistic Children

Recent evidence has demonstrated that salivary molecules, as well as bacterial populations, can be perturbed by several pathological conditions, including neuro-psychiatric diseases. This relationship between brain functionality and saliva composition could be exploited to unveil new pathological mechanisms of elusive diseases, such as Autistic Spectrum Disorder (ASD). We performed a combined approach of miRNA expression profiling by NanoString technology, followed by validation experiments in qPCR, and 16S rRNA microbiome analysis on saliva from 53 ASD and 27 neurologically unaffected control (NUC) children. MiR-29a-3p and miR-141-3p were upregulated, while miR-16-5p, let-7b-5p, and miR-451a were downregulated in ASD compared to NUCs. Microbiome analysis on the same subjects revealed that Rothia, Filifactor, Actinobacillus, Weeksellaceae, Ralstonia, Pasteurellaceae, and Aggregatibacter increased their abundance in ASD patients, while Tannerella, Moryella and TM7-3 decreased. Variations of both miRNAs and microbes were statistically associated to different neuropsychological scores related to anomalies in social interaction and communication. Among miRNA/bacteria associations, the most relevant was the negative correlation between salivary miR-141-3p expression and Tannerella abundance. MiRNA and microbiome dysregulations found in the saliva of ASD children are potentially associated with cognitive impairments of the subjects. Furthermore, a potential cross-talking between circulating miRNAs and resident bacteria could occur in saliva of ASD.

Phylum Synergistetes in the oral cavity: A possible contributor to periodontal disease

Microbial contributions to periodontal disease have been under renewed scrutiny with the advent of newer technologies to identify their presence and gene expression at the molecular level. Members of the phylum Synergistetes are some of the more recent bacteria to be associated with periodontal disease. Bacteria classified in this phylum can be found in a wide variety of habitats including both inside and outside of a mammalian host. Members of this phylum have been identified as part of the human microbiome. Indeed, many of the identified phylotypes have yet to be cultivated. Here we consider contributions of three named and formally described species to the oral microbial community and to pathogenesis of periodontal disease.

Periodontal inflammation: Integrating genes and dysbiosis

Biofilm bacteria co‐evolve and reach a symbiosis with the host on the gingival surface. The disruption of the homeostatic relationship between plaque bacteria and the host can initiate and promote periodontal disease progression. Recent advances in sequencing technologies allow researchers to profile disease‐associated microbial communities and quantify microbial metabolic activities and host transcriptional responses. In addition to confirming the findings from previous studies, new putative pathogens and novel genes that have not previously been associated with periodontitis, emerge. For example, multiple studies have reported that Synergistetes bacteria are associated with periodontitis. Genes involved in epithelial barrier defense were downregulated in periodontitis, while excessive expression of interleukin‐17 was associated with a hyperinflammatory response in periodontitis and with a unique microbial community. Bioinformatics‐enabled gene ontology pathway analyses provide a panoramic view of the bacterial and host activities as they shift from periodontal health to disease. Additionally, host innate factors, such as genetic variants identified by either a candidate‐gene approach or genome‐wide association analyses, have an impact on subgingival bacterial colonization. Transgenic mice carrying candidate genetic variants, or with the deletion of candidate genes mimicking the deleterious loss‐of‐function variant effect, provide experimental evidence validating the biologic relevance of the novel markers associated with the microbial phenotype identified through a statistical approach. Further refinement in bioinformatics, data management approaches, or statistical tools, are required to gain insight into host‐microbe interactions by harmonizing the multidimensional “big” data at the genomic, transcriptional, and proteomic levels.

Identification of Salivary Microbiota and Its Association With Host Inflammatory Mediators in Periodontitis

Periodontitis is a microbial-induced chronic inflammatory disease, which may not only result in tooth loss, but can also contribute to the development of various systemic diseases. The transition from healthy to diseased periodontium depends on microbial dysbiosis and impaired host immune response. Although periodontitis is a common disease as well as associated with various systemic inflammatory conditions, the taxonomic profiling of the salivary microbiota in periodontitis and its association with host immune and inflammatory mediators has not been reported. Therefore, the aim of this study was to identify key pathogens and their potential interaction with the host's inflammatory mediators in saliva samples for periodontitis risk assessment. The microbial 16S rRNA gene sequencing and the levels of inflammatory mediators were performed in saliva samples from patients with chronic periodontitis and periodontally healthy control subjects. The salivary microbial community composition differed significantly between patients with chronic periodontitis and healthy controls. Our analyses identified a number of microbes, including bacteria assigned to Eubacterium saphenum, Tannerella forsythia, Filifactor alocis, Streptococcus mitis/parasanguinis, Parvimonas micra, Prevotella sp., Phocaeicola sp., and Fretibacterium sp. as more abundant in periodontitis, compared to healthy controls. In samples from healthy individuals, we identified Campylobacter concisus, and Veillonella sp. as more abundant. Integrative analysis of the microbiota and inflammatory mediators/cytokines revealed associations that included positive correlations between the pathogens Treponema sp. and Selenomas sp. and the cytokines chitinase 3-like 1, sIL-6Rα, sTNF-R1, and gp130/sIL-6Rβ. In addition, a negative correlation was identified between IL-10 and Filifactor alocis. Our results reveal distinct and disease-specific patterns of salivary microbial composition between patients with periodontitis and healthy controls, as well as significant correlations between microbiota and host-mediated inflammatory cytokines. The positive correlations between the pathogens Treponema sp. and Selenomas sp. and the cytokines chitinase 3-like 1, sIL-6Rα, sTNF-R1, and gp130/sIL-6Rβ might have the future potential to serve as a combined bacteria-host salivary biomarker panel for diagnosis of the chronic infectious disease periodontitis. However, further studies are required to determine the capacity of these microbes and inflammatory mediators as a salivary biomarker panel for periodontitis.

Intra-oral single-site comparisons of periodontal and peri-implant microbiota in health and disease

OBJECTIVE

Periodontitis and peri-implantitis are oral infectious-inflammatory diseases that share similarities in their pathology and etiology. Our objective was to characterize the single-site subgingival and submucosal microbiomes of implant-rehabilitated, partially dentate Chinese subjects (n = 18) presenting with both periodontitis and peri-implantitis.

MATERIALS AND METHODS

Subgingival/submucosal plaque samples were collected from four clinically distinct sites in each subject: peri-implantitis submucosa (DI), periodontal pocket (DT), clinically healthy (unaffected) peri-implant submucosa (HI), and clinically healthy (unaffected) subgingival sulcus (HT). The bacterial microbiota present was analyzed using Illumina MiSeq sequencing.

RESULTS

Twenty-six phyla and 5,726 operational taxonomic units (OTUs, 97% sequence similarity cutoff) were identified. Firmicutes, Proteobacteria, Fusobacteria, Bacteroidetes, Actinobacteria, Synergistetes, TM7, and Spirochaetes comprised 99.6% of the total reads detected. Bacterial communities within the DI, DT, HI, and HT sites shared high levels of taxonomic similarity. Thirty-one "core species" were present in >90% sites, with Streptococcus infantis/mitis/oralis (HMT-070/HMT-071/HMT-638/HMT-677) and Fusobacterium sp. HMT-203/HMT-698 being particularly prevalent and abundant. Beta-diversity analyses (PERMANOVA test, weighted UniFrac) revealed the largest variance in the microbiota was at the subject level (46%), followed by periodontal health status (4%). Differing sets of OTUs were associated with periodontitis and peri-implantitis sites, respectively. This included putative "periodontopathogens," such as Prevotella, Porphyromonas, Tannerella, Bacteroidetes [G-5], and Treponema spp. Interaction network analysis identified several putative patterns underlying dysbiosis in periodontitis/peri-implantitis sites.

CONCLUSIONS

Species (OTU) composition of the periodontal and peri-implant microbiota varied widely between subjects. The inter-subject variations in subgingival/submucosal microbiome composition outweighed differences observed between implant vs. tooth sites, or between diseased vs. healthy (unaffected) peri-implant/periodontal sites.

Metabolic changes during periodontitis therapy assessed by real-time ambient mass spectrometry

It has been shown that bacteria in periodontally diseased patients can be recognized by the detection of volatile metabolites in the headspace of saliva by real-time ambient mass spectrometry. The aim of this study was to use this detection method to analyze the oral metabolome in diseased periodontitis patients before and after therapy to monitor disease evolution and healing events. Twelve patients with advanced chronic periodontal disease and 12 periodontally healthy controls served as test and control groups, respectively. Clinical data, subgingival plaque samples and saliva samples were collected at baseline (BL) and 3 months after treatment. The test group received non-surgical scaling and root planing using systemic antibiotics and the control group received one session of supragingival cleaning. Saliva samples from all subjects were analyzed with ambient mass spectrometry. Significant metabolic alterations were found in the headspace of saliva of periodontitis patients 3 months after the non-surgical periodontal treatment. Furthermore, the diseased group showed metabolic features after the treatment that were similar to the healthy control group. In addition, 29 metabolic features correlated with A. actinomycetemcomitans, 17 features correlated with P. gingivalis and one feature correlated with T. denticola. It was shown that headspace secondary electrospray ionization - mass spectrometry allows the detection of different volatile metabolites in healthy and diseased patients. It can be concluded that this rapid and minimally invasive method could have the potential to routinely diagnose and monitor periodontal diseases in the headspace of saliva samples and, eventually, in exhaled breath.

Cyclic Peptides in Neurological Disorders: The Case of Cyclo(His-Pro)

Communication between and within communities of cells or independent organisms is a crucial prerequisite for species survival. In response to variations in the extracellular environment, the collective behavior of cell populations can be coordinated by regulating community-level gene expression. This mechanism is strongly conserved during evolution, being shared both by bacterial communities and central nervous system cells. Notably, cyclic dipeptides (CDPs) are molecules that are implicated in these quorum sensing behaviors in both settings. Bacteria coordinate their collective behavior by producing CDPs (quorum sensing inducers) that enhance the capacity of individual members of the community to detect these signals and thus amplify the community-level response. In this review, we highlight recent data indicating that strikingly similar molecular mechanisms control communications between glial and neuronal cells to maintain homeostasis in the central nervous system, with a specific focus on the role of the thyrotropin-releasing hormone—derived CDP cyclo(His-Pro) in the protection against neurotoxic insults.

Unique subgingival microbiota associated with periodontitis in cirrhosis patients

Liver cirrhosis is a severe disease with major impact on the overall health of the patient including poor oral health. Lately, there has been increasing focus on oral diseases as cirrhosis-related complications due to the potential impact on systemic health and ultimately mortality. Periodontitis is one of the most common oral diseases in cirrhosis patients. However, no studies have investigated the composition of the subgingival microbiome in patients suffering from periodontitis and liver cirrhosis. We analysed the subgingival microbiome in 21 patients with periodontitis and cirrhosis using long-reads Illumina sequencing. The subgingival microbiota was dominated by bacteria belonging to the Firmicutes phylum and to a lesser extend the Actinobacteria and Bacteroidetes phyla. Bacteria usually considered periodontal pathogens, like Porhyromonas ginigivalis, Tannerella forsythia, Treponema denticola, generally showed low abundancy. Comparing the microbiota in our patients with that of periodontitis patients and healthy controls of three other studies revealed that the periodontitis-associated subgingival microbiota in cirrhosis patients is composed of a unique microbiota of bacteria not normally associated with periodontitis. We hypothesise that periodontitis in cirrhosis patients is a consequence of dysbiosis due to a compromised immune system that renders commensal bacteria pathogenic.

Identification of an early stage biofilm inhibitor from Veillonella tobetsuensis

Oral biofilm, the cause of dental caries and periodontal diseases, consists of multiple bacterial species. Streptococcus spp. and Veillonella spp. have been reported as to be initial and early colonizers of oral biofilms. Our previous studies showed that Veillonella tobetsuensis may play an important role on the development of S. gordonii biofilms without coaggregation involving extracellular biomolecules. In this study, the effect of a cyclic dipeptide autoinducer from culture supernatants from V. tobetsuensis at late-exponential growth phase on S. gordonii biofilm was examined. The cyclic dipeptide, identified as cyclo (-L-Leu-L-Pro) by gas chromatography/mass spectrometry, inhibited the development of S. gordonii biofilm. Furthermore, cyclo (-L-Leu-L-Pro) appeared not to cause bactericidal effects on planktonic cells of S. gordonii. This is the first report that oral Veillonella produces cyclo (-L-Leu-L-Pro) in their culture supernatants. Moreover, the results of this study suggest that cyclo (-L-Leu-L-Pro) may have an application to inhibit early stage development of oral biofilms.

Peptides as Quorum Sensing Molecules: Measurement Techniques and Obtained Levels In vitro and In vivo

The expression of certain bacterial genes is regulated in a cell-density dependent way, a phenomenon called quorum sensing. Both Gram-negative and Gram-positive bacteria use this type of communication, though the signal molecules (auto-inducers) used by them differ between both groups: Gram-negative bacteria use predominantly N-acyl homoserine lacton (AHL) molecules (autoinducer-1, AI-1) while Gram-positive bacteria use mainly peptides (autoinducer peptides, AIP or quorum sensing peptides). These quorum sensing molecules are not only involved in the inter-microbial communication, but can also possibly cross-talk directly or indirectly with their host. This review summarizes the currently applied analytical approaches for quorum sensing identification and quantification with additionally summarizing the experimentally found in vivo concentrations of these molecules in humans.

The subgingival microbiome, systemic inflammation and insulin resistance: The Oral Infections, Glucose Intolerance and Insulin Resistance Study

BACKGROUND

Inflammation might link microbial exposures to insulin resistance. We investigated the cross-sectional association between periodontal microbiota, inflammation and insulin resistance.

METHODS

The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) enrolled 152 diabetes-free adults (77% female) aged 20-55 years (mean = 34 ± 10). Three hundred and four subgingival plaque samples were analysed using the Human Oral Microbe Identification Microarray to measure the relative abundances of 379 taxa. C-reactive protein, interleukin-6, tumour necrosis factor-α and adiponectin were assessed from venous blood and their z-scores were summed to create an inflammatory score (IS). Insulin resistance was defined via the HOMA-IR. Associations between the microbiota and both inflammation and HOMA-IR were explored using multivariable linear regressions; mediation analyses assessed the proportion of the association explained by inflammation.

RESULTS

The IS was inversely associated with Actinobacteria and Proteobacteria and positively associated with Firmicutes and TM7 (p-values < 0.05). Proteobacteria levels were associated with insulin resistance (p < 0.05). Inflammation explained 30-98% of the observed associations between levels of Actinobacteria, Proteobacteria or Firmicutes and insulin resistance (p-values < 0.05). Eighteen individual taxa were associated with inflammation (p < 0.05) and 22 with insulin resistance (p < 0.05). No findings for individual taxa met Bonferroni-adjusted statistical significance.

CONCLUSION

Bacterial measures were related to inflammation and insulin resistance among diabetes-free adults.

Microbial signatures of oral dysbiosis, periodontitis and edentulism revealed by Gene Meter methodology

Conceptual models suggest that certain microorganisms (e.g., the "red" complex) are indicative of a specific disease state (e.g., periodontitis); however, recent studies have questioned the validity of these models. Here, the abundances of 500+ microbial species were determined in 16 patients with clinical signs of one of the following oral conditions: periodontitis, established caries, edentulism, and oral health. Our goal was to determine if the abundances of certain microorganisms reflect dysbiosis or a specific clinical condition that could be used as a 'signature' for dental research. Microbial abundances were determined by the analysis of 138,718 calibrated probes using Gene Meter methodology. Each 16S rRNA gene was targeted by an average of 194 unique probes (n=25nt). The calibration involved diluting pooled gene target samples, hybridizing each dilution to a DNA microarray, and fitting the probe intensities to adsorption models. The fit of the model to the experimental data was used to assess individual and aggregate probe behavior; good fits (R²>0.90) were retained for back-calculating microbial abundances from patient samples. The abundance of a gene was determined from the median of all calibrated individual probes or from the calibrated abundance of all aggregated probes. With the exception of genes with low abundances (<2 arbitrary units), the abundances determined by the different calibrations were highly correlated (r~1.0). Seventeen genera were classified as 'signatures of dysbiosis' because they had significantly higher abundances in patients with periodontitis and edentulism when contrasted with health. Similarly, 13 genera were classified as 'signatures of periodontitis', and 14 genera were classified as 'signatures of edentulism'. The signatures could be used, individually or in combination, to assess the clinical status of a patient (e.g., evaluating treatments such as antibiotic therapies). Comparisons of the same patient samples revealed high false negatives (45%) for next-generation-sequencing results and low false positives (7%) for Gene Meter results.

Cultivation strategies for growth of uncultivated bacteria

BACKGROUND

The majority of environmental bacteria and around a third of oral bacteria remain uncultivated. Furthermore, several bacterial phyla have no cultivable members and are recognised only by detection of their DNA by molecular methods. Possible explanations for the resistance of certain bacteria to cultivation in purity in vitro include: unmet fastidious growth requirements; inhibition by environmental conditions or chemical factors produced by neighbouring bacteria in mixed cultures; or conversely, dependence on interactions with other bacteria in the natural environment, without which they cannot survive in isolation. Auxotrophic bacteria, with small genomes lacking in the necessary genetic material to encode for essential nutrients, frequently rely on close symbiotic relationships with other bacteria for survival, and may therefore be recalcitrant to cultivation in purity.

HIGHLIGHT

Since in-vitro culture is essential for the comprehensive characterisation of bacteria, particularly with regard to virulence and antimicrobial resistance, the cultivation of uncultivated organisms has been a primary focus of several research laboratories. Many targeted and open-ended strategies have been devised and successfully used. Examples include: the targeted detection of specific bacteria in mixed plate cultures using colony hybridisation; growth in simulated natural environments or in co-culture with 'helper' strains; and modified media preparation techniques or development of customised media eg. supplementation of media with potential growth-stimulatory factors such as siderophores.

CONCLUSION

Despite significant advances in recent years in methodologies for the cultivation of previously uncultivated bacteria, a substantial proportion remain to be cultured and efforts to devise high-throughput strategies should be a high priority.

Gut Microbiota and Extreme Longevity

The study of the extreme limits of human lifespan may allow a better understanding of how human beings can escape, delay, or survive the most frequent age-related causes of morbidity, a peculiarity shown by long-living individuals. Longevity is a complex trait in which genetics, environment, and stochasticity concur to determine the chance to reach 100 or more years of age [1]. Because of its impact on human metabolism and immunology, the gut microbiome has been proposed as a possible determinant of healthy aging [2, 3]. Indeed, the preservation of host-microbes homeostasis can counteract inflammaging [4], intestinal permeability [5], and decline in bone and cognitive health [6, 7]. Aiming at deepening our knowledge on the relationship between the gut microbiota and a long-living host, we provide for the first time the phylogenetic microbiota analysis of semi-supercentenarians, i.e., 105-109 years old, in comparison to adults, elderly, and centenarians, thus reconstructing the longest available human microbiota trajectory along aging. We highlighted the presence of a core microbiota of highly occurring, symbiotic bacterial taxa (mostly belonging to the dominant Ruminococcaceae, Lachnospiraceae, and Bacteroidaceae families), with a cumulative abundance decreasing along with age. Aging is characterized by an increasing abundance of subdominant species, as well as a rearrangement in their co-occurrence network. These features are maintained in longevity and extreme longevity, but peculiarities emerged, especially in semi-supercentenarians, describing changes that, even accommodating opportunistic and allochthonous bacteria, might possibly support health maintenance during aging, such as an enrichment and/or higher prevalence of health-associated groups (e.g., Akkermansia, Bifidobacterium, and Christensenellaceae).

The Novel ASIC2 Locus is Associated with Severe Gingival Inflammation

An increasing body of evidence suggests a significant genetic regulation of inflammatory response mechanisms; however, little is known regarding the genetic determinants of severe gingival inflammation (GI). We conducted a genome-wide association study of severe GI among 4077 European American adults, participants in the Dental Atherosclerosis Risk In Communities cohort. The severe GI trait was defined dichotomously using the 90^th percentile of gingival index ≥2 extent score. Genotyping was performed with the Affymetrix 6.0 array platform and an imputed set of 2.5 million markers, based on HapMap Phase II CEU build 36, was interrogated. Genetic models were based on logistic regression and controlled for ancestry (10 principal components), sex, age, and examination center. One locus on chromosome 17 met genome-wide statistical significance criteria-lead single nucleotide polymorphism (SNP): rs11652874 [minor allele frequency=0.06, intronic to ASIC2 (acid sensing ionic channel-2, formerly named ACCN1); odds ratio=2.1, 95% confidence interval=1.6-2.7, p=3.9×10^-8]. This association persisted among subjects with severe periodontitis and was robust to adjustment for microbial plaque index. Moreover, the minor [G] allele was associated with higher levels of severe GI in stratified analyses among subsets of participants with high load of either "red" or "orange" complex pathogens, although this association was not statistically significant. While these results will require replication in independent samples and confirmation by mechanistic studies, this locus appears as a promising candidate for severe gingival inflammation. Our findings suggest that genetic variation in ASIC2 is significantly associated with severe gingival inflammation and the association is plaque-independent.

Sharpening Precision Medicine by a Thorough Interrogation of Metabolic Individuality

Precision medicine is an active component of medical practice today, but aspirations are to both broaden its reach to a greater diversity of individuals and improve its “precision” by enhancing the ability to define even more disease states in combination with associated treatments. Given complexity of human phenotypes, much work is required. In this review, we deconstruct this challenge at a high level to define what is needed to move closer toward these aspirations. In the context of the variables that influence the diverse array of phenotypes across human health and disease – genetics, epigenetics, environmental influences, and the microbiome – we detail the factors behind why an individual's biochemical (metabolite) composition is increasingly regarded as a key element to precisely defining phenotypes. Although an individual's biochemical (metabolite) composition is generally regarded, and frequently shown, to be a surrogate to the phenotypic state, we review how metabolites (and therefore an individual's metabolic profile) are also functionally related to the myriad of phenotypic influencers like genetics and the microbiota. We describe how using the technology to comprehensively measure an individual's biochemical profile – metabolomics – is integrative to defining individual phenotypes and how it is currently being deployed in efforts to continue to elaborate on human health and disease in large population studies. Finally, we summarize instances where metabolomics is being used to assess individual health in instances where signatures (i.e. biomarkers) have been defined.

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Untargeted biochemical profiling has the potential to phenotype individuals where genetic associations do not seem to show penetrance

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Metabolomics can be leveraged with other ‘omics data to discern phenotype information that is driven by environmental, microbiota, or epigenetic factors.

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Tracking the biochemical profile of individuals may help discern effectiveness or response to treatment or disease progression.