Identification of Potential Oral Microbial Biomarkers for the Diagnosis of Periodontitis

Insights into medieval rural lives: A paleo-odontological investigation of two central European communities

OBJECTIVE

Oral status is an important indicator of past lifestyles. Determining the presence and extent of oral pathologies helps reconstruct average oral health, paramasticatory activities and diet of ancient and historical populations.

DESIGN

In this study, the dental remains from the early medieval cemetery of Früebergstrasse in Baar (Canton of Zug, Switzerland) and the high medieval Dalheim cemetery (North Rhine-Westphalia, Germany) were analyzed. Caries, periodontal condition, periapical lesions, antemortem tooth loss, and enamel hypoplasia were assessed in 654 teeth (993 observable loci) from 68 individuals (Baar: n = 36; Dalheim: n = 32).

RESULTS

The oral status of both populations was affected by age with higher values of tooth wear in advanced age individuals. High tooth wear values in both populations point towards the consumption of abrasive foods. Pronounced anterior tooth wear in Baar may also be due to non-masticatory tooth usage. Finally, possible nutritional deficiencies were hypothesized for the Baar population. A higher caries prevalence was observed in the Baar group, probably due to differences in carbohydrate intake. The oral conditions observed in the two studied populations exhibited several analogies, suggesting comparable lifestyles despite their separation in space and time. The only differences observed are related to the use of teeth as "tools" and are thus determined by behavioral choices rather than diverse socioeconomic factors.

CONCLUSIONS

Using multiple dental parameters to examine the oral health of premodern individuals can provide useful insights into the interactions between humans and their environment, from dietary patterns to paramasticatory activities.

Periodontal disease and emerging point-of-care technologies for its diagnosis

Periodontal disease (PD), a chronic inflammatory disorder that damages the tooth and its supporting components, is a common global oral health problem. Understanding the intricacies of these disorders, from gingivitis to severe PD, is critical for efficient treatment, diagnosis, and prevention in dental care. Periodontal biosensors and biomarkers are critical in improving oral health diagnostic skills. Clinicians may accomplish early identification, tailored therapy, and efficient tracking of periodontal diseases by using these technologies, ushering in a new age of accurate oral healthcare. Traditional periodontitis diagnostic methods frequently rely on physical probing and visual examinations, necessitating the development of point-of-care (POC) devices. As periodontal disorders necessitate more precise and rapid diagnosis, incorporating novel innovations in biosensors and biomarkers becomes increasingly crucial. These innovations improve our capacity to diagnose, monitor, and adapt periodontal therapies, bringing in the next phase of customized and effective dental healthcare. The review discusses the characteristics and stages of PD, clinical treatment techniques, prominent biomarkers and infection-associated factors that may be employed to determine PD, biomedical sensing, and POC appliances that have been created so far to diagnose stages of PD and its progression profile, as well as predicting future developments in this field.

Newaskar D, Bacha S, V Manek P, Desai P. Analysis of microbiological profiles of Indian patients with peri-implantitis and periodontitis

The microbial profile of patients with periimplantis to inqure any unique composition of microorganisms in them is of interest to dentists. Hence, we evaluated the microbial profile of patients with peri-implantitis, patients with periodontitis and normal healthy subjects. 180 subjects were included in this study. Plaque samples were collected from 60periodontically healthy (PH) participants, 60 periodontitis (PT) subjects, and 60 periimplantitis (PI) subjects. Final concentrations were obtained for seven most common periodontal pathogens namely Aggregatibacter actinomycetemcomitans, Fusobacterium nucleatum, Treponema denticola, Porphyromonas gingivalis, Prevotella intermedia, Staphylococcus aureus and Haemophilus parainfluenzae. The abundance of microorganisms was represented in the form of Log10CFU (x103). The abundance of periopathogens evaluated in this research was different in periimplantitis patients, periodontitis patients and normal healthy subjects with slight greater abundance of peripathogens in patient with periimplantitis.

Dysrhythmic saliva microbiota in mobile phone addicts with sleep disorders and restored by acupuncture

Background

Mobile phone addiction (MPA) greatly affects the biological clock and sleep quality and is emerging as a behavioral disorder. The saliva microbiota has been linked to circadian rhythms, and our previous research revealed dysrhythmic saliva metabolites in MPA subjects with sleep disorders (MPASD). In addition, acupuncture had positive effects. However, the dysbiotic saliva microbiota in MPASD patients and the restorative effects of acupuncture are unclear.

Objectives

To probe the circadian dysrhythmic characteristics of the saliva microbiota and acupunctural restoration in MPASD patients.

Methods

MPASD patients and healthy volunteers were recruited by the Mobile Phone Addiction Tendency Scale (MPATS) and the Pittsburgh Sleep Quality Index (PSQI). Saliva samples were collected every 4 h for 72 h. After saliva sampling, six MPDSD subjects (group M) were acupuncturally treated (group T), and subsequent saliva sampling was conducted posttreatment. Finally, all the samples were subjected to 16S rRNA gene sequencing and bioinformatic analysis.

Results

Significantly increased MPATS and PSQI scores were observed in MPDSD patients (p< 0.01), but these scores decreased (p<0.001) after acupuncture intervention. Compared with those in healthy controls, the diversity and structure of the saliva microbiota in MPASD patients were markedly disrupted. Six genera with circadian rhythms were detected in all groups, including Sulfurovum, Peptostreptococcus, Porphyromonas and Prevotella. There were five genera with circadian rhythmicity in healthy people, of which the rhythmicities of the genera Rothia and Lautropia disappeared in MPASD patients but effectively resumed after acupuncture intervention.

Conclusions

This work revealed dysrhythmic salivary microbes in MPASD patients, and acupuncture, as a potential intervention, could be effective in mitigating this ever-rising behavioral epidemic.

Porphyromonas endodontalis HmuY differentially participates in heme acquisition compared to the Porphyromonas gingivalis and Tannerella forsythia hemophore-like proteins

Introduction

Porphyromonas gingivalis and Porphyromonas endodontalis belong to the Bacteroidota phylum. Both species inhabit the oral cavity and can be associated with periodontal diseases. To survive, they must uptake heme from the host as an iron and protoporphyrin IX source. Among the best-characterized heme acquisition systems identified in members of the Bacteroidota phylum is the P. gingivalis Hmu system, with a leading role played by the hemophore-like HmuY (HmuYPg) protein.

Methods

Theoretical analysis of selected HmuY proteins and spectrophotometric methods were employed to determine the heme-binding mode of the P. endodontalis HmuY homolog (HmuYPe) and its ability to sequester heme. Growth phenotype and gene expression analysis of P. endodontalis were employed to reveal the importance of the HmuYPe and Hmu system for this bacterium.

Results

Unlike in P. gingivalis, where HmuYPg uses two histidines for heme-iron coordination, other known HmuY homologs use two methionines in this process. P. endodontalis HmuYPe is the first characterized representative of the HmuY family that binds heme using a histidine-methionine pair. It allows HmuYPe to sequester heme directly from serum albumin and Tannerella forsythia HmuYTf, the HmuY homolog which uses two methionines for heme-iron coordination. In contrast to HmuYPg, which sequesters heme directly from methemoglobin, HmuYPe may bind heme only after the proteolytic digestion of hemoglobin.

Conclusions

We hypothesize that differences in components of the Hmu system and structure-based properties of HmuY proteins may evolved allowing different adaptations of Porphyromonas species to the changing host environment. This may add to the superior virulence potential of P. gingivalis over other members of the Bacteroidota phylum.

Clinical Study on the Implications of Immunological Markers in the Diagnosis of Periodontitis in People with Diabetes Mellitus

The basic idea from which the working hypothesis for this study started is the fact that the only systemic disease today that is clearly linked to periodontal disease by biochemical mechanisms is diabetes mellitus, as well as the clinical finding that diabetes causes a number of specific periodontal changes. Highlighting the biochemical markers of inflammation during periodontal disease in patients diagnosed with type 2 diabetes is the main aim of the study. To achieve this objective, we used the human ELISA kit from Boster Biological Technology Co., Ltd. (Pleasanton, CA, USA), for the detection of IL-1β, IL-4, IL-8 and TNF-α. The data analysis shows that plasma levels of these cytokines are associated with the progression of periodontitis. In conclusion, we can state that the involvement of immunological markers is evident in the pathogenesis of periodontal disease.

Comparison of the performance of MiSeq and NovaSeq in oral microbiome study

Objective

Next generation sequencing is commonly used to characterize the microbiome structure. MiSeq is most commonly used to analyze the microbiome due to its relatively long read length. Illumina also introduced the 250 × 2 chip for NovaSeq. The purpose of this study was to compare the performance of MiSeq and NovaSeq in the context of oral microbiome study.

Methods

Total read count, read quality score, relative bacterial abundance, community diversity, and correlation between two platforms were analyzed. Phylogenetic trees were analyzed for Streptococcus and periodontopathogens.

Results

NovaSeq produced significantly more read counts and assigned more operational taxonomic units (OTUs) compared to MiSeq. Community diversity was similar between MiSeq and NovaSeq. NovaSeq were able to detect more unique OTUs compared to MiSeq. When phylogenetic trees were constructed for Streptococcus and periodontopathogens, both platforms detected OTUs for most of the clades.

Conclusion

Taken together, while both MiSeq and NovaSeq platforms effectively characterize the oral microbiome, NovaSeq outperformed MiSeq in terms of read counts and detection of unique OTUs, highlighting its potential as a valuable tool for large scale oral microbiome studies.

A distinctive subgingival microbiome in patients with periodontitis and Alzheimer's disease compared with cognitively unimpaired periodontitis patients

AIM

Periodontitis is caused by dysbiosis of oral microbes and is associated with increased cognitive decline in Alzheimer's disease (AD), and recently, a potential functional link was proposed between oral microbes and AD. We compared the oral microbiomes of patients with or without AD to evaluate the association between oral microbes and AD in periodontitis.

MATERIALS AND METHODS

Periodontitis patients with AD (n = 15) and cognitively unimpaired periodontitis patients (CU) (n = 14) were recruited for this study. Each patient underwent an oral examination and neuropsychological evaluation. Buccal, supragingival and subgingival plaque samples were collected, and microbiomes were analysed by next-generation sequencing. Alpha diversity, beta diversity, linear discriminant analysis effect size, analysis of variance-like differential expression analysis and network analysis were used to compare group oral microbiomes.

RESULTS

All 29 participants had moderate to severe periodontitis. Group buccal and supragingival samples were indistinguishable, but subgingival samples demonstrated significant alpha and beta diversity differences. Differential analysis showed subgingival samples of the AD group had higher prevalence of Atopobium rimae, Dialister pneumosintes, Olsenella sp. HMT 807, Saccharibacteria (TM7) sp. HMT 348 and several species of Prevotella than the CU group. Furthermore, subgingival microbiome network analysis revealed a distinct, closely connected network in the AD group comprised of various Prevotella spp. and several anaerobic bacteria.

CONCLUSIONS

A unique microbial composition was discovered in the subgingival region in the AD group. Specifically, potential periodontal pathogens were found to be more prevalent in the subgingival plaque samples of the AD group. These bacteria may possess a potential to worsen periodontitis and other systemic diseases. We recommend that AD patients receive regular, careful dental check-ups to ensure proper oral hygiene management.

Machine learning techniques for periodontitis and dental caries detection: A narrative review

OBJECTIVES

In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing.

METHODS

An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect.

RESULTS

The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture.

CONCLUSION

While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction.

Oral Lactobacillus species and their probiotic capabilities in patients with periodontitis and periodontally healthy individuals

OBJECTIVES

This study aimed to identify oral Lactobacillus species and characterize their adhesion properties and antibacterial activity in patients with periodontitis compared with periodontally healthy individuals.

MATERIALS AND METHODS

Three hundred and fifty-four isolates from the saliva, subgingival, and tongue plaque of 59 periodontitis patients and 59 healthy individuals were analyzed. Oral Lactobacillus species were identified through the culture method in the modified MRS medium and confirmed by molecular testing. Moreover, the radial diffusion assay and cell culture methods were used to determine the antibacterial activities of oral strains against oral pathogens and their adhesion activity in vitro.

RESULTS

67.7% of the cases and 75.7% of the control samples were positive for the Lactobacillus species. Lacticaseibacillus paracasei and Limosilactobacillus fermentum were the dominant species in the case group, whereas Lacticaseibacillus casei and Lactiplantibacillus plantarum were dominant in the control group. Lactobacillus crispatus and Lactobacillus gasseri had higher antibacterial effects against oral pathogens. Moreover, Ligilactobacillus salivarius and L. fermentum demonstrated the highest ability to adhere to oral mucosal cells and salivary-coated hydroxyapatite.

CONCLUSION

L. crispatus, L. gasseri, L. fermentum, and L. salivarius can be introduced as probiotic candidates since they demonstrated appropriate adherence to oral mucosal cells and salivary-coated hydroxyapatite and also antibacterial activities. However, further studies should be conducted to assess the safety of probiotic interventions using these strains in patients with periodontal disease.

Comparative Analysis of Primers Used for 16S rRNA Gene Sequencing in Oral Microbiome Studies

Recent advances in genomic technologies have enabled more in-depth study of the oral microbiome. In this study, we compared the amplicons generated by primers targeting different sites of the 16S rRNA gene found in the Human Oral Microbiome Database (HOMD). Six sets of primer targeting V1-V2, V1-V3, V3-V4, V4-V5, V5-V7 and V6-V8 regions of 16S rRNA were tested via in silico simulation. Primers targeting the V1-V2, V3-V4, and V4-V5 regions generated more than 90% of the original input sequences. Primers targeting the V1-V2 and V1-V3 regions exhibited a low number of mismatches and unclassified sequences at the taxonomic level, but there were notable discrepancies at the species level. Phylogenetic tree comparisons showed primers targeting the V1-V2 and V3-V4 regions showed performances similar to primers targeting the whole 16s RNA region in terms of separating total oral microbiomes and periodontopathogens. In an analysis of clinical oral samples, V1-V2 primers showed superior performance for identifying more taxa and had better resolution sensitivity for Streptococcus than V3-V4 primers. In conclusion, primers targeting the V1-V2 region of 16S rRNA showed the best performance for oral microbiome studies. In addition, the study demonstrates the need for careful PCR primer selections.

Role of oxidative stress in the relationship between periodontitis and systemic diseases

Periodontitis is a common inflammatory disease. It is characterized by destruction of the supporting structures of the teeth and could lead to tooth loss and systemic inflammation. Bacteria in inflamed gingival tissue and virulence factors are capable of entering the bloodstream to induce systemic inflammatory response, thus influencing the pathological process of many diseases, such as cardiovascular diseases, diabetes, chronic kidney disease, as well as liver injury. An increasing body of evidence show the complex interplay between oxidative stress and inflammation in disease pathogenesis. When periodontitis occurs, increased reactive oxygen species accumulation leads to oxidative stress. Oxidative stress contributes to major cellular components damage, including DNA, proteins, and lipids. In this article, the focus will be on oxidative stress in periodontal disease, the relationship between periodontitis and systemic inflammation, and the impact of periodontal therapy on oxidative stress parameters.

The Oral Microbiome for Geographic Origin: An Italian Study

The human oral microbiome has primarily been studied in clinical settings and for medical purposes. More recently, oral microbial research has been incorporated into other areas of study. In forensics, research has aimed to exploit the variation in composition of the oral microbiome to answer forensic relevant topics, such as human identification and geographical provenience. Several studies have focused on the use of microbiome for continental, national, or ethnic origin evaluations. However, it is not clear how the microbiome varies between similar ethnic populations across different regions in a country. We report here a comparison of the oral microbiomes of individuals living in two regions of Italy - Lombardy and Piedmont. Oral samples were obtained by swabbing the donors' oral mucosa, and the V4 region of the 16S rRNA gene was sequenced from the extracted microbial DNA. Additionally, we compared the oral and the skin microbiome from a subset of these individuals, to provide an understanding of which anatomical region may provide more robust results that can be useful for forensic human identification. Initial analysis of the oral microbiota revealed the presence of a core oral microbiome, consisting of nine taxa shared across all oral samples, as well as unique donor characterising taxa in 31 out of 50 samples. We also identified a trend between the abundance of Proteobacteria and Bacteroidota and the smoking habits, and of Spirochaetota and Synergistota and the age of the enrolled participants. Whilst no significant differences were observed in the oral microbial diversity of individuals from Lombardy or Piedmont, we identified two bacterial families - Corynebacteriaceae and Actinomycetaceae - that showed abundance trends between the two regions. Comparative analysis of the skin and oral microbiota showed significant differences in the alpha (p = 0.0011) and beta (Pr(>F)= 9.999e-05) diversities. Analysis of skin and oral samples from the same donor further revealed that the skin microbiome contained more unique donor characterising taxa than the oral one. Overall, this study demonstrates that whilst the oral microbiome of individuals from the same country and of similar ethnicity are largely similar, there may be donor characterising taxa that might be useful for identification purposes. Furthermore, the bacterial signatures associated with certain lifestyles could provide useful information for investigative purposes. Finally, additional studies are required, the skin microbiome may be a better discriminant for human identification than the oral one.

Tobacco smoke exacerbates Filifactor alocis pathogenicity

AIM

Filifactor alocis has recently emerged as a periodontal pathobiont that appears to thrive in the oral cavity of smokers. We hypothesized that identification of smoke-responsive F. alocis genes would provide insight into adaptive strategies and that cigarette smoke would enhance F. alocis pathogenesis in vivo.

MATERIALS AND METHODS

F. alocis was grown in vitro and cigarette smoke extract-responsive genes determined by RNAseq. Mice were exposed, or not, to mainstream 1R6F research cigarette smoke and infected with F. alocis, or not, in an acute ligature model of periodontitis. Key clinical, infectious, and immune data were collected.

RESULTS

In culture, F. alocis growth was unaffected by smoke conditioning and only a small number of genes were specifically regulated by smoke exposure. Reduced murine mass, differences in F. alocis-cognizant antibody production, and altered immune profiles as well as altered alveolar bone loss were all attributable to smoke exposure and/or F. alocis infection in vivo.

CONCLUSIONS

F. alocis is well-adapted to tobacco-rich conditions and its pathogenesis is enhanced by tobacco smoke exposure. A smoke-exposed ligature model of periodontitis shows promise as a tool with which to further unravel mechanisms underlying tobacco-enhanced, bacteria-induced disease.

Characterization of the oral microbiome of children with type 1 diabetes in the acute and chronic phases

Background and Aim

The relationship between the oral microbiota and type 1 diabetes (T1D) remains unclear. We aimed to evaluate the variations in the oral microbiome in T1D and identify potentially associated bacterial factors.

Methods

We performed high-throughput sequencing of the V3-V4 area of the 16S rRNA gene to profile the oral bacterial composition of 47 healthy children (CON group), 46 children with new-onset T1D in the acute phase (NT1D group), and 10 children with T1D in the chronic phase receiving insulin treatment (CT1D group). Multivariate statistical analysis of sequencing data was performed.

Results

Compared to the CON group, the NT1D group was characterized by decreased diversity and increased abundance of genera harboring opportunistic pathogens, while this trend was partially reversed in the CT1D group. Differential genera between groups could distinguish the NT1D group from the CON group (AUC = 0.933) and CT1D group (AUC = 0.846), respectively. Moreover, T1D-enriched genera were closely correlated with HbA1c, FBG and WBCs levels.

Conclusion

Our results showed that the acute phase of T1D was characterized by oral microbiota dysbiosis, which could be partially ameliorated via glycemic control. The possible role of oral microbiota dysbiosis on oral health and systemic metabolic status in T1D warrants further mechanistic investigation.

Microbial profiling of peri-implantitis compared to the periodontal microbiota in health and disease using 16S rRNA sequencing

PURPOSE

The objective of this study was to analyze the microbial profile of individuals with peri-implantitis (PI) compared to those of periodontally healthy (PH) subjects and periodontitis (PT) subjects using Illumina sequencing.

METHODS

Buccal, supragingival, and subgingival plaque samples were collected from 109 subjects (PH: 30, PT: 49, and PI: 30). The V3-V4 region of 16S rRNA was sequenced and analyzed to profile the plaque microbiota.

RESULTS

Microbial community diversity in the PI group was higher than in the other groups, and the 3 groups showed significantly separated clusters in the buccal samples. The PI group showed different patterns of relative abundance from those in the PH and PT groups depending on the sampling site at both genus and phylum levels. In all samples, some bacterial species presented considerably higher relative abundances in the PI group than in the PH and PT groups, including Anaerotignum lactatifermentans, Bacteroides vulgatus, Faecalibacterium prausnitzii, Olsenella uli, Parasutterella excrementihominis, Prevotella buccae, Pseudoramibacter alactolyticus, Treponema parvum, and Slackia exigua. Network analysis identified that several well-known periodontal pathogens and newly recognized bacteria were closely correlated with each other.

CONCLUSIONS

The composition of the microbiota was considerably different in PI subjects compared to PH and PT subjects, and these results could shed light on the mechanisms involved in the development of PI.

Standardized studies of the oral microbiome: From technology-driven to hypothesis-driven

The microbiome is in a symbiotic relationship with the host. Among the microbial consortia in the human body, that in the oral cavity is complex. Instead of repeatedly confirming biomarkers of oral and systemic diseases, recent studies have focused on a unified clinical diagnostic standard in microbiology that reduces the heterogeneity caused by individual discrepancies. Research has also been conducted on other topics of greater clinical importance, including bacterial pathogenesis, and the effects of drugs and treatments. In this review, we divide existing research into technology-driven and hypothesis-driven, according to whether there is a clear research hypothesis. This classification allows the demonstration of shifts in the direction of oral microbiology research. Based on the shifts, we suggested that establishing clear hypotheses may be the solution to major research challenges.

Gingival Transcriptome of Innate Antimicrobial Factors and the Oral Microbiome With Aging and Periodontitis

The epithelial barrier at mucosal sites comprises an important mechanical protective feature of innate immunity, and is intimately involved in communicating signals of infection/tissue damage to inflammatory and immune cells in these local environments. A wide array of antimicrobial factors (AMF) exist at mucosal sites and in secretions that contribute to this innate immunity. A non-human primate model of ligature-induced periodontitis was used to explore characteristics of the antimicrobial factor transcriptome (n = 114 genes) of gingival biopsies in health, initiation and progression of periodontal lesions, and in samples with clinical resolution. Age effects and relationship of AMF to the dominant members of the oral microbiome were also evaluated. AMF could be stratified into 4 groups with high (n = 22), intermediate (n = 29), low (n = 18) and very low (n = 45) expression in healthy adult tissues. A subset of AMF were altered in healthy young, adolescent and aged samples compared with adults (e.g., APP, CCL28, DEFB113, DEFB126, FLG2, PRH1) and were affected across multiple age groups. With disease, a greater number of the AMF genes were affected in the adult and aged samples with skewing toward decreased expression, for example WDC12, PGLYRP3, FLG2, DEFB128, and DEF4A/B, with multiple age groups. Few of the AMF genes showed a >2-fold increase with disease in any age group. Selected AMF exhibited significant positive correlations across the array of AMF that varied in health and disease. In contrast, a rather limited number of the AMF significantly correlated with members of the microbiome; most prominent in healthy samples. These correlated microbes were different in younger and older samples and differed in health, disease and resolution samples. The findings supported effects of age on the expression of AMF genes in healthy gingival tissues showing a relationship to members of the oral microbiome. Furthermore, a dynamic expression of AMF genes was related to the disease process and showed similarities across the age groups, except for low/very low expressed genes that were unaffected in young samples. Targeted assessment of AMF members from this large array may provide insight into differences in disease risk and biomolecules that provide some discernment of early transition to disease.

In-Silico Detection of Oral Prokaryotic Species With Highly Similar 16S rRNA Sequence Segments Using Different Primer Pairs

Although clustering by operational taxonomic units (OTUs) is widely used in the oral microbial literature, no research has specifically evaluated the extent of the limitations of this sequence clustering-based method in the oral microbiome. Consequently, our objectives were to: 1) evaluate in-silico the coverage of a set of previously selected primer pairs to detect oral species having 16S rRNA sequence segments with ≥97% similarity; 2) describe oral species with highly similar sequence segments and determine whether they belong to distinct genera or other higher taxonomic ranks. Thirty-nine primer pairs were employed to obtain the in-silico amplicons from the complete genomes of 186 bacterial and 135 archaeal species. Each fasta file for the same primer pair was inserted as subject and query in BLASTN for obtaining the similarity percentage between amplicons belonging to different oral species. Amplicons with 100% alignment coverage of the query sequences and with an amplicon similarity value ≥97% (ASI97) were selected. For each primer, the species coverage with no ASI97 (SC-NASI97) was calculated. Based on the SC-NASI97 parameter, the best primer pairs were OP_F053-KP_R020 for bacteria (region V1-V3; primer pair position for Escherichia coli J01859.1: 9-356); KP_F018-KP_R002 for archaea (V4; undefined-532); and OP_F114-KP_R031 for both (V3-V5; 340-801). Around 80% of the oral-bacteria and oral-archaea species analyzed had an ASI97 with at least one other species. These very similar species play different roles in the oral microbiota and belong to bacterial genera such as Campylobacter, Rothia, Streptococcus and Tannerella, and archaeal genera such as Halovivax, Methanosarcina and Methanosalsum. Moreover, ~20% and ~30% of these two-by-two similarity relationships were established between species from different bacterial and archaeal genera, respectively. Even taxa from distinct families, orders, and classes could be grouped in the same possible OTU. Consequently, regardless of the primer pair used, sequence clustering with a 97% similarity provides an inaccurate description of oral-bacterial and oral-archaeal species, which can greatly affect microbial diversity parameters. As a result, OTU clustering conditions the credibility of associations between some oral species and certain health and disease conditions. This significantly limits the comparability of the microbial diversity findings reported in oral microbiome literature.

Comparison of the oral microbial composition between healthy individuals and periodontitis patients in different oral sampling sites using 16S metagenome profiling

Purpose

The purpose of this study was to compare the microbial composition of 3 types of oral samples through 16S metagenomic sequencing to determine how to resolve some sampling issues that occur during the collection of sub-gingival plaque samples.

Methods

In total, 20 subjects were recruited. In both the healthy and periodontitis groups, samples of saliva and supra-gingival plaque were collected. Additionally, in the periodontitis group, sub-gingival plaque samples were collected from the deepest periodontal pocket. After DNA extraction from each sample, polymerase chain reaction amplification was performed on the V3-V4 hypervariable region on the 16S rRNA gene, followed by metagenomic sequencing and a bioinformatics analysis.

Results

When comparing the healthy and periodontitis groups in terms of alpha-diversity, the saliva samples demonstrated much more substantial differences in bacterial diversity than the supra-gingival plaque samples. Moreover, in a comparison between the samples in the case group, the diversity score of the saliva samples was higher than that of the supra-gingival plaque samples, and it was similar to that of the sub-gingival plaque samples. In the beta-diversity analysis, the sub-gingival plaque samples exhibited a clustering pattern similar to that of the periodontitis group. Bacterial relative abundance analysis at the species level indicated lower relative frequencies of bacteria in the healthy group than in the periodontitis group. A statistically significant difference in frequency was observed in the saliva samples for specific pathogenic species (Porphyromonas gingivalis, Treponema denticola, and Prevotella intermedia). The saliva samples exhibited a similar relative richness of bacterial communities to that of sub-gingival plaque samples.

Conclusions

In this 16S oral microbiome study, we confirmed that saliva samples had a microbial composition that was more similar to that of sub-gingival plaque samples than to that of supra-gingival plaque samples within the periodontitis group.

Old age and other factors associated with salivary microbiome variation

BACKGROUND

Many factors can contribute to the exact makeup of the salivary microbiome. Differences in the oral microbiome occur with old age, which may be due to oral conditions and diseases associated with old age, such as edentulism, as well as other unknown causes.

METHODS

The salivary microbiome was sampled in patients from a large urban clinic. For all subjects age, gender, periodontal status, caries status, presence of edentulism, medications, and tobacco usage were recorded. Multifactor analysis was used to study variation in salivary microbiome profiles linked to these factors.

RESULTS

In the population sampled, there were significantly higher numbers of edentulous subjects, and increased levels of polypharmacy found with aging. Large differences in alpha diversity and beta diversity of the salivary microbiome in the old age group were largely linked to edentulism. However, multivariable analysis revealed, even after adjusting for differences in edentulism, polypharmacy, tobacco usage, periodontal disease, caries level, and gender, that old age itself was associated with lower levels of taxa Porphyromonas endodontalis, Alloprevotella tannerae, Filifactor alocis, Treponema, Lautropia Mirabilis and Pseudopropionibacterium sp._HMT_194. Surprisingly, of these taxa, most were ones known to reside on or near tooth surfaces.

CONCLUSIONS

Another factor or factors beyond edentulism, polypharmacy and periodontal disease play a role in the differences seen in oral microbiome with old age. The nature of this factor(s) is not known.

Metabolic phenotyping of saliva to identify possible biomarkers of periodontitis using proton nuclear magnetic resonance

AIM

The aim of this study was to propose biomarker candidates for periodontitis via untargeted metabolomics analysis.

MATERIALS AND METHODS

Metabolic profiling was performed using saliva samples from 92 healthy controls (H) and 129 periodontitis patients (P) in the discovery cohort using proton nuclear magnetic resonance spectroscopy. Random forest was applied to identify metabolites that significantly differentiated the control group from the periodontitis group. Candidate metabolites were then validated in an independent validation cohort.

RESULTS

In the discovery set, the metabolic profiles of the P group were clearly separated from those of the H group. A total of 31 metabolites were identified in saliva, and 7 metabolites were selected as candidate biomarkers. These metabolites were further confirmed in the validation set. Ethanol, taurine, isovalerate, butyrate, and glucose were finally confirmed as biomarkers. Furthermore, the biomarker panel showed more than 0.9 of the area under curve value in both discovery and validation sets, indicating that panels were more effective than individual metabolites for diagnosing periodontitis.

CONCLUSIONS

We identified five metabolite biomarkers that discriminated patients with periodontitis from healthy controls in two independent cohorts. These biomarkers have the potential for periodontal screening, detection of periodontitis, and monitoring of the outcome of periodontal therapy.

Washing- and Separation-Free Electrochemical Detection of Porphyromonas gingivalis in Saliva for Initial Diagnosis of Periodontitis

Indirect detection of Porphyromonas gingivalis in saliva, based on proteolytic cleavage by an Arg-specific gingipain (Arg-gingipain), has traditionally been used for simple, initial diagnosis of periodontitis. To accurately detect P. gingivalis using a point-of-care format, development of a simple biosensor that can measure the exact concentration of P. gingivalis is required. However, electrochemical detection in saliva is challenging due to the presence of various interfering electroactive species in different concentrations. Here, we report a washing- and separation-free electrochemical biosensor for sensitive detection of P. gingivalis in saliva. Glycine-proline-arginine conjugated with 4-aminophenol (AP) was used as an electrochemical substrate for a trypsin-like Arg-gingipain, and glycylglycine was used to increase the Arg-gingipain activity. The electrochemical signal of AP was increased using electrochemical-chemical (EC) redox cycling involving an electrode, AP, and tris(2-carboxyethyl)phosphine, and the electrochemical charge signal was corrected using the initial charge obtained before a 15 min incubation period. The EC redox cycling combined with the matrix-corrected signal facilitated a high and reproducible signal without requiring washing and separation steps. The proteolytic cleavage of the electrochemical substrate was specific to P. gingivalis. The calculated detection limit for P. gingivalis in artificial saliva was 5 × 10⁵ colony-forming units/mL, and the concentration of P. gingivalis in human saliva could be measured. The developed biosensor can be used as an initial diagnosis method to distinguish between healthy people and patients with periodontal diseases.

Filifactor alocis: Recent Insights and Advances

Filifactor alocis, a fastidious Gram-positive obligate anaerobic bacterium, is a newly appreciated member of the periodontal community that is now proposed to be a diagnostic indicator of periodontal disease. Its pathogenic characteristics are highlighted by its ability to survive in the oxidative stress-rich environment of the periodontal pocket and to significantly alter the microbial community dynamics by forming biofilms and interacting with several oral bacteria. Here, we describe the current understanding of F. alocis virulence attributes, such as its comparative resistance to oxidative stress, production of unique proteases and collagenases that can cause structural damage to host cells, and dysregulation of the immune system, which enable this bacterium to colonize, survive, and outcompete other traditional pathogens in the inflammatory environment of the periodontal pocket. Furthermore, we explore the recent advancements and future directions for F. alocis research, including the potential mechanisms for oxidative stress resistance and our evolving understanding of the interactions and mechanisms of bacterial survival inside neutrophils. We also discuss the current genetic tools and challenges involved in manipulating the F. alocis genome for the functional characterization of the putative virulence genes. Collectively, this information will expedite F. alocis research and should lead to the identification of prime targets for the development of novel therapeutics to aid in the control and prevention of periodontal disease.

Oral microbiome interactions with gingival gene expression patterns for apoptosis, autophagy and hypoxia pathways in progressing periodontitis

Oral mucosal tissues must react with and respond to microbes comprising the oral microbiome ecology. This study examined the interaction of the microbiome with transcriptomic footprints of apoptosis, autophagy and hypoxia pathways during periodontitis. Adult Macaca mulatta (n = 18; 12-23 years of age) exhibiting a healthy periodontium at baseline were used to induce progressing periodontitis through ligature placement around premolar/molar teeth. Gingival tissue samples collected at baseline, 0·5, 1 and 3 months of disease and at 5 months for disease resolution were analysed via microarray. Bacterial samples were collected at identical sites to the host tissues and analysed using MiSeq. Significant changes in apoptosis and hypoxia gene expression occurred with initiation of disease, while autophagy gene changes generally emerged later in disease progression samples. These interlinked pathways contributing to cellular homeostasis showed significant correlations between altered gene expression profiles in apoptosis, autophagy and hypoxia with groups of genes correlated in different directions across health and disease samples. Bacterial complexes were identified that correlated significantly with profiles of host genes in health, disease and resolution for each pathway. These relationships were more robust in health and resolution samples, with less bacterial complex diversity during disease. Using these pathways as cellular responses to stress in the local periodontal environment, the data are consistent with the concept of dysbiosis at the functional genomics level. It appears that the same bacteria in a healthy microbiome may be interfacing with host cells differently than in a disease lesion site and contributing to the tissue destructive processes.