Grading system for periodontitis by analyzing levels of periodontal pathogens in saliva

Highly accurate measurement of the relative abundance of oral pathogenic bacteria using colony-forming unit-based qPCR

PURPOSE

Quantitative polymerase chain reaction (qPCR) has recently been employed to measure the number of bacterial cells by quantifying their DNA fragments. However, this method can yield inaccurate bacterial cell counts because the number of DNA fragments varies among different bacterial species. To resolve this issue, we developed a novel optimized qPCR method to quantify bacterial colony-forming units (CFUs), thereby ensuring a highly accurate count of bacterial cells.

METHODS

To establish a new qPCR method for quantifying 6 oral bacteria namely, Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Prevotella intermedia, Fusobacterium nucleatum, and Streptococcus mutans, the most appropriate primer-probe sets were selected based on sensitivity and specificity. To optimize the qPCR for predicting bacterial CFUs, standard curves were produced by plotting bacterial CFU against Ct values. To validate the accuracy of the predicted CFU values, a spiking study was conducted to calculate the recovery rates of the predicted CFUs to the true CFUs. To evaluate the reliability of the predicted CFU values, the consistency between the optimized qPCR method and shotgun metagenome sequencing (SMS) was assessed by comparing the relative abundance of the bacterial composition.

RESULTS

For each bacterium, the selected primer-probe set amplified serial-diluted standard templates indicative of bacterial CFUs. The resultant Ct values and the corresponding bacterial CFU values were used to construct a standard curve, the linearity of which was determined by a coefficient of determination (r²) >0.99. The accuracy of the predicted CFU values was validated by recovery rates ranging from 95.1% to 106.8%. The reliability of the predicted CFUs was reflected by the consistency between the optimized qPCR and SMS, as demonstrated by a Spearman rank correlation coefficient (ρ) value of 1 for all 6 bacteria.

CONCLUSIONS

The CFU-based qPCR quantification method provides highly accurate and reliable quantitation of oral pathogenic bacteria.

Impact of Circulating Cell-Free DNA (cfDNA) as a Biomarker of the Development and Evolution of Periodontitis

In the last few decades, circulating cell-free DNA (cfDNA) has been shown to have an important role in cell apoptosis or necrosis, including in the development and evolution of several tumors and inflammatory diseases in humans. In this regard, periodontitis, a chronic inflammatory disease that can induce the destruction of supporting components of the teeth, could represent a chronic inflammatory stimulus linked to a various range of systemic inflammatory diseases. Recently, a possible correlation between periodontal disease and cfDNA has been shown, representing new important diagnostic-therapeutic perspectives. During the development of periodontitis, cfDNA is released in biological fluids such as blood, saliva, urine and other body fluids and represents an important index of inflammation. Due to the possibility of withdrawing some of these liquids in a non-invasive way, cfDNA could be used as a possible biomarker for periodontal disease. In addition, discovering a proportional relationship between cfDNA levels and the severity of periodontitis, expressed through the disease extent, could open the prospect of using cfDNA as a possible therapeutic target. The aim of this article is to report what researchers have discovered in recent years about circulating cfDNA in the development, evolution and therapy of periodontitis. The analyzed literature review shows that cfDNA has considerable potential as a diagnostic, therapeutic biomarker and therapeutic target in periodontal disease; however, further studies are needed for cfDNA to be used in clinical practice.

The Correlation between Periodontal Parameters and Cell-Free DNA in the Gingival Crevicular Fluid, Saliva, and Plasma in Chinese Patients: A Cross-Sectional Study

Purpose: To investigate the correlation between periodontal parameters and cell-free DNA (cfDNA) concentrations in gingival crevicular fluid (GCF), saliva, and plasma. Methods: Full mouth periodontal parameters, including probing depth (PD), bleeding on probing (BOP), and plaque index (PI) were recorded from 25 healthy volunteers, 31 patients with untreated gingivitis, and 25 patients with untreated periodontitis. GCF, saliva, and plasma samples were collected from all subjects. Extraction and quantification assays were undertaken to determine cfDNA concentrations of each sample. Results: GCF and salivary cfDNA levels were increased with aggravation of periodontal inflammation (GCF p < 0.0001; saliva p < 0.001). Plasma cfDNA concentrations in patients with periodontitis were significantly higher than those in healthy volunteers and patients with gingivitis. GCF and salivary cfDNA were positively correlated with mean PD, max PD, BOP, and mean PI (p < 0.0001), whereas plasma cfDNA was not correlated with BOP (p = 0.099). Conclusion: GCF, saliva, and plasma concentrations of cfDNA were significantly elevated in patients with periodontal disease. There were also positive correlations between cfDNA levels in GCF and saliva and periodontal parameters.

Salivary Microbiome Profile of Diabetes and Periodontitis in a Chinese Population

Aim

There is a bidirectional association between diabetes and periodontitis. However, the effect of diabetes on the periodontitis salivary microbiota has not been elucidated. The aim of this study was to determine the effect of the presence of diabetes on the microbiota among Chinese patients with periodontitis.

Materials and Methods

Unstimulated whole saliva samples were collected from the periodontitis with diabetes group (TC), chronic periodontitis group (CP), and periodontally healthy and systemically healthy group (H) by spitting method. Bacterial genomic DNA was PCR-amplified at the V4 variable region of 16S rRNA gene. The library was constructed according to the obtained sequence results, and biological analysis and statistical analysis were carried out. Functional prediction of three groups of microbial communities was performed by the PICRUSt algorithm.

Results

There was no significant difference in bacterial diversity between the TC and CP groups. Compared with the H group, the TC group and CP group presented a higher diversity of salivary flora. Firmicutes, Streptococcus, Haemophilus, Veillonella, and Haemophilus parainfluenzae dominated the H group. Corynebacterium, Leptotrichia, Dialister, Comamonas, Capnocytophaga, Catonella, Filifactor, Campylobacter, Treponema, Campylobacter concisus, Prevotella oralis, and Porphyromonas gingivalis were significantly enriched in the TC and CP groups. Among them, Treponema and P. oralis were the most abundant in the TC group. The PICRUSt results showed that many pathways related to cell motility and functional metabolism of the salivary microbial flora changed in the TC group and the CP group.

Conclusions

Diabetes was not the main factor causing the altered diversity of salivary microbiota in patients with periodontitis; however, the presence of diabetes altered the abundance of some microbiota in saliva.

Recent Advances on Possible Association Between the Periodontal Infection of Porphyromonas gingivalis and Central Nervous System Injury

Chronic periodontitis caused by Porphyromonas gingivalis (P. gingivalis) infection generally lasts for a lifetime. The long-term existence and development of P. gingivalis infection gradually aggravate the accumulation of inflammatory signals and toxic substances in the body. Recent evidence has revealed that P. gingivalis infection may be relevant to some central nervous system (CNS) diseases. The current work collects information and tries to explore the possible relationship between P. gingivalis infection and CNS diseases, including the interaction or pathways between peripheral infection and CNS injury, and the underlying neurotoxic mechanisms.

Microbiome of Saliva and Plaque in Children According to Age and Dental Caries Experience

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3-V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6-12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.

Clinical utility of subgingival plaque-specific bacteria in salivary microbiota for detecting periodontitis

Saliva contains diverse bacteria shed from various oral sites, including subgingival plaque. It is reasonable to focus on the total occupancy of subgingival plaque-specific bacteria (SUBP bacteria), which live in subgingival environments, in the saliva for detecting periodontitis using salivary testing. This study aimed to validate the clinical utility of SUBP bacteria in the salivary microbiota for the detection of periodontitis. We examined stimulated saliva samples collected from 125 subjects who visited three dental clinics. The relative abundances of previously identified 11 SUBP bacteria were determined using 16S ribosomal RNA gene sequencing and a reference-based approach. The prediction performance was evaluated using a receiver operating characteristic (ROC) curve. The SUBP bacteria accounted for 0-15.4% of the salivary microbiota, and the percentage distinguished periodontitis patients with at least 15 sites with probing depth ≥4 mm with a sensitivity of 0.90 (95% confidence interval [CI], 0.81-0.98) and specificity of 0.70 (95% CI, 0.60-0.80) (area under the ROC curve [AUC], 0.87). Among 2,047 combinations of 11 SUBP bacteria, combinations including Streptococcus constellatus, Porphyromonas gingivalis, and Fusobacterium nucleatum subsp. vincentii demonstrated significantly higher AUC values in their detection. These results suggest that examining SUBP bacteria in saliva may be useful for detecting periodontitis patients in mass screening.

Quantification of Bacteria in Mouth-Rinsing Solution for the Diagnosis of Periodontal Disease

This study aimed to evaluate the feasibility of diagnosing periodontitis via the identification of 18 bacterial species in mouth-rinse samples. Patients (n = 110) who underwent dental examinations in the Department of Periodontology at the Veterans Health Service Medical Center between 2018 and 2019 were included. They were divided into healthy and periodontitis groups. The overall number of bacteria, and those of 18 specific bacteria, were determined via real-time polymerase chain reaction in 92 mouth-rinse samples. Differences between groups were evaluated through logistic regression after adjusting for sex, age, and smoking history. There was a significant difference in the prevalence (healthy vs. periodontitis group) of Aggregatibacter actinomycetemcomitans (2.9% vs. 13.5%), Treponema denticola (42.9% vs. 69.2%), and Prevotella nigrescens (80% vs. 2.7%). Levels of Treponema denticola, Prevotella nigrescens, and Streptococcus mitis were significantly associated with severe periodontitis. We demonstrated the feasibility of detecting periopathogenic bacteria in mouth-rinse samples obtained from patients with periodontitis. As we did not comprehensively assess all periopathogenic bacteria, further studies are required to assess the potential of oral-rinsing solutions to indicate oral infection risk and the need to improve oral hygiene, and to serve as a complementary method for periodontal disease diagnosis.

Salivary microbiome and periodontal status of patients with periodontitis during the initial stage of orthodontic treatment

INTRODUCTION

Patients with severe periodontitis typically present with pathologic tooth migration. To improve esthetics and masticatory function, orthodontic treatment is required. Research on periodontal orthodontic treatment has been sparse, particularly from the microbial perspective. Hence, we analyzed the microbial and clinical changes in patients with well-controlled periodontitis in the early stage of orthodontic treatment.

METHODS

Ten patients with well-controlled periodontitis were asked to collect saliva before and 1 and 3 months after appliance placement (T0, T1, and T2, respectively) and underwent clinical examinations before and 1, 3, and 6 months after appliance placement (T0, T1, T2, and T3, respectively). The microbial community of saliva was analyzed by 16S rRNA gene sequencing. Gingival index, the plaque index, and the probing pocket depth were clinically assessed.

RESULTS

The plaque index significantly increased from T0 to T1 and decreased at T2 and T3. The probing pocket depth and gingival index increased slightly at T2, but not significantly, in both the high-risk site and low-risk site. The alpha and beta diversity increased at T1. The microbial community structure was similar at T0 and T2. The relative abundance of core genera and periodontal pathogens was stable during the initial 3 months of orthodontic treatment.

CONCLUSIONS

The orthodontic appliance promoted plaque accumulation and altered the microbial community of patients with well-controlled periodontitis during the first month of orthodontic treatment. The microbial community returned to the basal composition at 3 months after appliance placement, and the periodontal inflammation during the 6-months orthodontic treatment was under control.

Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number

Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified “healthy” and “moderate or severe” periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.

Detection of association between periodontitis and polymorphisms of IL-1β + 3954 and TNF-α -863 in the Korean population after controlling for confounding risk factors

Background and Objective

Interleukin (IL)‐1 and tumor necrosis factor (TNF)‐α are inflammatory cytokines that play an important role in periodontitis, and their genetic variations have been suggested to be associated with increased risk of periodontitis. Focusing on three single nucleotide polymorphisms (SNPs) of IL‐1α + 4845, IL‐1β + 3954, and TNF‐α −863, we aimed to investigate the relationship between periodontitis risk and the polymorphisms of IL‐1 α/β and TNF‐α in Koreans.

Material and Methods

Mouthwash samples from 548 subjects (135 controls without periodontitis, 387 generalized chronic periodontitis patients, and 26 generalized aggressive periodontitis patients) were collected for isolation of genomic DNA. Genotyping of selected SNPs was performed using real‐time PCR. Univariable associations between the polymorphisms and periodontitis were assessed by chi‐squared test or Fisher's exact test. To evaluate the association after controlling for confounding effects of various risk factors, we stratified the subjects according to the presence or absence of self‐reported diseases and employed multiple logistic regression model to adjust for age, smoking status, and oral hygiene indices and behaviors.

Results

Significant association of IL‐1β + 3954 and TNF‐α −863 polymorphisms with periodontitis was observed after adjusting for the confounding risk factors, but not in univariable association analysis. The significant association between genotype CT of IL‐1β + 3954 and increased risk of advanced periodontitis was consistently detected regardless of the status of self‐reported diseases. In the polymorphism of TNF‐α −863, the genotype with minor allele (CA + AA) was significantly associated with periodontitis susceptibility, which was observed only in the subjects with self‐reported diseases.

Conclusion

The results suggest that genetic variations of IL‐1β + 3954 and TNF‐α −863 are associated with increased risk of periodontitis in Koreans. In addition, our findings underscore the importance of controlling for confounding risk factors to detect significant association between genetic factors and risk of periodontitis. A further well‐designed large‐scale study is needed to warrant our results.

Comparison of Bacterial Load Parameters in Subgingival Plaque during Peri-implantitis and Periodontitis Using the RT-PCR Method

Objective

To estimate the actual parameters of bacterial load in subgingival plaque during periodontitis and peri-implantitis pathologies using the RT-PCR (real-time polymerase chain reaction) method and evaluate their associations with clinical periodontal indicators.

Materials and Methods

Five different groups of subjects were selected according to a formulated design of the study: with mild/moderate periodontitis, with severe periodontitis, with peri-implantitis, healthy periodontal group and healthy peri-implant group. Subgingival plaque samples were formed with paper points inserted in the pocket/sulcus area for 30 seconds. A standardized test the “ParodontoScreen” was provided for identification of target opportunistic pathogens (A. actinomycetemcomitans, P. gingivalis, T. forsythia, P. intermedia, T. denticola) by the RT-PCR.

Results

Bacterial load parameters demonstrated a significant tendency towards an increase within periodontitis progression and during the presence of peri-implantitis pathology. Each targeted mean bacterial load level was statistically associated with periodontitis or peri-implantitis pathology (p < 0, 05) according to the provided univariate analyses and upon condition that bacterial load parameters of healthy sites were used as reference for equiparation. The highest correlation values were found between periodontal probing depth and bacterial load parameters of A. actinomycetemcomitans (r=0, 37; p < 0, 05) and P. gingivalis (r=0, 28; p < 0, 05); and also between clinical attachment loss and bacterial load values of A. actinomycetemcomitans (r=0, 38; p < 0, 05) and P. gingivalis (r=0, 24; p < 0, 05).

Conclusions

Periodontitis and peri-implantitis are associated with the same microbial pathogens even though the distribution pattern of their bacterial load and detection frequency parameters registered with RT-PCR could be distinct and linked to the individual patient-related conditions and the severity stage of pathology.

Age and gender characteristics of the biochemical composition of saliva: Correlations with the composition of blood plasma

Background

Recently, the attention of researchers to the study of the properties of human saliva, as a material with unique properties and diagnostic capabilities, has increased.

Research objective

to study the connection of the biochemical composition of saliva and blood plasma in the norm, depending on gender and age.

Methods

107 volunteers took part in the study, including 46 female (37.2 ± 3.9 years old) and 61 male (36.1 ± 2.8 years old). In all samples of saliva and blood plasma, 16 biochemical parameters were determined, including mineral and protein composition, enzyme activity. Non-parametric statistical methods were used to process the data.

Results

It has been shown that it is difficult to establish an unambiguous relation between biochemical parameters of saliva and blood plasma. The calculation of the Spearman correlation coefficients showed that only 7 of the 16 parameters demonstrate the presence of a weak correlation between the content in saliva and plasma.

Conclusion

In general, the determination of the composition of saliva may have an independent diagnostic value; in this case, drawing a parallel with the composition of serum and blood plasma is not advisable. Nevertheless, the use of saliva in clinical laboratory diagnostics is associated with the need to establish criteria for the norm and pathology for each biochemical parameter.

The salivary microbiota in health and disease

The salivary microbiota (SM), comprising bacteria shed from oral surfaces, has been shown to be individualized, temporally stable and influenced by diet and lifestyle. SM reflects local bacterial alterations of the supragingival and subgingival microbiota, and periodontitis and dental-caries associated characteristics of SM have been reported. Also, data suggest an impact of systemic diseases on SM as demonstrated in patients with a wide variety of systemic diseases including diabetes, cancer, HIV and rheumatoid arthritis. The presence of systemic diseases seems to influence salivary levels of specific bacterial species, as well as α- and β-diversity of SM. The composition of SM might thereby potentially mirror oral and general health status. The contentious development of advanced molecular techniques such as metagenomics, metatranscriptomics and metabolomics has enabled the possibility to address bacterial functions rather than presence in microbial samples. However, at present only a few studies have employed such techniques on SM to reveal functional and metabolic characteristics in oral health and disease. Future studies are therefore warranted to illuminate the possible impact of metabolic functions of SM on oral and general health status. Ultimately, such an approach has the possibility to reveal novel and personalized therapeutic avenues in oral and general medicine.

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.