Prediction of Periodontal Inflammation via Metabolic Profiling of Saliva

Analysis of Salivary Metabolites by Nuclear Magnetic Resonance Before and After Oral Mucosa Cleaning of Infants in the Pre-dental Period

The aim of the present study was to verify if a protocol for cleaning the oral cavity of infants in the pre-dental period can reduce extrinsic salivary metabolites observed through Nuclear Magnetic Resonance (NMR). A cross-sectional clinical study with a convenience sample was conducted, and infants were recruited at the UFRJ Pediatric Dentistry Clinic. Participants who had used antibiotics and/or antifungals up to 3 months before and whose legal guardians did not consent or sign the Informed Consent Form were excluded. An anamnesis was performed with the guardians and the participants' intraoral clinical examination. Initial collection of unstimulated total saliva was performed using an automatic pipette with sterile plastic tips in the buccal floor region, at least 1 h after the last feeding. Subsequently, the infants' oral mucosa was cleaned with gauze moistened with filtered water, and after 5 min, a new collection was performed, using the same methodology. The obtained samples were immediately transferred on ice to the laboratory, centrifuged (10,000 g), and stored at −80°C. The NMR analyses were performed using a 500-MHz spectrometer Bruker, Germany); evaluations were done via the 1H and 1H-1H TOCSY spectra for metabolite signaling. Eleven pre-dental infants were evaluated, with a mean age of 3.8 months, including six girls (55%). Of these, nine participants (82%) were exclusively breastfed. The higher presence of components such as lactose, glucose, sugars, acetate, alanine, and lactate were observed in the samples before oral mucosa cleaning. Regarding the type of diet, more lactose was observed in the saliva of patients who were exclusively breastfed than those that received mixed feeding. We conclude that the oral mucosa cleaning of infants in the pre-dental period tends to reduce the concentration of extrinsic components from the diet, such as lactose, in the salivary metabolomic profile analyzed by NMR.

Resolving the Contradictory Functions of Lysine Decarboxylase and Butyrate in Periodontal and Intestinal Diseases

Periodontal disease is a common, bacterially mediated health problem worldwide. Mastication (chewing) repeatedly traumatizes the gingiva and periodontium, causing traces of inflammatory exudate, gingival crevicular fluid (GCF), to appear in crevices between the teeth and gingiva. Inadequate tooth cleaning causes a dentally adherent microbial biofilm composed of commensal salivary bacteria to appear around these crevices where many bacteria grow better on GCF than in saliva. We reported that lysine decarboxylase (Ldc) from Eikenella corrodens depletes the GCF of lysine by converting it to cadaverine and carbon dioxide. Lysine is an amino acid essential for the integrity and continuous renewal of dentally attached epithelium acting as a barrier to microbial products. Unless removed regularly by oral hygiene, bacterial products invade the lysine-deprived dental attachment where they stimulate inflammation that enhances GCF exudation. Cadaverine increases and supports the development of a butyrate-producing microbiome that utilizes the increased GCF substrates to slowly destroy the periodontium (dysbiosis). A long-standing paradox is that acid-induced Ldc and butyrate production support a commensal (probiotic) microbiome in the intestine. Here, we describe how the different physiologies of the respective tissues explain how the different Ldc and butyrate functions impact the progression and control of these two chronic diseases.

Untargeted and targeted gingival metabolome in rodents reveal metabolic links between high-fat diet-induced obesity and periodontitis

AIM

To characterize gingival metabolome in high-fat diet (HFD)-induced obesity in mice with/without periodontitis.

METHODS

HFD-induced obesity mouse model was established by 16-week feeding, and a lean control group was fed with low-fat diet (n = 21/group). Both models were induced for periodontitis on the left sides by molar ligation for 10 days, whereas the right sides were used as controls. Gingival metabolome and arginine metabolism were analysed by non-targeted/targeted liquid chromatography-mass spectrometry.

RESULTS

Of 2247 reference features, presence of periodontitis altered 165 in lean versus 885 in HFD mice; and HFD altered 525 in absence versus 1435 in presence of periodontitis. Compared with healthy condition, periodontitis and HFD had distinct effects on gingival metabolome. Metabolomic impacts of periodontitis were generally greater in HFD mice versus lean controls. K-medoids clustering showed that HFD amplified the impacts of periodontitis on gingival metabolome in both intensity and extensity. Ten metabolic pathways were enriched, including 2 specific to periodontitis, 5 specific to HFD and 3 shared ones. Targeted validation on arginine metabolism confirmed the additive effects between HFD and periodontitis.

CONCLUSION

The obese population consuming excessive HFD display amplified metabolic response to periodontitis, presenting a metabolic susceptibility to exacerbated periodontal destruction.

Emerging intraoral biosensors

Biomedical devices that involved continuous and real-time health-care monitoring have drawn much attention in modern medicine, of which skin electronics and implantable devices are widely investigated. Skin electronics are characterized for their non-invasive access to the physiological signals, and implantable devices are superior at the diagnosis and therapy integration. Despite the significant progress achieved, many gaps remain to be explored to provide a more comprehensive overview of human health. As the connecting point of the outer environment and human systems, the oral cavity contains many unique biomarkers that are absent in skin or inner organs, and hence, this could become a promising alternative locus for designing health-care monitoring devices. In this review, we outline the status of the oral cavity during the communication of the environment and human systems and compare the intraoral devices with skin electronics and implantable devices from the biophysical and biochemical aspects. We further summarize the established diagnosis database and technologies that could be adopted to design intraoral biosensors. Finally, the challenges and potential opportunities for intraoral biosensors are discussed. Intraoral biosensors could become an important complement for existing biomedical devices to constitute a more reliable health-care monitoring system.

Polyamine biomarkers as indicators of human disease

The significant increase of periodontitis, chronic kidney disease (CKD), Alzheimer's disease and cancer can be attributed to an ageing population. Each disease produces a range of biomarkers that can be indicative of disease onset and progression. Biomarkers are defined as cellular (intra/extracellular components and whole cells), biochemical (metabolites, ions and toxins) or molecular (nucleic acids, proteins and lipids) alterations which are measurable in biological media such as human tissues, cells or fluids. An interesting group of biomarkers that merit further investigation are the polyamines. Polyamines are a group of molecules consisting of cadaverine, putrescine, spermine and spermidine and have been implicated in the development of a range of systemic diseases, in part due to their production in periodontitis. Cadaverine and putrescine within the periodontal environment have demonstrated cell signalling interfering abilities, by way of leukocyte migration disruption. The polyamines spermine and spermidine in tumour cells have been shown to inhibit cellular apoptosis, effectively prolonging tumorigenesis and continuation of cancer within the host. Polyamine degradation products such as acrolein have been shown to exacerbate renal damage in CKD patients. Thus, the use of such molecules has merit to be utilized in the early indication of such diseases in patients.

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).

Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-surgical Periodontal Therapy: A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy

Pattern analysis of the salivary metabolic profile has been proven accurate in discriminating between generalized periodontitis (GP) patients and healthy individuals (HI), as this disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data from previous studies, this study aimed to evaluate if non-surgical periodontal therapy (NST) could affect the metabolomic profile in GP patients’ saliva and if it compares to that of HI. Unstimulated salivary samples were collected from 11 HI and 12 GP patients before and 3 months after NST. Nuclear Magnetic Resonance (NMR) spectroscopy, followed by a supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis, were performed to obtain metabolic profiles. In the GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in distinguishing between metabolic profiles of GP patients before and after NST. Orthogonal projection to latent structure was able to discriminate between the three groups of subjects with an accuracy of 85.6%. However, the post-NST metabolic profile of GP patients could not be completely assimilated to that of HI. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

Gingival fibroblasts dynamically reprogram cellular metabolism during infection of Porphyromonas gingivalis

OBJECTIVE

The purpose of the present study was to explore the sequential changes in the cellular metabolism in gingival fibroblasts (GFs) in response toPorphyromonas gingvalis (P. gingivalis) ATCC33277 infection.

DESIGN

GFs were treated withP. gingivalis at the MOI of 50 for 4, 24 and 48 h to mimic the early, medium, and late phase in the bacterial infection. LDH assay and cell counting kit-8 were utilized to explore cell death and proliferation. Real-time PCR was utilized to explore the gene transcription of pro-inflammatory genes. The relative levels of biomolecules in GFs were measured by gas chromatography-mass spectrometry. Principal component analysis and orthogonal partial least-squares-discriminant analysis were performed to visualize the metabolic difference among experimental groups. In addition, pathway analysis was conducted regarding differential metabolites in GFs.

RESULTS

P. gingivalis infection triggered significant gene transcription of IL-1β, IL 6, MCP 1, and MMP 1 in GFs. In addition, P. gingivalis stimulated cell proliferation of GFs at MOI of 10, 50 and 250. Moreover, P. gingivalis triggered significant cell death at higher MOI. 69, 173 and 148 metabolites were qualitatively detected at 4, 24 and 48 h after P. gingivalis infection respectively in GFs, showing a sequential change of different phase. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that ATP-binding cassette transporters, glutathione, purine and pyrimidine metabolism was significantly altered in different phase.

CONCLUSIONS

Human GFs may sequentially rewire metabolomics to shape the inflammatory responses and support the proliferation of host cells during P. gingivalis infection.

Salivary metabolite levels in perinatally HIV-infected youth with periodontal disease

INTRODUCTION

Salivary metabolite profiles are altered in adults with HIV compared to their uninfected counterparts. Less is known about youth with HIV and how oral disorders that commonly accompany HIV infection impact salivary metabolite levels.

OBJECTIVE

As part of the Adolescent Master Protocol multi-site cohort study of the Pediatric HIV/AIDS Cohort Study (PHACS) network we compared the salivary metabolome of youth with perinatally-acquired HIV (PHIV) and youth HIV-exposed, but uninfected (PHEU) and determined whether metabolites differ in PHIV versus PHEU.

METHODS

We used three complementary targeted and discovery-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) workflows to characterize salivary metabolite levels in 20 PHIV and 20 PHEU youth with and without moderate periodontitis. We examined main effects associated with PHIV and periodontal disease, and the interaction between them.

RESULTS

We did not identify differences in salivary metabolite profiles that remained significant under stringent control for both multiple between-group comparisons and multiple metabolites. Levels of cadaverine, a known periodontitis-associated metabolite, were more abundant in individuals with periodontal disease with the difference being more pronounced in PHEU than PHIV. In the discovery-based dataset, we identified a total of 564 endogenous peptides in the metabolite extracts, showing that proteolytic processing and amino acid metabolism are important to consider in the context of HIV infection.

CONCLUSION

The salivary metabolite profiles of PHIV and PHEU youth were overall very similar. Individuals with periodontitis particularly among the PHEU youth had higher levels of cadaverine, suggesting that HIV infection, or its treatment, may influence the metabolism of oral bacteria.

White Blood Cell Count Mediates the Association Between Periodontal Inflammation and Cognitive Performance Measured by Digit Symbol Substitution Test Among Older U.S. Adults

Background

Systemic effects of periodontal infection may increase the risk of central neuroinflammation, aggravating impaired cognition. This study aims to examine whether systemic inflammatory factors mediate the possible association between periodontal inflammation and cognitive function.

Methods

We conducted a cross-sectional analysis of 766 participants aged ≥ 60 years and who had completed periodontal and cognitive examinations in the National Health and Nutrition Examination Survey (NHANES) 2001–2002. We used multivariable linear regression to investigate the overall association between periodontal health and cognitive function as measured by the digit symbol substitution test (DSST). Bleeding on probing (BOP) and periodontal inflamed surface area (PISA) were used to assess the periodontal inflammatory activity and burden, respectively. Mediation analyses were used to test the indirect effects of the BOP/PISA on DSST via C-reactive protein, white blood cell (WBC) count, and fibrinogen.

Results

Participants with superior periodontal health obtained higher DSST scores than those with poorer periodontal health, adjusting for demographic factors and chronic conditions. Concerning the inflammatory activity, WBC count acted as a full mediator in the association between BOP and DSST (β = −0.091; 95% confidence interval [CI] = −0.174 to −0.008) and mediated 27.5% of the total association. Regarding the inflammatory burden, WBC count acted as a partial mediator in the association between PISA and DSST (β = −0.059; 95% CI = −0.087 to −0.031) and mediated 20.3% of the total association.

Conclusion

Our study indicated the potential role of systemic inflammatory factors as a mediator of associations between periodontal inflammation and cognitive function in the U.S. geriatric population.

A Preliminary Study on the Ability of the Trypsin-Like Peptidase Activity Assay Kit to Detect Periodontitis

This study aimed to explore whether the Trypsin-Like Peptidase Activity Assay Kit (TLP-AA-Kit), which measures the activity of N-benzoyl-dl-arginine peptidase (trypsin-like peptidase), can be used as a reliable tool for periodontitis detection in population-based surveillance. In total, 105 individuals underwent a full-mouth periodontal examination and provided tongue swabs as specimens for further analyses. The results of the TLP-AA-Kit were scored between 1 and 5; higher scores indicated higher trypsin concentrations. Receiver operating characteristic analyses were used to evaluate the predictive validity of the TLP-AA-Kit, where the periodontitis case definition provided by the Centers for Disease Control/American Academy of Periodontology served as the reference. Severe and moderate periodontitis were identified in 4.8% and 16.2% of the study population, respectively. The TLP-AA-Kit showed high diagnostic accuracy for severe periodontitis, with an area under the curve of 0.93 (95% confidence interval = 0.88-0.99). However, the diagnostic accuracy of the TLP-AA-Kit for moderate/severe periodontitis was not reliable. While further studies are necessary to validate our results, the results provided herein highlight the potential of the TLP-AA-Kit as a useful tool for the detection of periodontitis, particularly in severe cases, for population-based surveillance.

Electronic nicotine delivery system-induced alterations in oral health via saliva assessment

IMPACT STATEMENT

The use of traditional tobacco products is a known risk factor for the development of diseases including periodontal disease. To date, the potential oral health effects related to electronic nicotine delivery systems (ENDS) use is unknown. This study collected saliva from ENDS users and never tobacco users to examine differences in the oral cavity of inflammatory cytokines and metabolites. The identification and measurement of these ENDS-related changes provide insight into disease pathways potentially associated with ENDS use. The utilization of saliva samples collected from human participates enhances the application of the findings compared to the majority of studies using cell culture and animal models. In addition, these foundational findings can inform future studies to examining specific pathways identified, interventional approaches, and application of translatable biomarkers of ENDS use.

Microbial and metabolomic analysis of gingival crevicular fluid in general chronic periodontitis patients: lessons for a predictive, preventive, and personalized medical approach

Objectives

General chronic periodontitis (GCP) is a bacterial inflammatory disease with complex pathology. Despite extensive studies published on the variation in the oral microbiota and metabolic profiles of GCP patients, information is lacking regarding the correlation between host-bacterial interactions and biochemical metabolism. This study aimed to analyze the oral microbiome, the oral metabolome, and the link between them and to identify potential molecules as useful biomarkers for predictive, preventive, and personalized medicine (PPPM) in GCP.

Methods

In this study, gingival crevicular fluid (GCF) samples were collected from patients with GCP (n = 30) and healthy controls (n = 28). The abundance of oral microbiota constituents was obtained by Illumina sequencing, and the relative level of metabolites was measured by gas chromatography-mass spectrometry. Full-mouth probing depth, clinical attachment loss, and bleeding on probing were recorded as indices of periodontal disease.

Results

The relative abundances of 7 phyla and 82 genera differed significantly between the GCP and healthy groups. Seventeen differential metabolites involved in different metabolism pathways were selected based on variable influence on projection values (VIP > 1) and P values (P < 0.05). Through Spearman's correlation analysis, microorganisms, metabolites in GCF, and clinical data together showed a clear trend, and clinical data regarding periodontitis can be reflected in the shift of the oral microbial community and the change in metabolites in GCF. A combination of citramalic acid and N-carbamylglutamate yielded satisfactory accuracy (AUC = 0.876) for the predictive diagnosis of GCP.

Conclusions

Dysbiosis in the polymicrobial community structure and changes in metabolism could be mechanisms underlying periodontitis. The differential microorganisms and metabolites in GCF between periodontitis patients and healthy individuals are possibly biomarkers, pointing to a potential strategy for the prediction, diagnosis, prognosis, and management of personalized periodontal therapy.

Host-microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome

Periodontal studies using transcriptomics, proteomics, and metabolomics encompass the collection of mRNA transcripts, proteins, and small-molecule chemicals in the context of periodontal health and disease. The number of studies using these approaches has significantly increased in the last decade and they have provided new insight into the pathogenesis and host-microbe interactions that define periodontal diseases. This review provides an overview of current molecular findings using -omic approaches that underlie periodontal disease, including modulation of the host immune response, tissue homeostasis, and complex metabolic processes of the host and the oral microbiome. Integration of these -omic approaches will broaden our perspective of the molecular mechanisms involved in periodontal disease, advancing and improving the diagnosis and treatment of various stages and forms of periodontal disease.

Salivary metabolites to detect patients with cancer: a systematic review

Novel adjunctive screening aids are needed to reduce the morbidity and mortality related to cancer, and every effort should be made for early diagnosis. This systematic review aimed to evaluate salivary metabolites and their diagnostic value in patients with cancer.The systematic review was performed in two phases and included studies that focused on the diagnostic value of salivary metabolites in humans with solid malignant neoplasms. Five electronic databases were searched, and the risk of bias in individual studies was evaluated using the revised Quality Assessment of Diagnostic Accuracy Studies criteria (QUADAS-2). All procedures were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.Of the 1151 studies retrieved, 25 were included; 13 studies used targeted and 12 untargeted metabolomics approaches. Most studies included patients with breast and oral cancer. Except for one, all studies had case-control designs, and none fulfilled all quality assessments. Overall, 140 salivary metabolites were described. The most frequently reported metabolites were alanine, valine, and leucine. Among the 11 studies that reported diagnostic test accuracy (DTA) values, proline, threonine, and histidine in combination and monoacylglycerol alone demonstrated the highest DTA for breast cancer. Combined choline, betaine, pipecolinic acid, and L-carnitine showed better discriminatory performance for early oral cancer.This systematic review highlights the current evidence on salivary metabolites that may be used as a future strategy to diagnose cancer. Further studies including larger sample sizes with confirmation of the results by untargeted analysis are warranted.

Examining Bias and Reporting in Oral Health Prediction Modeling Studies

Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.

The Saliva Metabolome in Association to Oral Health Status

Periodontitis is one of the most prevalent oral diseases worldwide and is caused by multifactorial interactions between host and oral bacteria. Altered cellular metabolism of host and microbes releases a number of intermediary end products known as metabolites. There is an increasing interest in identifying metabolites from oral fluids such as saliva to widen the understanding of the complex pathogenesis of periodontitis. It is believed that some metabolites might serve as indicators toward early detection and screening of periodontitis and perhaps even for monitoring its prognosis in the future. Because contemporary periodontal screening methods are deficient, there is an urgent need for novel approaches in periodontal screening procedures. To this end, we associated oral parameters (clinical attachment level, periodontal probing depth, supragingival plaque, supragingival calculus, number of missing teeth, and removable denture) with a large set of salivary metabolites ( n = 284) obtained by mass spectrometry among a subsample ( n = 909) of nondiabetic participants from the Study of Health in Pomerania (SHIP-Trend-0). Linear regression analyses were performed in age-stratified groups and adjusted for potential confounders. A multifaceted image of associated metabolites ( n = 107) was revealed with considerable differences according to age groups. In the young (20 to 39 y) and middle-aged (40 to 59 y) groups, metabolites were predominantly associated with periodontal variables, whereas among the older subjects (≥60 y), tooth loss was strongly associated with metabolite levels. Metabolites associated with periodontal variables were clearly linked to tissue destruction, host defense mechanisms, and bacterial metabolism. Across all age groups, the bacterial metabolite phenylacetate was significantly associated with periodontal variables. Our results revealed alterations of the salivary metabolome in association with age and oral health status. Among our comprehensive panel of metabolites, periodontitis was significantly associated with the bacterial metabolite phenylacetate, a promising substance for further biomarker research.

Epigenetic findings in periodontitis in UK twins: a cross-sectional study

Background

Genetic and environmental risk factors contribute to periodontal disease, but the underlying susceptibility pathways are not fully understood. Epigenetic mechanisms are malleable regulators of gene function that can change in response to genetic and environmental stimuli, thereby providing a potential mechanism for mediating risk effects in periodontitis. The aim of this study is to identify epigenetic changes across tissues that are associated with periodontal disease.

Methods

Self-reported gingival bleeding and history of gum disease, or tooth mobility, were used as indicators of periodontal disease. DNA methylation profiles were generated using the Infinium HumanMethylation450 BeadChip in whole blood, buccal, and adipose tissue samples from predominantly older female twins (mean age 58) from the TwinsUK cohort. Epigenome-wide association scans (EWAS) of gingival bleeding and tooth mobility were conducted in whole blood in 528 and 492 twins, respectively. Subsequently, targeted candidate gene analysis at 28 genomic regions was carried out testing for phenotype-methylation associations in 41 (tooth mobility) and 43 (gingival bleeding) buccal, and 501 (tooth mobility) and 556 (gingival bleeding) adipose DNA samples.

Results

Epigenome-wide analyses in blood identified one CpG-site (cg21245277 in ZNF804A) associated with gingival bleeding (FDR = 0.03, nominal p value = 7.17e−8) and 58 sites associated with tooth mobility (FDR < 0.05) with the top signals in IQCE and XKR6. Epigenetic variation at 28 candidate regions (247 CpG-sites) for chronic periodontitis showed an enrichment for association with periodontal traits, and signals in eight genes (VDR, IL6ST, TMCO6, IL1RN, CD44, IL1B, WHAMM, and CXCL1) were significant in both traits. The methylation-phenotype association signals validated in buccal samples, and a subset (25%) also validated in adipose tissue.

Conclusions

Epigenome-wide analyses in adult female twins identified specific DNA methylation changes linked to self-reported periodontal disease. Future work will explore the environmental basis and functional impact of these results to infer potential for strategic personalized treatments and prevention of chronic periodontitis.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0614-4) contains supplementary material, which is available to authorized users.

Metabolomics study of oral cancers

BACKGROUND

Oral cancer is one of the most frequently occurring cancers. Metabolic reprogramming is an important hallmark of cancer. Metabolomics characterizes all the small molecules in a biological sample, and a complete set of small molecules in such sample is referred as metabolome. Nuclear magnetic resonance spectroscopy and mass spectrometry are two widely used techniques in metabolomics studies. Increasing evidence demonstrates that metabolomics techniques can be used to explore the metabolic signatures in oral cancer. Elucidation of metabolic alterations in oral cancer is also important for the understanding of its pathological mechanisms.

AIM OF REVIEW

In this paper, we summarize the latest progress of metabolomics study in oral cancer and provide the suggestions for the future studies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The metabolomics studies in saliva, serum, and tumor tissues revealed the existence of metabolic signatures in bio-fluids and tissues of oral cancer, and several tumor-specific metabolites identified in individual study could discriminate oral cancer from healthy controls or precancerous lesions, which are potential biomarkers for the screening or early diagnosis of oral cancer. Metabolomics study of oral cancers in the future should aim to establish a routine procedure with high sensitivity, profile intracellular metabolites to find out the metabolic characteristics of tumor cells, and investigate the mechanism behind metabolomic alterations and the metabolic response of cancer cells to chemotherapy.

Effects of inter-day and intra-day variation on salivary metabolomic profiles

BACKGROUND

Salivary secretion is an important parameter reflecting the health status of an individual and has been used clinically for the diagnosis of various oral diseases, such as xerostomia. Salivary metabolomic profiling is considered an emerging potential tool for the detection of various systemic diseases. To our knowledge, this is the first study to investigate the quantitative relationship between salivary secretion volume and salivary metabolomic profile.

METHODS

To evaluate inter- and intra-day variations in salivary secretion, 234 saliva samples were collected three times per day for three days from 13 subjects and analyzed. Capillary electrophoresis-mass spectrometry was used for non-targeted quantification of water-soluble metabolites.

RESULTS

No significant inter- or intra-day variations were observed in salivary secretion volume. No significant inter-day variations were observed in metabolomic patterns. In contrast, significant intra-day variations were observed in salivary metabolomic profiles. The difference was more obvious for stimulated saliva than for unstimulated saliva. These profile changes were independent of salivary secretion volume.

CONCLUSIONS

Our results indicated that diurnal change had a greater effect on salivary metabolomic profiles than the other factors. Hence, sampling time should be tightly controlled to minimize unexpected bias in the clinical use of salivary metabolomics.

Effect of non-surgical periodontal therapy on salivary metabolic fingerprint of generalized chronic periodontitis using nuclear magnetic resonance spectroscopy

OBJECTIVE

Metabolomic analysis of saliva proved its accuracy in discriminating patients with generalized chronic periodontitis (GCP) from healthy subjects by identifying specific molecular signatures of the disease. There is lack of investigations concerning the effect of periodontal treatment on individual metabolic fingerprints. Therefore, the aim of this study was to determine whether non-surgical periodontal therapy could change salivary metabolomic profile in GCP to one more similar to periodontal health.

DESIGN

Unstimulated whole saliva of 32 controls and 19 GCP patients were obtained prior to and 3 months after conventional staged non-surgical periodontal therapy. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by univariate and multivariate paired approaches to assess the changes introduced by the therapy.

RESULTS

In GCP group, periodontal treatment led to an improvement in all clinical parameters (p < 0.001). The accuracy of the multivariate model in discriminating the metabolomic profile of each GCP patient at two time points was 92.5%. Despite the almost perfect separation of the spectra in the metabolic space, the univariate analysis failed to identify significant variations in single metabolite content. The post-treatment metabolic profile of GCP patients could not be assimilated to that of healthy controls who exhibited different levels of lactate, pyruvate, valine, proline, tyrosine, and formate.

CONCLUSIONS

Based on these data, NMR-spectroscopic analysis revealed that, despite significant changes in the overall metabolomic fingerprint after non-surgical therapy, GCP patients maintained a distinctive metabolic profile compared to healthy individuals.

Analysis of salivary phenotypes of generalized aggressive and chronic periodontitis through nuclear magnetic resonance-based metabolomics

BACKGROUND

Recent findings about the differential gene expression signature of periodontal lesions have raised the hypothesis of distinctive biological phenotypes expressed by generalized chronic periodontitis (GCP) and generalized aggressive periodontitis (GAgP) patients. Therefore, this cross-sectional investigation was planned, primarily, to determine the ability of nuclear magnetic resonance (NMR) spectroscopic analysis of unstimulated whole saliva to discriminate GCP and GAgP disease-specific metabolomic fingerprint and, secondarily, to assess potential metabolites discriminating periodontitis patients from periodontally healthy individuals (HI).

METHODS

NMR-metabolomics spectra were acquired from salivary samples of patients with a clinical diagnosis of GCP (n = 33) or GAgP (n = 28) and from HI (n = 39). The clustering of HI, GCP, and GAgP patients was achieved by using a combination of the Principal Component Analysis and Canonical Correlation Analysis on the NMR profiles.

RESULTS

These analyses revealed a significant predictive accuracy discriminating HI from GCP, and discriminating HI from GAgP patients (both 81%). In contrast, the GAgP and GCP saliva samples seem to belong to the same metabolic space (60% predictive accuracy). Significantly lower levels (P < 0.05) of pyruvate, N-acetyl groups and lactate and higher levels (P < 0.05) of proline, phenylalanine, and tyrosine were found in GCP and GAgP patients compared with HI.

CONCLUSIONS

Within the limitations of this study, CGP and GAgP metabolomic profiles were not unequivocally discriminated through a NMR-based spectroscopic analysis of saliva.

Analysis of metabolic profiles of generalized aggressive periodontitis

BACKGROUND AND OBJECTIVE

The specific pathogenesis of generalized aggressive periodontitis (GAgP) has not yet been clarified, and few studies have focused on the association between GAgP and metabolomics. To elucidate the roles of metabolic profiles in the status of GAgP, this study aimed to identify the differential metabolic profiles between patients with GAgP and healthy controls using an untargeted metabolomic profiling method.

MATERIAL AND METHODS

Serum and gingival crevicular fluid samples were collected from healthy controls (n = 20) and patients with GAgP (n = 20) in this cross-sectional study. The relative levels of biomarkers in the samples were measured by gas chromatography-mass spectrometry. Principal components analysis and orthogonal partial least-squares discriminant analysis were used for statistical analysis. Metabolites were analysed qualitatively using the FiehnLib and NIST databases. Full-mouth probing depth and clinical attachment loss were recorded as indexes of periodontal disease.

RESULTS

A total of 349 metabolites were qualitatively detected in the gingival crevicular fluid samples, and 200 metabolites were detected in the serum samples. Compared with healthy controls, patients with GAgP showed significant increases in serum urea and allo-inositol levels. In contrast, glutathione, 2,5-dihydroxybenzaldehyde, adipic acid and 2-deoxyguanosine levels were decreased in patients with GAgP. In the gingival crevicular fluid samples, noradrenaline, uridine, α-tocopherol, dehydroascorbic acid, xanthine, galactose, glucose-1-phosphate and ribulose-5-phosphate levels were increased in patients with GAgP, while thymidine, glutathione and ribose-5-phosphate levels were decreased.

CONCLUSION

The metabolomics analysis by gas chromatography-mass spectrometry is an effective and minimally non-invasive way to differentiate the metabolites characteristic of patients with GAgP. Both serum and gingival crevicular fluid metabolomics are significantly different between patients with GAgP and healthy controls. These metabolic profiles have great potential in detecting GAgP and helping to understand its underlying mechanisms.

A bacterial metabolite ameliorates periodontal pathogen-induced gingival epithelial barrier disruption via GPR40 signaling

Several studies have demonstrated the remarkable properties of microbiota and their metabolites in the pathogenesis of several inflammatory diseases. 10-Hydroxy-cis-12-octadecenoic acid (HYA), a bioactive metabolite generated by probiotic microorganisms during the process of fatty acid metabolism, has been studied for its protective effects against epithelial barrier impairment in the intestines. Herein, we examined the effect of HYA on gingival epithelial barrier function and its possible application for the prevention and treatment of periodontal disease. We found that GPR40, a fatty acid receptor, was expressed on gingival epithelial cells; activation of GPR40 by HYA significantly inhibited barrier impairment induced by Porphyromonas gingivalis, a representative periodontopathic bacterium. The degradation of E-cadherin and beta-catenin, basic components of the epithelial barrier, was prevented in a GPR40-dependent manner in vitro. Oral inoculation of HYA in a mouse experimental periodontitis model suppressed the bacteria-induced degradation of E-cadherin and subsequent inflammatory cytokine production in the gingival tissue. Collectively, these results suggest that HYA exerts a protective function, through GPR40 signaling, against periodontopathic bacteria-induced gingival epithelial barrier impairment and contributes to the suppression of inflammatory responses in periodontal diseases.

On site visual detection of Porphyromonas gingivalis related periodontitis by using a magnetic-nanobead based assay for gingipains protease biomarkers

Porphyromonas gingivalis (P. gingivalis) is a pathogen causing periodontitis. A rapid assay is described for the diagnosis of periodontal infections related to P. gingivalis. The method is making use of gingipains, a group of P. gingivalis specific proteases as a detection biomarker. Magnetic-nanobeads were labeled with gingipain-specific peptide substrates and immobilized on a gold biosensing platform via gold-thiol linkage. As a result of this, the color of the gold layer turns black. Upon cleavage of the immobilized substrates by gingipains, the magnetic-nanobeads-peptide fragments were attracted by a magnet so that the golden surface color becomes visible again. This assay is highly sensitive and specific. It is capable of detecting as little as 49 CFU·mL^-1 of P. gingivalis within 30 s. Examination of periodontitis patients and healthy control saliva samples showed the potential of the assay. The simplicity and rapidity of the assay makes it an effective point-of-care device. Graphical abstract Schematic of the assay for the detection of P. gingivalis proteases as one of the promising biomarkers associated with periodontal diseases.

Polyamine patterns in plasma of patients with systemic lupus erythematosus and fever

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease with various clinical manifestations and serologic markers. In this study, we analyzed nine polyamine (PA) profiles of plasma from patients with SLE and healthy controls (HCs), and the relationship between the PA profiles and disease activity. PA alterations in plasma of 44 patients with SLE and fever were investigated using gas chromatography mass spectrometry (GC-MS) in selected ion monitoring mode using N-ethoxycarbonyl/ N-pentafluoropropionyl derivatives, and compared with those of 43 HCs. Patients with SLE and HCs showed differences in five of nine PA profiles. Among five changed PA levels, four PAs, namely N¹-acetylcadaverine, spermidine, N¹-acetylspermidine, and spermine, were dramatically decreased. However, the level of cadaverine was increased in patients with SLE. In the partial correlation with PA profiles and disease activity markers of SLE, several disease activity markers and nutritional markers were correlated with cadaverine, spermidine, and N ⁸-acetylspermidine. Thus, our results provide a comprehensive understanding of the relationship between PA metabolomics and disease activity markers in patients with SLE.

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution ¹H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis.

Distinct signatures of dental plaque metabolic byproducts dictated by periodontal inflammatory status

Onset of chronic periodontitis is associated with an aberrant polymicrobial community, termed dysbiosis. Findings regarding its etiology obtained using high-throughput sequencing technique suggested that dysbiosis holds a conserved metabolic signature as an emergent property. The purpose of this study was to identify robust biomarkers for periodontal inflammation severity. Furthermore, we investigated disease-associated metabolic signatures of periodontal microbiota using a salivary metabolomics approach. Whole saliva samples were obtained from adult subjects before and after removal of supragingival plaque (debridement). Periodontal inflamed surface area (PISA) was employed as an indicator of periodontal inflammatory status. Based on multivariate analyses using pre-debridement salivary metabolomics data, we found that metabolites associated with higher PISA included cadaverine and hydrocinnamate, while uric acid and ethanolamine were associated with lower PISA. Next, we focused on dental plaque metabolic byproducts by selecting salivary metabolites significantly decreased following debridement. Metabolite set enrichment analysis revealed that polyamine metabolism, arginine and proline metabolism, butyric acid metabolism, and lysine degradation were distinctive metabolic signatures of dental plaque in the high PISA group, which may be related to the metabolic signatures of disease-associated communities. Collectively, our findings identified potential biomarkers of periodontal inflammatory status and also provide insight into metabolic signatures of dysbiotic communities.

The dental calculus metabolome in modern and historic samples

INTRODUCTION

Dental calculus is a mineralized microbial dental plaque biofilm that forms throughout life by precipitation of salivary calcium salts. Successive cycles of dental plaque growth and calcification make it an unusually well-preserved, long-term record of host-microbial interaction in the archaeological record. Recent studies have confirmed the survival of authentic ancient DNA and proteins within historic and prehistoric dental calculus, making it a promising substrate for investigating oral microbiome evolution via direct measurement and comparison of modern and ancient specimens.

OBJECTIVE

We present the first comprehensive characterization of the human dental calculus metabolome using a multi-platform approach.

METHODS

Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) quantified 285 metabolites in modern and historic (200 years old) dental calculus, including metabolites of drug and dietary origin. A subset of historic samples was additionally analyzed by high-resolution gas chromatography-MS (GC-MS) and UPLC-MS/MS for further characterization of metabolites and lipids. Metabolite profiles of modern and historic calculus were compared to identify patterns of persistence and loss.

RESULTS

Dipeptides, free amino acids, free nucleotides, and carbohydrates substantially decrease in abundance and ubiquity in archaeological samples, with some exceptions. Lipids generally persist, and saturated and mono-unsaturated medium and long chain fatty acids appear to be well-preserved, while metabolic derivatives related to oxidation and chemical degradation are found at higher levels in archaeological dental calculus than fresh samples.

CONCLUSIONS

The results of this study indicate that certain metabolite classes have higher potential for recovery over long time scales and may serve as appropriate targets for oral microbiome evolutionary studies.