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

Metabolomics reveals impact of seven functional foods on metabolic pathways in a gut microbiota model

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Metabolomics was employed to assess 7 functional foods impact on gut microbiota.

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Insights regarding how functional foods alter gut metabolic pathways is presented.

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Increased GABA production was observed in polyphenol rich functional food.

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Purine alkaloids served as direct substrate in microbiota metabolism.

Functional food defined as dietary supplements that in addition to their nutritional values, can beneficially modulate body functions becomes more and more popular but the reaction of the intestinal microbiota to it is largely unknown. In order to analyse the impact of functional food on the microbiota itself it is necessary to focus on the physiology of the microbiota, which can be assessed in a whole by untargeted metabolomics. Obtaining a detailed description of the gut microbiota reaction to food ingredients can be a key to understand how these organisms regulate and bioprocess many of these food components. Extracts prepared from seven chief functional foods, namely green tea, black tea, Opuntia ficus-indica (prickly pear, cactus pear), black coffee, green coffee, pomegranate, and sumac were administered to a gut consortium culture encompassing 8 microbes which are resembling, to a large extent, the metabolic activities found in the human gut. Samples were harvested at 0.5 and 24 h post addition of functional food extract and from blank culture in parallel and analysed for its metabolites composition using gas chromatography coupled to mass spectrometry detection (GC-MS). A total of 131 metabolites were identified belonging to organic acids, alcohols, amino acids, fatty acids, inorganic compounds, nitrogenous compounds, nucleic acids, phenolics, steroids and sugars, with amino acids as the most abundant class in cultures. Considering the complexity of such datasets, multivariate data analyses were employed to classify samples and investigate how functional foods influence gut microbiota metabolisms. Results from this study provided a first insights regarding how functional foods alter gut metabolism through either induction or inhibition of certain metabolic pathways, i.e. GABA production in the presence of higher acidity induced by functional food metabolites such as polyphenols. Likewise, functional food metabolites i.e., purine alkaloids acted themselves as direct substrate in microbiota metabolism.

Salivary Metabolomics Fingerprint of Chronic Apical Abscess with Sinus Tract: A Pilot Study

Chronic apical abscess (CAA) is a lesion of apical periodontitis mostly characterized by areas of liquefactive necrosis with disintegrating polymorphonuclear neutrophils surrounded by macrophages. Its presence leads to local bacterial infection, systemic inflammatory response, pain, and swelling. The use of a novel approach for the study of CAA, such as metabolomics, seems to be important since it has proved to be a powerful tool for biomarkers discovery which could give novel molecular insight on CAA. So, the aim of this study was to verify the possibility to identify the metabolic fingerprint of CAA through the analysis of saliva samples. Nineteen patients were selected for this study: eleven patients affected by CAA with a sinus tract constituted the study group whereas eight patients without clinical and radiographic signs of CAA formed the healthy control group. Saliva samples were collected from each subject and immediately frozen at −80°C. Metabolomic profiles were obtained using a gas chromatography/mass spectrometry instrument. Subsequently, in order to compare the two groups, a multivariate statistical model was built that resulted to be statistically significant. The class of metabolites characterizing the CAA patients was closely related to the bacterial catabolism, tissue necrosis, and presence of a sinus tract. These preliminary results, for the first time, indicate that saliva samples analyzed by means of GC/MS metabolomics may be useful for identifying the presence of CAA, leading to new insights into this disease.

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification

INTRODUCTION

Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models.

OBJECTIVES

We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis.

METHODS

We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks.

RESULTS

There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice.

CONCLUSION

The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm.

The subgingival microbiome associated with periodontitis in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a systemic disease, predisposing patients to other inflammatory conditions including periodontitis. The subgingival microbiome, a key player in periodontitis pathogenesis, is not well characterized in T2DM population. To better understand whether the subgingival microbiome is different between T2DM and systemically healthy, nondiabetic (ND) subjects, we performed a longitudinal analysis of the subgingival microbiome in T2DM patients (n = 15) compared with ND subjects (n = 16). Using metagenomic shotgun sequencing, we investigated the microbiome in the healthy periodontal state, periodontitis state, and resolved state after treatment. We found that in the periodontitis state, the shift in the subgingival microbiome from the healthy state was less prominent in T2DM compared with ND subjects, yet the clinical signs of disease were similar for both. Furthermore, we revealed highly correlated presence of pathogenic species in relative abundance not only in the periodontitis state, but also in the healthy state in T2DM, suggesting an elevated risk of progression to periodontitis in this cohort. We further investigated the functional potentials of the subgingival microbiome and identified a set of microbial marker genes associated with the clinical states. These genes were significantly enriched in 21 pathways, some of which are associated with periodontitis and some potentially link T2DM and periodontitis. This study identified the longitudinal changes of the subgingival microbiome associated with periodontitis in T2DM and suggests that T2DM patients are more susceptible to shifts in the subgingival microbiome toward dysbiosis, potentially due to impaired host metabolic and immune regulation.

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.

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.

Discriminating high-risk cervical Human Papilloma Virus infections with urinary biomarkers via non-targeted GC-MS-based metabolomics

Genital human papillomavirus (HPV) is the world’s most commonly diagnosed sexually transmitted infection, and high-risk HPV types are strongly linked to cervical dysplasia and carcinoma. Puerto Ricans are among the US citizens with higher HPV prevalence and lower screening rates and access to treatment. This bleak statistic was as a motivation to detect biomarkers for early diagnosis of HPV in this population. We collected both urine and cervical swabs from 43 patients attending San Juan Clinics. Cervical swabs were used for genomic DNA extractions and HPV genotyping with the HPV SPF10-LiPA25 kit, and gas chromatography-mass spectrometry (GC-MS) was employed on the urine-derived products for metabolomics analyses. We aimed at discriminating between patients with different HPV categories: HPV negative (HPV-), HPV positive with simultaneous low and high-risk infections (HPV+B) and HPV positive exclusively high-risk (HPV+H). We found that the metabolome of HPV+B is closer to HPV- than to HPV+H supporting evidence that suggests HPV co-infections may be antagonistic due to viral interference leading to a lower propensity for cervical cancer development. In contrast, metabolites of patients with HPV+H were significantly different from those that were HPV-. We identified three urinary metabolites 5-Oxoprolinate, Erythronic acid and N-Acetylaspartic acid that discriminate HPV+H cases from negative controls. These metabolites are known to be involved in a variety of biochemical processes related to energy and metabolism and may likely be biomarkers for HPV high-risk cervical infection. However, further validation should follow using a larger patient cohort and diverse populations to confirm our finding.

A novel pH-responsive quaternary ammonium chitosan-liposome nanoparticles for periodontal treatment

The aim of this study was to evaluate the antibacterial activity and cytocompatibility of novel pH-activated nanoparticles (NPs) in vitro and in vivo. The NPs were synthesized from a quaternary ammonium chitosan, i.e., N,N,N-trimethyl chitosan, a liposome, and doxycycline (TMC-Lip-DOX NPs). The cytocompatibility of the NPs was evaluated. The TMC-Lip-DOX NPs achieved superb inhibition of free mixed bacteria and biofilm formation. They also showed excellent biocompatibility with human periodontal ligament fibroblasts. Animal experiments showed that the NPs strongly inhibited biofilm formation and prevented alveolar bone absorption in vivo. All the results indicate that the TMC-Lip-DOX NPs have good potential for use in the treatment of periodontal and other inflammatory diseases.

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.

Circulation levels of acute phase proteins pentraxin 3 and serum amyloid A in atherosclerosis have correlations with periodontal inflamed surface area

Objectives One of the plausible mechanisms in the relationship between periodontitis and coronary artery disease (CAD) is the systemic inflammatory burden comprised of circulating cytokines/mediators related to periodontitis. This study aims to test the hypothesis that periodontal inflamed surface area (PISA) is correlated with higher circulating levels of acute phase reactants (APR) and pro-inflammatory cytokines/mediators and lower anti-inflammatory cytokines/mediators in CAD patients. Material and Methods Patients aged from 30 to 75 years who underwent coronary angiography with CAD suspicion were included. Clinical periodontal parameters (probing depth - PD, clinical attachment loss, and bleeding on probing - BOP) were previously recorded and participants were divided into four groups after coronary angiography: Group 1: CAD (+) with periodontitis (n=20); Group 2: CAD (+) without periodontitis (n=20); Group 3: CAD (-) with periodontitis (n=21); Group 4: CAD (-) without periodontitis (n = 16). Serum interleukin (IL) -1, -6, -10, tumor necrosis factor (TNF)-α, serum amyloid A (SAA), pentraxin (PTX) 3, and high-sensitivity C-reactive protein (hs-CRP) levels were measured with ELISA. Results Groups 1 and 3 showed periodontal parameter values higher than Groups 2 and 4 (p<0.0125). None of the investigated serum parameters were statistically significantly different between the study groups (p>0.0125). In CAD (-) groups (Groups 3 and 4), PISA has shown positive correlations with PTX3 and SAA (p<0.05). Age was found to predict CAD significantly according to the results of the multivariate regression analysis (Odds Ratio: 1.17; 95% Confidence Interval: 1.08-1.27; p<0.001). Conclusions Although age was found to predict CAD significantly, the positive correlations between PISA and APR in CAD (-) groups deserve further attention, which might depend on the higher PISA values of periodontitis patients. In further studies conducted in a larger population, the stratification of age groups would provide us more accurate results.

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.

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.