Saliva Metabolome Research Articles

Utilizing Saliva Metabolomics for Diagnosing Gastric Cancer and Exploring the Changes in Saliva Metabolites After Surgery.

Gastric cancer (GC) is a disease with high prevalence and mortality, but we lack convenient and accurate methods to screen for this disease. Thus, we aimed to search for some salivary biomarkers and explore changes in metabolites in patients' saliva after radical gastrectomy. A total of 152 subjects were divided into three groups (healthy group, GC group, and one-week postoperative group). After simple processing, saliva samples were analyzed by liquid chromatography-mass spectrometry. First, we used total ion chromatography and principal component analysis to determine the metabolite profiles. Next, t-test, partial least squares discriminant analysis, support vector machine, and receiver operating characteristics curve analysis were performed to identify biomarkers. Then, Fisher discriminant analysis and hierarchical clustering analysis were performed to determine the discriminating ability of biomarkers. Finally, we established a generalized linear model to predict GC based on biomarkers, and used bootstrapping for internal validation. Between the healthy and GC groups, we identified four biomarkers: lactic acid, kynurenic acid, 3-hydroxystachydrine, and S-(1,2,2-trichlorovinyl)-L-cysteine. We used stepwise regression to select five metabolites and develop a model with areas under the curve equal to 0.973 in the training dataset and 0.924 in the validation dataset. Between the GC and one-week postoperative groups, we found two differential metabolites: 19-hydroxyprostaglandin E2 and DG (14:0/0:0/18:2n6). Differential metabolites were observed among the three groups. GC could be initially diagnosed on the basis of detection of these biomarkers. Moreover, changes in salivary metabolites in postoperative patients could provide important insights for basic studies.

Saliva metabolomics: concepts and applications in oral disorders.

The purpose of this review was to present the basic concepts of metabolomics methodology and the use of saliva for diagnostic, prognostic, and predictive strategies. This review followed the focus in: "saliva metabolomics" and "oral diseases". The authors searched studies on PubMed database. The inclusion criteria were original studies and reviews that assessed metabolomics techniques. A descriptive analysis was performed considering the study design, approach system, clinical steps, and tools for the determination of profile or biomarkers metabolites, and the advantages and disadvantages. Metabolomic analyses use a combination of analytical instrumentation and informatic tools to provide information on metabolite characteristics. In this review we described different technologies applied and the advantages and limitations of each technique. Furthermore, in the literature search, we retrieved 25 studies that investigated saliva metabolites in oral diseases: 8 studies used targeted analysis and 17 untargeted metabolomics approaches. Most studies included patients with periodontal diseases, oral squamous cell carcinoma, and Sjögren Syndrome. The most frequently reported metabolites were glycine, leucine, phenylalanine, dipeptides, linoleic acid, arachidonic acid, tyrosine, choline, taurine, lactate, valine, and proline. Metabolomics analysis has emerged as a powerful tool for tumor diagnosis and to enhance tumor classification, including salivary gland tumors (SGTs). It also holds promise for developing personalized treatment plans and defining more precise prognostic categories. Metabolomics is the most functional and comprehensive technique for monitoring and understanding gene functions and identifying the biochemical state of an organism in response to genetic and environmental changes.

Targeted saliva metabolomics in Sjögren's syndrome

ObjectiveSjögren's Syndrome (SS) is a chronic inflammatory autoimmune exocrinopathy, and although, the role of metabolism in the autoimmune responses has been discussed in diseases such as lupus erythematosus, rheumatoid arthritis, psoriasis and scleroderma. There is a lack of information regarding the metabolic implications of SS. Considering that the disease affects primarily salivary glands; the aim of this study is to evaluate the metabolic changes in the salivary glands' microenvironment using a targeted metabolomics approach. MethodsThe saliva from 10 patients diagnosed with SS by the American-European consensus and 10 healthy volunteers was analyzed in an Ultra-high Performance Liquid Chromatograph Coupled Mass Spectrometry (UPLC-MS). ResultsThe results showed an increased concentration in SS of metabolites involved in oxidative stress such as lactate, alanine and malate, and amino acids involved in the growth and proliferation of T-cells, such as arginine, leucine valine and isoleucine. ConclusionsThese results revealed that is possible to differentiate the metabolic profile of SS and healthy individuals using a small amount of saliva, which in its turn may reflect the cellular changes observed in the microenvironments of damaged salivary glands from these patients.

Crystalloscopic analysis in detection of saliva metabolome specialties in elderly people

The purpose of this study was to assess the features of saliva crystallization in middle-aged and elderly people. The study included middle-aged (45-60 years old; n=63) and elderly (over 60 years old; n=78) men who signed an informed consent to participate. Crystalloscopic analysis of saliva in order to characterize the metabolic characteristics of the body and the composition of biological fluids. The intrinsic crystallization of mixed saliva was studied using the method of classical crystalloscopy. The structure index, crystallizability, facia destruction degree and the clearity of the marginal protein zone were determined. Each of the parameters was expressed on a 5-point scale. In general, the study made it possible to establish the presence of shifts in the crystallogenic properties of mixed saliva in the older age group (over 60 years old) relative to middle-aged people. Thus, the features of the crystalloscopic picture of biological fluid in elderly subjects included a decrease in the total crystallogenic potential of biological fluid, which was manifested in a decrease in crystallizability and the structural index of dehydrated saliva facies. In addition, the involutive shifts of the crystallograms were represented by an increase in the degree of destruction of facies and the expansion of the marginal zone of micropreparations. The revealed transformations of the crystallogenic properties of the biological medium allow us to speak about age-dependent characteristics of the component composition and physico-chemical parameters of saliva and, therefore, indirectly describe the involutive changes in the metabolome of this biological fluid.

Tear and Saliva Metabolomics in Evaporative Dry Eye Disease in Females.

Accurate diagnosis of dry eye disease (DED) is challenging, and even today there is no gold standard biomarker of DED. Hypothesis-free global metabolomic studies of tears from DED patients have great potential to discover metabolites and pathways affected in the pathophysiology of DED, and to identify possible future biomarkers. These metabolites and biomarkers could be important for diagnosing and monitoring disease as well as for new therapeutic targets and strategies. As DED is associated with dry mouth, this study aimed to perform metabolomic analyses of tears and saliva from patients with decreased tear film break-up time but normal Schirmer test, and age-matched controls with both tear production and stability within physiological range. We applied strict inclusion criteria to reduce sampling bias in the metabolomic analyses and selected only age-matched females with Schirmer test values between 10-15 mm/5 min. The tear film analysis arm included 19 patients (with tear film break-up time 0-5 s) and 12 controls (with tear film break-up time 10-30 s), while the salivary analysis arm consisted of a subset which included 18 patients and six controls. Metabolomic analyses were performed using liquid chromatography and high-resolution mass spectrometry. Analyses using a global database search detected a total of 56 metabolites in tear samples that were significantly different between the groups. Of these, several have known associations with DED. These metabolites are present in meibum and have anti-oxidative characteristics or associations with the ocular microbiome, and altered concentrations suggest that they may play a significant role in DED associated with decreased tear film stability. In saliva, hypotaurine levels were lower among patients with tear film instability. In this pilot study, we found different levels of several metabolites in patients with decreased tear film break-up time that may have associations with DED. Future studies are required to replicate our findings and clarify the exact roles of these metabolites.

Effects of medwakh smoking on salivary metabolomics and its association with altered oral redox homeostasis among youth

The use of alternative tobacco products, particularly medwakh, has expanded among youth in the Middle East and around the world. The present study is conducted to investigate the biochemical and pathophysiological changes caused by medwakh smoking, and to examine the salivary metabolomics profile of medwakh smokers. Saliva samples were collected from 30 non-smokers and 30 medwakh smokers and subjected to metabolomic analysis by UHPLC-ESI-QTOF-MS. The CRP and Glutathione Peroxidase 1 activity levels in the study samples were quantified by ELISA and the total antioxidant capacity (TAC) by TAC assay kits. Statistical measurements and thorough validation of data obtained from untargeted metabolomics identified 37 uniquely and differentially abundant metabolites in saliva of medwakh smokers. The levels of phthalate, L-sorbose, cytosine, uridine, alpha-hydroxy hippurate, and L-nicotine were noticeably high in medwakh smokers. Likewise, 20 metabolic pathways were differentially altered in medwakh smokers. This study identified a distinctive saliva metabolomics profile in medwakh smokers associated with altered redox homeostasis, metabolic pathways, antioxidant system, and CRP levels. The impact of the altered metabolites in medwakh smokers and their diagnostic utility require further research in large cohorts.

Characteristics of menstrual cycle disorder and saliva metabolomics of young women in a high-temperature environment.

Objective: Menstrual disorders induced by high-temperature environments, can seriously damage women's reproductive health and workability. The regulation mechanism underlying it is not yet to be elucidated. Saliva is an information-rich biological fluid that can reflect systemic diseases. Here, we investigated the characteristics of menstrual cycle disorders and saliva metabolomics to provide a deeper insight of the regulation mechanism of young women in high-temperature environments. Methods: Women from high and normal temperature areas of China were selected and divided into two groups-high-temperature (H group) and control (C group). A questionnaire survey was conducted in summer (July) to investigate the incidence rate of menstrual disorders, characteristics of the disorders, and factors influencing the risk of these disorders in different regions. Metabolomics was applied to analyze the characteristics of the salivary metabolites and neurotransmitters in the two groups of women with menstrual disorders. Results: The incidence rate of menstrual disorders was significantly higher in the H group than that in the C group (p < 0.05). High-temperature environment, stress, and sleep quality were identified as critical factors associated with menstrual disorders. Non-targeted saliva metabolomics identified 64 significantly different metabolites between two groups, which mainly enriched in metabolic pathways such as carbohydrate metabolism, membrane transport, digestive system, and nucleotide metabolism (p < 0.05). N-acetylneuraminic acid, MYO, and tyramine may be candidate markers for early diagnosis of menstrual disorders in high temperature environments. Metabolites may be involving in the acute-phase response during an inflammatory process, to affecting the reproductive system by influencing the HPA axis loop. Regulations about oocyte membrane production and the luteal functions would be exerted in menstrual disorders. Targeted metabolomics of neurotransmitters revealed increased expression of histamine (HA) and glutamine and decreased expression of 5-hydroxyindole acetic acid (5-HIAA) (p < 0.05). Conclusion: Menstrual disorder characteristics induced by high temperature environments were specific. Anxiety, sleep quality and temperature feeling were the key factors to the menstrual disorder. endocrine regulation mechanism and inflammatory reactions might contribute to the development of menstrual disorders through influencing the formation of the follicular cell membrane.

Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.

Maternal saliva metabolomics: the prediction and pathogenesis of fetal isolated congenital heart defect

Maternal saliva metabolomics: the prediction and pathogenesis of fetal isolated congenital heart defect

Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity

The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes-and whether markers can be found in different biofluids-are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum. Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques. Positive percent agreement of 1.00 between a partial least squares-discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified. In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.

Untargeted saliva metabolomics by liquid chromatography - mass spectrometry reveals markers of COVID-19 severity

Untargeted saliva metabolomics by liquid chromatography - mass spectrometry reveals markers of COVID-19 severity

Analysis of the saliva metabolic signature in patients with primary Sjögren's syndrome.

The saliva metabolome has been applied to explore disease biomarkers. In this study we characterized the metabolic profile of primary Sjögren's syndrome (pSS) patients and explored metabolomic biomarkers. This work presents a liquid chromatography-mass spectrometry-based metabolomic study of the saliva of 32 patients with pSS and 38 age- and sex-matched healthy adults. Potential pSS saliva metabolite biomarkers were explored using test group saliva samples (20 patients with pSS vs. 25 healthy adults) and were then verified by a cross-validation group (12 patients with pSS vs. 13 healthy adults). Metabolic pathways, including tryptophan metabolism, tyrosine metabolism, carbon fixation, and aspartate and asparagine metabolism, were found to be significantly regulated and related to inflammatory injury, neurological cognitive impairment and the immune response. Phenylalanyl-alanine was discovered to have good predictive ability for pSS, with an area under the curve (AUC) of 0.87 in the testing group (validation group: AUC = 0.75). Our study shows that salivary metabolomics is a useful strategy for differential analysis and biomarker discovery in pSS.

MAFFIN: metabolomics sample normalization using maximal density fold change with high-quality metabolic features and corrected signal intensities.

Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms. MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics datasets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (i) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (ii) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis and more confirmed significantly altered metabolites. The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction and demo data are available at https://github.com/HuanLab/MAFFIN. Other data in this work are available on request. Supplementary data are available at Bioinformatics online.

Evaluation of the Use of Saliva Metabolome as a Surrogate of Blood Metabolome in Assessing Internal Exposures to Traffic-Related Air Pollution.

In the omics era, saliva, a filtrate of blood, may serve as an alternative, noninvasive biospecimen to blood, although its use for specific metabolomic applications has not been fully evaluated. We demonstrated that the saliva metabolome may provide sensitive measures of traffic-related air pollution (TRAP) and associated biological responses via high-resolution, longitudinal metabolomics profiling. We collected 167 pairs of saliva and plasma samples from a cohort of 53 college student participants and measured corresponding indoor and outdoor concentrations of six air pollutants for the dormitories where the students lived. Grand correlation between common metabolic features in saliva and plasma was moderate to high, indicating a relatively consistent association between saliva and blood metabolites across subjects. Although saliva was less associated with TRAP compared to plasma, 25 biological pathways associated with TRAP were detected via saliva and accounted for 69% of those detected via plasma. Given the slightly higher feature reproducibility found in saliva, these findings provide some indication that the saliva metabolome offers a sensitive and practical alternative to blood for characterizing individual biological responses to environmental exposures.

The Oxidative Potential of Fine Particulate Matter and Biological Perturbations in Human Plasma and Saliva Metabolome.

Particulate oxidative potential may comprise a key health-relevant parameter of particulate matter (PM) toxicity. To identify biological perturbations associated with particulate oxidative potential and examine the underlying molecular mechanisms, we recruited 54 participants from two dormitories near and far from a congested highway in Atlanta, GA. Fine particulate matter oxidative potential ("FPMOP") levels at the dormitories were measured using dithiothreitol assay. Plasma and saliva samples were collected from participants four times for longitudinal high-resolution metabolic profiling. We conducted metabolome-wide association studies to identify metabolic signals with FPMOP. Leukotriene metabolism and galactose metabolism were top pathways associated with ≥5 FPMOP-related indicators in plasma, while vitamin E metabolism and leukotriene metabolism were found associated with most FPMOP indicators in saliva. We observed different patterns of perturbed pathways significantly associated with water-soluble and -insoluble FPMOPs, respectively. We confirmed five metabolites directly associated with FPMOP, including hypoxanthine, histidine, pyruvate, lactate/glyceraldehyde, and azelaic acid, which were implications of perturbations in acute inflammation, nucleic acid damage and repair, and energy perturbation. The unique metabolic signals were specific to FPMOP, but not PM mass, providing initial indication that FPMOP might constitute a more sensitive, health-relevant measure for elucidating etiologies related to PM2.5 exposures.

Saliva Metabolomics in Dry Mouth Patients with Head and Neck Cancer or Sjögren's Syndrome.

The etiology of dry mouth conditions is multi-faceted. Patients radiated after head and neck cancer (HNC) and those with primary Sjögren’s syndrome (pSS) share many of the same symptoms despite different causes. With the aim of better understanding the pathophysiology and biochemical processes behind dry mouth with different etiologies, we investigated the metabolic profile of 10 HNC patients, 9 pSS patients and 10 healthy controls using high-performance liquid chromatography-high resolution mass spectrometry (HPLC-MS) metabolomics. Principal component analysis (PCA) revealed different metabolic profiles when comparing all subjects included in the study. Both patient groups showed higher ratios of several pyrimidine nucleotides and nucleosides when compared to controls. This finding may indicate that purinergic signaling plays a role in dry mouth conditions. Moreover, significantly increased levels of DL-3-aminoisobutyric acid were found in HNC patients when compared to controls, and a similar tendency was observed in the pSS patients. Furthermore, a dysregulation in amino acid metabolism was observed in both patient groups. In conclusion, metabolomics analysis showed separate metabolic profiles for HNC and pSS patients as compared to controls that could be useful in diagnostics and for elucidating the different pathophysiologies. The demonstrated dysregulation of pyrimidine nucleotides and levels of metabolites derived from amino acids in the patient groups should be studied further.

A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019.

Background and AimsThe SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients.MethodsSaliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients.ResultsWe identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population.ConclusionThis is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

Potential of Prebiotic D-Tagatose for Prevention of Oral Disease.

Recent studies have shown phenotypic and metabolic heterogeneity in related species including Streptococcus oralis, a typical oral commensal bacterium, Streptococcus mutans, a cariogenic bacterium, and Streptococcus gordonii, which functions as an accessory pathogen in periodontopathic biofilm. In this study, metabolites characteristically contained in the saliva of individuals with good oral hygiene were determined, after which the effects of an identified prebiotic candidate, D-tagatose, on phenotype, gene expression, and metabolic profiles of those three key bacterial species were investigated. Examinations of the saliva metabolome of 18 systemically healthy volunteers identified salivary D-tagatose as associated with lower dental biofilm abundance in the oral cavity (Spearman’s correlation coefficient; r = -0.603, p = 0.008), then the effects of D-tagatose on oral streptococci were analyzed in vitro. In chemically defined medium (CDM) containing D-tagatose as the sole carbohydrate source, S. mutans and S. gordonii each showed negligible biofilm formation, whereas significant biofilms were formed in cultures of S. oralis. Furthermore, even in the presence of glucose, S. mutans and S. gordonii showed growth suppression and decreases in the final viable cell count in a D-tagatose concentration-dependent manner. In contrast, no inhibitory effects of D-tagatose on the growth of S. oralis were observed. To investigate species-specific inhibition by D-tagatose, the metabolomic profiles of D-tagatose-treated S. mutans, S. gordonii, and S. oralis cells were examined. The intracellular amounts of pyruvate-derived amino acids in S. mutans and S. gordonii, but not in S. oralis, such as branched-chain amino acids and alanine, tended to decrease in the presence of D-tagatose. This phenomenon indicates that D-tagatose inhibits growth of those bacteria by affecting glycolysis and its downstream metabolism. In conclusion, the present study provides evidence that D-tagatose is abundant in saliva of individuals with good oral health. Additionally, experimental results demonstrated that D-tagatose selectively inhibits growth of the oral pathogens S. mutans and S. gordonii. In contrast, the oral commensal S. oralis seemed to be negligibly affected, thus highlighting the potential of administration of D-tagatose as an oral prebiotic for its ability to manipulate the metabolism of those targeted oral streptococci.

Low-Dose Doxycycline Treatment Normalizes Levels of Some Salivary Metabolites Associated with Oral Microbiota in Patients with Primary Sjögren's Syndrome.

Saliva is a complex oral fluid, and plays a major role in oral health. Primary Sjögren’s syndrome (pSS), as an autoimmune disease that typically causes hyposalivation. In the present study, salivary metabolites were studied from stimulated saliva samples (n = 15) of female patients with pSS in a group treated with low-dose doxycycline (LDD), saliva samples (n = 10) of non-treated female patients with pSS, and saliva samples (n = 14) of healthy age-matched females as controls. Saliva samples were analyzed with liquid chromatography mass spectrometry (LC-MS) based on the non-targeted metabolomics method. The saliva metabolite profile differed between pSS patients and the healthy control (HC). In the pSS patients, the LDD treatment normalized saliva levels of several metabolites, including tyrosine glutamine dipeptide, phenylalanine isoleucine dipeptide, valine leucine dipeptide, phenylalanine, pantothenic acid (vitamin B5), urocanic acid, and salivary lipid cholesteryl palmitic acid (CE 16:0), to levels seen in the saliva samples of the HC. In conclusion, the data showed that pSS is associated with an altered saliva metabolite profile compared to the HC and that the LLD treatment normalized levels of several metabolites associated with dysbiosis of oral microbiota in pSS patients. The role of the saliva metabolome in pSS pathology needs to be further studied to clarify if saliva metabolite levels can be used to predict or monitor the progress and treatment of pSS.

Effects of acute heat stress on salivary metabolites in growing pigs: an analysis using nuclear magnetic resonance-based metabolomics profiling.

Heat stress (HS) causes adverse impacts on pig production and health. A potential biomarker of HS is required to predict its occurrence and thereby better manage pigs under HS. Information about the saliva metabolome in heat-stressed pigs is limited. Therefore, this study was aimed to investigate the effects of acute HS on the saliva metabolome and identify metabolites that could be used as potential biomarkers. Growing pigs (n = 6, 3 boars, and 3 gilts) were raised in a thermal neutral (TN; 25°C) environment for a 5-d adaptation period (CON). After adaptation, the pigs were first exposed to HS (30°C; HS30) and then exposed to higher HS (33°C; HS33) for 24 h. Saliva was collected after adaptation, first HS, and second HS, respectively, for metabolomic analysis using 1H-nuclear magnetic resonance spectroscopy. Four metabolites had significantly variable importance in the projection (VIP > 1; p < 0.05) different levels in TN compared to HS groups from all genders (boars and gilts). However, sex-specific characteristics affected metabolites (glutamate and leucine) by showing the opposite results, indicating that HS was less severe in females than in males. A decrease in creatine levels in males and an increase in creatine phosphate levels in females would have contributed to a protective effect from protein degradation by muscle damage. The results showed that HS led to an alteration in metabolites related to energy and protein. Protection from muscle damage may be attributed to the alteration in protein-related metabolites. However, energy-related metabolites showed opposing results according to sex-specific characteristics, such as sex hormone levels and subcutaneous fat layer. This study had shown that saliva samples could be used as a noninvasive method to evaluate heat-stressed pigs. And the results in this study could be contributed to the development of a diagnostic tool as a noninvasive biomarker for managing heat-stressed pigs.