Urinary Metabolomics Study Research Articles

Impact of the TRPV2 Inhibitor on Advanced Heart Failure in Patients with Muscular Dystrophy: Exploratory Study of Biomarkers Related to the Efficacy of Tranilast.

Cardiomyopathy is the leading cause of death in patients with muscular dystrophy (MD). Tranilast, a widely used anti-allergic drug, has displayed inhibitory activity against the transient receptor potential cation channel subfamily V member 2 and improved cardiac function in MD patients. To identify urinary biomarkers that assess improved cardiac function after tranilast administration, we performed a urinary metabolomic study focused on oxidative fatty acids. Accompanying the clinical trial of tranilast, urine specimens were collected over 24 weeks from MD patients with advanced heart failure. Urinary levels of tetranor-PGDM (tetranor-prostaglandin D metabolite), a metabolite of prostaglandin D2, significantly decreased 12 weeks after tranilast administration and were correlated with BNP. These results suggest that prostaglandin-mediated inflammation, which increases with the pathological progression of heart failure in MD patients, was attenuated. Urinary prostaglandin E3 (PGE3) levels significantly increased 4 weeks after tranilast administration. There were positive correlations between the urinary levels of PGE3 and 8-hydroxy-2'-deoxyguanosine, an oxidative stress marker. High PGE3 levels may have a protective effect against cardiomyopathy in MD patients with high oxidative stress. Although further validation studies are necessary, urinary tetranor-PGDM and PGE3 levels may help the current understanding of the extent of advanced heart failure in patients with MD after tranilast administration.

The metabolites of de novo NAD+ synthesis are a valuable predictor of acute kidney injury.

Acute kidney injury (AKI) is often iatrogenic and potentially preventable. Reduced renal nicotinamide adenine dinucleotide (NAD+) is reported to increase the susceptibility of AKI. The present study explored the predictive value of urinary de novo NAD+ synthetic metabolites for AKI using two independent cohorts. The expression of de novo NAD+ synthetic enzymes in human kidney was examined by immunohistochemistry and single-cell transcriptomes. Urine samples were collected from two independent cohorts: the methotrexate (MTX) cohort with high-dose MTX treatment for lymphoma (n=189) and the liver transplantation cohort with orthotopic liver transplantation (n=49). Urinary metabolomics study of NAD+ de novo synthesis was performed by liquid chromatography with mass spectrometry, screening for AKI predictive biomarkers. Nephroseq database and immunohistochemistry were used to analyze kidney de novo NAD+ synthetic enzymes expression in AKI-susceptible conditions. Human proximal tubule was the main structure in the kidney that expressed the necessary enzymes for NAD+ de novo synthesis. In the MTX cohort, the urinary quinolinic acid (QA)/3-hydroxyanthranilic acid (3-OH AA) ratio before chemotherapy was significantly lower in those who developed AKI after chemotherapy compared with those who did not. This finding was consistent in the liver transplantation cohort. The area under the receiver-operating characteristic curve (AUC) of urinary QA/3-OH AA for AKI prediction was 0.749 and 0.729 in two cohorts, respectively. 3-Hydroxyanthranilic acid dioxygenase (HAAO), the enzyme catalyzing QA synthesis from 3-OH AA, decreased in AKI-susceptible diabetic kidneys. The human proximal tubules were important source of NAD+ from the de novo pathway. Reduced urinary QA/3-OH AA ratio, which possibly suggested decreased HAAO activity, could be a potential AKI predictive biomarker.

Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy

Hypertensive disorders of pregnancy account for 3% to 10% of maternal-fetal morbidity and mortality worldwide. This condition has been considered one of the leading causes of maternal deaths in developing countries, such as India. This study aimed to discover hypertensive disorders of pregnancy-specific candidate urine metabolites as markers for hypertensive disorders of pregnancy by applying integrated metabolomics and machine learning approaches. The targeted urinary metabolomics study was conducted in 70 healthy pregnant controls and 133 pregnant patients having hypertension as cases. Hypertensive disorders of pregnancy-specific metabolites for disease prediction were further extracted using univariate and multivariate statistical analyses. For machine learning analysis, 80% of the data were used for training (79 for hypertensive disorders of pregnancy and 42 for healthy pregnancy) and validation (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy), and 20% of the data were used for test sets (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy). The statistical analysis using an unpaired t test revealed 44 differential metabolites. Pathway analysis showed mainly that purine and thiamine metabolism were altered in the group with hypertensive disorders of pregnancy compared with the healthy pregnancy group. The area under the receiver operating characteristic curves of the 5 most predominant metabolites were 0.98 (adenosine), 0.92 (adenosine monophosphate), 0.89 (deoxyadenosine), 0.81 (thiamine), and 0.81 (thiamine monophosphate). The best prediction accuracies were obtained using 2 machine learning models (95% for the gradient boost model and 98% for the decision tree) among the 5 used models. The machine learning models showed higher predictive performance for 3 metabolites (ie, thiamine monophosphate, adenosine monophosphate, and thiamine) among 5 metabolites. The combined accuracies of adenosine from all models were 98.6 in the training set and 95.6 in the test set. Moreover, the predictive performance of adenosine was higher than other metabolites. The relative feature importance of adenosine was also observed in the decision tree and the gradient boost model. Among other metabolites, adenosine and thiamine metabolites were found to differentiate participants with hypertensive disorders of pregnancy from participants with healthy pregnancies; hence, these metabolites can serve as a promising noninvasive marker for the detection of hypertensive disorders of pregnancy.

Urinary Metabolomics Study on the Protective Role of Cocoa in Zucker Diabetic Rats via 1H-NMR-Based Approach.

Cocoa constitutes one of the richest sources of dietary flavonoids with demonstrated anti-diabetic potential. However, the metabolic impact of cocoa intake in a diabetic context remains unexplored. In this study, metabolomics tools have been used to investigate the potential metabolic changes induced by cocoa in type 2 diabetes (T2D). To this end, male Zucker diabetic fatty rats were fed on standard (ZDF) or 10% cocoa-rich diet (ZDF-C) from week 10 to 20 of life. Cocoa supplementation clearly decreased serum glucose levels, improved glucose metabolism and produced significant changes in the urine metabolome of ZDF animals. Fourteen differential urinary metabolites were identified, with eight of them significantly modified by cocoa. An analysis of pathways revealed that butanoate metabolism and the synthesis and degradation of branched-chain amino acids and ketone bodies are involved in the beneficial impact of cocoa on diabetes. Moreover, correlation analysis indicated major associations between some of these urine metabolites (mainly valine, leucine, and isoleucine) and body weight, glycemia, insulin sensitivity, and glycated hemoglobin levels. Overall, this untargeted metabolomics approach provides a clear metabolic fingerprint associated to chronic cocoa intake that can be used as a marker for the improvement of glucose homeostasis in a diabetic context.

Urinary Metabolomic Study in a Healthy Children Population and Metabolic Biomarker Discovery of Attention-Deficit/Hyperactivity Disorder (ADHD).

ObjectivesKnowledge of the urinary metabolomic profiles of healthy children and adolescents plays a promising role in the field of pediatrics. Metabolomics has also been used to diagnose disease, discover novel biomarkers, and elucidate pathophysiological pathways. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in childhood. However, large-sample urinary metabolomic studies in children with ADHD are relatively rare. In this study, we aimed to identify specific biomarkers for ADHD diagnosis in children and adolescents by urinary metabolomic profiling.MethodsWe explored the urine metabolome in 363 healthy children aged 1–18 years and 76 patients with ADHD using high-resolution mass spectrometry.ResultsMetabolic pathways, such as arachidonic acid metabolism, steroid hormone biosynthesis, and catecholamine biosynthesis, were found to be related to sex and age in healthy children. The urinary metabolites displaying the largest differences between patients with ADHD and healthy controls belonged to the tyrosine, leucine, and fatty acid metabolic pathways. A metabolite panel consisting of FAPy-adenine, 3-methylazelaic acid, and phenylacetylglutamine was discovered to have good predictive ability for ADHD, with a receiver operating characteristic area under the curve (ROC–AUC) of 0.918. A panel of FAPy-adenine, N-acetylaspartylglutamic acid, dopamine 4-sulfate, aminocaproic acid, and asparaginyl-leucine was used to establish a robust model for ADHD comorbid tic disorders and controls with an AUC of 0.918.

Distinct Urinary Metabolic Biomarkers of Human Colorectal Cancer.

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with high mortality rate due to its poor diagnosis in the early stage. Here, we report a urinary metabolomic study on a cohort of CRC patients (n =67) and healthy controls (n =21) using ultraperformance liquid chromatography triple quadrupole mass spectrometry. Pathway analysis showed that a series of pathways that belong to amino acid metabolism, carbohydrate metabolism, and lipid metabolism were dysregulated, for instance the glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism, glycolysis, and TCA cycle. A total of 48 differential metabolites were identified in CRC compared to controls. A panel of 12 biomarkers composed of chenodeoxycholic acid, vanillic acid, adenosine monophosphate, glycolic acid, histidine, azelaic acid, hydroxypropionic acid, glycine, 3,4-dihydroxymandelic acid, 4-hydroxybenzoic acid, oxoglutaric acid, and homocitrulline were identified by Random Forest (RF), Support Vector Machine (SVM), and Boruta analysis classification model and validated by Gradient Boosting (GB), Logistic Regression (LR), and Random Forest diagnostic model, which were able to discriminate CRC subjects from healthy controls. These urinary metabolic biomarkers provided a novel and promising molecular approach for the early diagnosis of CRC.

HBM4EU Chromates Study: Urinary Metabolomics Study of Workers Exposed to Hexavalent Chromium.

Exposure to hexavalent chromium Cr(VI) may occur in several occupational activities, placing workers in many industries at risk for potential related health outcomes. Untargeted metabolomics was applied to investigate changes in metabolic pathways in response to Cr(VI) exposure. We obtained our data from a study population of 220 male workers with exposure to Cr(VI) and 102 male controls from Belgium, Finland, Poland, Portugal and the Netherlands within the HBM4EU Chromates Study. Urinary metabolite profiles were determined using liquid chromatography mass spectrometry, and differences between post-shift exposed workers and controls were analyzed using principal component analysis. Based on the first two principal components, we observed clustering by industrial chromate application, such as welding, chrome plating, and surface treatment, distinct from controls and not explained by smoking status or alcohol use. The changes in the abundancy of excreted metabolites observed in workers reflect fatty acid and monoamine neurotransmitter metabolism, oxidative modifications of amino acid residues, the excessive formation of abnormal amino acid metabolites and changes in steroid and thyrotropin-releasing hormones. The observed responses could also have resulted from work-related factors other than Cr(VI). Further targeted metabolomics studies are needed to better understand the observed modifications and further explore the suitability of urinary metabolites as early indicators of adverse effects associated with exposure to Cr(VI).

A Population-Based Urinary and Plasma Metabolomics Study of Environmental Exposure to Cadmium

A Population-Based Urinary and Plasma Metabolomics Study of Environmental Exposure to Cadmium

Urinary metabolomics study of vancomycin-associated nephrotoxicity based on ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry.

Drug-induced nephrotoxicity is widespread and seriously affects human health. Vancomycin is a classical glycopeptide antibiotic. Vancomycin is widely used for severe infections caused by Gram-positive bacteria, especially methicillin-resistant Staphylococcus aureus but its obvious nephrotoxicity affects the safety of its clinical application. However, the etiology of vancomycin induced kidney injury is not well understood. This study aimed to explore the potential mechanism of vancomycin-induced nephrotoxicity in rats. Vancomycin (400mgkg-1) was used to establish kidney injury models in rats. A metabonomic approach was employed using ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) to delineate metabolic alterations. As a result, 15, 22, and 37 biomarkers were identified in urine samples from the treatment group compared to the control model on D2, D4, and D7, respectively. Changes in the levels of these metabolites indicated that amino acid metabolism and energy metabolism were disturbed in rats with vancomycin-associated nephrotoxicity. This study revealed the kidney effect of vancomycin, which may provide novel and promising research approaches to vancomycin-induced renal toxicity.

Urinary Metabolomics Study of Vancomycin-Associated Nephrotoxicitybased on UHPLC-QTOF-MS/MS

Urinary Metabolomics Study of Vancomycin-Associated Nephrotoxicitybased on UHPLC-QTOF-MS/MS

Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease.

Bicuspid aortic valve (BAV) is the most common congenital heart defect responsible for valvular and aortic complications in affected patients. Causes and mechanisms of this pathology are still elusive and thus the lack of early detection biomarkers leads to challenges in its diagnosis and prevention of associated cardiovascular anomalies. The aim of this study was to explore the potential use of urine Nuclear Magnetic Resonance (NMR) metabolomics to evaluate a molecular fingerprint of BAV. Both multivariate and univariate statistical analyses were performed to compare the urinary metabolome of 20 patients with BAV with that of 24 matched controls. Orthogonal partial least squared discriminant analysis (OPLS-DA) showed statistically significant discrimination between cases and controls, suggesting seven metabolites (3-hydroxybutyrate, alanine, betaine, creatine, glycine, hippurate, and taurine) as potential biomarkers. Among these, glycine, hippurate and taurine individually displayed medium sensitivity and specificity by receiver operating characteristic (ROC) analysis. Pathway analysis indicated two metabolic pathways likely perturbed in BAV subjects. Possible contributions of gut microbiota activity and energy imbalance are also discussed. These results constitute encouraging preliminary findings in favor of the use of urine-based metabolomics for early diagnosis of BAV.

Urinary Metabolomics Study of Vancomycin-Associated Nephrotoxicity Based on UHPLC-QTOF-MS/MS

Urinary Metabolomics Study of Vancomycin-Associated Nephrotoxicity Based on UHPLC-QTOF-MS/MS

Serum biomarkers of the calcium-deficient rats identified by metabolomics based on UPLC/Q-TOF MS/MS

BackgroundWe previously identified the urinary biomarkers to diagnose calcium deficiency and nutritional rickets by ultra-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF MS/MS). To find biomarkers of calcium deficiency and further confirm these biomarkers in serum, we performed serum metabolomics analysis of calcium-deficient rats.MethodsA calcium-deficient rat model was established with a low-calcium diet for 12 weeks. Serum metabolomics based UPLC/Q-TOF MS/MS and multivariate statistical analysis was performed to identify the alterations in metabolites associated with calcium deficiency in rats.ResultsBone mineral density, serum parathyroid hormone and alkaline phosphatase were significantly decreased in the low-calcium diet group (LCG) compared to the normal calcium diet group (NCG). Serum metabolic-profiling analysis could definitively distinguish between the LCG and NCG and identified 24 calcium-deficient biomarkers. Three metabolites (indoxyl sulfate, phosphate, and taurine) of the 24 biomarkers were found in our previous urinary metabolomics study of rats with a calcium deficiency and nutritional rickets. The areas under the curve (AUCs) of these three biomarkers were greater than 0.8, and the combination of any two biomarkers was higher than 0.95.ConclusionDietary calcium deficiency induced the alterations of metabolites in the serum of rats, and the three identified biomarkers had relatively high diagnostic values for calcium deficiency in rats.

Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes

Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.

Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS.

Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body’s water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC×GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine.

Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in Multisample Studies.

Data-independent acquisition mass spectrometry (DIA-MS) is essential for information-rich spectral annotations in untargeted metabolomics. However, the acquired MS2 spectra are highly complex, posing significant annotation challenges. We have developed a correlation-based deconvolution (CorrDec) method that uses ion abundance correlations in multisample studies using DIA-MS as an update of our MS-DIAL software. CorrDec is based on the assumption that peak intensities of precursor and fragment ions correlate across samples and exploits this quantitative information to deconvolute complex DIA spectra. CorrDec clearly improved deconvolution of the original MS-DIAL deconvolution method (MS2Dec) in a dilution series of chemical standards and a 224-sample urinary metabolomics study. The primary advantage of CorrDec over MS2Dec is the ability to discriminate coeluting low-abundance compounds. CorrDec requires the measurement of multiple samples to successfully deconvolute DIA spectra; however, our randomized assessment demonstrated that CorrDec can contribute to studies with as few as 10 unique samples. The presented methodology improves compound annotation and identification in multisample studies and will be useful for applications in large cohort studies.

A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry.

Human urine gives evidence of the metabolism in the body and contains different metabolites at various concentrations. A number of analytical techniques including mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been used to obtain metabolites levels in urine samples. However, gas chromatography-mass spectrometry (GC-MS) is one of the most widely used techniques for urinary metabolomics studies due to its higher sensitivity, resolution, reproducibility, reliability, relatively low cost and ease of operation compared to liquid chromatography-mass spectrometry and NMR. This review looks at various aspects of urine preparation prior to analysis by GC-MS including sample storage, urease pretreatment, derivatization, use of internal standard and quality control samples for data correction. In addition, most common types of inlet liners, ionization techniques and columns are discussed and a summary of mass analyzers are also highlighted. Lastly, the role of retention index in metabolite identification and data normalization methods are presented. The purpose of this review is summarizing methods of sample storage, pretreatment, and GC-MS analysis that are mostly used in urine metabolomics studies. Specific emphasis is given to the critical steps within the GC-MS urine metabolomics that those new to this field need to be aware of and the remaining challenges that require further attention and studies.

A urinary metabolomic study from subjects after long-term occupational exposure to low concentration acrylamide using UPLC-QTOF/MS

Because long-term occupational exposure to low concentrations of acrylamide (ACR) has the potential to cause neurological damage, it is important to identify biomarkers that can be used to evaluate this risk. In the present study, urine metabolomics of the ACR-exposed and non-exposed groups to identify potential metabolites was carried out using ultra high performance liquid chromatography coupled with quadrupole time of flight mass spectrometry. Serum biochemical indexes of the exposed and non-exposed groups were also determined. Principal component analysis showed a differential separation between exposed group and non-exposed group and a total of 7 metabolites were identified in positive and negative ionization modes; Area under curve of anthranilic acid, β-guanidinopropionic acid and mesobilirubinogen were 0.980, 0.843 and 0.801 respectively and these metabolites showed high sensitivity and specificity. The 13 biochemical indexes were divided into three classes based on physiological functions. Only biomarkers of dysregulated liver function including alanine aminotransferase, aspartic transaminase, total bilirubin, direct bilirubin and triglyceride were significantly higher in the exposed group than in the non-exposed group. This study identifies important related metabolic changes in the bodies of workers after long-term occupational exposure to low concentration ACR and suggests new biomarkers of nervous system injury caused by ACR. The study also provides a sound basis for exploring the biochemical mechanisms and metabolic pathways of nervous system toxicity caused by ACR.

A urinary metabolomics study of a Polish subpopulation environmentally exposed to arsenic

BackgroundAlmost every organ in the human body can be affected by arsenic (As) exposure associated with various industrial processes, as well as with contaminated food, drinking water and polluted air. Much is known about high exposure to inorganic As but there is little data on the metabolic changes connected to a low exposure e.g. in people living in smelter areas. ObjectivesThe objectives of the study were: (1) characterise urinary concentration of total arsenic (AsT) in Polish inhabitants of the vicinity of a copper smelter area, (2) speciation analysis of various forms of arsenic in girls (GL), boys (BL), women (WL) and men (ML) with a slightly elevated AsT concentration and age/sex matched groups with a substantially higher AsT concentration, (GH, BH, WH and MH – respectively), (3) comparison of metabolomics profiles of urine between the age/sex matched people with low and high AsT concentrations. MethodsUrine samples were analysed for total arsenic and its chemical forms (AsIII; AsV, methylarsonic acid, dimethylarsinic acid, arsenobetaine) using HPLC-ICP-MS. Untargeted metabolomics analysis of the urine samples was performed using UPLC system connected to Q-TOF-MS equipped with an electrospray source. The XCMS Online program was applied for feature detection, retention time correction, alignment, statistics, annotation and identification. Potentially identified compounds were fragmented and resulting spectra were compared to the spectra in the Human Metabolome Database. ResultsUrine concentration of AsT was, as follows: GL 16.40 ± 0.83; GH 115.23 ± 50.52; BL 16.48 ± 0.83; BH 95.00 ± 50.03; WL 16.93 ± 1.21; WH 170.13 ± 96.47; ML 16.91 ± 1.20; MH 151.71 ± 84.31 μg/l and percentage of arsenobetaine in AsT was, as follows: GL 65.5 ± 13.8%, GH 87.2 ± 4.7%, BL 59.8 ± 12.5%, BH 90.5 ± 2.4%, WL 50.8 ± 14.1%, WH 90.4 ± 3.5%, ML 53.3 ± 10.0%, MH 74.6 ± 20.2%.In the people with low and high AsT concentrations there were significant differences in the intensity of signal (is.) from numerous compounds being metabolites of neurotransmitters, nicotine and hormones transformation (serotonin in the girls and women; catecholamines in the girls, boys and women; mineralocorticoids and glucocorticoids in the boys, androgens in the women and men and nicotine in the boys, women and men). These changes might have been associated with higher is. from metabolites of leucine, tryptophan, purine degradation (in the GH, WH), urea cycle (in the WH and MH), glycolysis (in the WH) and with lower is. from metabolites of tricarboxylic acid cycle (in the BH) in comparison with low AsT matched groups. In the MH vs. ML higher is. from metabolite of lipid peroxidation (4-hydroxy-2-nonenal) was observed. Additionally, the presence of significant differences was reported in is. from food components metabolites, which might have modulated the negative effects of As (vitamin C in the girls, boys and men, vitamin B6 in the girls, boys and women as well as phenolic compounds in the boys and girls). We hypothesize that the observed higher is. from metabolites of sulphate (in MH) and glucoronate degradation (in BH, WH and MH) than in the matched low AsT groups may be related to the impaired glucuronidation and sulfonation and higher is. from catecholamines, nicotine and hormones. ConclusionOur results indicated that even a low exposure to As is associated with metabolic changes and that urine metabolomics studies could be a good tool to reflect their wide spectrum connected to specific environmental exposure to As, e.g. in smelter areas.

Pharmacodynamic and urinary metabolomics studies on the mechanism of Schisandra polysaccharide in the treatment of Alzheimer's disease.

Schisandra chinensis (Turcz.) Baill is produced mainly in northeast China, Korea and Japan. Its fruit has been used in food as a nutritional and functional ingredient for centuries. Polysaccharide is an important chemical component in Schisandra. Previous studies have shown that Schisandra polysaccharide (SCP) could be used to improve cognitive function clinically and treat age-related neurodegenerative disorders. In this study, a urinary metabolomics method based on ultra-high-performance liquid chromatography combined with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was established to investigate the change of endogenous metabolites in an amyloid β-protein (Aβ) 25-35-induced Alzheimer's disease (AD) rat model. Meanwhile, levels of 9 neurotransmitters were evaluated with ultrahigh-performance liquid chromatography-triple-quadrupole mass spectrometry (UHPLC-TQ-MS) to explore the therapeutic mechanisms of SCP on the AD rat model. Additionally, the synthesis of phosphorylated tau protein (p-tau), acetylcholinesterase (AchE) activity and oxidative damage in the brain of the AD rats were assessed using glycogen synthase kinase-3β (GSK3β), AchE and antioxidant assays, NOS (nitric oxide synthase) and SOD (superoxide dismutase), respectively. The results indicated that the AD model was established successfully and the inducement of Aβ25-35 caused the phosphorylation of tau protein and the deposition of Aβ. In the AD model rats, the levels of AchE, GSK-3β and NOS were significantly elevated and SOD activity was reduced. In the hippocampus of the model rats, the contents of γ-aminobutyric acid, acetylcholine, glycine, norepinephrine, taurine, serotonin and dopamine were significantly decreased and the contents of glutamate and aspartic acid were increased significantly. However, SCP could reduce the degree of phosphorylation of tau protein, the deposition of Aβ and oxidative damage and reverse these changes of neurotransmitters in the AD rats. In a metabolomics study, a total of 38 metabolites were finally identified as potential biomarkers of AD and all of them had a significant recovery compared with the AD model after SCP administration. Metabolomics studies have shown that SCP plays a role in protecting the central nervous system, regulating intestinal microbial metabolism, regulating energy metabolism, and promoting antioxidant effects by regulating the levels of endogenous metabolites in related pathways. This is first report of the use of urine metabolomics combined with the evaluation of 9 neurotransmitter levels to investigate the mechanism of SCP on the treatment of AD.