Urinary Metabolomic Analysis Research Articles

Urinary metabolomics analysis to reveal metabolic mechanism of guanxinning injection on heart failure with renal dysfunction

Consistently, the multiple heart-kidney interactions make pharmaceutical research for cardiorenal syndrome difficult and complex. Guanxinning Injection (GXN) has been reported to provide unique advantage for treating cardiac and renal diseases compared to typical monotherapies. However, the protection mechanism of GXN is largely unknown. This study explored the acting mechanism of GXN on heart failure with renal dysfunction from a metabolic perspective. Transverse aortic constriction (TAC) surgery was performed on C57/BL/6 mice to induce heart failure with renal dysfunction. Using telmisartan as a positive control, GXN treatment was applied during the 12th to 16th week after TAC. Cardiac function and structure were examined using M-mode echocardiography, and renal function was evaluated via representative biochemical parameters and hematoxylin-eosin staining. Moreover, untargeted metabolomic analyses of urine were conducted to screen for differential substances associated with the cardiorenal protection effect of GXN. As a result, GXN provided good cardioprotective effects on left ventricular ejection fraction elevation, fractional shortening, internal diastolic, and mass maintenance. GXN also reduced TAC-induced elevation of blood urea nitrogen, and serum Cystatin C and relieved kidney pathological damage. Metabolomic analyses identified 21 differential metabolites in the TAC model group. Ten metabolites involving the metabolic pathways of carnitine synthesis, valine, leucine and isoleucine degradation, and glutamate metabolism, taurine and hypotaurine metabolism, tryptophan metabolism, arginine and proline metabolism, and purine metabolism were restored by GXN. The main cardiorenal protection mechanism of GXN was found to be related to energy metabolism and oxidative stress. Taken together, this study provides the first evidence of the metabolic protection mechanism of GXN on heart failure with renal dysfunction for the first time and provides a research basis for the application of GXN in CRS-2 pharmaceuticals.

Dietary rumen-protected choline supplementation regulates blood biochemical profiles and urinary metabolome and improves growth performance of growing lambs

This study aimed to assess the growth performance and blood metabolites, as well as metabolic profiles in the urine of lambs fed on dietary rumen-protected choline (RPC). Thirty-six Dorper × Hu lambs weighing approximately 20 kg were equally assigned to three groups, and fed on three diets supplemented with different RPC concentrations (0, 0.25% and 0.75%) for 45 days. Supplementation of RPC significantly increased average daily gain (ADG) and decreased feed-to-gain ratio (F/G) of lambs (p < 0.05). Dietary RPC was significantly associated with elevated plasma high-density lipoprotein (HDL) and suppressed low-density lipoprotein (LDL) levels when compared to the control group (p < 0.05). Moreover, concentrations of very-low-density lipoprotein (VLDL) exhibited an increasing trend (p = 0.065), whereas β-hydroxybutyrate (BHBA) levels decreased (p = 0.086) in plasma. Analysis of urine metabolome revealed that RPC supplementation significantly suppressed urinary concentrations of pyruvate (p < 0.05), while increased urinary concentrations of trimethylamine oxide, p-cresol, phenylacetylglycine and hippurate (p < 0.05). These findings suggest that RPC supplementation can promote weight gain, alter plasma lipid metabolism and modify urinary metabolome which is correlated with energy metabolism, lipid metabolism and intestinal microbial metabolism in lambs. In conclusion, based on our findings, we recommend 0.25% RPC as a supplement for growing lambs.

Identification of the metabolic signatures of prostate cancer by mass spectrometry-based plasma and urine metabolomics analysis.

Prostate cancer (PCa) is one of the most commonly diagnosed cancers among men which is associated with profound metabolic changes. Systematic analysis of the metabolic alterations and identification of new biomarkers may benefit PCa diagnosis and a deep understanding of the pathological mechanism. The purpose of this study was to determine the metabolic features of PCa. Plasma and urine metabolites from 89 prostate cancer (PCa) patients, 84 benign prostatic hyperplasia (BPH) patients, and 70 healthy males were analyzed using LC-MS/MS and GC-MS. The Orthogonalised Partial Least Squares Discriminant Analysis (OPLS-DA) was used to find the significantly changed metabolites. The clinical value of the candidate markers was examined by receiver operating characteristic curve analysis and compared with prostate-specific antigen (PSA). Multivariate statistical analyses found a series of altered metabolites, which related to the urea cycle, tricarboxylic acid cycle (TCA), fatty acid metabolism, and the glycine cleavage system. Plasma Glu/Gln showed the highest predictive value (AUC = 0.984) when differentiating PCa patients from healthy controls, with a higher sensitivity than PSA (96.6% vs. 94.4%). Both Glu/Gln and PSA displayed a low specificity when differentiating PCa patients from BPH patients (<53.2%), while the combination of Glu/Gln and PSA can further increase the diagnostic specificity to 66.9%. The present study showed the metabolic features of PCa, provided strong evidence that the amide nitrogen and the energy metabolic pathways could be a valuable source of markers for PCa. Several candidate markers identified in this study were clinically valuable for further assessment.

Relationship Between Inflammation and Metabolism in Patients With Newly Presenting Rheumatoid Arthritis.

BackgroundSystemic inflammation in rheumatoid arthritis (RA) is associated with metabolic changes. We used nuclear magnetic resonance (NMR) spectroscopy–based metabolomics to assess the relationship between an objective measure of systemic inflammation [C-reactive protein (CRP)] and both the serum and urinary metabolome in patients with newly presenting RA.MethodsSerum (n=126) and urine (n=83) samples were collected at initial presentation from disease modifying anti-rheumatic drug naïve RA patients for metabolomic profile assessment using 1-dimensional 1H-NMR spectroscopy. Metabolomics data were analysed using partial least square regression (PLS-R) and orthogonal projections to latent structure discriminant analysis (OPLS-DA) with cross validation.ResultsUsing PLS-R analysis, a relationship between the level of inflammation, as assessed by CRP, and the serum (p=0.001) and urinary (p<0.001) metabolome was detectable. Likewise, following categorisation of CRP into tertiles, patients in the lowest CRP tertile and the highest CRP tertile were statistically discriminated using OPLS-DA analysis of both serum (p=0.033) and urinary (p<0.001) metabolome. The most highly weighted metabolites for these models included glucose, amino acids, lactate, and citrate. These findings suggest increased glycolysis, perturbation in the citrate cycle, oxidative stress, protein catabolism and increased urea cycle activity are key characteristics of newly presenting RA patients with elevated CRP.ConclusionsThis study consolidates our understanding of a previously identified relationship between serum metabolite profile and inflammation and provides novel evidence that there is a relationship between urinary metabolite profile and inflammation as measured by CRP. Identification of these metabolic perturbations provides insights into the pathogenesis of RA and may help in the identification of therapeutic targets.

Urinary GC-MS steroid metabotyping in treated children with congenital adrenal hyperplasia

Abstract Background Treatment of children with classic congenital adrenal hyperplasia (CAH) is a difficult balance between hypercortisolism and hyperandrogenism. Biochemical monitoring of treatment is not well defined. Objective Cluster analysis of the urinary steroid metabolome obtained by targeted gas chromatography–mass spectrometry (GC–MS) for treatment monitoring of children with CAH. Methods We evaluated 24-h urinary steroid metabolome analyses of 109 prepubertal children aged 7.0 ± 1.6 years with classic CAH due to 21-hydroxylase deficiency treated with hydrocortisone and fludrocortisone. 24-h urinary steroid metabolite excretions were transformed into CAH-specific z-scores. Subjects were divided into groups (metabotypes) by k-means clustering algorithm. Urinary steroid metabolome and clinical data of patients of each metabotype were analyzed. Results Four unique metabotypes were generated. Metabotype 1 (N = 21 (19%)) revealed adequate metabolic control with low cortisol metabolites (mean: −0.57z) and suppressed androgen and 17α-hydroxyprogesterone (17OHP) metabolites (−0.79z). Metabotype 2 (N = 23 (21%)) showed overtreatment consisting of a constellation of elevated urinary cortisol metabolites (0.62z) and low metabolites of androgens and 17OHP (−0.75z). Metabotype 3 (N = 32 (29%)) demonstrated undertreated patients with low cortisol metabolites (−0.69z) and elevated metabolites of androgens and 17OHP (0.50z). Metabotype 4 (N = 33 (30%)) presented patients with treatment failure reflected by unsuppressed androgen- and 17OHP metabolites (0.71z) despite elevated urinary cortisol metabolites (0.39z). Conclusion Metabotyping, which means grouping metabolically similar individuals, helps to monitor treatment of children with CAH using GC–MS urinary steroid metabolome analysis. This method allows classification in adequately-, over-, or undertreated children as well as identification of patients with treatment failure.

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

PD06-11 CHANGES IN URINE METABOLITES AFTER ONABOTULINUMTOXIN-A INJECTIONS FOR REFRACTORY OVERACTIVE BLADDER

You have accessJournal of UrologyUrodynamics/Lower Urinary Tract Dysfunction/Female Pelvic Medicine: Female Incontinence: Therapy I (PD06)1 Sep 2021PD06-11 CHANGES IN URINE METABOLITES AFTER ONABOTULINUMTOXIN-A INJECTIONS FOR REFRACTORY OVERACTIVE BLADDER Laura Tellechea, Samantha Freeman, Ilir Agalliu, Melissa Laudano, Sylvia Suadicani, and Nitya Abraham Laura TellecheaLaura Tellechea More articles by this author , Samantha FreemanSamantha Freeman More articles by this author , Ilir AgalliuIlir Agalliu More articles by this author , Melissa LaudanoMelissa Laudano More articles by this author , Sylvia SuadicaniSylvia Suadicani More articles by this author , and Nitya AbrahamNitya Abraham More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001974.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Overactive Bladder (OAB) is diagnosed based on symptoms and often treated based on patient preference. A urine biomarker could phenotype refractory OAB and identify those who would benefit from chemodenervation vs neuromodulation. Metabolomics is the analysis of metabolic profiles. Urine metabolites may be potential biomarkers for OAB yet little is known about the effect of third line treatments like bladder onabotulinumtoxin-A (BTX-A) on the urinary metabolome. Our study objective was to evaluate changes in the urinary metabolome and correlate with OAB-V8 symptom score change after BTX-A injections in women with refractory OAB. METHODS: Women over 18 years with non-neurogenic refractory OAB were recruited from our female urology clinic. OAB-V8 questionnaires and urine samples were collected before and after 100 units BTX-A injection. Samples were aliquoted, frozen on dry ice, and stored in a -80ºC freezer. Urine metabolomic analysis was conducted by Human Metabolome Technology Inc., Tsuruoka, Japan, and data was expressed as percent change from pre-treatment values. These were correlated with improvement in OAB-V8 Scores categorized as: no improvement (<8), mild improvement (8-16), marked improvement (>17). Change in metabolite levels was compared between groups using ANOVA, and correlations between metabolites and OAB-V8 scores evaluated using linear regression. RESULTS: Fifty-four urine samples were collected from 21 women. Mean age was 56 (46-56). 3 patients had diabetes, 10 had chronic pain disorders, 8 had prior pelvic surgery. Of 42 metabolites, Quinic Acid (p=0.01), N-o-Toluoyl-glycine-1-Phenaceturic acid-1 (p=0.02) and Adenosine (p=0.01) levels were significantly lower and N8-Acetylspermidine (p=0.01) levels significantly higher in the marked improvement vs. no improvement group. In linear regression models, N8-acetylspermidine (p=0.02) and Quinic Acid levels (p=0.009) significantly correlated with improvement in OABV-8 scores, Figure 1. CONCLUSIONS: Bladder BTX-A injection changes the urinary metabolome, with corresponding OAB score improvement. Further investigation of urine metabolites could provide objective data and biomarkers to follow OAB treatment response, and ultimately identify a subset of women who would benefit from certain third line therapies. Source of Funding: Urology Department © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e104-e104 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Laura Tellechea More articles by this author Samantha Freeman More articles by this author Ilir Agalliu More articles by this author Melissa Laudano More articles by this author Sylvia Suadicani More articles by this author Nitya Abraham More articles by this author Expand All Advertisement Loading ...

EXHES study reveals the impact of prenatal exposure to metals, PFASs, organophosphates, and organochlorines on early child development

BACKGROUND AND AIM: Unraveling the exposome may be a cornerstone towards precision prevention in public health. The presented approach was applied to a cohort of 50 mother-child pairs in the framework of HEALS project. METHODS: The links between in utero exposure to metals, PFOS, PFOA, organophosphates, and organochlorines, metabolic pathway deregulation, and clinically observed phenotypes were drawn through a urinary and serum untargeted metabolomics analysis using UPLC-Q-TOF/MS and NMR, followed by integrative bioinformatics and exposome-wide association algorithms. Spectral pre-processing was performed using the Bioconductor R packages XCMS and CAMERA. The databases HMDB, Metlin, and Lipid Maps, were used for metabolites identification. Enrichment and pathway analyses were performed using GeneSpring GX, which mapped significant biomarkers to known biochemical pathways based on the information contained in public databases (MetaCyc, Wikipathways, and KEGG). The Exposome-Wide Association Study (EWAS) approach was adopted to comprehensively and systematically associate multiple exposure factors discovering robust correlations with metabolites levels and dysregulated pathways. RESULTS:Metabolite identification revealed that the total number of unique annotated metabolites in urine and serum samples analysis using LC-HRMS was 751, and 7830, respectively. The detected metabolites on serum samples were mapped on 246 pathways, while urinary metabolites on 163. According to EWAS analysis, birth weight is positively affected by S-Adenosylhomocysteine levels during the first trimester of pregnancy, and negatively associated with the levels of Citrulline, and DEAMPY, at delivery. In addition, higher exposure levels to Hexachlorocyclohexane (HCH), 2,2',4,5,5'-Pentachlorobiphenyl (PCB101) and 2.4’-DDT, can lead to height increasement. The same outcome is associated to citric acid levels. Head circumference is positively associated with exposure to 4.4’-DDT at the first trimester. CONCLUSIONS:Overall, functionally coupling advanced bioinformatics algorithms applied on omics data with exposome-derived information on exposures and health indicators can support the high-dimension-biology-based association of environmental exposures and adverse health outcomes in early life. KEYWORDS: Exposome, Metabolomics, Birth outcomes

Effects of prenatal exposure to phthalates and Hexamoll® DINCH derived from urinary untargeted metabolomics analysis

BACKGROUND AND AIM: Phthalates and their substitute Hexamoll® DINCH® are a group of plasticisers with a production volume of millions of tons per year found in daily use products. There is a societ...

Urinary metabolomics analysis of the protective effects of Daming capsule on hyperlipidemia rats using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry.

Hyperlipidemia is recognized as one of the most important risk factors for morbidity and mortality due to cardiovascular diseases. Daming capsule, a Chinese patent medicine, has shown definitive efficacy in patients with hyperlipidemia. In this study, serum biochemistry and histopathology assessment were used to investigate the lipid-lowering effect of Daming capsule. Furthermore, urinary metabolomics based on ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry was conducted to identify the urinary biomarkers associated with hyperlipidemia and discover the underlying mechanisms of the antihyperlipidemic action of Daming capsule. After 10weeks of treatment, Daming capsule significantly lowered serum lipid levels and ameliorated hepatic steatosis induced by a high-fat diet. A total of 33 potential biomarkers associated with hyperlipidemia were identified, among which 26 were robustly restored to normal levels after administration of Daming capsule. Pathway analysis revealed that the lipid-lowering effect of Daming capsule is related to the regulation of multiple metabolic pathways including vitamin B and amino acid metabolism, tricarboxylic acid cycle, and pentose phosphate pathway. Notably, the study demonstrates that metabolomics is a powerful tool to elucidate the multitarget mechanism of traditional Chinese medicines, thereby promoting their research and development.

Amniotic fluid and urine metabolomic alterations associated with pregnant women with Down syndrome fetuses

Background Down syndrome (DS) is a chromosomal disorder caused by a third copy of all or part of chromosome 21. Clinical observations and preclinical studies both suggest that DS may be associated with significant metabolic and bioenergetic alterations. But the metabolic alterations in pregnant women carrying DS fetuses still remains unclear. In this study, we investigated the characteristic metabolomics and lipidomics changes during fetal development of DS. Methods The AF and random urine specimens were selected from 20 pregnant women carrying DS fetuses and 20 pregnant women carrying healthy fetuses. The diagnosis of DS was screened according to chromosome karyotype analysis, and untargeted metabolomic and lipidomic analyses were performed. Results Through the analyses of AF, 308 differential metabolites were selected between DS and controls. The metabolites with significant changes mainly involved lipid molecules, organic acids, nucleotides and carbon. Further analysis of lipidomics showed 64 differential metabolites, mainly involving glycerides, sphingolipids and glycerolipids. As for urine metabolomic and lipidomic analyses, there existed consistent metabolites with AF, but the number was much less. Conclusions Compared with the controls, carbon metabolism, amino acid metabolism, glyceride metabolism, sphingolipid metabolism and glycerophospholipid metabolism were significantly changed in DS cases. In addition, characterized biomarkers in AF and urine were screened for DS diagnosis, and these metabolites were mainly involved in energy metabolism and liver dysfunction. This finding may help improve the efficiency of prenatal screening for DS.

Corrigendum to: PSXII-19 Urine Metabolomics Analysis Associated with Feed Efficiency on Crossbred Steers during the Growing and Finishing Period on Forage- and Concentrate-Based Diets.

Corrigendum to: PSXII-19 Urine Metabolomics Analysis Associated with Feed Efficiency on Crossbred Steers during the Growing and Finishing Period on Forage- and Concentrate-Based Diets.

Assessment of Oral Vancomycin-Induced Alterations in Gut Bacterial Microbiota and Metabolome of Healthy Men.

Several classes of antibiotics have reduced the mortality caused by infectious diseases; however, orally administered antibiotics alter the composition of gut microbiota, leading to dysbiosis-related disease. Therefore, in this study, we used 16S rRNA gene sequencing- and metabolomics-based approaches to investigate the effects of oral vancomycin on gut bacterial microbiota and the metabolome in biospecimens collected from healthy men. Samples collected from 11 healthy men were analyzed using 16S rRNA gene sequencing and metabolomics. 16S rRNA gene sequencing was performed to analyze the gut bacterial microbiota, and GC-TOFMS-based untargeted metabolomics was performed to analyze fecal, urine, and plasma metabolomics. Spearman’s rank correlation was utilized to explore the associations between gut bacterial microbiota and metabolome. Fecal 16S rRNA gene sequencing analysis showed decreased relative abundance of genera belonging to the phyla Bacteroidetes and Firmicutes, and increased relative abundance of genera of the phyla Proteobacteria and Fusobacteria. Fecal metabolomics analysis showed that levels of uracil, L-aspartic acid, lithocholic acid, and deoxycholic acid were significantly higher at baseline, whereas that of dihydrouracil was significantly higher after vancomycin administration. No significant metabolic markers were selected from urine and plasma metabolomics analysis. This study demonstrates that oral vancomycin administration induces alterations in gut bacterial microbiota and metabolome. Correlation analysis between our two datasets shows that alteration of the gut bacterial microbiota, induced by oral vancomycin, potentially affected the systemic activity of dihydropyrimidine dehydrogenase. This correlation should be further examined in future studies to define the effects of gut bacterial microbiota on drug-metabolizing enzymes, thereby contributing to the development of personalized therapy.

Differential Metabolites in Chinese Autistic Children: A Multi-Center Study Based on Urinary 1H-NMR Metabolomics Analysis.

Background: Autism spectrum disorder (ASD) is a group of early-onset neurodevelopmental disorders. However, there is no valuable biomarker for the early diagnosis of ASD. Our large-scale and multi-center study aims to identify metabolic variations between ASD and healthy children and to investigate differential metabolites and associated pathogenic mechanisms.Methods: One hundred and seventeen autistic children and 119 healthy children were recruited from research centers of 7 cities. Urine samples were assayed by 1H-NMR metabolomics analysis to detect metabolic variations. Multivariate statistical analysis, including principal component analysis (PCA), and orthogonal projection to latent structure discriminant analysis (OPLS-DA), as well as univariate analysis were used to assess differential metabolites between the ASD and control groups. The differential metabolites were further analyzed by receiver operating characteristics (ROC) curve analysis and metabolic pathways analysis.Results: Compared with the control group, the ASD group showed higher levels of glycine, guanidinoacetic acid, creatine, hydroxyphenylacetylglycine, phenylacetylglycine, and formate and lower levels of 3-aminoisobutanoic acid, alanine, taurine, creatinine, hypoxanthine, and N-methylnicotinamide. ROC curve showed relatively significant diagnostic values for hypoxanthine [area under the curve (AUC) = 0.657, 95% CI 0.588 to 0.726], creatinine (AUC = 0.639, 95% CI 0.569 to 0.709), creatine (AUC = 0.623, 95% CI 0.552 to 0.694), N-methylnicotinamide (AUC = 0.595, 95% CI 0.523 to 0.668), and guanidinoacetic acid (AUC = 0.574, 95% CI 0.501 to 0.647) in the ASD group. Combining the metabolites creatine, creatinine and hypoxanthine, the AUC of the ROC curve reached 0.720 (95% CI 0.659 to 0.777). Significantly altered metabolite pathways associated with differential metabolites were glycine, serine and threonine metabolism, arginine and proline metabolism, and taurine and hypotaurine metabolism.Conclusions: Urinary amino acid metabolites were significantly altered in children with ASD. Amino acid metabolic pathways might play important roles in the pathogenic mechanisms of ASD.

GC–MS urinary metabolomics analysis of inherited metabolic diseases and stable metabolic biomarker screening by a comprehensive chemometric method

Screening of stable metabolic biomarkers objectively measuring the underlying pathophysiological changes are vital to boost early diagnosis and treatment prognosis of inherited metabolic diseases (IMDs). In this study, gas chromatography-mass spectrometry (GC–MS) was invoked to perform urinary metabolomics analysis for IMDs and a comprehensive chemometric method was presented to screen stable metabolic biomarkers effectively. For this chemometric method, a novel hybrid perturbation framework based on partial least squares discriminant analysis (PLS-DA) was adopted, in which the data level utilized bootstrap (BS) method and the function level employed the strategy of multiple informative vector fusion (MIVF), forming an algorithm BS-MIVF-PLSDA. Investigated by two common IMDs (methylmalonic acidemia and propionic acidemia), GC–MS allowed the detection of a rich profile of metabolites, characterizing subtle differences between disease and healthy control groups. The proposed chemometric method BS-MIVF-PLSDA exhibited superiorities to simpler techniques in terms of identifying candidate metabolic biomarkers more biologically correlated to the metabolic mechanisms with desirable selection stability. Moreover, the chemometric method enabled good classification performance in discriminating between disease and healthy control groups using the identified top-ranked candidate metabolic biomarkers. All the results showed that GC–MS urinary metabolomics coupled with chemometrics was feasible to offer an efficient path to achieve stable metabolic biomarkers, supporting early diagnosis and guiding treatment in IMDs clinical practice.

Metabolomics study of urine with Benzene, Toluene and Xylene combined exposure based on ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry

Objective: To study the effects of combined occupational exposure of benzene, toluene, and xylene on human metabolism at an overall level, and to screen biomarkers related to the combined occupational exposure of benzene, toluene, and xylene, and to explore the mechanism of early health effects preliminarily caused by combined occupational exposure of benzene, toluene, and xylene by identification of biomarkers and retrieval of metabolic pathways. Methods: A shoe-making company was selected as the research site. Twenty subjects for the exposed group and the control group were selected separately, and urine of the subjects was collected. The metabolic profiles of the samples were collected by liquid chromatography time-of-flight mass spectrometry, and professional metabolomics and multivariate statistical analysis software were used to establish PCA and OPLS-DA analysis models to screen potential biomarkers and identify biomarkers. Finally, based on the dynamic changes and trends of potential biomarkers between groups, the mechanism of body damage caused by benzene, toluene, and xylene was initially explored. Results: Urine metabolomics analysis showed that the metabolic profile of urine samples of the benzene, toluene, and xylene combined exposure group was different from that of the control group. 27 potential biomarkers that were closely related to the combined exposure of benzene, toluene, and xylene were screened and identified. These potential biomarkers were enriched in 16 metabolic pathways, of which 3 pathways were significantly enriched (P<0.05) , respectively, lysine metabolism, amino sugar metabolism, and nucleotide sugar metabolism. Conclusion: The metabonomics method can well reflect the changes in the metabolome of urine samples in the occupational population after the combined exposure of benzene, toluene, and xylene, which will help us better evaluate the risk of combined exposure of benzene, toluene, and xylene and prevent and control their health risks.

Dose-Related Urinary Metabolic Alterations of a Combination of Quercetin and Resveratrol-Treated High-Fat Diet Fed Rats.

Most herbal polyphenols and flavonoids reveals multiple ameliorative benefits for obesity caused by chronic metabolic disorders. Accumulated studies have revealed that preferable therapeutic effects can be obtained through clinical combination of these two kinds of natural compounds for obesity improvement. The typical representative research was the combination of quercetin and resveratrol (CQR), in which the ratio of quercetin and resveratrol is 2:1, demonstrating a synergistic effect in anti-obesity process. Although there exists reports clarifying the mechanism of the combination of two to improve obesity from the perspective of improving adipose tissue inflammation or modulating the composition of intestinal flora, there are few further studies on the mechanism of drug action from the perspective of metabolites transformation. In this research, we mainly focused on the alterations of endogenous metabolites in rats, and analyzed the urine metabolites of obese and intervention model. Therefore, a gas chromatography-mass spectrometry (GC-MS) based metabolomics approach was applied to assess the potential effects and mechanisms of CQR at different dosages (45, 90, and 180 mg/kg) in high fat diet (HFD)-induced obesity rats. Body weight gain and visceral fat weight were reduced by CQR, as well as blood lipid and inflammatory factor levels were increased by CQR in a dose-related manner. Urinary metabolomics revealed 22 differential metabolites related to the HFD-induced obesity, which were reversed in a dose-dependent manner by CQR, of which 8 were reversed in the 45 mg/kg CQR group, 15 were reversed in the 90 mg/kg CQR group, and 18 were reversed in the 180 mg/kg CQR group. Combined with bioinformatics and pattern recognition, the results demonstrated that the key differential metabolites were basically involved in amino acid metabolism, galactose metabolism, pantothenate and CoA biosynthesis, pyruvate metabolism and lysine degradation. In summary, our results showed significant therapeutic action by CQR administration and remarkable metabolomic changes after HFD feeding and CQR intervention. Urinary metabolomic analysis was highlighted on account of providing holistic and comprehensive insights into the pathophysiological mechanisms of the HFD-induced obesity, which also supplied clues for the future mechanism studies of CQR’s anti-obesity effects.

Urinary metabolomic analysis to identify potential markers for the diagnosis of tuberculosis and latent tuberculosis

Tuberculosis (TB) is a serious infectious disease with high infection and mortality rates. 5%–10% of the latent tuberculosis infections (LTBI) are likely to develop into active TB, and there are currently no clinical biomarkers that can distinguish between LTBI, active TB and other non-tuberculosis populations. Therefore, it is necessary to develop rapid diagnostic methods for active TB and LTBI. In this study, urinary metabolome of 30 active TB samples and the same number of LTBI and non-TB control samples were identified and analyzed by UPLC-Q Exactive MS. In total, 3744 metabolite components were obtained in ESI- mode and 4086 in ESI + mode. Orthogonal partial least square discriminant analysis (OPLS-DA) and hierarchical cluster analysis (HCA) showed that there were significant differences among LTBI, active TB and non-TB. Six differential metabolites were screened in positive and negative mode, 3-hexenoic acid, glutathione (GSH), glycochenodeoxycholate-3-sulfate, N-[4′-hydroxy-(E)-cinnamoyl]-l-aspartic acid, deoxyribose 5-phosphate and histamine. The overlapping pathways differential metabolites involved were mainly related to immune regulation and urea cycle. The results showed that the urine metabolism of TB patients was disordered and many metabolic pathways changed. Multivariate statistical analysis revealed that GSH and histamine were selected as potential molecular markers, with area under curve of receiver operating characteristic curve over 0.75. Among the multiple differential metabolites, GSH and histamine changed to varying degrees in active TB, LTBI and the non-TB control group. The levels of GSH and histamine in 48 urinary samples were measured by ELISA in validation phase, and the result in our study provided the potential for non-invasive biomarkers of TB.

Urine metabolomics analysis based on ultra performance liquid chromatography-high resolution mass spectrometry combined with osmolality calibration sample concentration variability

尿液是代谢组学研究中主要关注的体液样本之一。尿液样本中的代谢物浓度受饮食、疾病等因素影响变异较大,这极大阻碍了高质量组学数据的获取和可靠生物标志物的鉴定。研究为克服尿液样本的浓度变异性,在原始数据采集前,根据样本渗透压的大小,针对性地调整进样量或者稀释样本,从而确保代谢组学分析样本的渗透压与进样量的乘积相当,再经超高效液相色谱-高分辨质谱技术(UPLC-HRMS)分析,采用总离子丰度或总有用峰面积(MSTUS)对数据集进行归一化处理。研究利用临床样本及其梯度稀释的溶液,对该方法与现有研究普遍使用的方法进行了比较,随后通过先天性肾积水患者及健康志愿者的尿液样本做了进一步的方法学验证。数据集经校正后,峰面积RSD<30%的提取峰数量增加,主成分分析结果较校正前有更高的组内聚集和组间分群效应,正交偏最小二乘判别分析的统计模型更不易过拟合。与肌酐比较,渗透压值与质谱信号间呈现了更好的线性关系。以上结果表明,数据采集前通过样本渗透压进行校正,能有效消除因样本本身代谢物浓度变化引起的组内差异,提高方法的重复性和统计模型的可靠度。以渗透压为基准的校正策略,比肌酐校正法适用范围更广,结果也更准确。研究可对后续各类来源的尿液代谢组学研究提供数据归一化的指导和参考。

Reliability of urinary charged metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

Currently, large-scale cohort studies for metabolome analysis have been launched globally. However, only a few studies have evaluated the reliability of urinary metabolome analysis. This study aimed to establish the reliability of urinary metabolomic profiling in cohort studies. In the Tsuruoka Metabolomics Cohort Study, 123 charged metabolites were identified and routinely quantified using capillary electrophoresis-mass spectrometry (CE-MS). We evaluated approximately 750 quality control (QC) samples and 6,720 participants’ spot urine samples. We calculated inter- and intra-batch coefficients of variation in the QC and participant samples and technical intraclass correlation coefficients (ICC). A correlation of metabolite concentrations between spot and 24-h urine samples obtained from 32 sub-cohort participants was also evaluated. The coefficient of variation (CV) was less than 20% for 87 metabolites (70.7%) and 20–30% for 19 metabolites (15.4%) in the QC samples. There was less than 20% inter-batch CV for 106 metabolites (86.2%). Most urinary metabolites would have reliability for measurement. The 96 metabolites (78.0%) was above 0.75 for the estimated ICC, and those might be useful for epidemiological analysis. Among individuals, the Pearson correlation coefficient of 24-h and spot urine was more than 70% for 59 of the 99 metabolites. These results show that the profiling of charged metabolites using CE-MS in morning spot human urine is suitable for epidemiological metabolomics studies.