Comprehensive Metabolic Signature of Renal Dysplasia in Children. A Multiplatform Metabolomics Concept.
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SUBMITTER: Macioszek S
PROVIDER: S-EPMC8358436 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.
| S-EPMC9363947 | biostudies-literature
Project description:IntroductionPatient-derived skin fibroblasts offer a unique translational model to study molecular mechanisms of multiple human diseases. Metabolomics profiling allows to track changes in a broad range of metabolites and interconnected metabolic pathways that could inform on molecular mechanisms involved in disease development and progression, and on the efficacy of therapeutic interventions. Therefore, it is important to establish standardized protocols for metabolomics analysis in human skin fibroblasts for rigorous and reliable metabolic assessment.ObjectivesWe aimed to develop an optimized protocol for concurrent measure of the concentration of amino acids, acylcarnitines, and components of the tricarboxylic acid (TCA) cycle in human skin fibroblasts using gas (GC) and liquid chromatography (LC) coupled with mass spectrometry (MS).MethodsThe suitability of four different methods of cell harvesting on the recovery of amino acids, acylcarnitines, and TCA cycle metabolites was established using GC/MS and LC/MS analytical platforms. For each method, metabolite stability was determined after 48 h, 2 weeks and 1 month of storage at - 80 °C.ResultsHarvesting cells in 80% methanol solution allowed the best recovery and preservation of metabolites. Storage of samples in 80% methanol up to 1 month at - 80 °C did not significantly impact metabolite concentrations.ConclusionWe developed a robust workflow for metabolomics analysis in human skin fibroblasts suitable for a high-throughput multiplatform analysis. This method allows a direct side-by-side comparison of metabolic changes in samples collected at different time that could be used for studies in large patient cohorts.
| S-EPMC7928486 | biostudies-literature
Project description:BackgroundMetabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.Methodology/principal findings40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).Conclusions/significanceOur study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.
| S-EPMC2978704 | biostudies-literature
Project description:BackgroundMetabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis.MethodsA case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men.FindingsWe characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…).InterpretationThese findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS.FundingThe NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frost Chair funded by La Fondation de l'Université de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB French infrastructure (ANR-INBS-0010). All authors had full access to the full data in the study and accept responsibility to submit for publication.
| S-EPMC8237302 | biostudies-literature
Project description:RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n?=?42) and controls (n?=?49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced.
| S-EPMC5114559 | biostudies-literature
Project description:Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD.
| S-EPMC7240949 | biostudies-literature
Project description:ObjectiveGraves' disease (GD) related hyperthyroidism (HT) has profound effects on metabolic activity and metabolism of macromolecules affecting energy homeostasis. In this study, we aimed to get a comprehensive understanding of the metabolic changes and their clinical relevance in GD children.MethodsWe investigated serum substances from 30 newly diagnosed GD children and 30 age- and gender-matched healthy controls. We explored the metabolomics using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) analysis, and then analyzed the metabolomic data via multivariate statistical analysis.ResultsBy untargeted metabolomic analysis, a total of 730 metabolites were identified in all participants, among which 48 differential metabolites between GD and control groups were filtered out, including amino acids, dipeptides, lipids, purines, etc. Among these metabolites, 33 were detected with higher levels, while 15 with lower levels in GD group compared to controls. Pathway analysis showed that HT had a significant impact on aminoacyl-transfer ribonucleic acid (tRNA) biosynthesis, several amino acids metabolism, purine metabolism, and pyrimidine metabolism.ConclusionIn this study, via untargeted metabolomics analysis, significant variations of serum metabolomic patterns were detected in GD children.
| S-EPMC8635134 | biostudies-literature
Project description:Honeybee (Apis mellifera) venom (HBV) has been a subject of extensive proteomics research; however, scarce information on its metabolite composition can be found in the literature. The aim of the study was to identify and quantify the metabolites present in HBV. To gain the highest metabolite coverage, three different mass spectrometry (MS)-based methodologies were applied. In the first step, untargeted metabolomics was used, which employed high-resolution, accurate-mass Orbitrap MS. It allowed obtaining a broad overview of HBV metabolic components. Then, two targeted metabolomics approaches, which employed triple quadrupole MS, were applied to quantify metabolites in HBV samples. The untargeted metabolomics not only confirmed the presence of amines, amino acids, carbohydrates, and organic acids in HBV, but also provided information on venom components from other metabolite classes (e.g., nucleosides, alcohols, purine and pyrimidine derivatives). The combination of three MS-based metabolomics platforms facilitated the identification of 214 metabolites in HBV samples, among which 138 were quantified. The obtaining of the wide free amino acid profiles of HBV is one of the project's achievements. Our study contributed significantly to broadening the knowledge about HBV composition and should be continued to obtain the most comprehensive metabolite profile of HBV.
| S-EPMC7729905 | biostudies-literature
Project description:The gut microbiota is susceptible to modulation by environmental stimuli and therefore can serve as a biological sensor. Recent evidence suggests that xenobiotics can disrupt the interaction between the microbiota and host. Here, we describe an approach that combines in vitro microbial incubation (isolated cecal contents from mice), flow cytometry, and mass spectrometry- and 1H nuclear magnetic resonance (NMR)-based metabolomics to evaluate xenobiotic-induced microbial toxicity. Tempol, a stabilized free radical scavenger known to remodel the microbial community structure and function in vivo, was studied to assess its direct effect on the gut microbiota. The microbiota was isolated from mouse cecum and was exposed to tempol for 4 h under strict anaerobic conditions. The flow cytometry data suggested that short-term tempol exposure to the microbiota is associated with disrupted membrane physiology as well as compromised metabolic activity. Mass spectrometry and NMR metabolomics revealed that tempol exposure significantly disrupted microbial metabolic activity, specifically indicated by changes in short-chain fatty acids, branched-chain amino acids, amino acids, nucleotides, glucose, and oligosaccharides. In addition, a mouse study with tempol (5 days gavage) showed similar microbial physiologic and metabolic changes, indicating that the in vitro approach reflected in vivo conditions. Our results, through evaluation of microbial viability, physiology, and metabolism and a comparison of in vitro and in vivo exposures with tempol, suggest that physiologic and metabolic phenotyping can provide unique insight into gut microbiota toxicity. IMPORTANCE The gut microbiota is modulated physiologically, compositionally, and metabolically by xenobiotics, potentially causing metabolic consequences to the host. We recently reported that tempol, a stabilized free radical nitroxide, can exert beneficial effects on the host through modulation of the microbiome community structure and function. Here, we investigated a multiplatform phenotyping approach that combines high-throughput global metabolomics with flow cytometry to evaluate the direct effect of tempol on the microbiota. This approach may be useful in deciphering how other xenobiotics directly influence the microbiota.
| S-EPMC6222046 | biostudies-other
Project description:BackgroundMetabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine.MethodsWe used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18-75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4-5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS.ResultsMetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (17 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N-oxide, and p-cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.83 and 0.87). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome.ConclusionsUrine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome.
| S-EPMC8320177 | biostudies-literature