Project description:Urease pre-treatment of urine has been utilized since the early 1960s to remove high levels of urea from samples prior to further processing and analysis by gas chromatography-mass spectrometry (GC-MS). Aside from the obvious depletion or elimination of urea, the effect, if any, of urease pre-treatment on the urinary metabolome has not been studied in detail. Here, we report the results of three separate but related experiments that were designed to assess possible indirect effects of urease pre-treatment on the urinary metabolome as measured by GC-MS. In total, 235 GC-MS analyses were performed and over 106 identified and 200 unidentified metabolites were quantified across the three experiments. The results showed that data from urease pre-treated samples 1) had the same or lower coefficients of variance among reproducibly detected metabolites, 2) more accurately reflected quantitative differences and the expected ratios among different urine volumes, and 3) increased the number of metabolite identifications. Overall, we observed no negative consequences of urease pre-treatment. In contrast, urease pretreatment enhanced the ability to distinguish between volume-based and biological sample types compared to no treatment. Taken together, these results show that urease pretreatment of urine offers multiple beneficial effects that outweigh any artifacts that may be introduced to the data in urinary metabolomics analyses.
Project description:ObjectivesGout is a common type of inflammatory arthritis. The aim of this study was to detect urinary metabolic changes in gout patients which may contribute to understanding the pathological mechanism of gout and discovering potential metabolite markers.MethodsUrine samples from 35 gout patients and 29 healthy volunteers were analyzed by gas chromatography-mass spectrometry (GC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed to screen differential metabolites between two groups, and the variable importance for projection (VIP) values and Student's t-test results were combined to define the significant metabolic changes caused by gout. Further, binary logistic regression analysis was performed to establish a model to distinguish gout patients from healthy people, and receiver operating characteristic (ROC) curve was made to evaluate the potential for diagnosis of gout.ResultA total of 30 characteristic metabolites were significantly different between gout patients and controls, mainly including amino acids, carbohydrates, organic acids, and their derivatives, associated with perturbations in purine nucleotide synthesis, amino acid metabolism, purine metabolism, lipid metabolism, carbohydrate metabolism, and tricarboxylic acid cycle. Binary logistic regression and ROC curve analysis showed the combination of urate and isoxanthopterin can effectively discriminate the gout patients from controls with the area under the curve (AUC) of 0.879.ConclusionThus, the urinary metabolomics study is an efficient tool for a better understanding of the metabolic changes of gout, which may support the clinical diagnosis and pathological mechanism study of gout.
Project description:We developed a set of methods for the quantification of four major components of microbial biomass using gas chromatography/mass spectrometry (GC/MS). Specifically, methods are described to quantify amino acids, RNA, fatty acids, and glycogen, which comprise an estimated 88% of the dry weight of Escherichia coli. Quantification is performed by isotope ratio analysis with fully (13)C-labeled biomass as internal standard, which is generated by growing E. coli on [U-(13)C]glucose. This convenient, reliable, and accurate single-platform (GC/MS) workflow for measuring biomass composition offers significant advantages over existing methods. We demonstrate the consistency, accuracy, precision, and utility of this procedure by applying it to three metabolically unique E. coli strains. The presented methods will have widespread applicability in systems microbiology and bioengineering.
Project description:Microbial communities exchange molecules with their environment, which plays a major role in regulating global biogeochemical cycles and climate. While extracellular metabolites are commonly measured in terrestrial and limnic ecosystems, the presence of salt in marine habitats limits the nontargeted analyses of the ocean exometabolome using mass spectrometry (MS). Current methods require salt removal prior to sample measurements, which can alter the molecular composition of the metabolome and limit the types of compounds detected by MS. To overcome these limitations, we developed a gas chromatography MS (GC-MS) method that avoids sample altering during salt removal and that detects metabolites down to nanomolar concentrations from less than 1 ml of seawater. We applied our method (SeaMet) to explore marine metabolomes in vitro and in vivo First, we measured the production and consumption of metabolites during the culture of a heterotrophic bacterium, Marinobacter adhaerens Our approach revealed successional uptake of amino acids, while sugars were not consumed. These results show that exocellular metabolomics provides insights into nutrient uptake and energy conservation in marine microorganisms. We also applied SeaMet to explore the in situ metabolome of coral reef and mangrove sediment porewaters. Despite the fact that these ecosystems occur in nutrient-poor waters, we uncovered high concentrations of sugars and fatty acids, compounds predicted to play a key role for the abundant and diverse microbial communities in coral reef and mangrove sediments. Our data demonstrate that SeaMet advances marine metabolomics by enabling a nontargeted and quantitative analysis of marine metabolites, thus providing new insights into nutrient cycles in the oceans.IMPORTANCE Nontargeted approaches using metabolomics to analyze metabolites that occur in the oceans is less developed than those for terrestrial and limnic ecosystems. One of the challenges in marine metabolomics is that salt limits metabolite analysis in seawater to methods requiring salt removal. Building on previous sample preparation methods for metabolomics, we developed SeaMet, which overcomes the limitations of salt on metabolite detection. Considering that the oceans contain the largest dissolved organic matter pool on Earth, describing the marine metabolome using nontargeted approaches is critical for understanding the drivers behind element cycles, biotic interactions, ecosystem function, and atmospheric CO2 storage. Our method complements both targeted marine metabolomic investigations as well as other "omics" (e.g., genomics, transcriptomics, and proteomics) approaches by providing an avenue for studying the chemical interaction between marine microbes and their habitats.
Project description:The effect of puffing treatment on the volatile components of green tea has been studied. A total of 155 volatile compounds were identified by using HS-SPME and SPE extraction methods, combined with gas chromatography-mass spectrometry (GC-MS). The total concentration of volatile compounds in puffed green tea increased by 2.25 times compared to that in before puffing. 12 key volatile compounds in green tea were identified before and after puffing using a combination of multivariate statistical analysis, GC-O, AEDA dilution analysis, and relative odor activity value (rOAV). The puffing process generates the Maillard reaction, where sugars react with amino acids to produce Maillard reaction products (such as pyrazine, pyrrole, furan, and their derivatives), giving them a unique baking aroma. The proportion of these compounds in the total volatile matter increased. The research results provided guidance and a theoretical basis for improving the aroma processing of green tea.
Project description:The diverse characteristics and large number of entities make metabolite separation challenging in metabolomics. To date, there is not a singular instrument capable of analyzing all types of metabolites. In order to achieve a better separation for higher peak capacity and accurate metabolite identification and quantification, we integrated GC × GC-MS and parallel 2DLC-MS for analysis of polar metabolites. To test the performance of the developed system, 13 rats were fed different diets to form two animal groups. Polar metabolites extracted from rat livers were analyzed by GC × GC-MS, parallel 2DLC-MS (-) and parallel 2DLC-MS (+), respectively. By integrating all data together, 58 metabolites were detected with significant change in their abundance levels between groups (p≤ 0.05). Of the 58 metabolites, three metabolites were detected in two platforms and two in all three platforms. Manual examination showed that discrepancy of metabolite regulation measured by different platforms was mainly caused by the poor shape of chromatographic peaks resulting from low instrument response. Pathway analysis demonstrated that integrating the results from multiple platforms increased the confidence of metabolic pathway assignment.
Project description:BackgroundPlasma oxalate measurements can be used for the screening and therapeutic monitoring of primary hyperoxaluria. We developed a gas chromatography-mass spectrometry (GC-MS) assay for plasma oxalate measurements with high sensitivity and suitable testing volumes for pediatric populations.MethodsPlasma oxalate was extracted, derivatized, and analyzed by GC-MS. We measured the ion at m/z 261.10 to quantify oxalate and the 13C2-oxalate ion (m/z: 263.15) as the internal standard. Method validation included determination of the linear range, limit of blank, limit of detection, lower limit of quantification, precision, recovery, carryover, interference, and dilution effect. The cut-off value between primary and non-primary hyperoxaluria in a pediatric population was analyzed.ResultsThe detection limit was 0.78 μmol/L, and the linear range was up to 80.0 μmol/L. The between-day precision was 5.7% at 41.3 μmol/L and 13.1% at 1.6 μmol/L. The carryover was <0.2%. The recovery rate ranged from 90% to 110%. Interference analysis showed that Hb did not interfere with plasma oxalate quantification, whereas intralipids and bilirubin caused false elevation of oxalate concentrations. A cut-off of 13.9 μmol/L showed 63% specificity and 77% sensitivity, whereas a cut-off of 4.15 μmol/L showed 100% specificity and 20% sensitivity. The minimum required sample volume was 250 μL. The detected oxalate concentrations showed interference from instrument conditioning, sample preparation procedures, medications, and various clinical conditions.ConclusionsGC-MS is a sensitive assay for quantifying plasma oxalate and is suitable for pediatric patients. Plasma oxalate concentrations should be interpreted in a clinical context.
Project description:The ability to identify and quantify small molecule metabolites derived from gut microbial-mammalian cometabolism is essential for the understanding of the distinct metabolic functions of the microbiome. To date, analytical protocols that quantitatively measure a complete panel of microbial metabolites in biological samples have not been established but are urgently needed by the microbiome research community. Here, we report an automated high-throughput quantitative method using a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) platform to simultaneously measure over one hundred microbial metabolites in human serum, urine, feces, and Escherichia coli cell samples within 15 min per sample. A reference library was developed consisting of 145 methyl and ethyl chloroformate (MCF and ECF) derivatized compounds with their mass spectral and retention index information for metabolite identification. These compounds encompass different chemical classes including fatty acids, amino acids, carboxylic acids, hydroxylic acids, and phenolic acids as well as benzoyl and phenyl derivatives, indoles, etc., that are involved in a number of important metabolic pathways. Within an optimized range of concentrations and sample volumes, most derivatives of both reference standards and endogenous metabolites in biological samples exhibited satisfactory linearity (R2 > 0.99), good intrabatch reproducibility, and acceptable stability within 6 days (RSD < 20%). This method was further validated by examination of the analytical variability of 76 paired human serum, urine, and fecal samples as well as quality control samples. Our method involved using high-throughput sample preparation, measurement with automated derivatization, and rapid GC/TOFMS analysis. Both techniques are well suited for microbiome metabolomics studies.
Project description:Individuals are exposed to a wide variety of chemicals over their lifetime, yet current understanding of mixture toxicology is still limited. We present a two-step analytical method using a gas chromatograph-triple quadrupole mass spectrometer that requires less than 1 mL of sample. The method is applied to 183 plasma samples from a study population of children with autism spectrum disorder, their parents, and unrelated neurotypical children. We selected 156 environmental chemical compounds and ruled out chemicals with detection rates less than 20% of our study cohort (n = 61), as well as ones not amenable to the selected extraction and analytical methods (n = 34). The targeted method then focused on remaining chemicals (n = 61) plus 8 additional polychlorinated biphenyls (PCBs). Persistent pollutants, such as p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) and PCB congeners 118 and 180, were detected at high frequencies and several previously unreported chemicals, including 2,4,6-trichlorophenol, isosafrole, and hexachlorobutadiene, were frequently detected in our study cohort. This work highlights the benefits of employing a multi-step analytical method in exposure studies and demonstrates the efficacy of such methods for reporting novel information on previously unstudied pollutant exposures.