Project description:The development of improved mass spectrometers and supporting computational tools is expected to enable the rapid annotation of whole metabolomes. Essential for the progress is the identification of strengths and weaknesses of novel instrumentation in direct comparison to previous instruments. Orbitrap liquid chromatography (LC)-mass spectrometry (MS) technology is now widely in use, while Orbitrap gas chromatography (GC)-MS introduced in 2015 has remained fairly unexplored in its potential for metabolomics research. This study aims to evaluate the additional knowledge gained in a metabolomics experiment when using the high-resolution Orbitrap GC-MS in comparison to a commonly used unit-mass resolution single-quadrupole GC-MS. Samples from an osmotic stress treatment of a non-model organism, the microalga Skeletonema costatum, were investigated using comparative metabolomics with low- and high-resolution methods. Resulting datasets were compared on a statistical level and on the level of individual compound annotation. Both MS approaches resulted in successful classification of stressed vs. non-stressed microalgae but did so using different sets of significantly dysregulated metabolites. High-resolution data only slightly improved conventional library matching but enabled the correct annotation of an unknown. While computational support that utilizes high-resolution GC-MS data is still underdeveloped, clear benefits in terms of sensitivity, metabolic coverage, and support in structure elucidation of the Orbitrap GC-MS technology for metabolomics studies are shown here.
Project description:We report here that a straightforward change of the standard derivatization procedure for GC⁻MS metabolomics is leading to a strong increase in metabolite signal intensity. Drying samples between methoxymation and trimethylsilylation significantly increased signals by two- to tenfold in extracts of yeast cells, plant and animal tissue, and human urine. This easy step reduces the cost of sample material and the need for expensive new hardware.
Project description:Fenofibrate, a peroxisome proliferator-activated receptor ? (PPAR?) agonist, was found to exacerbate inflammation and tissue injury in experimental acute colitis mice. Through lipidomics analysis, bioactive sphingolipids were significantly up-regulated in the colitis group. In this study, to provide further insight into the PPAR?-dependent exacerbation of colitis, gas chromatography-mass spectrometry (GC/MS) based metabolomics was employed to investigate the serum and colon of dextran sulfate sodium (DSS)-induced colitis mice treated with fenofibrate, with particular emphasis on changes in low-molecular-weight metabolites. With the aid of multivariate analysis and metabolic pathway analysis, potential metabolite markers in the amino acid metabolism, urea cycle, purine metabolism, and citrate cycle were highlighted, such as glycine, serine, threonine, malic acid, isocitric acid, uric acid, and urea. The level changes of these metabolites in either serum or colons of colitis mice were further potentiated following fenofibrate treatment. Accordingly, the expression of threonine aldolase and phosphoserine aminotransferase 1 was significantly up-regulated in colitis mice and further potentiated in fenofibrate/DSS-treated mice. It was revealed that beyond the control of lipid metabolism, PPAR? also shows effects on the above pathways, resulting in enhanced protein catabolism and energy expenditure, increased bioactive sphingolipid metabolism and proinflammatory state, which were possibly related to the exacerbated colitis.
Project description:Plum brandy (Slivovitz (en); Šljivovica(sr)) is an alcoholic beverage that is increasingly consumed all over the world. Its quality assessment has become of great importance. In our study, the main volatiles and aroma compounds of 108 non-aged plum brandies originating from three plum cultivars, and fermented using different conditions, were investigated. The chemical profiles obtained after two-step GC-FID-MS analysis were subjected to multivariate data analysis to reveal the peculiarity in different cultivars and fermentation process. Correlation of plum brandy chemical composition with its sensory characteristics obtained by expert commission was also performed. The utilization of PCA and OPLS-DA multivariate analysis methods on GC-FID-MS, enabled discrimination of brandy samples based on differences in plum varieties, pH of plum mash, and addition of selected yeast or enzymes during fermentation. The correlation of brandy GC-FID-MS profiles with their sensory properties was achieved by OPLS multivariate analysis. Proposed workflow confirmed the potential of GC-FID-MS in combination with multivariate data analysis that can be applied to assess the plum brandy quality.
Project description:Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC-MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC-MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC-MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC-MS and GC-MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC-MS and GC × GC-MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC-MS processing compared to targeted GC-MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC-MS were somewhat higher than with GC-MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC-MS was demonstrated; many additional candidate biomarkers were found with GC × GC-MS compared to GC-MS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0219-6) contains supplementary material, which is available to authorized users.
Project description:BackgroundColorectal cancer (CRC) is one of the most common malignant gastrointestinal cancers in the world with a 5-year survival rate of approximately 68%. Although researchers accumulated many scientific studies, its pathogenesis remains unclear yet. Detecting and removing these malignant polyps promptly is the most effective method in CRC prevention. Therefore, the analysis and disposal of malignant polyps is conducive to preventing CRC.MethodsIn the study, metabolic profiling as well as diagnostic biomarkers for CRC was investigated using untargeted GC-MS-based metabolomics methods to explore the intervention approaches. In order to better characterize the variations of tissue and serum metabolic profiles, orthogonal partial least-square discriminant analysis was carried out to further identify significant features. The key differences in tR-m/z pairs were screened by the S-plot and VIP value from OPLS-DA. Identified potential biomarkers were leading in the KEGG in finding interactions, which show the relationships among these signal pathways.ResultsFinally, 17 tissue and 13 serum candidate ions were selected based on their corresponding retention time, p-value, m/z, and VIP value. Simultaneously, the most influential pathways contributing to CRC were inositol phosphate metabolism, primary bile acid biosynthesis, phosphatidylinositol signaling system, and linoleic acid metabolism.ConclusionsThe preliminary results suggest that the GC-MS-based method coupled with the pattern recognition method and understanding these cancer-specific alterations could make it possible to detect CRC early and aid in the development of additional treatments for the disease, leading to improvements in CRC patients' quality of life.
Project description:Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.
Project description:The fermentation metabolites significantly influence the quality of jujube wine. However, the dynamics of these metabolites during fermentation are not well understood. In this study, a total of 107 volatile and 1758 non-volatile compounds were identified using a flavor-directed research strategy and non-targeted metabolomics. The increase in esters and alcohols during fermentation shifted the aroma from grassy, mushroomy, and earthy to a floral and fruity flavor in the jujube wine. Leucine and phenylalanine were notably enriched during fermentation, potentially benefiting human health and enriching the flavor of fruit wines. Moreover, pathway analysis identified four key metabolic pathways and two crucial metabolic substrates, pyruvate and l-aspartate. This study provides a theoretical reference for optimizing the fermentation process and enhancing the quality of jujube wine.
Project description:(1) Background: The ability to determine the age of ginseng is very important because the price of ginseng depends on the cultivation period. Since morphological observation is subjective, a new scientific and systematic method for determining the age of ginseng is required. (2) Methods: Three techniques were used for a metabolomics approach. High-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy was used to analyze powdered ginseng samples without extraction. Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and gas chromatography quadrupole time-of-fight mass spectrometry (GC-TOF/MS) were used to analyze the extracts of 4-, 5-, and 6-year-old ginseng. (3) Results: A metabolomics approach has the potential to discriminate the age of ginseng. Among the primary metabolites detected from NMR spectroscopy, the levels of fumarate and choline showed moderate prediction with an area under the curve (AUC) value of more than 0.7. As a result of UPLC-QTOF/MS-based profiling, 61 metabolites referring to the VIP (variable importance in the projection) score contributed to discriminating the age of ginseng. The results of GC×GC-TOF/MS showed clear discrimination of 4-, 5-, and 6-year-old ginseng using orthogonal partial least-squares discriminant analysis (OPLS-DA) to 100% of the discrimination rate. The results of receiver operating characteristic (ROC) analysis, 16 metabolites between 4- and 5-year-old ginseng, and 18 metabolites between 5- and 6-year-old ginseng contributed to age discrimination in all regions. (4) Conclusions: These results showed that metabolic profiling and multivariate statistical analyses can distinguish the age of ginseng. Especially, it is meaningful that ginseng samples from different areas had the same metabolites for age discrimination. In future studies, it will be necessary to identify the unknown variables and to collaboratively study with other fields the biochemistry of aging in ginseng.