Journal articles: 'Metabolomics (LC-MS)'

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Zhou, Bin, Jun Feng Xiao, Leepika Tuli, and Habtom W. Ressom. "LC-MS-based metabolomics." Mol. BioSyst. 8, no. 2 (2012): 470–81. http://dx.doi.org/10.1039/c1mb05350g.

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Wishart, David S., Leo L. Cheng, Valérie Copié, Arthur S. Edison, Hamid R. Eghbalnia, Jeffrey C. Hoch, Goncalo J. Gouveia, et al. "NMR and Metabolomics—A Roadmap for the Future." Metabolites 12, no. 8 (July 23, 2022): 678. http://dx.doi.org/10.3390/metabo12080678.

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Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021—the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

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D’eon, Jessica C., Brian P. Lankadurai, André J. Simpson, Eric J. Reiner, David G. Poirier, Greg C. Vanlerberghe, and Myrna J. Simpson. "Cross-Platform Comparison of Amino Acid Metabolic Profiling in Three Model Organisms Used in Environmental Metabolomics." Metabolites 13, no. 3 (March 8, 2023): 402. http://dx.doi.org/10.3390/metabo13030402.

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Environmental metabolomics is a promising approach to study pollutant impacts to target organisms in both terrestrial and aquatic environments. To this end, both nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based methods are used to profile amino acids in different environmental metabolomic studies. However, these two methods have not been compared directly which is an important consideration for broader comparisons in the environmental metabolomics field. We compared the quantification of 18 amino acids in the tissue extracts of Daphnia magna, a common model organism used in both ecotoxicology and ecology, using both 1H NMR spectroscopy and liquid chromatography with tandem MS (LC-MS/MS). 1H NMR quantification of amino acids agreed with the LC-MS/MS quantification for 17 of 18 amino acids measured. We also tested both quantitative methods in a D. magna sub-lethal exposure study to copper and lithium. Again, both NMR and LC-MS/MS measurements showed agreement. We extended our analyses with extracts from the earthworm Eisenia fetida and the plant model Nicotiana tabacum. The concentrations of amino acids by both 1H NMR and LC-MS/MS, agreed and demonstrated the robustness of both techniques for quantitative metabolomics. These findings demonstrate the compatibility of these two analytical platforms for amino acid profiling in environmentally relevant model organisms and emphasizes that data from either method is robust for comparisons across studies to further build the knowledge base related to pollutant exposure impacts and toxic responses of diverse environmental organisms.

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Emwas, Abdul-Hamid, Raja Roy, Ryan T. McKay, Leonardo Tenori, Edoardo Saccenti, G. A. Nagana Gowda, Daniel Raftery, et al. "NMR Spectroscopy for Metabolomics Research." Metabolites 9, no. 7 (June 27, 2019): 123. http://dx.doi.org/10.3390/metabo9070123.

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Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

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Sawada, Yuji, and Masami Yokota Hirai. "INTEGRATED LC-MS/MS SYSTEM FOR PLANT METABOLOMICS." Computational and Structural Biotechnology Journal 4, no. 5 (January 2013): e201301011. http://dx.doi.org/10.5936/csbj.201301011.

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Rojo, David, Coral Barbas, and Francisco J. Rupérez. "LC–MS metabolomics of polar compounds." Bioanalysis 4, no. 10 (June 2012): 1235–43. http://dx.doi.org/10.4155/bio.12.100.

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Fang, Zhong-Ze, and Frank J. Gonzalez. "LC–MS-based metabolomics: an update." Archives of Toxicology 88, no. 8 (April 8, 2014): 1491–502. http://dx.doi.org/10.1007/s00204-014-1234-6.

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Kim, Gyoung-Deuck, Jin Lee, and Joong-Hyuck Auh. "Metabolomic Screening of Anti-Inflammatory Compounds from the Leaves of Actinidia arguta (Hardy Kiwi)." Foods 8, no. 2 (February 1, 2019): 47. http://dx.doi.org/10.3390/foods8020047.

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The metabolomic screening of potential anti-inflammatory compounds in the leaves of Actinidia arguta was performed by using LC-MS/MS. Ethanol extracts were prepared, and the anti-inflammatory effects were investigated based on nitric oxide (NO) synthesis and inducible nitric oxide synthase expression in lipopolysaccharide-induced RAW 264.7 macrophages. The 75% ethanol extract showed the highest inhibitory effect on nitric oxide (NO) production, and it was further separated by in vitro bioassay-guided fractionation using preparative LC with reversed-phase column separation. Through multiple steps of fractionation, sub-fraction 1-3 was finally purified, and caffeic acid derivatives, such as caffeoylthreonic acid and danshensu (salvianic acid A), were successfully identified as key anti-inflammatory compounds by LC-MS/MS and metabolomics analyses. This is the first study identifying anti-inflammatory compounds in A. arguta (Actinidia arguta) leaves through bioassay-guided fractionation and metabolomics screening. Results of this study would be useful for the application of A. arguta leaves as a nutraceutical.

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Fecke, Antonia, Nay Min Min Thaw Saw, Dipali Kale, Siva Swapna Kasarla, Albert Sickmann, and Prasad Phapale. "Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics." Metabolites 13, no. 7 (July 13, 2023): 844. http://dx.doi.org/10.3390/metabo13070844.

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Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound’s individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a “quantitative chromatogram library” with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.

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Liu, Xiaoyan, Xiaoyi Tian, Shi Qinghong, Haidan Sun, Li Jing, Xiaoyue Tang, Zhengguang Guo, et al. "Characterization of LC-MS based urine metabolomics in healthy children and adults." PeerJ 10 (June 22, 2022): e13545. http://dx.doi.org/10.7717/peerj.13545.

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Previous studies reported that sex and age could influence urine metabolomics, which should be considered in biomarker discovery. As a consequence, for the baseline of urine metabolomics characteristics, it becomes critical to avoid confounding effects in clinical cohort studies. In this study, we provided a comprehensive lifespan characterization of urine metabolomics in a cohort of 348 healthy children and 315 adults, aged 1 to 78 years, using liquid chromatography coupled with high resolution mass spectrometry. Our results suggest that sex-dependent urine metabolites are much greater in adults than in children. The pantothenate and CoA biosynthesis and alanine metabolism pathways were enriched in early life. Androgen and estrogen metabolism showed high activity during adolescence and youth stages. Pyrimidine metabolism was enriched in the geriatric stage. Based on the above analysis, metabolomic characteristics of each age stage were provided. This work could help us understand the baseline of urine metabolism characteristics and contribute to further studies of clinical disease biomarker discovery.

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Wandy, Joe, Vinny Davies, Justin J. J. van der Hooft, Stefan Weidt, Rónán Daly, and Simon Rogers. "In Silico Optimization of Mass Spectrometry Fragmentation Strategies in Metabolomics." Metabolites 9, no. 10 (October 9, 2019): 219. http://dx.doi.org/10.3390/metabo9100219.

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Liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimize strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data to allow the testing of different fragmentation strategies. To demonstrate its utility, we show how ViMMS can be used to optimize N for Top-N data-dependent acquisition (DDA) acquisition, giving results comparable to modifying N on the mass spectrometer. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimization of the fragmentation strategy for a particular experiment.

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Chen, Chi, Frank J. Gonzalez, and Jeffrey R. Idle. "LC-MS-Based Metabolomics in Drug Metabolism." Drug Metabolism Reviews 39, no. 2-3 (January 2007): 581–97. http://dx.doi.org/10.1080/03602530701497804.

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Tian, He, Bowen Li, and Guanghou Shui. "Untargeted LC–MS Data Preprocessing in Metabolomics." Journal of Analysis and Testing 1, no. 3 (July 2017): 187–92. http://dx.doi.org/10.1007/s41664-017-0030-8.

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Bueschl, C., R. Krska, B. Kluger, and R. Schuhmacher. "Isotopic labeling-assisted metabolomics using LC–MS." Analytical and Bioanalytical Chemistry 405, no. 1 (September 26, 2012): 27–33. http://dx.doi.org/10.1007/s00216-012-6375-y.

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Deda, Olga, Olga Begou, Helen Gika, Georgios Theodoridis, and Agapios Agapiou. "Optimization of Carob Products Preparation for Targeted LC-MS/MS Metabolomics Analysis." Metabolites 13, no. 5 (May 9, 2023): 645. http://dx.doi.org/10.3390/metabo13050645.

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Carob (Ceratonia siliqua) is an exceptional source of significant bioactive compounds with great economic importance in the Mediterranean region, where it is widely cultivated. Carob fruit is used for the production of a variety of products and commodities such as powder, syrup, coffee, flour, cakes, and beverages. There is growing evidence of the beneficial effects of carob and the products made from it on a range of health problems. Therefore, metabolomics could be used to explore the nutrient-rich compounds of carob. Sample preparation is a crucial step in metabolomics-based analysis and has a great impact on the quality of the data obtained. Herein, sample preparation of carob syrup and powder was optimized, to enable highly efficient metabolomics-based HILIC-MS/MS analysis. Pooled powder and syrup samples were extracted under different conditions by adjusting pH, solvent type, and sample weight to solvent volume ratio (Wc/Vs). The metabolomics profiles obtained were evaluated using the established criteria of total area and number of maxima. It was observed that the Wc/Vs ratio of 1:2 resulted in the highest number of metabolites, regardless of solvent type or pH. Aqueous acetonitrile with a Wc/Vs ratio of 1:2 satisfied all established criteria for both carob syrup and powder samples. However, when the pH was adjusted, basic aqueous propanol 1:2 Wc/Vs and acidic aqueous acetonitrile 1:2 Wc/Vs provided the best results for syrup and powder, respectively. We strongly believe that the current study could support the standardization of the metabolomics sample preparation process to enable more efficient LC-MS/MS carob analysis.

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Deng, Chaoyang, Yang Yue, Hefei Zhang, Meng Liu, Yansong Ge, Enshuang Xu, and Jiasan Zheng. "Serum Metabolomics and Ionomics Analysis of Hoof-Deformed Cows Based on LC-MS/MS and ICP-OES/MS." Animals 13, no. 9 (April 23, 2023): 1440. http://dx.doi.org/10.3390/ani13091440.

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In order to explore the metabolic and ionic changes of hoof-deformed cows, the serum samples of 10 healthy cows (group C) and 10 hoof-deformed cows (group T) were analyzed by LC-MS/MS and ICP-OES/MS. The pathway enrichment of differential metabolites was analyzed by screening and identifying differential metabolites and ions and using a bioinformatics method. The integration of metabolomics and ionics was analyzed with ggplot2 software in R language, and verified by MRM target metabolomics. The results showed that 127 metabolites were screened by metabolomics, of which 81 were up-regulated (p < 0.05) and 46 were down-regulated (p < 0.05). The results of ICP-OES/MS showed that 13 kinds of ions such as K, Li, and Pb in serum of dairy cows were up-regulated, while 18 kinds of ions such as Al, Cu and Sb were down-regulated. The integrated analysis of metabolomics and ionics found that potassium ions were positively correlated with L-tyrosine, L-proline, thiamine and L-valine. Sodium ions were positively correlated with L-valine and negatively correlated with α-D-glucose. The results of high-throughput target metabolomics showed that the contents of L-proline, L-phenylalanine and L-tryptophan in serum of dairy cows increased significantly, which was consistent with the results of non-target metabolomics. In a word, the metabolism and ion changes in dairy cows with hoof deformation were revealed by metabolomics and ionics.

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Chong, Jasmine, Mai Yamamoto, and Jianguo Xia. "MetaboAnalystR 2.0: From Raw Spectra to Biological Insights." Metabolites 9, no. 3 (March 22, 2019): 57. http://dx.doi.org/10.3390/metabo9030057.

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Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.

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Klåvus, Anton, Marietta Kokla, Stefania Noerman, Ville M. Koistinen, Marjo Tuomainen, Iman Zarei, Topi Meuronen, et al. "“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling." Metabolites 10, no. 4 (March 31, 2020): 135. http://dx.doi.org/10.3390/metabo10040135.

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Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

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Shahid, Mohammad, Udai B. Singh, and Mohammad Saghir Khan. "Metabolomics-Based Mechanistic Insights into Revealing the Adverse Effects of Pesticides on Plants: An Interactive Review." Metabolites 13, no. 2 (February 8, 2023): 246. http://dx.doi.org/10.3390/metabo13020246.

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In plant biology, metabolomics is often used to quantitatively assess small molecules, metabolites, and their intermediates in plants. Metabolomics has frequently been applied to detect metabolic alterations in plants exposed to various biotic and abiotic stresses, including pesticides. The widespread use of pesticides and agrochemicals in intensive crop production systems is a serious threat to the functionality and sustainability of agroecosystems. Pesticide accumulation in soil may disrupt soil–plant relationships, thereby posing a pollution risk to agricultural output. Application of metabolomic techniques in the assessment of the biological consequences of pesticides at the molecular level has emerged as a crucial technique in exposome investigations. State-of-the-art metabolomic approaches such as GC–MS, LC–MS/MS UHPLC, UPLC–IMS–QToF, GC/EI/MS, MALDI-TOF MS, and 1H-HR-MAS NMR, etc., investigating the harmful effects of agricultural pesticides have been reviewed. This updated review seeks to outline the key uses of metabolomics related to the evaluation of the toxicological impacts of pesticides on agronomically important crops in exposome assays as well as bench-scale studies. Overall, this review describes the potential uses of metabolomics as a method for evaluating the safety of agricultural chemicals for regulatory applications. Additionally, the most recent developments in metabolomic tools applied to pesticide toxicology and also the difficulties in utilizing this approach are discussed.

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Zheng, Jiamin, Mathew Johnson, Rupasri Mandal, and David S. Wishart. "A Comprehensive Targeted Metabolomics Assay for Crop Plant Sample Analysis." Metabolites 11, no. 5 (May 11, 2021): 303. http://dx.doi.org/10.3390/metabo11050303.

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Metabolomics plays an important role in various fields from health to agriculture. However, the comprehensive quantitative metabolomic analysis of plants and plant metabolites has not been widely performed. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based plant metabolomics offers the sensitivity and breadth of coverage for both phenotyping and disease diagnosis of plants. Here, we report a high-coverage and quantitative MS-based assay for plant metabolite analysis. The assay detects and quantifies 206 primary and secondary plant metabolites, including many key plant hormones. In total, it measures 28 amino acids and derivatives, 27 organic acids, 20 biogenic amines and derivatives, 40 acylcarnitines, 90 phospholipids and C-6 sugars. All the analysis methods in this assay are based on LC-MS/MS techniques using both positive and negative-mode multiple reaction monitoring (MRM). The recovery rates of spiked plant samples at three different concentration levels (low, medium and high) ranged from 80% to 120%, with satisfactory precision values of less than 20%. This targeted plant metabolomic assay has been successfully applied to the analysis of large numbers of pine and spruce needle samples, canola root samples, as well as cannabis samples. Moreover, the assay was specifically developed in a 96-well plate format, which enables automated, high-throughput sample analysis. This assay has already been used to analyze over 1500 crop plant samples in less than two months.

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Xiao, Jun Feng, Bin Zhou, and Habtom W. Ressom. "Metabolite identification and quantitation in LC-MS/MS-based metabolomics." TrAC Trends in Analytical Chemistry 32 (February 2012): 1–14. http://dx.doi.org/10.1016/j.trac.2011.08.009.

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Afifah, Enik Nurlaili, and Sastia Prama Putri. "Food metabolomics for improvement of nutrition and well-being." BIO Web of Conferences 127 (2024): 07001. http://dx.doi.org/10.1051/bioconf/202412707001.

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Food metabolomics is an emerging field that employs comprehensive analytical techniques, such as Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), and Nuclear Magnetic Resonance (NMR), to identify and quantify essential nutrients and bioactive compounds in foods, and to link their impact on human health. By integrating metabolomic data with nutritional science, researchers can better elucidate how dietary components influence metabolic processes and contribute to overall health and well-being. This review highlights recent studies in food metabolomics, providing a detailed understanding of its application in assessing nutritional value, optimizing dietary recommendations, and improving food quality. The role of food metabolomics in precision nutrition and well-being is significant, and recent advancements in this research area are discussed.

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Mitchell, Joshua M., Yuanye Chi, Maheshwor Thapa, Zhiqiang Pang, Jianguo Xia, and Shuzhao Li. "Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline." PLOS Computational Biology 20, no. 6 (June 6, 2024): e1011912. http://dx.doi.org/10.1371/journal.pcbi.1011912.

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To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

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Plewa, Szymon, Paweł Dereziński, Jolanta Florczak-Wyspiańska, Karolina Popławska-Domaszewicz, Wojciech Kozubski, Bartosz Sokół, Roman Jankowski, Jan Matysiak, and Zenon J. Kokot. "LC-MS/MS based targeted metabolomics method for analysis of serum and cerebrospinal fluid." Journal of Medical Science 88, no. 1 (February 7, 2019): 12–20. http://dx.doi.org/10.20883/jms.2019.335.

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Introduction. Recent instrumentation and software advancement enabled to develop new, high‑throughput targeted metabolomics methods for in‑depth exploration of metabolome in a quantitative manner.Material and Methods. The presented targeted metabolomics approach allows to analyze both of serum and CSF in the same way, with identical sample preparation procedures. The analyses were carried out using high‑performance liquid chromatography system coupled to triple quadrupole tandem mass spectrometer with electrospray ion source (LC‑ESI‑QqQ‑MS/MS). Results. The applied targeted metabolomics approach enabled to determine a wide panel of metabolites from different chemical classes of compounds including: acylcarnitines, amino acids and biogenic amines, glycerophospholipids, sphingolipids and sum of hexoses. Finally, 148 metabolites in serum and 57 in cerebrospinal fluid were determined.Conclusions. Here we presented the results of successful implementation of the method of analysis of low‑molecular weight compounds in human serum and CSF using targeted metabolomics. The evaluation of selected groups of metabolites resulted in obtaining the mean concentrations of panel of metabolites in serum and CSF, which gives a valuable information about the metabolome of these matrices.

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Zhao, Shuang, and Liang Li. "Chemical derivatization in LC-MS-based metabolomics study." TrAC Trends in Analytical Chemistry 131 (October 2020): 115988. http://dx.doi.org/10.1016/j.trac.2020.115988.

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Lu, Wenyun, Bryson D. Bennett, and Joshua D. Rabinowitz. "Analytical strategies for LC–MS-based targeted metabolomics." Journal of Chromatography B 871, no. 2 (August 2008): 236–42. http://dx.doi.org/10.1016/j.jchromb.2008.04.031.

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Becker, Susen, Linda Kortz, Christin Helmschrodt, Joachim Thiery, and Uta Ceglarek. "LC–MS-based metabolomics in the clinical laboratory." Journal of Chromatography B 883-884 (February 2012): 68–75. http://dx.doi.org/10.1016/j.jchromb.2011.10.018.

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Chen, Chi, and Sangyub Kim. "LC-MS-BASED METABOLOMICS OF XENOBIOTIC-INDUCED TOXICITIES." Computational and Structural Biotechnology Journal 4, no. 5 (January 2013): e201301008. http://dx.doi.org/10.5936/csbj.201301008.

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Wu, Huan, and Fang Feng. "Untargeted metabolomic analysis using LC-TOF/MS and LC-MS/MS for revealing metabolic alterations linked to alcohol-induced hepatic steatosis in rat serum and plasma." RSC Advances 6, no. 34 (2016): 28279–88. http://dx.doi.org/10.1039/c5ra27910k.

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Shi, Zhan, Haohui Li, Wei Zhang, Youxiang Chen, Chunyan Zeng, Xiuhua Kang, Xinping Xu, et al. "A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies." Metabolites 12, no. 12 (November 24, 2022): 1168. http://dx.doi.org/10.3390/metabo12121168.

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As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

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Banoei, Mohammad M., Sarah J. Donnelly, Beata Mickiewicz, Aalim Weljie, Hans J. Vogel, and Brent W. Winston. "Metabolomics in critical care medicine: a new approach to biomarker discovery." Clinical & Investigative Medicine 37, no. 6 (December 1, 2014): 363. http://dx.doi.org/10.25011/cim.v37i6.22241.

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Purpose: To present an overview and comparison of the main metabolomics techniques (1H NMR, GC-MS, and LC-MS) and their current and potential use in critical care medicine. Source: This is a focused review, not a systematic review, using the PubMed database as the predominant source of references to compare metabolomics techniques. Principal Findings: 1H NMR, GC-MS, and LC-MS are complementary techniques that can be used on a variety of biofluids for metabolomics analysis of patients in the Intensive Care Unit (ICU). These techniques have been successfully used for diagnosis and prognosis in the ICU and other clinical settings; for example, in patients with septic shock and community-acquired pneumonia. Conclusion: Metabolomics is a powerful tool that has strong potential to impact diagnosis and prognosis and to examine responses to treatment in critical care medicine through diagnostic and prognostic biomarker and biopattern identification

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Riquelme, Gabriel, Nicolás Zabalegui, Pablo Marchi, Christina M. Jones, and María Eugenia Monge. "A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows." Metabolites 10, no. 10 (October 16, 2020): 416. http://dx.doi.org/10.3390/metabo10100416.

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Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.

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Tambay, Vincent, Valérie-Ann Raymond, Corentine Goossens, Louise Rousseau, Simon Turcotte, and Marc Bilodeau. "Metabolomics-Guided Identification of a Distinctive Hepatocellular Carcinoma Signature." Cancers 15, no. 12 (June 18, 2023): 3232. http://dx.doi.org/10.3390/cancers15123232.

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Background: Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming. Methods: HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography–mass spectrometry (LC/MS). Results: Principal component analysis (PCA) within our 1H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS–DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS. Conclusions: Together, 1H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis.

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Habra, Hani, Jennifer L. Meijer, Tong Shen, Oliver Fiehn, David A. Gaul, Facundo M. Fernández, Kaitlin R. Rempfert, et al. "metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics." Metabolites 14, no. 2 (February 15, 2024): 125. http://dx.doi.org/10.3390/metabo14020125.

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Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a “primary” feature list is used as a template for matching compounds in “target” feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution’s in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

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Alpay Savasan, Zeynep, Ali Yilmaz, Zafer Ugur, Buket Aydas, Ray Bahado-Singh, and Stewart Graham. "Metabolomic Profiling of Cerebral Palsy Brain Tissue Reveals Novel Central Biomarkers and Biochemical Pathways Associated with the Disease: A Pilot Study." Metabolites 9, no. 2 (February 2, 2019): 27. http://dx.doi.org/10.3390/metabo9020027.

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Cerebral palsy (CP) is one of the most common causes of motor disability in childhood, with complex and heterogeneous etiopathophysiology and clinical presentation. Understanding the metabolic processes associated with the disease may aid in the discovery of preventive measures and therapy. Tissue samples (caudate nucleus) were obtained from post-mortem CP cases (n = 9) and age- and gender-matched control subjects (n = 11). We employed a targeted metabolomics approach using both 1H NMR and direct injection liquid chromatography-tandem mass spectrometry (DI/LC-MS/MS). We accurately identified and quantified 55 metabolites using 1H NMR and 186 using DI/LC-MS/MS. Among the 222 detected metabolites, 27 showed significant concentration changes between CP cases and controls. Glycerophospholipids and urea were the most commonly selected metabolites used to develop predictive models capable of discriminating between CP and controls. Metabolomics enrichment analysis identified folate, propanoate, and androgen/estrogen metabolism as the top three significantly perturbed pathways. We report for the first time the metabolomic profiling of post-mortem brain tissue from patients who died from cerebral palsy. These findings could help to further investigate the complex etiopathophysiology of CP while identifying predictive, central biomarkers of CP.

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Mo, Liang, Bing Wei, Renji Liang, Zhi Yang, Shouzhi Xie, Shengrong Wu, and Yong You. "Exploring potential biomarkers for lung adenocarcinoma using LC-MS/MS metabolomics." Journal of International Medical Research 48, no. 4 (April 2020): 030006051989721. http://dx.doi.org/10.1177/0300060519897215.

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Background The average 5-year survival rate of lung adenocarcinoma patients is only 15% to 17%, which is primarily due to late-stage diagnosis and a lack of specific prognostic evaluations that can recommend effective therapies. Additionally, there is no clinically recognized biomarker that is effective for early-stage diagnosis. Methods Tissue samples from 10 lung adenocarcinoma patients (both tumor and non-tumor tissues) and 10 benign lung tumor samples were collected. The significantly differentially represented metabolites from the three groups were analyzed by liquid chromatography and tandem mass spectrometry. Results Pathway analysis indicated that central carbon metabolism was the top altered pathway in lung adenocarcinoma, while protein digestion and absorption, and central carbon metabolism were the top altered pathways in benign lung tumors. Receiver operating characteristic curve analysis revealed that adenosine 3′-monophosphate, creatine, glycerol, and 14 other differential metabolites were potential sensitive and specific biomarkers for the diagnosis and prognosis of lung adenocarcinoma. Conclusion Our findings suggest that the metabolomics approach may be a useful method to detect potential biomarkers in lung adenocarcinoma patients.

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Zeki, Özge Cansın, Cemil Can Eylem, Tuba Reçber, Sedef Kır, and Emirhan Nemutlu. "Integration of GC–MS and LC–MS for untargeted metabolomics profiling." Journal of Pharmaceutical and Biomedical Analysis 190 (October 2020): 113509. http://dx.doi.org/10.1016/j.jpba.2020.113509.

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Wang, Xusheng, Ji-Hoon Cho, Suresh Poudel, Yuxin Li, Drew R. Jones, Timothy I. Shaw, Haiyan Tan, Boer Xie, and Junmin Peng. "JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics." Metabolites 10, no. 5 (May 12, 2020): 190. http://dx.doi.org/10.3390/metabo10050190.

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Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rule of chemistry. We illustrated the utility of JUMPm by detecting metabolite formulas and structures from liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analyses of unlabeled and stable-isotope labeled yeast samples. We also benchmarked the performance of JUMPm by analyzing a mixed sample from a commercially available metabolite library in both hydrophilic and hydrophobic LC-MS/MS. These analyses confirm that metabolite identification can be significantly improved by estimating the element composition in formulas using stable isotope labeling, or by introducing LC retention time during a spectral library search, which are incorporated into JUMPm functions. Finally, we compared the performance of JUMPm and two commonly used programs, Compound Discoverer 3.1 and MZmine 2, with respect to putative metabolite identifications. Our results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.

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Stettin, Daniel, Remington X. Poulin, and Georg Pohnert. "Metabolomics Benefits from Orbitrap GC–MS—Comparison of Low- and High-Resolution GC–MS." Metabolites 10, no. 4 (April 4, 2020): 143. http://dx.doi.org/10.3390/metabo10040143.

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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.

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Hanifah, Abu, Awang Maharijaya, Sastia P. Putri, Walter A. Laviña, and Sobir. "Untargeted Metabolomics Analysis of Eggplant (Solanum melongena L.) Fruit and Its Correlation to Fruit Morphologies." Metabolites 8, no. 3 (September 1, 2018): 49. http://dx.doi.org/10.3390/metabo8030049.

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Eggplant is one of the most widely cultivated vegetables in the world and has high biodiversity in terms of fruit shape, size, and color. Therefore, fruit morphology and nutrient content become important considerations for both consumers and breeders who develop new eggplant-based products. To gain insight on the diversity of eggplant metabolites, twenty-one eggplant accessions were analyzed by untargeted metabolomics using GC-MS and LC-MS. The dataset of eggplant fruit morphologies, and metabolites specific to different eggplant fruit accessions were used for correlation analysis. Untargeted metabolomics analysis using LC-MS and GC-MS was able to detect 136 and 207 peaks, respectively. Fifty-one (51) metabolites from the LC-MS analysis and 207 metabolites from the GC-MS analysis were putatively identified, which included alkaloids, terpenes, terpenoids, fatty acids, and flavonoids. Spearman correlation analysis revealed that 14 fruit morphologies were correlated with several metabolites. This information will be very useful for the development of strategies for eggplant breeding.

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Beyoğlu, Diren, Yury V. Popov, and Jeffrey R. Idle. "The Metabolomic Footprint of Liver Fibrosis." Cells 13, no. 16 (August 11, 2024): 1333. http://dx.doi.org/10.3390/cells13161333.

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Both experimental and clinical liver fibrosis leave a metabolic footprint that can be uncovered and defined using metabolomic approaches. Metabolomics combines pattern recognition algorithms with analytical chemistry, in particular, 1H and 13C nuclear magnetic resonance spectroscopy (NMR), gas chromatography–mass spectrometry (GC–MS) and various liquid chromatography–mass spectrometry (LC–MS) platforms. The analysis of liver fibrosis by each of these methodologies is reviewed separately. Surprisingly, there was little general agreement between studies within each of these three groups and also between groups. The metabolomic footprint determined by NMR (two or more hits between studies) comprised elevated lactate, acetate, choline, 3-hydroxybutyrate, glucose, histidine, methionine, glutamine, phenylalanine, tyrosine and citrate. For GC–MS, succinate, fumarate, malate, ascorbate, glutamate, glycine, serine and, in agreement with NMR, glutamine, phenylalanine, tyrosine and citrate were delineated. For LC–MS, only β-muricholic acid, tryptophan, acylcarnitine, p-cresol, valine and, in agreement with NMR, phosphocholine were identified. The metabolomic footprint of liver fibrosis was upregulated as regards glutamine, phenylalanine, tyrosine, citrate and phosphocholine. Several investigators employed traditional Chinese medicine (TCM) treatments to reverse experimental liver fibrosis, and a commentary is given on the chemical constituents that may possess fibrolytic activity. It is proposed that molecular docking procedures using these TCM constituents may lead to novel therapies for liver fibrosis affecting at least one-in-twenty persons globally, for which there is currently no pharmaceutical cure. This in-depth review summarizes the relevant literature on metabolomics and its implications in addressing the clinical problem of liver fibrosis, cirrhosis and its sequelae.

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Szczerbinski, Lukasz, Gladys Wojciechowska, Adam Olichwier, Mark A. Taylor, Urszula Puchta, Paulina Konopka, Adam Paszko, et al. "Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity." Nutrients 14, no. 1 (January 4, 2022): 214. http://dx.doi.org/10.3390/nu14010214.

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Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.

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Hemmer, Selina, Sascha K. Manier, Svenja Fischmann, Folker Westphal, Lea Wagmann, and Markus R. Meyer. "Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data." Metabolites 10, no. 9 (September 21, 2020): 378. http://dx.doi.org/10.3390/metabo10090378.

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The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

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Shi, Wen, Xiang Yuan, Kuiqing Cui, Hui Li, Penghui Fu, Saif-Ur Rehman, Deshun Shi, Qingyou Liu, and Zhipeng Li. "LC-MS/MS Based Metabolomics Reveal Candidate Biomarkers and Metabolic Changes in Different Buffalo Species." Animals 11, no. 2 (February 20, 2021): 560. http://dx.doi.org/10.3390/ani11020560.

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Consumers have shown more and more interest in high-quality and healthy dairy products and buffalo milk is commercially more viable than other milks in producing superior dairy products due to its higher contents of fat, crude protein, and total solids. Metabolomics is one of the most powerful strategies in molecular mechanism research however, little study has been focused on the milk metabolites in different buffalo species. Therefore, the aim of this study was to explore the underlying molecular mechanism of the fatty synthesis and candidate biomarkers by analyzing the metabolomic profiles. Milk of three groups of buffaloes, including 10 Mediterranean, 12 Murrah, and 10 crossbred buffaloes (Murrah × local swamp buffalo), were collected and UPLC-Q-Orbitrap HRMS was used to obtain the metabolomic profiles. Results showed that milk fatty acid in Mediterranean buffalo was significantly higher than Murrah buffalo and crossbred buffalo. A total of 1837/726 metabolites was identified in both positive and negative electrospray ionization (ESI±) mode, including 19 significantly different metabolites between Mediterranean and Murrah buffalo, and 18 different metabolites between Mediterranean and crossbred buffalo. We found 11 of the different metabolites were both significantly different between Mediterranean vs. Murrah group and Mediterranean vs crossbred group, indicating that they can be used as candidate biomarkers of Mediterranean buffalo milk. Further analysis found that the different metabolites were mainly enriched in fat synthesis related pathways such as fatty acid biosynthesis, unsaturated fatty acid biosynthesis, and linoleic acid metabolism, indicating that the priority of different pathways affected the milk fat content in different buffalo species. These specific metabolites may be used as biomarkers in the identification of milk quality and molecular breeding of high milk fat buffalo.

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Galeano Garcia, Paula, Fábio Neves dos Santos, Samantha Zanotta, Marcos Eberlin, and Chiara Carazzone. "Metabolomics of Solanum lycopersicum Infected with Phytophthora infestans Leads to Early Detection of Late Blight in Asymptomatic Plants." Molecules 23, no. 12 (December 15, 2018): 3330. http://dx.doi.org/10.3390/molecules23123330.

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Tomato crops suffer attacks of various pathogens that cause large production losses. Late blight caused by Phytophthora infestans is a devastating disease in tomatoes because of its difficultly to control. Here, we applied metabolomics based on liquid chromatography–mass spectrometry (LC-MS) and metabolic profiling by matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in combination with multivariate data analysis in the early detection of late blight on asymptomatic tomato plants and to discriminate infection times of 4, 12, 24, 36, 48, 60, 72 and 96 h after inoculation (hpi). MALDI-MS and LC-MS profiles of metabolites combined with multivariate data analysis are able to detect early-late blight-infected tomato plants, and metabolomics based on LC-MS discriminates infection times in asymptomatic plants. We found the metabolite tomatidine as an important biomarker of infection, saponins as early infection metabolite markers and isocoumarin as early and late asymptomatic infection marker along the post infection time. MALDI-MS and LC-MS analysis can therefore be used as a rapid and effective method for the early detection of late blight-infected tomato plants, offering a suitable tool to guide the correct management and application of sanitary defense approaches. LC-MS analysis also appears to be a suitable tool for identifying major metabolites of asymptomatic late blight-infected tomato plants.

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Goossens, Corentine, Vincent Tambay, Valérie-Ann Raymond, Louise Rousseau, Simon Turcotte, and Marc Bilodeau. "Impact of the delay in cryopreservation timing during biobanking procedures on human liver tissue metabolomics." PLOS ONE 19, no. 6 (June 10, 2024): e0304405. http://dx.doi.org/10.1371/journal.pone.0304405.

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The liver is a highly specialized organ involved in regulating systemic metabolism. Understanding metabolic reprogramming of liver disease is key in discovering clinical biomarkers, which relies on robust tissue biobanks. However, sample collection and storage procedures pose a threat to obtaining reliable results, as metabolic alterations may occur during sample handling. This study aimed to elucidate the impact of pre-analytical delay during liver resection surgery on liver tissue metabolomics. Patients were enrolled for liver resection during which normal tissue was collected and snap-frozen at three timepoints: before transection, after transection, and after analysis in Pathology. Metabolomics analyses were performed using 1H Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS). Time at cryopreservation was the principal variable contributing to differences between liver specimen metabolomes, which superseded even interindividual variability. NMR revealed global changes in the abundance of an array of metabolites, namely a decrease in most metabolites and an increase in β-glucose and lactate. LC-MS revealed that succinate, alanine, glutamine, arginine, leucine, glycerol-3-phosphate, lactate, AMP, glutathione, and NADP were enhanced during cryopreservation delay (all p<0.05), whereas aspartate, iso(citrate), ADP, and ATP, decreased (all p<0.05). Cryopreservation delays occurring during liver tissue biobanking significantly alter an array of metabolites. Indeed, such alterations compromise the integrity of metabolomic data from liver specimens, underlining the importance of standardized protocols for tissue biobanking in hepatology.

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Cai, Qingpo, Jessica A. Alvarez, Jian Kang, and Tianwei Yu. "Network Marker Selection for Untargeted LC–MS Metabolomics Data." Journal of Proteome Research 16, no. 3 (February 17, 2017): 1261–69. http://dx.doi.org/10.1021/acs.jproteome.6b00861.

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Burton, Lyle, Gordana Ivosev, Stephen Tate, Gary Impey, Julie Wingate, and Ron Bonner. "Instrumental and experimental effects in LC–MS-based metabolomics." Journal of Chromatography B 871, no. 2 (August 2008): 227–35. http://dx.doi.org/10.1016/j.jchromb.2008.04.044.

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Shah, Punit, Richard Searfoss, Valerie Bussberg, Bennett Greenwood, Shraddha Karmacharya, Allison MacDonald, Kennedy Ofori-Mensa, et al. "Abstract 5319: Treatment of K562 leukemia cells with an experimental UBE2K modifier identifies multi-omic changes associated with altered oncogenic processes." Cancer Research 82, no. 12_Supplement (June 15, 2022): 5319. http://dx.doi.org/10.1158/1538-7445.am2022-5319.

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Abstract Ubiquitination is a conserved post translation modification involving covalent attachment of ubiquitin protein and is known to regulate many biological processes, including proteasomal degradation. Three major families of enzymes are involved in the regulation of ubiquitination, including activating enzyme E1, Ubiquitin conjugating enzyme E2 and ubiquitin ligase E3. UBE2K is an E2 conjugating ligase that was identified as an anti-cancer drug target from the BERG Interrogative Biology® platform, an artificial intelligence multi-omics analytical method employing Bayesian algorithms. Herein, we used proteomics, lipidomics and metabolomics to investigate the impact of the treatment of UBE2K small molecule ligand (BRG0451) on K562 leukemia cells. K562 cells were treated with 30, 100 and 300 nM concentrations for 24 hours with BRG0451 or Paclitaxel or 0.1% DMSO (Control). Cells were pelleted and analyzed using a multi-omics approach. Proteomic analysis was performed using Thermo Q-Exactive+ LC MS/MS analysis. Lipidomic analysis was performed using SCIEX TripleTOF MS/MS ALL shotgun workflow and metabolomics was performed using 3 different platforms (High resolution RP-LC-MS, HILIC QqQ LC-MS/MS and GC-TOF MS). Unsupervised clustering and differential analysis were used to investigate the impact of the treatments. Proteomic analysis identified and quantified 6930 proteins from K562 cells using TMT labelling with offline 24 fractions and LC-MS/MS. Structural lipidomics analysis evaluated 1980 lipid molecular species and metabolomics analysis identified over 700 metabolites using GC-MS, LC-MS and LC-MS/MS. Multiomics and regression analysis for 30 nM BRG0451 treatment revealed no distinct pattern of omics variables. However, treatment on K562 cells with 300 nM treatment demonstrated 97 differentially expressed proteins compared to control. Pathway analysis revealed chromatin remodeling, and more specifically, regulation of chromatin silencing and localization to nucleolus as major pathways impacted by differentially expressed proteins. Similar pathways were impacted by Paclitaxel and Nocodazole treatment compared to control. Additionally, metabolomic and lipidomic differentials were observed with 300 nM BRG0451 treatment. Structural lipidomics revealed dose -dependent changes in triacylglycerols and cholesterol esters, glycolipid monounsaturated species, and glycolipid medium carbon chain subgroups. Dose dependent impact on amino acids metabolism, purine metabolism, and pyrimidine metabolism was observed with a high degree of similarity for compared drugs. Herein, we demonstrated the use of multi-omics technology in deconvoluting the impact of BRG0451 on independent biological pathways, revealing the intricate mechanisms targeting cell cycle as well as ubiquitin regulator components in a leukemia cell line. Citation Format: Punit Shah, Richard Searfoss, Valerie Bussberg, Bennett Greenwood, Shraddha Karmacharya, Allison MacDonald, Kennedy Ofori-Mensa, Vladimir Tolstikov, Pragalath Sundararajan, Maria-Dorothea Nastke, Eric M. Grund, Gregory M. Miller, Stephane Gesta, Rangaprasad Sarangarajan, Elder Granger, Niven R. Narain, Vivek K. Vishnudas, Michael A. Kiebish. Treatment of K562 leukemia cells with an experimental UBE2K modifier identifies multi-omic changes associated with altered oncogenic processes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5319.

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George, Saby, Kyoung-Soo Choi, Roberto Pili, and Abdul Latif Kazim. "The lipid metabolome of clear cell renal cell carcinoma (CCRCC)." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 10609. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.10609.

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10609 Background: The commonest type of kidney cancer is CCRCC. Treatment approaches mostly target aberrant vasculature. However, kidney cancer is also known to accumulate lipids and a detailed knowledge of the lipid species present in these tumors could lead to a better understanding of the underlying aberrant metabolic pathways and suggest possible treatment strategies. Lipidomics is an emerging field driven by rapid advances in mass spectrometry (MS), and is widely used to discover biomarkers. We attempt to identify the lipidomic profile of CCRCC using a liquid chromatography MS-based approach (LC-MS). Methods: We utilized 6 fresh frozen representative samples of CCRCC and matching non-tumor areas of kidney from nephrectomy samples. Lipids and other non-polar cellular constituents were extracted from both CCRCC and control tissues by methyl-t-butyl ether /methanol. LC-MS based lipid profiling was performed on a Waters Q-ToF Premier MS coupled with Ultra Performance LC. The peak detection and alignment across all chromatograms were performed using the XCMS software (v 1.14.1, Scripps Center for Metabolomics). Statistical comparisons of the intensities of aligned peaks were performed using the XCMS-built-in Welch's t-test. Results: The outcome of XCMS was converted to a table that contains fold change, p-value and mass to charge ratio (m/z) for each peak, its corresponding retention time, and the integrated peak intensities from all samples. 224 peaks out of 1419 differed between CCRCC and the control group, with p <0.05, calculated by XCMS. About an equal number of analytes increased or decreased in CCRCC compared with control samples. Preliminary attempts to identify the analytes included use of METLIN (Scripps Center for Metabolomics) and HMDB (Human metabolome database, Genome Alberta & Genome Canada) databases. Many of the hits identified phosphatidylcholines, phosphatidylethanolamines, triacylglycerols and diacylglycerols, as well as other lipid species. Conclusions: The lipid metabolomic profile varied significantly between CCRCC and control. Further studies are required to confirm the identities of the lipid species contributing to this variation by obtaining structural information using tandem MS (LC-MS/MS).

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Journal articles on the topic 'Metabolomics (LC-MS)'

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