Journal articles: 'Mass spectrometry metabolomic'

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Tsuchida, Sachio, and Tomohiro Nakayama. "Metabolomics Research in Periodontal Disease by Mass Spectrometry." Molecules 27, no. 9 (April 30, 2022): 2864. http://dx.doi.org/10.3390/molecules27092864.

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Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.

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Furina, R. R., N. N. Mitrakova, V. L. Ryzhkov, and I. K. Safiullin. "Metabolomic research in medicine." Kazan medical journal 95, no. 1 (February 15, 2014): 1–6. http://dx.doi.org/10.17816/kmj1445.

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The paper covers the questions of metabolomic research in medicine. The central idea of metabolomics is to identify the specific biomarkers in biological samples for diagnosis of a number of conditions. The biomarkers include volatile organic compounds - metabolites isolated from various tissues and biological fluids (blood, urine, sputum, exhaled air). Main methods of separation and identification of volatile organic compounds (gas chromatography, mass spectrometry, nuclear magnetic resonance spectroscopy) applied in metabolomics, are reviewed. Mass spectrometry and nuclear magnetic resonance spectroscopy are compared as the main methods of volatile metabolites detection. The method of solid phase microextraction, used for sample preparation, is described. The paper reviews laboratory research results aimed at the detection of cancer, chronic infections and inherited diseases biomarkers. The qualitative characteristics of biological sample metabolome taken from patients with different diseases are discussed. Besides, special attention is paid to the possible use of metabolomics in experimental medicine. The results of volatile metabolome changes in cell culture in vitro depending on the additives to nutrient media, β-carotene volatile decomposition products as suspected carcinogens, volatile organic compounds emitted at vertebrates decay are described. In addition, the method of two-dimensional gas chromatography aimed to increase the sensitivity and specificity of metabolomics tests is portrayed. The presented approach adds to early diagnosis of a number of diseases.

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Lokhov, Petr G., Oxana P. Trifonova, Dmitry L. Maslov, and Elena E. Balashova. "In Situ Mass Spectrometry Diagnostics of Impaired Glucose Tolerance Using Label-Free Metabolomic Signature." Diagnostics 10, no. 12 (December 5, 2020): 1052. http://dx.doi.org/10.3390/diagnostics10121052.

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In metabolomics, mass spectrometry is used to detect a large number of low-molecular substances in a single analysis. Such a capacity could have direct application in disease diagnostics. However, it is challenging because of the analysis complexity, and the search for a way to simplify it while maintaining the diagnostic capability is an urgent task. It has been proposed to use the metabolomic signature without complex data processing (mass peak detection, alignment, normalization, and identification of substances, as well as any complex statistical analysis) to make the analysis more simple and rapid. Methods: A label-free approach was implemented in the metabolomic signature, which makes the measurement of the actual or conditional concentrations unnecessary, uses only mass peak relations, and minimizes mass spectra processing. The approach was tested on the diagnosis of impaired glucose tolerance (IGT). Results: The label-free metabolic signature demonstrated a diagnostic accuracy for IGT equal to 88% (specificity 85%, sensitivity 90%, and area under receiver operating characteristic curve (AUC) of 0.91), which is considered to be a good quality for diagnostics. Conclusions: It is possible to compile label-free signatures for diseases that allow for diagnosing the disease in situ, i.e., right at the mass spectrometer without complex data processing. This achievement makes all mass spectrometers potentially versatile diagnostic devices and accelerates the introduction of metabolomics into medicine.

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Patel, Manish, Sonika Pandey, Manoj Kumar, Md Haque, Sikander Pal, and Narendra Yadav. "Plants Metabolome Study: Emerging Tools and Techniques." Plants 10, no. 11 (November 8, 2021): 2409. http://dx.doi.org/10.3390/plants10112409.

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Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

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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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Deutsch, Jessica M., Madison O. Green, Priyanka Akavaram, Ashleigh C. Davis, Sarth S. Diskalkar, Isabelle A. Du Plessis, Hannah A. Fallon, et al. "Limited Metabolomic Overlap between Commensal Bacteria and Marine Sponge Holobionts Revealed by Large Scale Culturing and Mass Spectrometry-Based Metabolomics: An Undergraduate Laboratory Pedagogical Effort at Georgia Tech." Marine Drugs 21, no. 1 (January 14, 2023): 53. http://dx.doi.org/10.3390/md21010053.

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Sponges are the richest source of bioactive organic small molecules, referred to as natural products, in the marine environment. It is well established that laboratory culturing-resistant symbiotic bacteria residing within the eukaryotic sponge host matrix often synthesize the natural products that are detected in the sponge tissue extracts. However, the contributions of the culturing-amenable commensal bacteria that are also associated with the sponge host to the overall metabolome of the sponge holobiont are not well defined. In this study, we cultured a large library of bacteria from three marine sponges commonly found in the Florida Keys. Metabolomes of isolated bacterial strains and that of the sponge holobiont were compared using mass spectrometry to reveal minimal metabolomic overlap between commensal bacteria and the sponge hosts. We also find that the phylogenetic overlap between cultured commensal bacteria and that of the sponge microbiome is minimal. Despite these observations, the commensal bacteria were found to be a rich resource for novel natural product discovery. Mass spectrometry-based metabolomics provided structural insights into these cryptic natural products. Pedagogic innovation in the form of laboratory curricula development is described which provided undergraduate students with hands-on instruction in microbiology and natural product discovery using metabolomic data mining strategies.

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Gil-de-la-Fuente, Alberto, Maricruz Mamani-Huanca, María C. Stroe, Sergio Saugar, Alejandra Garcia-Alvarez, Axel A. Brakhage, Coral Barbas, and Abraham Otero. "Aspergillus Metabolome Database for Mass Spectrometry Metabolomics." Journal of Fungi 7, no. 5 (May 15, 2021): 387. http://dx.doi.org/10.3390/jof7050387.

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The Aspergillus Metabolome Database is a free online resource to perform metabolite annotation in mass spectrometry studies devoted to the genus Aspergillus. The database was created by retrieving and curating information on 2811 compounds present in 601 different species and subspecies of the genus Aspergillus. A total of 1514 scientific journals where these metabolites are mentioned were added as meta-information linked to their respective compounds in the database. A web service to query the database based on m/z (mass/charge ratio) searches was added to CEU Mass Mediator; these queries can be performed over the Aspergillus database only, or they can also include a user-selectable set of other general metabolomic databases. This functionality is offered via web applications and via RESTful services. Furthermore, the complete content of the database has been made available in .csv files and as a MySQL database to facilitate its integration into third-party tools. To the best of our knowledge, this is the first database and the first service specifically devoted to Aspergillus metabolite annotation based on m/z searches.

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Lains, Ines, Kevin M. Mendez, João Q. Gil, John B. Miller, Rachel S. Kelly, Patrícia Barreto, Ivana K. Kim, et al. "Urinary Mass Spectrometry Profiles in Age-Related Macular Degeneration." Journal of Clinical Medicine 11, no. 4 (February 11, 2022): 940. http://dx.doi.org/10.3390/jcm11040940.

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We and others have shown that patients with different severity stages of age-related macular degeneration (AMD) have distinct plasma metabolomic profiles compared to controls. Urine is a biofluid that can be obtained non-invasively and, in other fields, urine metabolomics has been proposed as a feasible alternative to plasma biomarkers. However, no studies have applied urinary mass spectrometry (MS) metabolomics to AMD. This study aimed to assess urinary metabolomic profiles of patients with different stages of AMD and a control group. We included two prospectively designed, multicenter, cross-sectional study cohorts: Boston, US (n = 185) and Coimbra, Portugal (n = 299). We collected fasting urine samples, which were used for metabolomic profiling (Ultrahigh Performance Liquid chromatography—Mass Spectrometry). Multivariable logistic and ordinal logistic regression models were used for analysis, accounting for gender, age, body mass index and use of AREDS supplementation. Results from both cohorts were then meta-analyzed. No significant differences in urine metabolites were seen when comparing patients with AMD and controls. When disease severity was considered as an outcome, six urinary metabolites differed significantly (p < 0.01). In particular, two of the metabolites identified have been previously shown by our group to also differ in the plasma of patients of AMD compared to controls and across severity stages. While there are fewer urinary metabolites associated with AMD than plasma metabolites, this study identified some differences across stages of disease that support previous work performed with plasma, thus highlighting the potential of these metabolites as future biomarkers for AMD.

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Fukushima, Atsushi, Mikiko Takahashi, Hideki Nagasaki, Yusuke Aono, Makoto Kobayashi, Miyako Kusano, Kazuki Saito, Norio Kobayashi, and Masanori Arita. "Development of RIKEN Plant Metabolome MetaDatabase." Plant and Cell Physiology 63, no. 3 (December 17, 2021): 433–40. http://dx.doi.org/10.1093/pcp/pcab173.

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Abstract The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography–MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.

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Gamache, Paul H., David F. Meyer, Michael C. Granger, and Ian N. Acworth. "Metabolomic applications of electrochemistry/Mass spectrometry." Journal of the American Society for Mass Spectrometry 15, no. 12 (December 2004): 1717–26. http://dx.doi.org/10.1016/j.jasms.2004.08.016.

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Kaji, Sanae, Masatoshi Kusuhara, Rie Makuuchi, Yushi Yamakawa, Masanori Tokunaga, Yutaka Tanizawa, Etsuro Bando, et al. "Metabolomic profling in gastric cancer tissues using time-of-flight mass spectrometry." Journal of Clinical Oncology 35, no. 4_suppl (February 1, 2017): 66. http://dx.doi.org/10.1200/jco.2017.35.4_suppl.66.

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66 Background: To explore carcinogenic and prognostic biomarkers for gastric cancer, genomics, transcriptomics and proteomics approaches have been extensively applied;however, little has been investigated regarding the role of metabolomics profiles on progression and prognosis of gastric cancer. Therefore, in order to elucidate the role of metabolome on prognosis of gastric cancer, we investigated the metabolic profiles of gastric cancer tissue using time-of-flight mass spectrometry (TOFMS) Methods: A total of 162 patients with gastric cancer underwent gastrectomy from February 2010 to March 2013 were enrolled in this study. Cancer tissues (CA) and adjacent non-cancerous tissues (NC) were obtained from surgically resected sample and were snap-frozen in liquid nitrogen. The samples were then homogenized and then applied to capillary electrophoresis TOFMS (CE-TOFMS) The metabolomics dates were analyzed using principal component analysis (PCA) and hierachical clustering analysis(HCA) in order to compare the metabolic profiles of NC and CA. Metabolites date were further assessed according by the non-parametric Mann–Whitney U-test on the presence or absence of recurrence . Results: A total 96 metabolites were detected and quantified. PCA of the date well-distinguished CA from NC. In CA, lactate / pyruvate ratio was significantly higher, while adenylate energy charge was significantly lower than NC, which reaffirms the Warburug effect of cancer. Total glutathione and reducted glutathione/oxidized glutathione ratio in CA were significantly higher than in NC which possibly contributes to the homeostasis of redox status in CA. Intriguingly, in patients with recurrence, tumor concentrations of β-Ala, Asp, GDP and Gly were significantly lower than in those without recurrence. Conclusions: Metabolomic profiling clearly differentiated CA from NC. Considerably high lactate and amino acids levels expectedly highlighted the metabolome of tumors. Certain metabolite will be a candidate for biomarker in gastric cancer.

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Salzer, Liesa, and Michael Witting. "Quo Vadis Caenorhabditis elegans Metabolomics—A Review of Current Methods and Applications to Explore Metabolism in the Nematode." Metabolites 11, no. 5 (April 29, 2021): 284. http://dx.doi.org/10.3390/metabo11050284.

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Metabolomics and lipidomics recently gained interest in the model organism Caenorhabditis elegans (C. elegans). The fast development, easy cultivation and existing forward and reverse genetic tools make the small nematode an ideal organism for metabolic investigations in development, aging, different disease models, infection, or toxicology research. The conducted type of analysis is strongly depending on the biological question and requires different analytical approaches. Metabolomic analyses in C. elegans have been performed using nuclear magnetic resonance (NMR) spectroscopy, direct infusion mass spectrometry (DI-MS), gas-chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) or combinations of them. In this review we provide general information on the employed techniques and their advantages and disadvantages in regard to C. elegans metabolomics. Additionally, we reviewed different fields of application, e.g., longevity, starvation, aging, development or metabolism of secondary metabolites such as ascarosides or maradolipids. We also summarised applied bioinformatic tools that recently have been used for the evaluation of metabolomics or lipidomics data from C. elegans. Lastly, we curated metabolites and lipids from the reviewed literature, enabling a prototypic collection which serves as basis for a future C. elegans specific metabolome database.

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Morabito, Aurelia, Giulia De Simone, Manuela Ferrario, Francesca Falcetta, Roberta Pastorelli, and Laura Brunelli. "EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing." Metabolites 13, no. 1 (December 21, 2022): 13. http://dx.doi.org/10.3390/metabo13010013.

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Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and direct feature annotation. We developed EASY-FIA, a free standalone application that can be employed for FIA-HRMS metabolomic data pre-processing by users with no bioinformatics/programming skills. We validated the tool's performance and applicability in two clinical metabolomics case studies. The main functions of our application are blank subtraction, alignment of the metabolites, and direct feature annotation by means of the Human Metabolome Database (HMDB) using a minimum number of mass spectrometry parameters. In a scenario where FIA-HRMS is increasingly recognized as a reliable strategy for fast metabolomics analysis, EASY-FIA could become a standardized and feasible tool easily usable by all scientists dealing with MS-based metabolomics. EASY-FIA was implemented in MATLAB with the App Designer tool and it is freely available for download.

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Li, Haorong, Martine Uittenbogaard, Ling Hao, and Anne Chiaramello. "Clinical Insights into Mitochondrial Neurodevelopmental and Neurodegenerative Disorders: Their Biosignatures from Mass Spectrometry-Based Metabolomics." Metabolites 11, no. 4 (April 10, 2021): 233. http://dx.doi.org/10.3390/metabo11040233.

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Mitochondria are dynamic multitask organelles that function as hubs for many metabolic pathways. They produce most ATP via the oxidative phosphorylation pathway, a critical pathway that the brain relies on its energy need associated with its numerous functions, such as synaptic homeostasis and plasticity. Therefore, mitochondrial dysfunction is a prevalent pathological hallmark of many neurodevelopmental and neurodegenerative disorders resulting in altered neurometabolic coupling. With the advent of mass spectrometry (MS) technology, MS-based metabolomics provides an emerging mechanistic understanding of their global and dynamic metabolic signatures. In this review, we discuss the pathogenetic causes of mitochondrial metabolic disorders and the recent MS-based metabolomic advances on their metabolomic remodeling. We conclude by exploring the MS-based metabolomic functional insights into their biosignatures to improve diagnostic platforms, stratify patients, and design novel targeted therapeutic strategies.

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Václavík, L., J. Ovesná, L. Kučera, J. Hodek, K. Demnerová, and J. Hajšlová. "Application of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) metabolomic fingerprinting to characterise GM and conventional maize varieties." Czech Journal of Food Sciences 31, No. 4 (July 19, 2013): 368–75. http://dx.doi.org/10.17221/177/2013-cjfs.

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The feasibility of metabolomic fingerprinting approach based on ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-QTOFMS) was studied to assess its ability to discriminate between maize varieties, and to show the associations between them on the metabolomic level. The non-targeted metabolomic analysis was applied to assess the variability within two varieties grown under different environmental conditions and to characterise the association within a sample set comprising both conventional and transgenic (MON-ØØ81Ø-6) maize varieties cultivated under the same environmental conditions (locality). Typical metabolomic fingerprints were established for individual plants. The plants representing two varieties formed well separated clusters. Metabolomic fingerprints of the second sample set enabled their unambiguous discrimination. The differences in metabolomic fingerprints between maize varieties were identified and documented by grouping in PCA and/or CA. The results indicate a similar genetic basis of transgenic maize varieties as they descend from a MON 810 event. The results explicitly showed that the variability of the metabolites in MON 810 did not exceed the ranges measured within the conventional varieties, thus supporting the concept of substantial equivalence.  

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Pérez-Míguez, Raquel, María Castro-Puyana, Elena Sánchez-López, Merichel Plaza, and María Luisa Marina. "Untargeted HILIC-MS-Based Metabolomics Approach to Evaluate Coffee Roasting Process: Contributing to an Integrated Metabolomics Multiplatform." Molecules 25, no. 4 (February 17, 2020): 887. http://dx.doi.org/10.3390/molecules25040887.

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An untargeted metabolomics strategy using hydrophilic interaction chromatography-mass spectrometry (HILIC-MS) was developed in this work enabling the study of the coffee roasting process. Green coffee beans and coffee beans submitted to three different roasting degrees (light, medium, and strong) were analyzed. Chromatographic separation was carried out using water containing 10 mM ammonium formate with 0.2 % formic acid (mobile phase A) and acetonitrile containing 10 mM ammonium formate with 0.2 % formic acid (mobile phase B). A total of 93 molecular features were considered from which 31 were chosen as the most statistically significant using variable in the projection values. 13 metabolites were tentatively identified as potential biomarkers of the coffee roasting process using this metabolomic platform. Results obtained in this work were complementary to those achieved using orthogonal techniques such as reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) since only one metabolite was found to be common between HILIC-MS and RPLC-MS platforms (caffeoylshikimic acid isomer) and other between HILIC-MS and CE-MS platforms (choline). On the basis of these results, an untargeted metabolomics multiplatform is proposed in this work based on the integration of the three orthogonal techniques as a powerful tool to expand the coverage of the roasted coffee metabolome.

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Fan, Sili, Muhammad Shahid, Peng Jin, Arash Asher, and Jayoung Kim. "Identification of Metabolic Alterations in Breast Cancer Using Mass Spectrometry-Based Metabolomic Analysis." Metabolites 10, no. 4 (April 24, 2020): 170. http://dx.doi.org/10.3390/metabo10040170.

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Breast cancer (BC) is a major global health issue and remains the second leading cause of cancer-related death in women, contributing to approximately 41,760 deaths annually. BC is caused by a combination of genetic and environmental factors. Although various molecular diagnostic tools have been developed to improve diagnosis of BC in the clinical setting, better detection tools for earlier diagnosis can improve survival rates. Given that altered metabolism is a characteristic feature of BC, we aimed to understand the comparative metabolic differences between BC and healthy controls. Metabolomics, the study of metabolism, can provide incredible insight and create useful tools for identifying potential BC biomarkers. In this study, we applied two analytical mass spectrometry (MS) platforms, including hydrophilic interaction chromatography (HILIC) and gas chromatography (GC), to generate BC-associated metabolic profiles using breast tissue from BC patients. These metabolites were further analyzed to identify differentially expressed metabolites in BC and their associated metabolic networks. Additionally, Chemical Similarity Enrichment Analysis (ChemRICH), MetaMapp, and Metabolite Set Enrichment Analysis (MSEA) identified significantly enriched clusters and networks in BC tissues. Since metabolomic signatures hold significant promise in the clinical setting, more effort should be placed on validating potential BC biomarkers based on identifying altered metabolomes.

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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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Gaffney, Isabella, Jonathan Brett Sallach, Julie Wilson, Edmund Bergström, and Jane Thomas-Oates. "Metabolomic Approaches to Studying the Response to Drought Stress in Corn (Zea mays) Cobs." Metabolites 11, no. 7 (July 3, 2021): 438. http://dx.doi.org/10.3390/metabo11070438.

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Metabolomics is a technique that allows for the evaluation of the entire extractable chemical profile of a plant, for example, using high-resolution mass spectrometry (HRMS) and can be used to evaluate plant stress responses, such as those due to drought. Metabolomic analysis is dependent upon the efficiency of the extraction protocol. Currently, there are two common extraction procedures widely used in metabolomic experiments, those that extract from plant tissue processed in liquid nitrogen or extraction from lyophilised plant tissues. Here, we evaluated the two using non-targeted metabolomics to show that lyophilisation can stabilise the maize (Zea mays) extractable metabolome, increasing throughput and efficiency of extraction as compared to the more traditional processing in liquid nitrogen. Then, we applied the lyophilisation approach to explore the effect of drought upon the maize metabolome in a non-targeted HRMS metabolomics approach. Metabolomics revealed differences in the mature maize metabolome having undergone three drought conditions imposed at two critical development stages (three-leaf stage and grain-fill stage); moreover, this difference was observed across two tissue types (kernel and inner cob/pith). It was shown that under ideal conditions, the biochemical make-up of the tissue types is different. However, under stress conditions, the stress response dominates the metabolic profile. Drought-related metabolites known from other plant systems have been identified and metabolomics has revealed potential novel drought-stress indicators in our maize system.

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Chowdhury, Shamiha, Sultan Mohammed Faheem, Shaik Sarfaraz Nawaz, and Khalid Siddiqui. "The role of metabolomics in personalized medicine for diabetes." Personalized Medicine 18, no. 5 (September 2021): 501–8. http://dx.doi.org/10.2217/pme-2021-0083.

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Metabolomics is rapidly evolving omics technology in personalized medicine, it offers a new avenue for identification of multiple novel metabolic mediators of impaired glucose tolerance and dysglycemia. Liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy are most commonly used analytical methods in the field of metabolomics. Recent evidences showed that metabolomic profiles are link to the incidence of diabetes. In this review, an overview of metabolomics studies in diabetes revealed several diabetes-associated metabolites including 1,5-anhydroglycitol, branch chain amino acids, glucose, α-hydroxybutyric acid, 3-hydroundecanoyl-carnitine and phosphatidylcholine that could be potential biomarkers associated with diabetes. These identified metabolites can be used to develop personalized prognostics and diagnostic, and help in diabetes management.

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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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Kapoore, Rahul Vijay, and Seetharaman Vaidyanathan. "Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2079 (October 28, 2016): 20150363. http://dx.doi.org/10.1098/rsta.2015.0363.

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Metabolome analyses are a suite of analytical approaches that enable us to capture changes in the metabolome (small molecular weight components, typically less than 1500 Da) in biological systems. Mass spectrometry (MS) has been widely used for this purpose. The key challenge here is to be able to capture changes in a reproducible and reliant manner that is representative of the events that take place in vivo . Typically, the analysis is carried out in vitro , by isolating the system and extracting the metabolome. MS-based approaches enable us to capture metabolomic changes with high sensitivity and resolution. When developing the technique for different biological systems, there are similarities in challenges and differences that are specific to the system under investigation. Here, we review some of the challenges in capturing quantitative changes in the metabolome with MS based approaches, primarily in microbial and mammalian systems. This article is part of the themed issue ‘Quantitative mass spectrometry’.

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Yanagihara, M., F. Nakajima, and T. Tobino. "Metabolomic responses of an estuarine benthic amphipod to heavy metals at urban-runoff concentrations." Water Science and Technology 78, no. 11 (December 20, 2018): 2349–54. http://dx.doi.org/10.2166/wst.2018.518.

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Abstract Heavy metals released from urban areas have toxic effects on aquatic organisms. Heavy metals in aquatic environments exist in various forms and methods designed to assess their effects need to consider their bioavailability. This study aimed to explore biomarkers in an estuarine amphipod, Grandidierella japonica, for exposure to heavy metals using metabolomics. We exposed G. japonica to different heavy metals and analyzed their metabolomes using high-resolution mass spectrometry. Partial least squares discriminant analysis (PLS-DA) was used to extract biomarkers of exposure for each heavy metal. As a result, three models were built to predict discrimination based on metabolomic profiles, and 70, 106, and 168 metabolites were extracted as biomarkers for exposure to Cu, Zn, and Cd, respectively. Our results suggest that PLS-DA was effective in extracting biomarkers, and this study demonstrated the usefulness of metabolomics as biomarkers.

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Taylor, Kurt, Nancy McBride, Neil J Goulding, Kimberley Burrows, Dan Mason, Lucy Pembrey, Tiffany Yang, Rafaq Azad, John Wright, and Deborah A Lawlor. "Metabolomics datasets in the Born in Bradford cohort." Wellcome Open Research 5 (September 10, 2021): 264. http://dx.doi.org/10.12688/wellcomeopenres.16341.2.

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Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks’ gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts’ extensive phenotype data from questionnaires, medical, educational and social record linkage, and other ‘omics data.

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Taylor, Kurt, Nancy McBride, Neil J Goulding, Kimberley Burrows, Dan Mason, Lucy Pembrey, Tiffany Yang, Rafaq Azad, John Wright, and Deborah A Lawlor. "Metabolomics datasets in the Born in Bradford cohort." Wellcome Open Research 5 (November 5, 2020): 264. http://dx.doi.org/10.12688/wellcomeopenres.16341.1.

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Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks’ gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts’ extensive phenotype data from questionnaires, medical, educational and social record linkage, and other ‘omics data.

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Climaco Pinto, Rui, Ibrahim Karaman, Matthew R. Lewis, Jenny Hällqvist, Manuja Kaluarachchi, Gonçalo Graça, Elena Chekmeneva, et al. "Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography–Mass Spectrometry Metabolomics Datasets." Analytical Chemistry 94, no. 14 (March 31, 2022): 5493–503. http://dx.doi.org/10.1021/acs.analchem.1c03592.

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Ramabulana, Anza-Tshilidzi, Paul A. Steenkamp, Ntakadzeni E. Madala, and Ian A. Dubery. "Application of Plant Growth Regulators Modulates the Profile of Chlorogenic Acids in Cultured Bidens pilosa Cells." Plants 10, no. 3 (February 25, 2021): 437. http://dx.doi.org/10.3390/plants10030437.

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Plant cell culture offers an alternative to whole plants for the production of biologically important specialised metabolites. In cultured plant cells, manipulation by auxin and cytokinin plant growth regulators (PGRs) may lead to in vitro organogenesis and metabolome changes. In this study, six different combination ratios of 2,4-dichlorophenoxyacetic acid (2,4-D) and benzylaminopurine (BAP) were investigated with the aim to induce indirect organogenesis from Bidens pilosa callus and to investigate the associated induced changes in the metabolomes of these calli. Phenotypic appearance of the calli and total phenolic contents of hydromethanolic extracts indicated underlying biochemical differences that were investigated using untargeted metabolomics, based on ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC–qTOF–MS), combined with multivariate data analysis. The concentration and combination ratios of PGRs were shown to induce differential metabolic responses and, thus, distinct metabolomic profiles, dominated by chlorogenic acids consisting of caffeoyl- and feruloyl-derivatives of quinic acid. Although organogenesis was not achieved, the results demonstrate that exogenous application PGRs can be used to manipulate the metabolome of B. pilosa for in vitro production of specialised metabolites with purported pharmacological properties.

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Yelamanchi, Soujanya D., Sumaithangi Thattai Arun Kumar, Archita Mishra, Thottethodi Subrahmanya Keshava Prasad, and Avadhesha Surolia. "Metabolite Dysregulation by Pranlukast in Mycobacterium tuberculosis." Molecules 27, no. 5 (February 24, 2022): 1520. http://dx.doi.org/10.3390/molecules27051520.

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Mycobacterium tuberculosis has been infecting millions of people worldwide over the years, causing tuberculosis. Drugs targeting distinct cellular mechanisms including synthesis of the cell wall, lipids, proteins, and nucleic acids in Mtb are currently being used for the treatment of TB. Although extensive research is being carried out at the molecular level in the infected host and pathogen, the identification of suitable drug targets and drugs remains under explored. Pranlukast, an allosteric inhibitor of MtArgJ (Mtb ornithine acetyltransferase) has previously been shown to inhibit the survival and virulence of Mtb. The main objective of this study was to identify the altered metabolic pathways and biological processes associated with the differentially expressed metabolites by PRK in Mtb. Here in this study, metabolomics was carried out using an LC-MS/MS-based approach. Collectively, 50 metabolites were identified to be differentially expressed with a significant p-value through a global metabolomic approach using a high-resolution mass spectrometer. Metabolites downstream of argJ were downregulated in the arginine biosynthetic pathway following pranlukast treatment. Predicted human protein interactors of pranlukast-treated Mtb metabolome were identified in association with autophagy, inflammation, DNA repair, and other immune-related processes. Further metabolites including N-acetylglutamate, argininosuccinate, L-arginine, succinate, ergothioneine, and L-phenylalanine were validated by multiple reaction monitoring, a targeted mass spectrometry-based metabolomic approach. This study facilitates the understanding of pranlukast-mediated metabolic changes in Mtb and holds the potential to identify novel therapeutic approaches using metabolic pathways in Mtb.

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Zhao, Kai, Jianzhong Zhang, Zhen Xu, Yue Xu, Aiming Xu, Wei Chen, Chenkui Miao, Shouyong Liu, Zengjun Wang, and Ruipeng Jia. "Metabolomic Profiling of Human Spermatozoa in Idiopathic Asthenozoospermia Patients Using Gas Chromatography-Mass Spectrometry." BioMed Research International 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/8327506.

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The purpose of this study was to describe the first metabolic profile of human sperm cells through the application of an untargeted platform based on gas chromatography-mass spectrometry (GC-MS). Sperm cell samples from patients diagnosed with idiopathic asthenozoospermia (n=30) and healthy subjects (n=30) were analyzed using a nontargeted metabolomics method based on GC-MS spectroscopy. The mass spectrometric data were collected using multivariate and univariate analyses to identify metabolites related to idiopathic asthenozoospermia. By using metabolomic strategies, we identified 33 metabolites, 27 of which were decreased in the idiopathic asthenozoospermia group compared with the normozoospermic group and six were increased in idiopathic asthenozoospermia. With respect to human sperm cells, some of these metabolites are reported here for the first time. Pathways for nucleoside, amino acid and energy metabolism, and the Krebs cycle were disturbed and were associated with idiopathic asthenozoospermia. The metabolic profiling provides an important first step in studying the pathophysiological mechanisms involved in IAS, and the identified metabolites may become potential biomarkers for its diagnosis and treatment.

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Darghouth, Dhouha, Bérengère Koehl, Geoffrey Madalinski, Jean-François Heilier, Petra Bovee, Ying Xu, Marie-Françoise Olivier, et al. "Pathophysiology of sickle cell disease is mirrored by the red blood cell metabolome." Blood 117, no. 6 (February 10, 2011): e57-e66. http://dx.doi.org/10.1182/blood-2010-07-299636.

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Abstract Emerging metabolomic tools can now be used to establish metabolic signatures of specialized circulating hematopoietic cells in physiologic or pathologic conditions and in human hematologic diseases. To determine metabolomes of normal and sickle cell erythrocytes, we used an extraction method of erythrocytes metabolites coupled with a liquid chromatography-mass spectrometry–based metabolite profiling method. Comparison of these 2 metabolomes identified major changes in metabolites produced by (1) endogenous glycolysis characterized by accumulation of many glycolytic intermediates; (2) endogenous glutathione and ascorbate metabolisms characterized by accumulation of ascorbate metabolism intermediates, such as diketogulonic acid and decreased levels of both glutathione and glutathione disulfide; (3) membrane turnover, such as carnitine, or membrane transport characteristics, such as amino acids; and (4) exogenous arginine and NO metabolisms, such as spermine, spermidine, or citrulline. Finally, metabolomic analysis of young and old normal red blood cells indicates metabolites whose levels are directly related to sickle cell disease. These results show the relevance of metabolic profiling for the follow-up of sickle cell patients or other red blood cell diseases and pinpoint the importance of metabolomics to further depict the pathophysiology of human hematologic diseases.

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Zhu, Guangsu, Min Guo, Jianxin Zhao, Hao Zhang, Gang Wang, and Wei Chen. "Integrative Metabolomic Characterization Reveals the Mediating Effect of Bifidobacterium breve on Amino Acid Metabolism in a Mouse Model of Alzheimer’s Disease." Nutrients 14, no. 4 (February 9, 2022): 735. http://dx.doi.org/10.3390/nu14040735.

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Alzheimer’s disease (AD) is commonly accompanied by global alterations in metabolic profiles, resulting in cognitive impairment and neuroinflammation in the brain. Using ultraperformance liquid chromatography-mass spectrometry, we performed integrative untargeted metabolomic analysis of metabolite alterations in the serum and hippocampal tissues of amyloid-β (Aβ)-injected AD model mice and sham controls. Multivariate analysis revealed that a Bifidobacterium breve CCFM1025 intervention significantly restored the differential metabolites induced by Aβ-injection, resulting in B. breve CCFM1025 serum and hippocampal metabolomes clustering between control and model mice. Furthermore, pathway and metabolite set enrichment analysis found that these altered metabolites were predominantly linked to amino acid metabolism. Overall, the integrative metabolome analysis indicated that B. breve CCFM1025 supplementation could modulate serum and hippocampal metabolomes in the early stage of AD, with amino acids as a potential driver.

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Patterson, Jeffrey, Xiaojian Shi, William Bresette, Ryan Eghlimi, Sarah Atlas, Kristin Farr, Sonia Vega-López, and Haiwei Gu. "A Metabolomic Analysis of the Sex-Dependent Hispanic Paradox." Metabolites 11, no. 8 (August 20, 2021): 552. http://dx.doi.org/10.3390/metabo11080552.

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In Mexican Americans, metabolic conditions, such as obesity and type 2 diabetes (T2DM), are not necessarily associated with an increase in mortality; this is the so-called Hispanic paradox. In this cross-sectional analysis, we used a metabolomic analysis to look at the mechanisms behind the Hispanic paradox. To do this, we examined dietary intake and body mass index (BMI; kg/m2) in men and women and their effects on serum metabolomic fingerprints in 70 Mexican Americans (26 men, 44 women). Although having different BMI values, the participants had many similar anthropometric and biochemical parameters, such as systolic and diastolic blood pressure, total cholesterol, and LDL cholesterol, which supported the paradox in these subjects. Plasma metabolomic phenotypes were measured using liquid chromatography tandem mass spectrometry (LC-MS/MS). A two-way ANOVA assessing sex, BMI, and the metabolome revealed 23 significant metabolites, such as 2-pyrrolidinone (p = 0.007), TMAO (p = 0.014), 2-aminoadipic acid (p = 0.019), and kynurenine (p = 0.032). Pathway and enrichment analyses discovered several significant metabolic pathways between men and women, including lysine degradation, tyrosine metabolism, and branch-chained amino acid (BCAA) degradation and biosynthesis. A log-transformed OPLS-DA model was employed and demonstrated a difference due to BMI in the metabolomes of both sexes. When stratified for caloric intake (<2200 kcal/d vs. >2200 kcal/d), a separate OPLS-DA model showed clear separation in men, while females remained relatively unchanged. After accounting for caloric intake and BMI status, the female metabolome showed substantial resistance to alteration. Therefore, we provide a better understanding of the Mexican-American metabolome, which may help demonstrate how this population—particularly women—possesses a longer life expectancy despite several comorbidities, and reveal the underlying mechanisms of the Hispanic paradox.

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Yan, Katie, Jeffrey Brumbaugh, Jeremy Barrett, Sarah Jung, and Shen Hu. "Capillary Electrophoresis with Mass Spectrometry: Applications to Metabolomic Analysis." Current Metabolomics 1, no. 2 (March 1, 2013): 121–27. http://dx.doi.org/10.2174/2213235x11301020002.

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González-Domínguez, Raúl, Ana Sayago, and Ángeles Fernández-Recamales. "Direct infusion mass spectrometry for metabolomic phenotyping of diseases." Bioanalysis 9, no. 1 (January 2017): 131–48. http://dx.doi.org/10.4155/bio-2016-0202.

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Paul, Alessandra, and Peter de Boves Harrington. "Chemometric applications in metabolomic studies using chromatography-mass spectrometry." TrAC Trends in Analytical Chemistry 135 (February 2021): 116165. http://dx.doi.org/10.1016/j.trac.2020.116165.

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Kamleh, M. A., Y. Hobani, J. A. T. Dow, and D. G. Watson. "Metabolomic profiling ofDrosophilausing liquid chromatography Fourier transform mass spectrometry." FEBS Letters 582, no. 19 (July 25, 2008): 2916–22. http://dx.doi.org/10.1016/j.febslet.2008.07.029.

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Dupont, Félix, René Gagnon, Joe T. R. Clarke, and Christiane Auray-Blais. "41. Metabolomic studies in Fabry disease using mass spectrometry." Molecular Genetics and Metabolism 99, no. 2 (February 2010): S16. http://dx.doi.org/10.1016/j.ymgme.2009.10.058.

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Kiseleva, Olga, Ilya Kurbatov, Ekaterina Ilgisonis, and Ekaterina Poverennaya. "Defining Blood Plasma and Serum Metabolome by GC-MS." Metabolites 12, no. 1 (December 24, 2021): 15. http://dx.doi.org/10.3390/metabo12010015.

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Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

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Zhang, Lin-Ning, Long Wang, Zi-Qi Shi, Ping Li, and Hui-Jun Li. "A metabolomic strategy based on integrating headspace gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry to differentiate the five cultivars of Chrysanthemum flower." RSC Advances 8, no. 17 (2018): 9074–82. http://dx.doi.org/10.1039/c7ra13503c.

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Li, Zhen, Yue Mu, Chunlan Guo, Xin You, Xiaoyan Liu, Qian Li, and Wei Sun. "Analysis of the saliva metabolic signature in patients with primary Sjögren’s syndrome." PLOS ONE 17, no. 6 (June 2, 2022): e0269275. http://dx.doi.org/10.1371/journal.pone.0269275.

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Background The saliva metabolome has been applied to explore disease biomarkers. In this study we characterized the metabolic profile of primary Sjögren’s syndrome (pSS) patients and explored metabolomic biomarkers. Methods This work presents a liquid chromatography-mass spectrometry-based metabolomic study of the saliva of 32 patients with pSS and 38 age- and sex-matched healthy adults. Potential pSS saliva metabolite biomarkers were explored using test group saliva samples (20 patients with pSS vs. 25 healthy adults) and were then verified by a cross-validation group (12 patients with pSS vs. 13 healthy adults). Results Metabolic pathways, including tryptophan metabolism, tyrosine metabolism, carbon fixation, and aspartate and asparagine metabolism, were found to be significantly regulated and related to inflammatory injury, neurological cognitive impairment and the immune response. Phenylalanyl-alanine was discovered to have good predictive ability for pSS, with an area under the curve (AUC) of 0.87 in the testing group (validation group: AUC = 0.75). Conclusion Our study shows that salivary metabolomics is a useful strategy for differential analysis and biomarker discovery in pSS.

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Diab, Joseph, Terkel Hansen, Rasmus Goll, Hans Stenlund, Einar Jensen, Thomas Moritz, Jon Florholmen, and Guro Forsdahl. "Mucosal Metabolomic Profiling and Pathway Analysis Reveal the Metabolic Signature of Ulcerative Colitis." Metabolites 9, no. 12 (November 27, 2019): 291. http://dx.doi.org/10.3390/metabo9120291.

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The onset of ulcerative colitis (UC) is characterized by a dysregulated mucosal immune response triggered by several genetic and environmental factors in the context of host–microbe interaction. This complexity makes UC ideal for metabolomic studies to unravel the disease pathobiology and to improve the patient stratification strategies. This study aims to explore the mucosal metabolomic profile in UC patients, and to define the UC metabolic signature. Treatment- naïve UC patients (n = 18), UC patients in deep remission (n = 10), and healthy volunteers (n = 14) were recruited. Mucosa biopsies were collected during colonoscopies. Metabolomic analysis was performed by combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). In total, 177 metabolites from 50 metabolic pathways were identified. The most prominent metabolome changes among the study groups were in lysophosphatidylcholine, acyl carnitine, and amino acid profiles. Several pathways were found perturbed according to the integrated pathway analysis. These pathways ranged from amino acid metabolism (such as tryptophan metabolism) to fatty acid metabolism, namely linoleic and butyrate. These metabolic changes during UC reflect the homeostatic disturbance in the gut, and highlight the importance of system biology approaches to identify key drivers of pathogenesis which prerequisite personalized medicine.

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Lavarello, Chiara, Sebastiano Barco, Martina Bartolucci, Isabella Panfoli, Emanuele Magi, Gino Tripodi, Andrea Petretto, and Giuliana Cangemi. "Development of an Accurate Mass Retention Time Database for Untargeted Metabolomic Analysis and Its Application to Plasma and Urine Pediatric Samples." Molecules 26, no. 14 (July 13, 2021): 4256. http://dx.doi.org/10.3390/molecules26144256.

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Liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is currently the method of choice for untargeted metabolomic analysis. The availability of established protocols to achieve a high confidence identification of metabolites is crucial. The aim of this work is to describe the workflow that we have applied to build an Accurate Mass Retention Time (AMRT) database using a commercial metabolite library of standards. LC-HRMS analysis was carried out using a Vanquish Horizon UHPLC system coupled to a Q-Exactive Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Fisher Scientific, Milan, Italy). The fragmentation spectra, obtained with 12 collision energies, were acquired for each metabolite, in both polarities, through flow injection analysis. Several chromatographic conditions were tested to obtain a protocol that yielded stable retention times. The adopted chromatographic protocol included a gradient separation using a reversed phase (Waters Acquity BEH C18) and a HILIC (Waters Acquity BEH Amide) column. An AMRT database of 518 compounds was obtained and tested on real plasma and urine samples analyzed in data-dependent acquisition mode. Our AMRT library allowed a level 1 identification, according to the Metabolomics Standards Initiative, of 132 and 124 metabolites in human pediatric plasma and urine samples, respectively. This library represents a starting point for future metabolomic studies in pediatric settings.

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Bachman, Martin, Ian Sinclair, Delyan Ivanov, and Jonathan Wingfield. "Information-rich high-throughput cellular assays using acoustic mist ionisation mass spectrometry." Analyst 146, no. 1 (2021): 315–21. http://dx.doi.org/10.1039/d0an01519a.

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Sakurai, Nozomu, Takeshi Ara, Mitsuo Enomoto, Takeshi Motegi, Yoshihiko Morishita, Atsushi Kurabayashi, Yoko Iijima, et al. "Tools and Databases of the KOMICS Web Portal for Preprocessing, Mining, and Dissemination of Metabolomics Data." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/194812.

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A metabolome—the collection of comprehensive quantitative data on metabolites in an organism—has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study onJatropha curcasis presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data.

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Stastny, Kamil, Kristina Putecova, Lenka Leva, Milan Franek, Petr Dvorak, and Martin Faldyna. "Profiling of Metabolomic Changes in Plasma and Urine of Pigs Caused by Illegal Administration of Testosterone Esters." Metabolites 10, no. 8 (July 27, 2020): 307. http://dx.doi.org/10.3390/metabo10080307.

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The use of anabolic steroid hormones as growth promoters in feed for farm animals has been banned in the European Union since 1988 on the basis of Council Directive 96/22/EC. However, there is still ongoing monitoring and reporting of positive findings of these banned substances in EU countries. The aim of this work was to investigate the efficacy and discriminatory ability of metabolic fingerprinting after the administration of 17β-testosterone esters to pigs. Plasma and urine samples were chromatographically separated on a Hypersil Gold C18 column. High resolution mass spectrometry metabolomic fingerprints were analysed on a hybrid mass spectrometer Q-Exactive. Three independent multivariate statistical methods, namely principal component analysis, clustre analysis, and orthogonal partial least squares discriminant analysis showed significant differences between the treated and control groups of pigs even 14 days after the administration of the hormonal drug. Plasma samples were also analysed by a conventional quantitative analysis using liquid chromatography with tandem mass spectrometry and a pharmacokinetic curve was constructed based on the results. In this case, no testosterone residue was detected 14 days after the administration. The results clearly showed that a metabolomics approach can be a useful and effective tool for the detection and monitoring of banned anabolic steroids used illegally in pig fattening.

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Huang, Zhengyan, and Chi Wang. "A Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data." Metabolites 12, no. 4 (March 30, 2022): 305. http://dx.doi.org/10.3390/metabo12040305.

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This review presents an overview of the statistical methods on differential abundance (DA) analysis for mass spectrometry (MS)-based metabolomic data. MS has been widely used for metabolomic abundance profiling in biological samples. The high-throughput data produced by MS often contain a large fraction of zero values caused by the absence of certain metabolites and the technical detection limits of MS. Various statistical methods have been developed to characterize the zero-inflated metabolomic data and perform DA analysis, ranging from simple tests to more complex models including parametric, semi-parametric, and non-parametric approaches. In this article, we discuss and compare DA analysis methods regarding their assumptions and statistical modeling techniques.

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Lankadurai, Brian P., Edward G. Nagato, and Myrna J. Simpson. "Environmental metabolomics: an emerging approach to study organism responses to environmental stressors." Environmental Reviews 21, no. 3 (September 2013): 180–205. http://dx.doi.org/10.1139/er-2013-0011.

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Metabolomics is the analysis of endogenous and exogenous low molecular mass metabolites within a cell, tissue, or biofluid of an organism in response to an external stressor. The sub-discipline of environmental metabolomics is the application of metabolomic techniques to analyze the interactions of organisms with their environment. There has been a rapid growth in environmental metabolomics over the past decade. This growth can be attributed to the comprehensive and rapid nature of nontargeted metabolomics and the ability to generate hypotheses involving complex environmental stressors, especially when the mode of action is unknown. Using a wide variety of model organisms, metabolomic studies have detected stress from abiotic factors such as xenobiotic exposure and temperature shifts as well as biotic stressors such as herbivory and competition. Nuclear magnetic resonance (NMR)-based metabolomics has been the dominant analytical platform used for environmental metabolomics studies, owing to its nonselectivity and ease of sample preparation. However, the number of mass spectrometry (MS)-based metabolomic studies is also increasing rapidly, owing to its high sensitivity for the detection of trace levels of metabolites. In this review, we provide an overview of the general experimental design, extraction methods, analytical instrumentation, and statistical methods used in environmental metabolomics. We then highlight some of the recent studies that have used metabolomics to elucidate hitherto unknown biochemical modes of actions of various environmental stressors to both terrestrial and aquatic organisms, as well as identify potential metabolite shifts as early bioindicators of these stressors. Through this, we emphasize the immense potential and versatility of environmental metabolomics as a routine tool for characterizing the responses of organisms to numerous types of environmental stressors.

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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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Putluri, N., Y. Zhang, V. Putluri, S. Vareed, V. T. Vasu, S. M. Fischer, C. Chad, and A. Sreekumar. "Androgen-regulated metabolome in prostate cancer." Journal of Clinical Oncology 29, no. 7_suppl (March 1, 2011): 25. http://dx.doi.org/10.1200/jco.2011.29.7_suppl.25.

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25 Background: Prostate cancer (PC) is the second most prevalent cancer among American men which is primarily treated by androgen ablation therapy. Although a number of patients respond to this regimen, a significant subset fail and the tumor invariably progresses into a hormone refractory metastatic state, which is lethal. Earlier we had reported the first unbiased metabolomic signature for localized and metastatic prostate cancer tissues. Advancing further, we attempt to delineate the subset of metabolome in prostate cancer which is regulated by androgen-action. Methods: Androgen responsive (R22V1, LnCap and VCAP) and independent (PC3, DU145) PC cells and benign prostate epithelial cells (RWPE) were profiled for their metabolomic alterations using mass spectrometry. Extracted metabolome from these cells were profiles using a combination quadrupole-time-of-flight (Q-TOF) and triple quadrupole (QQQ) mass spectrometers coupled to reverse phase and aqueous normal phase chromatography. The metabolomic profiles were analyzed to delineate class-specific signatures which were interrogated for altered bioprocesses using Oncomine Concept Map (OCM, www.oncomine.org ). The androgen receptor (AR) regulated metabolome was verified using treatment of PC cells with synthetic androgen, R1881. Results: A total of 3,092 metabolites (113 named) were detected across the 4 cells lines, of which 869 compounds were significantly (ANOVA P<0.01) different between androgen responsive and non-responsive cells. The differential compendia included 28 named metabolites, including sarcosine which was earlier shown to be elevated during PC development and progression. Bioprocess mapping of AR-regulated metabolome revealed enrichment of amino acid metabolism and methylation potential, both of which were earlier defined to be the hallmarks of PC development and progression. Conclusions: The study defines AR-regulated metabolic signature which portrays enrichment of amino acid metabolism and methylation potential that are hallmarks of PC development and progression. No significant financial relationships to disclose.

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Suzuki, Ryuichiro, Hisahiro Kai, Yoshihiro Uesawa, Koji Matsuno, Yoshihito Okada, and Yoshiaki Shirataki. "Electron Ionization Mass Spectrometry-based Metabolomics Studies of Sophora Flavescens can Identify the Geographical Origin of Root Samples." Natural Product Communications 11, no. 1 (January 2016): 1934578X1601100. http://dx.doi.org/10.1177/1934578x1601100121.

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An electron ionization mass spectrometry (EI-MS)-based metabolomic approach was applied to Sophora flavescens to identify the geographical origin of each sample. The score plot from principal component analysis using the EI-MS data showed that Japanese S. flavescens samples tended to cluster away from Chinese S. flavescens samples. Statistical techniques showed that ions arising from kurarinol and kushenol H, which we previously identified as marker molecules for Japanese S. flavescens, were characteristic of Japanese S. flavescens. Therefore, metabolomics based on EI-MS data is a valuable tool for confirming the geographical origins of S. flavescens samples. The results suggest that EI-MS-based metabolomics is suitable for the quality control of traditional medicines containing many components.

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Journal articles on the topic 'Mass spectrometry metabolomic'

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