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Matsuta R, Yamamoto H, Tomita M, Saito R. iDMET: network-based approach for integrating differential analysis of cancer metabolomics. BMC Bioinformatics 2022; 23:508. [PMID: 36443658 PMCID: PMC9706903 DOI: 10.1186/s12859-022-05068-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 11/18/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study. RESULTS We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered. CONCLUSIONS We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.
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Affiliation(s)
- Rira Matsuta
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, 252-8520, Japan
- Human Metabolome Technologies, Inc., 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan
| | - Hiroyuki Yamamoto
- Human Metabolome Technologies, Inc., 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, 252-8520, Japan
| | - Rintaro Saito
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, 252-8520, Japan
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2
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Sakurai N, Yamazaki S, Suda K, Hosoki A, Akimoto N, Takahashi H, Shibata D, Aoki Y. The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites. Nucleic Acids Res 2022; 51:D660-D677. [PMID: 36417935 PMCID: PMC9825447 DOI: 10.1093/nar/gkac1058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/21/2022] [Accepted: 10/25/2022] [Indexed: 11/25/2022] Open
Abstract
The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources for discovering and prioritizing the unknowns that should be subject to practical identification, as well as further detailed study of spending costs and the risks of misprediction, are lacking. As such a resource, we released databases, Food-, Plant- and Thing-Metabolome Repository (http://metabolites.in/foods, http://metabolites.in/plants, and http://metabolites.in/things, referred to as XMRs) in which the sample-specific localization of unknowns detected by liquid chromatography-mass spectrometry in a wide variety of samples can be examined, helping to discover and prioritize the unknowns. A set of application programming interfaces for the XMRs facilitates the use of metabolome data for large-scale analysis and data mining. Several applications of XMRs, including integrated metabolome and genome analyses, are presented. Expanding the concept of XMRs will accelerate the identification of unknowns and increase the discovery of new knowledge.
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Affiliation(s)
- Nozomu Sakurai
- To whom correspondence should be addressed. Tel: +81 55 981 6895; Fax: +81 55 981 9448; ;
| | | | - Kunihiro Suda
- Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Ai Hosoki
- Bioinformation and DDBJ Center, National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
| | - Nayumi Akimoto
- Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Haruya Takahashi
- Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
| | - Daisuke Shibata
- Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Yuichi Aoki
- Correspondence may also be addressed to Yuichi Aoki. Tel: +81 22 274 6040; Fax: +81 22 274 6040;
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Hoffmann N, Mayer G, Has C, Kopczynski D, Al Machot F, Schwudke D, Ahrends R, Marcus K, Eisenacher M, Turewicz M. A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics. Metabolites 2022; 12:metabo12070584. [PMID: 35888710 PMCID: PMC9319858 DOI: 10.3390/metabo12070584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/17/2022] [Accepted: 06/19/2022] [Indexed: 12/13/2022] Open
Abstract
Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography−mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.
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Affiliation(s)
- Nils Hoffmann
- Forschungszentrum Jülich GmbH, Institute for Bio- and Geosciences (IBG-5), 52425 Jülich, Germany
- Correspondence: (N.H.); (M.T.); Tel.: +49-(0)521-106-86780 (N.H.)
| | - Gerhard Mayer
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany;
| | - Canan Has
- Biological Mass Spectrometry, Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany;
- University Hospital Carl Gustav Carus, 01307 Dresden, Germany
- CENTOGENE GmbH, 18055 Rostock, Germany
| | - Dominik Kopczynski
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Fadi Al Machot
- Faculty of Science and Technology, Norwegian University for Life Science (NMBU), 1433 Ås, Norway;
| | - Dominik Schwudke
- Bioanalytical Chemistry, Forschungszentrum Borstel, Leibniz Lung Center, 23845 Borstel, Germany;
- Airway Research Center North, German Center for Lung Research (DZL), 23845 Borstel, Germany
- German Center for Infection Research (DZIF), TTU Tuberculosis, 23845 Borstel, Germany
| | - Robert Ahrends
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Katrin Marcus
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
| | - Martin Eisenacher
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr University Bochum, 44801 Bochum, Germany
| | - Michael Turewicz
- Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, 85764 Neuherberg, Germany
- Correspondence: (N.H.); (M.T.); Tel.: +49-(0)521-106-86780 (N.H.)
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Fukushima A, Takahashi M, Nagasaki H, Aono Y, Kobayashi M, Kusano M, Saito K, Kobayashi N, Arita M. Development of RIKEN Plant Metabolome MetaDatabase. PLANT & CELL PHYSIOLOGY 2022; 63:433-440. [PMID: 34918130 PMCID: PMC8917833 DOI: 10.1093/pcp/pcab173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/15/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
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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Affiliation(s)
| | - Mikiko Takahashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | | | - Yusuke Aono
- Degree Programs in Life and Earth Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Makoto Kobayashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Miyako Kusano
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Faculty of Life and Environmental Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
- Tsukuba Plant Innovation Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Kazuki Saito
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Norio Kobayashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Data Knowledge Organization Unit, RIKEN Information R&D and Strategy Headquarters, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Masanori Arita
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Bioinformation and DDBJ Center, National Institute of Genetics, Yata 1111, Mishima, Shizuoka 411-8540, Japan
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Patel MK, Pandey S, Kumar M, Haque MI, Pal S, Yadav NS. Plants Metabolome Study: Emerging Tools and Techniques. PLANTS (BASEL, SWITZERLAND) 2021; 10:2409. [PMID: 34834772 PMCID: PMC8621461 DOI: 10.3390/plants10112409] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/31/2021] [Accepted: 11/01/2021] [Indexed: 05/06/2023]
Abstract
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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Affiliation(s)
- Manish Kumar Patel
- Department of Postharvest Science of Fresh Produce, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel
| | - Sonika Pandey
- Independent Researcher, Civil Line, Fathepur 212601, India;
| | - Manoj Kumar
- Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel;
| | - Md Intesaful Haque
- Fruit Tree Science Department, Newe Ya’ar Research Center, Agriculture Research Organization, Volcani Center, Ramat Yishay 3009500, Israel;
| | - Sikander Pal
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India;
| | - Narendra Singh Yadav
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
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6
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Gómez-Cebrián N, Vázquez Ferreiro P, Carrera Hueso FJ, Poveda Andrés JL, Puchades-Carrasco L, Pineda-Lucena A. Pharmacometabolomics by NMR in Oncology: A Systematic Review. Pharmaceuticals (Basel) 2021; 14:ph14101015. [PMID: 34681239 PMCID: PMC8539252 DOI: 10.3390/ph14101015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 12/14/2022] Open
Abstract
Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.
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Affiliation(s)
- Nuria Gómez-Cebrián
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain;
| | | | | | | | - Leonor Puchades-Carrasco
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain;
- Correspondence: (L.P.-C.); (A.P.-L.); Tel.: +34-963246713 (L.P.-C.)
| | - Antonio Pineda-Lucena
- Molecular Therapeutics Program, Centro de Investigación Médica Aplicada, 31008 Navarra, Spain
- Correspondence: (L.P.-C.); (A.P.-L.); Tel.: +34-963246713 (L.P.-C.)
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7
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Krantz M, Zimmer D, Adler SO, Kitashova A, Klipp E, Mühlhaus T, Nägele T. Data Management and Modeling in Plant Biology. FRONTIERS IN PLANT SCIENCE 2021; 12:717958. [PMID: 34539712 PMCID: PMC8446634 DOI: 10.3389/fpls.2021.717958] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/29/2021] [Indexed: 05/25/2023]
Abstract
The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.
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Affiliation(s)
- Maria Krantz
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Zimmer
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Stephan O. Adler
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Anastasia Kitashova
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| | - Edda Klipp
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Thomas Nägele
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
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Takahashi H, Ochiai K, Sasaki K, Izumi A, Shinyama Y, Mohri S, Nomura W, Jheng HF, Kawada T, Inoue K, Goto T. Metabolome analysis revealed that soybean-Aspergillus oryzae interaction induced dynamic metabolic and daidzein prenylation changes. PLoS One 2021; 16:e0254190. [PMID: 34214105 PMCID: PMC8253397 DOI: 10.1371/journal.pone.0254190] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/21/2021] [Indexed: 01/13/2023] Open
Abstract
Several isoflavonoids are well known for their ability to act as soybean phytoalexins. However, the overall effects of the soybean-Aspergillus oryzae interaction on metabolism remain largely unknown. The aim of this study is to reveal an overview of nutritive and metabolic changes in germinated and A. oryzae-elicited soybeans. The levels of individual nutrients were measured using the ustulation, ashing, Kjeldahl, and Folch methods. The levels of individual amino acids were measured using high-performance liquid chromatography. Low-molecular-weight compounds were measured through metabolome analysis using liquid chromatography-mass spectrometry. Although the levels of individual nutrients and amino acids were strongly influenced by the germination process, the elicitation process had little effect on the change in the contents of individual nutrients and amino acids. However, after analyzing approximately 700 metabolites using metabolome analysis, we found that the levels of many of the metabolites were strongly influenced by soybean-A. oryzae interactions. In particular, the data indicate that steroid, terpenoid, phenylpropanoid, flavonoid, and fatty acid metabolism were influenced by the elicitation process. Furthermore, we demonstrated that not the germination process but the elicitation process induced daidzein prenylation, suggesting that the soybean-A. oryzae interactions produce various phytoalexins that are valuable for health promotion and/or disease prevention.
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Affiliation(s)
- Haruya Takahashi
- Laboratory of Molecular Function of Food, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
| | | | | | | | | | - Shinsuke Mohri
- Laboratory of Molecular Function of Food, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
| | - Wataru Nomura
- Laboratory of Molecular Function of Food, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
- Research Unit for Physiological Chemistry, Kyoto University, Kyoto, Japan
| | - Huei-Fen Jheng
- Laboratory of Molecular Function of Food, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
- National Applied Research Laboratories, National Laboratory Animal Center, Taipei, Taiwan
| | - Teruo Kawada
- Laboratory of Molecular Function of Food, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
- Research Unit for Physiological Chemistry, Kyoto University, Kyoto, Japan
| | - Kazuo Inoue
- Laboratory of Molecular Function of Food, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
- Research Unit for Physiological Chemistry, Kyoto University, Kyoto, Japan
| | - Tsuyoshi Goto
- Laboratory of Molecular Function of Food, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
- Research Unit for Physiological Chemistry, Kyoto University, Kyoto, Japan
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9
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Ara T, Sakurai N, Takahashi S, Waki N, Suganuma H, Aizawa K, Matsumura Y, Kawada T, Shibata D. TOMATOMET: A metabolome database consists of 7118 accurate mass values detected in mature fruits of 25 tomato cultivars. PLANT DIRECT 2021; 5:e00318. [PMID: 33969254 PMCID: PMC8082711 DOI: 10.1002/pld3.318] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 01/18/2021] [Accepted: 03/09/2021] [Indexed: 06/02/2023]
Abstract
The total number of low-molecular-weight compounds in the plant kingdom, most of which are secondary metabolites, is hypothesized to be over one million, although only a limited number of plant compounds have been characterized. Untargeted analysis, especially using mass spectrometry (MS), has been useful for understanding the plant metabolome; however, due to the limited availability of authentic compounds for MS-based identification, the identities of most of the ion peaks detected by MS remain unknown. Accurate mass values of peaks obtained by high accuracy mass measurement and, if available, MS/MS fragmentation patterns provide abundant annotation for each peak. Here, we carried out an untargeted analysis of compounds in the mature fruit of 25 tomato cultivars using liquid chromatography-Orbitrap MS for accurate mass measurement, followed by manual curation to construct the metabolome database TOMATOMET (http://metabolites.in/tomato-fruits/). The database contains 7,118 peaks with accurate mass values, in which 1,577 ion peaks are annotated as members of a chemical group. Remarkably, 71% of the mass values are not found in the accurate masses detected previously in Arabidopsis thaliana, Medicago truncatula or Jatropha curcas, indicating significant chemical diversity among plant species that remains to be solved. Interestingly, substantial chemical diversity exists also among tomato cultivars, indicating that chemical profiling from distinct cultivars contributes towards understanding the metabolome, even in a single organ of a species, and can prioritize some desirable metabolic targets for further applications such as breeding.
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Affiliation(s)
- Takeshi Ara
- Graduate School of AgricultureKyoto UniversityUjiJapan
| | - Nozomu Sakurai
- Kazusa DNA Research InstituteKisarazuJapan
- National Institute of GeneticsMishimaJapan
| | - Shingo Takahashi
- Graduate School of AgricultureKyoto UniversityUjiJapan
- KAGOME CO., LTD.NasushiobaraJapan
| | - Naoko Waki
- Graduate School of AgricultureKyoto UniversityUjiJapan
- KAGOME CO., LTD.NasushiobaraJapan
| | | | | | | | - Teruo Kawada
- Graduate School of AgricultureKyoto UniversityUjiJapan
| | - Daisuke Shibata
- Graduate School of AgricultureKyoto UniversityUjiJapan
- Kazusa DNA Research InstituteKisarazuJapan
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10
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Yang Y, Zhu Y, Li X, Zhang X, Yu B. Identification of potential biomarkers and metabolic pathways based on integration of metabolomic and transcriptomic data in the development of breast cancer. Arch Gynecol Obstet 2021; 303:1599-1606. [PMID: 33791842 DOI: 10.1007/s00404-021-06015-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 02/23/2021] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Breast cancer (BC) is the most common type of malignant tumor and the most common cause of cancer-related mortality among women. Metabolic reprogramming is considered a hallmark of cancer, and the study of BC metabolism may be the key to the development of new strategies for diagnosis and treatment. In this study, we aimed to explore the potential metabolites and gene biomarkers for BC through the integration of metabolomics and transcriptomic data, which could further understand BC tumor biology. METHODS Transcriptome dataset GSE139038 was downloaded to explore the differentially expressed genes (DEGs) between BC and normal control (NC) samples. Metabolomics dataset MTBLS326 was downloaded and preprocessed to obtain altered metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEGs-metabolites relations. Finally, the pathway enrichment analysis of altered metabolites was performed. RESULTS A total of 280 DEGs and eight metabolites were explored between BC and NC samples. The liner module analysis investigated 28 DEGs-metabolites interactions including WASP family member 3 (WASF3)-lactate, ras-related protein Rab-7B (RAB7B)-lactate, and methyltransferase-like 7A (METTL7A)-pyruvate. Finally, pathways analysis showed that these metabolites (such as lactate and pyruvate) were mainly enriched in pathways like disorders of the Krebs cycle. CONCLUSIONS Combining with the transcriptomic and metabolomics data, we found that lactate, pyruvate, WASF3, RAB7B, and METTL7A might be used as novel biomarkers and potential therapeutic targets for BC. In addition, the disorders of the Krebs cycle pathway might affect the progression of BC.
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Affiliation(s)
- Yifei Yang
- Department of Thyroid Mammary Surgery, The First People's Hospital of Yuhang, No. 369 Yingbin Road, Linping, Yuhang District, Hangzhou, 330110, Zhejiang, China
| | - Yunhua Zhu
- Department of Thyroid Mammary Surgery, The First People's Hospital of Yuhang, No. 369 Yingbin Road, Linping, Yuhang District, Hangzhou, 330110, Zhejiang, China
| | - Xiaoyan Li
- Department of Thyroid Mammary Surgery, The First People's Hospital of Yuhang, No. 369 Yingbin Road, Linping, Yuhang District, Hangzhou, 330110, Zhejiang, China
| | - Xiuxia Zhang
- Department of Thyroid Mammary Surgery, The First People's Hospital of Yuhang, No. 369 Yingbin Road, Linping, Yuhang District, Hangzhou, 330110, Zhejiang, China
| | - Bin Yu
- Department of Thyroid Mammary Surgery, The First People's Hospital of Yuhang, No. 369 Yingbin Road, Linping, Yuhang District, Hangzhou, 330110, Zhejiang, China.
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Ara T, Sakurai N, Suzuki H, Aoki K, Saito K, Shibata D. MassBase: A large-scaled depository of mass spectrometry datasets for metabolome analysis. PLANT BIOTECHNOLOGY (TOKYO, JAPAN) 2021; 38:167-171. [PMID: 34177338 PMCID: PMC8215474 DOI: 10.5511/plantbiotechnology.20.0911a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Depository of low-molecular-weight compounds or metabolites detected in various organisms in a non-targeted manner is indispensable for metabolomics research. Due to the diverse chemical compounds, various mass spectrometry (MS) setups with state-of-the-art technologies have been used. Over the past two decades, we have analyzed various biological samples by using gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, or capillary electrophoresis-mass spectrometry, and archived the datasets in the depository MassBase (http://webs2.kazusa.or.jp/massbase/). As the format of MS datasets depends on the MS setup used, we converted each raw binary dataset of the mass chromatogram to text file format, and thereafter, information of the chromatograph peak was extracted in the text file from the converted file. In total, the depository comprises 46,493 datasets, of which 38,750 belong to the plant species and 7,743 are authentic or mixed chemicals as well as other sources (microorganisms, animals, and foods), as on August 1, 2020. All files in the depository can be downloaded in bulk from the website. Mass chromatograms of 90 plant species obtained by LC-Fourier transform ion cyclotron resonance MS or Orbitrap MS, which detect the ionized molecules with high accuracy allowing speculation of chemical compositions, were converted to text files by the software PowerGet, and the chemical annotation of each peak was added. The processed datasets were deposited in the annotation database KomicMarket2 (http://webs2.kazusa.or.jp/km2/). The archives provide fundamental resources for comparative metabolomics and functional genomics, which may result in deeper understanding of living organisms.
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Affiliation(s)
- Takeshi Ara
- Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan
- E-mail: Tel: +81-774-38-3653 Fax: +81-774-38-3682
| | - Nozomu Sakurai
- Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Hideyuki Suzuki
- Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Koh Aoki
- Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan
- Graduate School of Life and Environmental Sciences, Osaka Prefecture University, 1-1 Gakuencho, Naka, Sakai, Osaka 599-8531, Japan
| | - Kazuki Saito
- Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo, Chiba, Chiba 260-8675, Japan
| | - Daisuke Shibata
- Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan
- Department of Biological Sciences, Kisarazu Campus of Tohoku University, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan
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Rodrigues AM, Miguel C, Chaves I, António C. Mass spectrometry-based forest tree metabolomics. MASS SPECTROMETRY REVIEWS 2021; 40:126-157. [PMID: 31498921 DOI: 10.1002/mas.21603] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 08/05/2019] [Indexed: 05/24/2023]
Abstract
Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.
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Affiliation(s)
- Ana Margarida Rodrigues
- Plant Metabolomics Laboratory, GreenIT-Bioresources for Sustainability, Instituto de Tecnologia Química e Biológica António Xavie, Universidade Nova de Lisboa (ITQB NOVA) Avenida da República, Oeiras, 2780-157, Portugal
| | - Célia Miguel
- Forest Genomics & Molecular Genetics Lab, BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1749-016, Lisboa, Portugal
- Instituto de Biologia Experimental e Tecnológica (iBET), 2780-157, Oeiras, Portugal
| | - Inês Chaves
- Forest Genomics & Molecular Genetics Lab, BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1749-016, Lisboa, Portugal
- Instituto de Biologia Experimental e Tecnológica (iBET), 2780-157, Oeiras, Portugal
| | - Carla António
- Plant Metabolomics Laboratory, GreenIT-Bioresources for Sustainability, Instituto de Tecnologia Química e Biológica António Xavie, Universidade Nova de Lisboa (ITQB NOVA) Avenida da República, Oeiras, 2780-157, Portugal
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Ara T, Suda K, Amagai M, Namai K, Suzuki H, Sakurai N, Shibata D. Metabolome profile of Negi-Nira chive, an interspecies hybrid of green spring onion ( Allium fistulosum) and Chinese chive ( A. tuberosum). PLANT BIOTECHNOLOGY (TOKYO, JAPAN) 2020; 37:383-387. [PMID: 33088206 PMCID: PMC7557663 DOI: 10.5511/plantbiotechnology.20.0605b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/05/2020] [Indexed: 06/11/2023]
Abstract
Metabolome analysis of flavored vegetables, green spring onion (Allium fistulosum), Chinese chive (A. tuberosum), and their interspecies hybrid Negi-Nira chive, was conducted using liquid chromatography-Fourier transform ion cyclotron resonance-mass spectrometry, with ca. 2 ppm mass accuracy. Ion peaks in the chromatograms of four biological replicates of the vegetable leaves were processed using the alignment software PowerGet for metabolite comparison, from which we obtained the potential chemical formulae. In total, 860 ion peaks were reproducibly detected; of these, 506, 525, and 336 peaks were found in the hybrid, A. tuberosum, and A. fistulosum, respectively. There were 130 peaks specific to the hybrid; from these, 31 metabolites were annotated by searching compound databases. The sulfur-containing compounds and flavonoids were further analyzed using bioinformatics, to examine the sulfur metabolism of Allium volatiles and the flavonoid pathways in these species. In conclusion, our metabolome analysis of this interspecies hybrid and its parents provides a unique opportunity to elucidate their metabolic background.
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Affiliation(s)
- Takeshi Ara
- Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Kunihiro Suda
- Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Masayuki Amagai
- Tochigi Agricultural Experimental Station, 1080 Kawaraya, Utsunomiya, Tochigi 320-0002, Japan
- Department of Biological Sciences, Kisarazu Campus of Tohoku University, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Kiyoshi Namai
- Tochigi Agricultural Experimental Station, 1080 Kawaraya, Utsunomiya, Tochigi 320-0002, Japan
| | - Hideyuki Suzuki
- Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Nozomu Sakurai
- Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Daisuke Shibata
- Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
- Department of Biological Sciences, Kisarazu Campus of Tohoku University, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
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Hiraga Y, Ara T, Nagashima Y, Shimada N, Sakurai N, Suzuki H, Kera K. Metabolome analysis using multiple data mining approaches suggests luteolin biosynthesis in Physcomitrella patens. PLANT BIOTECHNOLOGY (TOKYO, JAPAN) 2020; 37:377-381. [PMID: 33088205 PMCID: PMC7557656 DOI: 10.5511/plantbiotechnology.20.0525b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 05/25/2020] [Indexed: 06/11/2023]
Abstract
The model land plant Physcomitrella patens synthesizes flavonoids which may act as protectant of ultraviolet-B radiation. We aimed to uncover its flavonoid profile, for which metabolome analysis using liquid chromatography coupled with Ion trap/Orbitrap mass spectrometry was performed. From the 80% methanol extracts, 661 valid peaks were detected. Prediction of the elemental compositions within a mass accuracy of 2 ppm indicated that 217 peaks had single elemental composition. A compound database search revealed 47 peaks to be annotated as secondary metabolites based on the compound database search. Comprehensive substituent search by ShiftedIonsFinder showed there were 13 peaks of potential flavonoid derivatives. Interestingly, a peak having m/z 287.0551, corresponding to that of luteolin, was detected, even though flavone synthase has never been identified in P. patens. Using P. patens labeled with stable isotopes (13C-, 15N-, 18O-, and 34S), we confirmed the elemental composition of the peak as C15H10O6. By a comparison of MS/MS spectra with that of authentic standard, the peak was identified as luteolin or related flavone isomers. This is the first report of luteolin or related flavones synthesis and the possibility of the existence of an unknown enzyme with flavone synthase activity in P. patens.
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Affiliation(s)
- Yasuhide Hiraga
- Research and Development Department, Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
- Department of Research and Development, Hirata Corporation, 111 Hitotsugi, Ueki, Kita, Kumamoto, Kumamoto 861-0198, Japan
| | - Takeshi Ara
- Research and Development Department, Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Yoshiki Nagashima
- Research and Development Department, Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Norimoto Shimada
- Research and Development Department, Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Nozomu Sakurai
- Research and Development Department, Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Hideyuki Suzuki
- Research and Development Department, Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
- Department of Research and Development, Hirata Corporation, 111 Hitotsugi, Ueki, Kita, Kumamoto, Kumamoto 861-0198, Japan
| | - Kota Kera
- Research and Development Department, Kazusa DNA Research Institute, 2-6-7 Kazusa Kamatari, Kisarazu, Chiba 292-0818, Japan
- Department of Nutritional Science and Food Safety, Faculty of Applied Bioscience, Tokyo University of Agriculture, 1-1-1 Sakuragaoka, Setagaya, Tokyo 156-8502, Japan
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Advances in Liquid Chromatography–Mass Spectrometry-Based Lipidomics: A Look Ahead. JOURNAL OF ANALYSIS AND TESTING 2020. [DOI: 10.1007/s41664-020-00135-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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16
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Palermo A, Huan T, Rinehart D, Rinschen MM, Li S, O'Donnell VB, Fahy E, Xue J, Subramaniam S, Benton HP, Siuzdak G. Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology. ANALYTICAL SCIENCE ADVANCES 2020; 1:70-80. [PMID: 35190800 PMCID: PMC8858440 DOI: 10.1002/ansa.202000042] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.
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Affiliation(s)
- Amelia Palermo
- Scripps Center for MetabolomicsThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Tao Huan
- Scripps Center for MetabolomicsThe Scripps Research InstituteLa JollaCaliforniaUSA
- Department of ChemistryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Duane Rinehart
- Scripps Center for MetabolomicsThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Markus M. Rinschen
- Scripps Center for MetabolomicsThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Shuzhao Li
- The Jackson Laboratory for Genomic MedicineFarmingtonConnecticutUSA
| | | | - Eoin Fahy
- Department of BioengineeringUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Jingchuan Xue
- Scripps Center for MetabolomicsThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Shankar Subramaniam
- Department of BioengineeringUniversity of California San DiegoLa JollaCaliforniaUSA
| | - H. Paul Benton
- Scripps Center for MetabolomicsThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Gary Siuzdak
- Scripps Center for MetabolomicsThe Scripps Research InstituteLa JollaCaliforniaUSA
- Department of ChemistryMolecular and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
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Sakurai N, Mardani-Korrani H, Nakayasu M, Matsuda K, Ochiai K, Kobayashi M, Tahara Y, Onodera T, Aoki Y, Motobayashi T, Komatsuzaki M, Ihara M, Shibata D, Fujii Y, Sugiyama A. Metabolome Analysis Identified Okaramines in the Soybean Rhizosphere as a Legacy of Hairy Vetch. Front Genet 2020; 11:114. [PMID: 32153648 PMCID: PMC7049541 DOI: 10.3389/fgene.2020.00114] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 01/30/2020] [Indexed: 11/13/2022] Open
Abstract
Inter-organismal communications below ground, such as plant-microbe interactions in the rhizosphere, affect plant growth. Metabolites are shown to play important roles in biological communication, but there still remain a large number of metabolites in soil to be uncovered. Metabolomics, a technique for the comprehensive analysis of metabolites in samples, may uncover the molecules that intermediate these interactions. We conducted a multivariate analysis using liquid chromatography (LC)-mass spectrometry (MS)-based untargeted metabolomics in several soil samples and also targeted metabolome analysis for the identification of the candidate compounds in soil. We identified okaramine A, B, and C in the rhizosphere soil of hairy vetch. Okaramines are indole alkaloids first identified in soybean pulp (okara) inoculated with Penicillium simplicissimum AK-40 and are insecticidal. Okaramine B was detected in the rhizosphere from an open field growing hairy vetch. Okaramine B was also detected in both bulk and rhizosphere soils of soybean grown following hairy vetch, but not detected in soils of soybean without hairy vetch growth. These results suggested that okaramines might be involved in indirect defense of plants against insects. To our knowledge, this is the first report of okaramines in the natural environment. Untargeted and targeted metabolomics would be useful to uncover the chemistry of the rhizosphere.
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Affiliation(s)
- Nozomu Sakurai
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Japan.,Kazusa DNA Research Institute, Kisarazu, Japan
| | - Hossein Mardani-Korrani
- Department of International Environmental and Agricultural Sciences, Faculty of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Japan
| | - Masaru Nakayasu
- Research Institute for Sustainable Humanosphere, Kyoto University, Gokasho, Japan
| | - Kazuhiko Matsuda
- Department of Applied Biological Chemistry, Faculty of Agriculture, Kindai University, Nara, Japan
| | - Kumiko Ochiai
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
| | - Masaru Kobayashi
- Division of Applied Life Sciences, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
| | - Yusuke Tahara
- Research and Development Center for Five-Sense Devices, Kyushu University, Fukuoka, Japan
| | - Takeshi Onodera
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | - Yuichi Aoki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Takashi Motobayashi
- Department of International Environmental and Agricultural Sciences, Faculty of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Japan
| | - Masakazu Komatsuzaki
- Center for Field Agriculture Research & Education, Ibaraki University, Ami, Japan
| | - Makoto Ihara
- Department of Applied Biological Chemistry, Faculty of Agriculture, Kindai University, Nara, Japan
| | | | - Yoshiharu Fujii
- Department of International Environmental and Agricultural Sciences, Faculty of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Japan
| | - Akifumi Sugiyama
- Research Institute for Sustainable Humanosphere, Kyoto University, Gokasho, Japan
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Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics. Methods Mol Biol 2020; 2064:191-217. [PMID: 31565776 DOI: 10.1007/978-1-4939-9831-9_15] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
In this age of -omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.
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19
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Hidayati NA, Yamada‐Oshima Y, Iwai M, Yamano T, Kajikawa M, Sakurai N, Suda K, Sesoko K, Hori K, Obayashi T, Shimojima M, Fukuzawa H, Ohta H. Lipid remodeling regulator 1 (LRL1) is differently involved in the phosphorus-depletion response from PSR1 in Chlamydomonas reinhardtii. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 100:610-626. [PMID: 31350858 PMCID: PMC6899820 DOI: 10.1111/tpj.14473] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 07/03/2019] [Accepted: 07/09/2019] [Indexed: 05/05/2023]
Abstract
The elucidation of lipid metabolism in microalgae has attracted broad interest, as their storage lipid, triacylglycerol (TAG), can be readily converted into biofuel via transesterification. TAG accumulates in the form of oil droplets, especially when cells undergo nutrient deprivation, such as for nitrogen (N), phosphorus (P), or sulfur (S). TAG biosynthesis under N-deprivation has been comprehensively studied in the model microalga Chlamydomonas reinhardtii, during which TAG accumulates dramatically. However, the resulting rapid breakdown of chlorophyll restricts overall oil yield productivity and causes cessation of cell growth. In contrast, P-deprivation results in oil accumulation without disrupting chloroplast integrity. We used a reverse genetics approach based on co-expression analysis to identify a transcription factor (TF) that is upregulated under P-depleted conditions. Transcriptomic analysis revealed that the mutants showed repression of genes typically associated with lipid remodeling under P-depleted conditions, such as sulfoquinovosyl diacylglycerol 2 (SQD2), diacylglycerol acyltransferase (DGTT1), and major lipid droplet protein (MLDP). As accumulation of sulfoquinovosyl diacylglycerol and TAG were suppressed in P-depleted mutants, we designated the protein as lipid remodeling regulator 1 (LRL1). LRL1 mutants showed slower growth under P-depletion. Moreover, cell size in the mutant was significantly reduced, and TAG and starch accumulation per cell were decreased. Transcriptomic analysis also suggested the repression of several genes typically upregulated in adaptation to P-depletion that are associated with the cell cycle and P and lipid metabolism. Thus, our analysis of LRL1 provides insights into P-allocation and lipid remodeling under P-depleted conditions in C. reinhardtii. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The sequencing data were made publicly available under the BioProject Accession number PRJDB6733 and an accession number LC488724 at the DNA Data Bank of Japan (DDBJ). The data is available at https://trace.ddbj.nig.ac.jp/BPSearch/bioproject?acc=PRJDB6733; http://getentry.ddbj.nig.ac.jp/getentry/na/LC488724. The metabolome data were made publicly available and can be accessed at http://metabolonote.kazusa.or.jp/SE195:/; http://webs2.kazusa.or.jp/data/nur/.
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Affiliation(s)
- Nur A. Hidayati
- Graduate School of Bioscience and BiotechnologyTokyo Institute of Technology4259‐B‐65 Nagatsuta‐cho, Midori‐kuYokohama226‐8501Japan
| | - Yui Yamada‐Oshima
- Graduate School of Bioscience and BiotechnologyTokyo Institute of Technology4259‐B‐65 Nagatsuta‐cho, Midori‐kuYokohama226‐8501Japan
| | - Masako Iwai
- School of Life Science and TechnologyTokyo Institute of Technology4259‐B‐65 Nagatsuta‐cho, Midori‐kuYokohama226‐8501Japan
| | - Takashi Yamano
- Graduate School of BiostudiesKyoto UniversityKyoto606‐8502Japan
| | | | - Nozomu Sakurai
- Technology DevelopmentKazusa DNA Research InstituteKazusa‐kamatari 2‐6‐7KisarazuChiba292‐0818Japan
- Present address:
National Institute of Genetics Bioinformation & DDBJ Center1111 YataMishimaShizuoka411‐8540Japan
| | - Kunihiro Suda
- Technology DevelopmentKazusa DNA Research InstituteKazusa‐kamatari 2‐6‐7KisarazuChiba292‐0818Japan
| | - Kanami Sesoko
- School of Life Science and TechnologyTokyo Institute of Technology4259‐B‐65 Nagatsuta‐cho, Midori‐kuYokohama226‐8501Japan
| | - Koichi Hori
- School of Life Science and TechnologyTokyo Institute of Technology4259‐B‐65 Nagatsuta‐cho, Midori‐kuYokohama226‐8501Japan
| | - Takeshi Obayashi
- Graduate School of Information SciencesTohoku University6‐3‐09, Aramaki‐Aza‐Aoba, Aoba‐kuSendai980‐8679Japan
| | - Mie Shimojima
- School of Life Science and TechnologyTokyo Institute of Technology4259‐B‐65 Nagatsuta‐cho, Midori‐kuYokohama226‐8501Japan
| | - Hideya Fukuzawa
- Graduate School of BiostudiesKyoto UniversityKyoto606‐8502Japan
| | - Hiroyuki Ohta
- School of Life Science and TechnologyTokyo Institute of Technology4259‐B‐65 Nagatsuta‐cho, Midori‐kuYokohama226‐8501Japan
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20
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Pinu FR, Beale DJ, Paten AM, Kouremenos K, Swarup S, Schirra HJ, Wishart D. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites 2019; 9:E76. [PMID: 31003499 PMCID: PMC6523452 DOI: 10.3390/metabo9040076] [Citation(s) in RCA: 306] [Impact Index Per Article: 61.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 04/15/2019] [Accepted: 04/16/2019] [Indexed: 02/07/2023] Open
Abstract
The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.
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Affiliation(s)
- Farhana R Pinu
- The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand.
| | - David J Beale
- Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosciences Precinct, Dutton Park, Dutton Park, QLD 4102, Australia.
| | - Amy M Paten
- Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Research and Innovation Park, Acton, ACT 2601, Australia.
| | - Konstantinos Kouremenos
- Trajan Scientific and Medical, Ringwood, VIC 3134, Australia.
- Bio21 Institute, The University of Melbourne, Parkville, VIC 3010, Australia.
| | - Sanjay Swarup
- Department of Biological Sciences, National University of Singapore, Singapore 117411, Singapore.
| | - Horst J Schirra
- Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD 4072, Australia.
| | - David Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.
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21
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Perez de Souza L, Naake T, Tohge T, Fernie AR. From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics. Gigascience 2017; 6:1-20. [PMID: 28520864 PMCID: PMC5499862 DOI: 10.1093/gigascience/gix037] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 05/08/2017] [Accepted: 05/12/2017] [Indexed: 01/19/2023] Open
Abstract
The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.
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Affiliation(s)
- Leonardo Perez de Souza
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Thomas Naake
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Takayuki Tohge
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
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22
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Akimoto N, Ara T, Nakajima D, Suda K, Ikeda C, Takahashi S, Muneto R, Yamada M, Suzuki H, Shibata D, Sakurai N. FlavonoidSearch: A system for comprehensive flavonoid annotation by mass spectrometry. Sci Rep 2017; 7:1243. [PMID: 28455528 PMCID: PMC5430893 DOI: 10.1038/s41598-017-01390-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 03/29/2017] [Indexed: 11/17/2022] Open
Abstract
Currently, in mass spectrometry-based metabolomics, limited reference mass spectra are available for flavonoid identification. In the present study, a database of probable mass fragments for 6,867 known flavonoids (FsDatabase) was manually constructed based on new structure- and fragmentation-related rules using new heuristics to overcome flavonoid complexity. We developed the FlavonoidSearch system for flavonoid annotation, which consists of the FsDatabase and a computational tool (FsTool) to automatically search the FsDatabase using the mass spectra of metabolite peaks as queries. This system showed the highest identification accuracy for the flavonoid aglycone when compared to existing tools and revealed accurate discrimination between the flavonoid aglycone and other compounds. Sixteen new flavonoids were found from parsley, and the diversity of the flavonoid aglycone among different fruits and vegetables was investigated.
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Affiliation(s)
- Nayumi Akimoto
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan
| | - Takeshi Ara
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan.,Kyoto University, Graduate School of Agriculture, Gokasho, Uji, Kyoto, 611-0011, Japan
| | - Daisuke Nakajima
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan
| | - Kunihiro Suda
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan
| | - Chiaki Ikeda
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan
| | - Shingo Takahashi
- Kyoto University, Graduate School of Agriculture, Gokasho, Uji, Kyoto, 611-0011, Japan.,KAGOME CO., LTD., Nishitomiyama 17, Nasushiobara, Tochigi, 329-2762, Japan
| | - Reiko Muneto
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan
| | - Manabu Yamada
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan
| | - Hideyuki Suzuki
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan
| | - Daisuke Shibata
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan
| | - Nozomu Sakurai
- Kazusa DNA Research Institute, Kazusa-kamatari 2-6-7, Kisarazu, Chiba, 292-0818, Japan.
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23
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Goveia J, Pircher A, Conradi LC, Kalucka J, Lagani V, Dewerchin M, Eelen G, DeBerardinis RJ, Wilson ID, Carmeliet P. Meta-analysis of clinical metabolic profiling studies in cancer: challenges and opportunities. EMBO Mol Med 2016; 8:1134-1142. [PMID: 27601137 PMCID: PMC5048364 DOI: 10.15252/emmm.201606798] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Cancer cell metabolism has received increasing attention. Despite a boost in the application of clinical metabolic profiling (CMP) in cancer patients, a meta‐analysis has not been performed. The primary goal of this study was to assess whether public accessibility of metabolomics data and identification and reporting of metabolites were sufficient to assess which metabolites were consistently altered in cancer patients. We therefore retrospectively curated data from CMP studies in cancer patients published during 5 recent years and used an established vote‐counting method to perform a semiquantitative meta‐analysis of metabolites in tumor tissue and blood. This analysis confirmed well‐known increases in glycolytic metabolites, but also unveiled unprecedented changes in other metabolites such as ketone bodies and amino acids (histidine, tryptophan). However, this study also highlighted that insufficient public accessibility of metabolomics data, and inadequate metabolite identification and reporting hamper the discovery potential of meta‐analyses of CMP studies, calling for improved standardization of metabolomics studies.
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Affiliation(s)
- Jermaine Goveia
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, VIB, Leuven, Belgium
| | - Andreas Pircher
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, VIB, Leuven, Belgium
| | - Lena-Christin Conradi
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, VIB, Leuven, Belgium
| | - Joanna Kalucka
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, VIB, Leuven, Belgium
| | - Vincenzo Lagani
- Computer Science Department, University of Crete, Heraklion, Greece
| | - Mieke Dewerchin
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, VIB, Leuven, Belgium
| | - Guy Eelen
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, VIB, Leuven, Belgium
| | - Ralph J DeBerardinis
- Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ian D Wilson
- Department of Surgery and Cancer, Imperial College, London, UK
| | - Peter Carmeliet
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, VIB, Leuven, Belgium
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24
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Misra BB, van der Hooft JJJ. Updates in metabolomics tools and resources: 2014-2015. Electrophoresis 2015; 37:86-110. [DOI: 10.1002/elps.201500417] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2015] [Revised: 10/04/2015] [Accepted: 10/05/2015] [Indexed: 12/12/2022]
Affiliation(s)
- Biswapriya B. Misra
- Department of Biology, Genetics Institute; University of Florida; Gainesville FL USA
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25
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Analysis of Metabolomics Datasets with High-Performance Computing and Metabolite Atlases. Metabolites 2015; 5:431-42. [PMID: 26287255 PMCID: PMC4588804 DOI: 10.3390/metabo5030431] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 07/07/2015] [Accepted: 07/13/2015] [Indexed: 01/07/2023] Open
Abstract
Even with the widespread use of liquid chromatography mass spectrometry (LC/MS) based metabolomics, there are still a number of challenges facing this promising technique. Many, diverse experimental workflows exist; yet there is a lack of infrastructure and systems for tracking and sharing of information. Here, we describe the Metabolite Atlas framework and interface that provides highly-efficient, web-based access to raw mass spectrometry data in concert with assertions about chemicals detected to help address some of these challenges. This integration, by design, enables experimentalists to explore their raw data, specify and refine features annotations such that they can be leveraged for future experiments. Fast queries of the data through the web using SciDB, a parallelized database for high performance computing, make this process operate quickly. By using scripting containers, such as IPython or Jupyter, to analyze the data, scientists can utilize a wide variety of freely available graphing, statistics, and information management resources. In addition, the interfaces facilitate integration with systems biology tools to ultimately link metabolomics data with biological models.
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