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Lu Y, Pang Z, Xia J. Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data. Brief Bioinform 2023; 24:bbac553. [PMID: 36572652 PMCID: PMC9851290 DOI: 10.1093/bib/bbac553] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/31/2022] [Accepted: 11/15/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.
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Affiliation(s)
- Yao Lu
- Department of Microbiology and Immunology, McGill University, Quebec, Canada
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Quebec, Canada
| | - Jianguo Xia
- Department of Microbiology and Immunology, McGill University, Quebec, Canada
- Institute of Parasitology, McGill University, Quebec, Canada
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2
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Peralbo-Molina Á, Solà-Santos P, Perera-Lluna A, Chicano-Gálvez E. Data Processing and Analysis in Mass Spectrometry-Based Metabolomics. Methods Mol Biol 2023; 2571:207-239. [PMID: 36152164 DOI: 10.1007/978-1-0716-2699-3_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.
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Affiliation(s)
- Ángela Peralbo-Molina
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain.
| | - Pol Solà-Santos
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Alexandre Perera-Lluna
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Eduardo Chicano-Gálvez
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain
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3
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MAVEN2: An Updated Open-Source Mass Spectrometry Exploration Platform. Metabolites 2022; 12:metabo12080684. [PMID: 35893250 PMCID: PMC9330773 DOI: 10.3390/metabo12080684] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022] Open
Abstract
MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, MAVEN2, which supports LC-MS/MS analysis of metabolomics and lipidomics samples. We have developed algorithms to support MS/MS spectral matching and efficient search of large-scale fragmentation libraries. We explore the ability of our approach to separate authentic from spurious metabolite identifications using a set of standards spiked into water and yeast backgrounds. To support our improved lipid identification workflow, we introduce a novel in-silico lipidomics library covering major lipid classes and compare searches using our novel library to searches with existing in-silico lipidomics libraries. MAVEN2 source code and cross-platform application installers are freely available for download from GitHub under a GNU permissive license [ver 3], as are the in silico lipidomics libraries and corresponding code repository.
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4
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Yan S, Bhawal R, Yin Z, Thannhauser TW, Zhang S. Recent advances in proteomics and metabolomics in plants. MOLECULAR HORTICULTURE 2022; 2:17. [PMID: 37789425 PMCID: PMC10514990 DOI: 10.1186/s43897-022-00038-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/20/2022] [Indexed: 10/05/2023]
Abstract
Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.
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Affiliation(s)
- Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Ruchika Bhawal
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA
| | - Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | | | - Sheng Zhang
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA.
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5
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Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
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6
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Place BJ, Ulrich EM, Challis JK, Chao A, Du B, Favela K, Feng YL, Fisher CM, Gardinali P, Hood A, Knolhoff AM, McEachran AD, Nason SL, Newton SR, Ng B, Nuñez J, Peter KT, Phillips AL, Quinete N, Renslow R, Sobus JR, Sussman EM, Warth B, Wickramasekara S, Williams AJ. An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group. Anal Chem 2021; 93:16289-16296. [PMID: 34842413 PMCID: PMC8848292 DOI: 10.1021/acs.analchem.1c02660] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Non-targeted analysis (NTA) encompasses a rapidly evolving set of mass spectrometry techniques aimed at characterizing the chemical composition of complex samples, identifying unknown compounds, and/or classifying samples, without prior knowledge regarding the chemical content of the samples. Recent advances in NTA are the result of improved and more accessible instrumentation for data generation and analysis tools for data evaluation and interpretation. As researchers continue to develop NTA approaches in various scientific fields, there is a growing need to identify, disseminate, and adopt community-wide method reporting guidelines. In 2018, NTA researchers formed the Benchmarking and Publications for Non-Targeted Analysis Working Group (BP4NTA) to address this need. Consisting of participants from around the world and representing fields ranging from environmental science and food chemistry to 'omics and toxicology, BP4NTA provides resources addressing a variety of challenges associated with NTA. Thus far, BP4NTA group members have aimed to establish a consensus on NTA-related terms and concepts and to create consistency in reporting practices by providing resources on a public Web site, including consensus definitions, reference content, and lists of available tools. Moving forward, BP4NTA will provide a setting for NTA researchers to continue discussing emerging challenges and contribute to additional harmonization efforts.
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Affiliation(s)
- Benjamin J. Place
- National Institute of Standards and Technology, Gaithersburg, MD, USA 20899,Corresponding author,
| | - Elin M. Ulrich
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA 27711
| | | | - Alex Chao
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA 27711
| | - Bowen Du
- Southern California Coastal Water Research Project Authority, Costa Mesa, CA, USA 92626
| | - Kristin Favela
- Southwest Research Institute, San Antonio, TX, USA 78238
| | - Yong-Lai Feng
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada, K1A 0K9
| | - Christine M. Fisher
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, MD, USA 20740
| | - Piero Gardinali
- Institute of Environment & Department of Chemistry and Biochemistry, Florida International University, North Miami, FL 33181
| | - Alan Hood
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA 20993
| | - Ann M. Knolhoff
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, MD, USA 20740
| | | | - Sara L. Nason
- Connecticut Agricultural Experiment Station, New Haven, CT, USA 06511
| | - Seth R. Newton
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA 27711
| | - Brian Ng
- Institute of Environment & Department of Chemistry and Biochemistry, Florida International University, North Miami, FL 33181
| | - Jamie Nuñez
- Pacific Northwest National Laboratory, Richland, WA, USA 99352
| | - Katherine T. Peter
- National Institute of Standards and Technology, Charleston, SC, USA 29412
| | - Allison L. Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Research Triangle Park, NC, USA 27711
| | - Natalia Quinete
- Institute of Environment & Department of Chemistry and Biochemistry, Florida International University, North Miami, FL 33181
| | - Ryan Renslow
- Pacific Northwest National Laboratory, Richland, WA, USA 99352
| | - Jon R. Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA 27711
| | - Eric M. Sussman
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA 20993
| | - Benedikt Warth
- Department of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
| | - Samanthi Wickramasekara
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA 20993
| | - Antony J. Williams
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA 27711
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7
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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: 34] [Impact Index Per Article: 8.5] [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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8
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Chen L, Lu W, Wang L, Xing X, Chen Z, Teng X, Zeng X, Muscarella AD, Shen Y, Cowan A, McReynolds MR, Kennedy BJ, Lato AM, Campagna SR, Singh M, Rabinowitz JD. Metabolite discovery through global annotation of untargeted metabolomics data. Nat Methods 2021; 18:1377-1385. [PMID: 34711973 PMCID: PMC8733904 DOI: 10.1038/s41592-021-01303-3] [Citation(s) in RCA: 135] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 09/16/2021] [Indexed: 11/08/2022]
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
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Affiliation(s)
- Li Chen
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Wenyun Lu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Lin Wang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Xi Xing
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Ziyang Chen
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Xin Teng
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Xianfeng Zeng
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Antonio D Muscarella
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yihui Shen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Alexis Cowan
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Melanie R McReynolds
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Brandon J Kennedy
- Lotus Separations, LLC, Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Ashley M Lato
- Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA
| | - Shawn R Campagna
- Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Joshua D Rabinowitz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA.
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9
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Uppal K. Models of Metabolomic Networks. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11615-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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10
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Chaker J, Gilles E, Léger T, Jégou B, David A. From Metabolomics to HRMS-Based Exposomics: Adapting Peak Picking and Developing Scoring for MS1 Suspect Screening. Anal Chem 2020; 93:1792-1800. [DOI: 10.1021/acs.analchem.0c04660] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Erwann Gilles
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Thibaut Léger
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Bernard Jégou
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
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11
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Ludwig M, Nothias LF, Dührkop K, Koester I, Fleischauer M, Hoffmann MA, Petras D, Vargas F, Morsy M, Aluwihare L, Dorrestein PC, Böcker S. Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. NAT MACH INTELL 2020. [DOI: 10.1038/s42256-020-00234-6] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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12
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Kachman M, Habra H, Duren W, Wigginton J, Sajjakulnukit P, Michailidis G, Burant C, Karnovsky A. Deep annotation of untargeted LC-MS metabolomics data with Binner. Bioinformatics 2020; 36:1801-1806. [PMID: 31642507 DOI: 10.1093/bioinformatics/btz798] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 06/20/2019] [Accepted: 10/22/2019] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION When metabolites are analyzed by electrospray ionization (ESI)-mass spectrometry, they are usually detected as multiple ion species due to the presence of isotopes, adducts and in-source fragments. The signals generated by these degenerate features (along with contaminants and other chemical noise) obscure meaningful patterns in MS data, complicating both compound identification and downstream statistical analysis. To address this problem, we developed Binner, a new tool for the discovery and elimination of many degenerate feature signals typically present in untargeted ESI-LC-MS metabolomics data. RESULTS Binner generates feature annotations and provides tools to help users visualize informative feature relationships that can further elucidate the underlying structure of the data. To demonstrate the utility of Binner and to evaluate its performance, we analyzed data from reversed phase LC-MS and hydrophilic interaction chromatography (HILIC) platforms and demonstrated the accuracy of selected annotations using MS/MS. When we compared Binner annotations of 75 compounds previously identified in human plasma samples with annotations generated by three similar tools, we found that Binner achieves superior performance in the number and accuracy of annotations while simultaneously minimizing the number of incorrectly annotated principal ions. Data reduction and pattern exploration with Binner have allowed us to catalog a number of previously unrecognized complex adducts and neutral losses generated during the ionization of molecules in LC-MS. In summary, Binner allows users to explore patterns in their data and to efficiently and accurately eliminate a significant number of the degenerate features typically found in various LC-MS modalities. AVAILABILITY AND IMPLEMENTATION Binner is written in Java and is freely available from http://binner.med.umich.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maureen Kachman
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Hani Habra
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - William Duren
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI 48109, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Janis Wigginton
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Peter Sajjakulnukit
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - George Michailidis
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI 48109, USA.,Department of Statistics, University of Florida, Gainesville, FL 32611, USA
| | - Charles Burant
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI 48109, USA.,Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Alla Karnovsky
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI 48109, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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13
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Fraisier-Vannier O, Chervin J, Cabanac G, Puech V, Fournier S, Durand V, Amiel A, André O, Benamar OA, Dumas B, Tsugawa H, Marti G. MS-CleanR: A Feature-Filtering Workflow for Untargeted LC-MS Based Metabolomics. Anal Chem 2020; 92:9971-9981. [PMID: 32589017 DOI: 10.1021/acs.analchem.0c01594] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the gold-standard technique to determine the full chemical diversity in biological samples. However, this approach still has many limitations; notably, the difficulty of accurately estimating the number of unique metabolites profiled among the thousands of MS ion signals arising from chromatograms. Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER suite, which tackles feature degeneracy and improves annotation rates. We show that implementation of MS-CleanR reduces the number of signals by nearly 80% while retaining 95% of unique metabolite features. Moreover, the annotation results from MS-FINDER can be ranked according to the database chosen by the user, which enhance identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions fostered class separation resulting from multivariate data analysis and led to annotation of 75% of the final features. The full workflow was applied to metabolomic profiles from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches. A group of glycosylated triterpenoids overrepresented in resistant lines were identified as candidate compounds conferring pathogen resistance. MS-CleanR is implemented through a Shiny interface for intuitive use by end-users (available at https://github.com/eMetaboHUB/MS-CleanR).
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Affiliation(s)
- Ophélie Fraisier-Vannier
- Pharma Dev, Université de Toulouse, IRD, UPS, 31400 Toulouse, France.,Institut de Recherche en Informatique de Toulouse, Université de Toulouse, UPS, Toulouse 31400, France
| | - Justine Chervin
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Metatoul-AgromiX Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, LRSV, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Guillaume Cabanac
- Institut de Recherche en Informatique de Toulouse, Université de Toulouse, UPS, Toulouse 31400, France
| | - Virginie Puech
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Metatoul-AgromiX Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, LRSV, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Sylvie Fournier
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Metatoul-AgromiX Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, LRSV, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Virginie Durand
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Aurélien Amiel
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,De Sangosse, Bonnel, 47480 Pont-Du-Casse, France
| | - Olivier André
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,De Sangosse, Bonnel, 47480 Pont-Du-Casse, France
| | - Omar Abdelaziz Benamar
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,De Sangosse, Bonnel, 47480 Pont-Du-Casse, France
| | - Bernard Dumas
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, Yokohama 230-0045, Japan.,RIKEN Center for Integrative Medical Science, Yokohama 230-0045, Japan
| | - Guillaume Marti
- Pharma Dev, Université de Toulouse, IRD, UPS, 31400 Toulouse, France.,Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Metatoul-AgromiX Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, LRSV, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Institut de Recherche en Informatique de Toulouse, Université de Toulouse, UPS, Toulouse 31400, France
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14
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Senan O, Aguilar-Mogas A, Navarro M, Capellades J, Noon L, Burks D, Yanes O, Guimerà R, Sales-Pardo M. CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network. Bioinformatics 2020; 35:4089-4097. [PMID: 30903689 PMCID: PMC6792096 DOI: 10.1093/bioinformatics/btz207] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 01/30/2019] [Accepted: 03/21/2019] [Indexed: 11/26/2022] Open
Abstract
Motivation The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample. Results Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools. Availability and implementation https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Oriol Senan
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
| | - Antoni Aguilar-Mogas
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
| | - Miriam Navarro
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Jordi Capellades
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Luke Noon
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain.,Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Deborah Burks
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain.,Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Oscar Yanes
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Roger Guimerà
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,ICREA, Barcelona, Spain
| | - Marta Sales-Pardo
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
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15
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Razzaq A, Sadia B, Raza A, Khalid Hameed M, Saleem F. Metabolomics: A Way Forward for Crop Improvement. Metabolites 2019; 9:E303. [PMID: 31847393 PMCID: PMC6969922 DOI: 10.3390/metabo9120303] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 12/02/2019] [Accepted: 12/11/2019] [Indexed: 12/15/2022] Open
Abstract
Metabolomics is an emerging branch of "omics" and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment-gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.
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Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
| | - Bushra Sadia
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
| | - Ali Raza
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Wuhan 430062, China;
| | - Muhammad Khalid Hameed
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China;
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
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16
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From mass to metabolite in human untargeted metabolomics: Recent advances in annotation of metabolites applying liquid chromatography-mass spectrometry data. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2018.11.022] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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17
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Del Carratore F, Schmidt K, Vinaixa M, Hollywood KA, Greenland-Bews C, Takano E, Rogers S, Breitling R. Integrated Probabilistic Annotation: A Bayesian-Based Annotation Method for Metabolomic Profiles Integrating Biochemical Connections, Isotope Patterns, and Adduct Relationships. Anal Chem 2019; 91:12799-12807. [PMID: 31509381 DOI: 10.1021/acs.analchem.9b02354] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
In a typical untargeted metabolomics experiment, the huge amount of complex data generated by mass spectrometry necessitates automated tools for the extraction of useful biological information. Each metabolite generates numerous mass spectrometry features. The association of these experimental features to the underlying metabolites still represents one of the major bottlenecks in metabolomics data processing. While certain identification (e.g., by comparison to authentic standards) is always desirable, it is usually achievable only for a limited number of compounds, and scientists often deal with a significant amount of putatively annotated metabolites. The confidence in a specific annotation is usually assessed by considering different sources of information (e.g., isotope patterns, adduct formation, chromatographic retention times, and fragmentation patterns). IPA (integrated probabilistic annotation) offers a rigorous and reproducible method to automatically annotate metabolite profiles and evaluate the resulting confidence of the putative annotations. It is able to provide a rigorous measure of our confidence in any putative annotation and is also able to update and refine our beliefs (i.e., background prior knowledge) by incorporating different sources of information in the annotation process, such as isotope patterns, adduct formation and biochemical relations. The IPA package is freely available on GitHub ( https://github.com/francescodc87/IPA ), together with the related extensive documentation.
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Affiliation(s)
- Francesco Del Carratore
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Kamila Schmidt
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Maria Vinaixa
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Katherine A Hollywood
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Caitlin Greenland-Bews
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Eriko Takano
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Simon Rogers
- School of Computing Science , University of Glasgow , Glasgow , G12 8RZ , U.K
| | - Rainer Breitling
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
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18
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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19
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Peters DL, Wang W, Zhang X, Ning Z, Mayne J, Figeys D. Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome. Proteomics 2019; 19:e1800363. [PMID: 31321880 DOI: 10.1002/pmic.201800363] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 06/27/2019] [Indexed: 12/14/2022]
Abstract
The gut microbiome has been shown to play a significant role in human healthy and diseased states. The dynamic signaling that occurs between the host and microbiome is critical for the maintenance of host homeostasis. Analyzing the human microbiome with metaproteomics, metabolomics, and integrative multi-omics analyses can provide significant information on markers for healthy and diseased states, allowing for the eventual creation of microbiome-targeted treatments for diseases associated with dysbiosis. Metaproteomics enables functional activity information to be gained from the microbiome samples, while metabolomics provides insight into the overall metabolic states affecting/representing the host-microbiome interactions. Combining these functional -omic platforms together with microbiome composition profiling allows for a holistic overview on the functional and metabolic state of the microbiome and its influence on human health. Here the benefits of metaproteomics, metabolomics, and the integrative multi-omic approaches to investigating the gut microbiome in the context of human health and diseases are reviewed.
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Affiliation(s)
- Danielle L Peters
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Wenju Wang
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Xu Zhang
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Zhibin Ning
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Janice Mayne
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Daniel Figeys
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada.,Canadian Institute for Advanced Research, 661 University Ave, Toronto, ON, M5G 1M1, Canada.,The University of Ottawa and Shanghai Institute of Materia Medica Joint Research Center on Systems and Personalized Pharmacology, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
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20
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Seitzer PM, Searle BC. Incorporating In-Source Fragment Information Improves Metabolite Identification Accuracy in Untargeted LC–MS Data Sets. J Proteome Res 2018; 18:791-796. [DOI: 10.1021/acs.jproteome.8b00601] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Phillip M. Seitzer
- Proteome Software, 1340 Southwest Bertha Boulevard Suite 10, Portland, Oregon 97219, United States
| | - Brian C. Searle
- Proteome Software, 1340 Southwest Bertha Boulevard Suite 10, Portland, Oregon 97219, United States
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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21
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Wang X, Jones DR, Shaw TI, Cho JH, Wang Y, Tan H, Xie B, Zhou S, Li Y, Peng J. Target-Decoy-Based False Discovery Rate Estimation for Large-Scale Metabolite Identification. J Proteome Res 2018; 17:2328-2334. [PMID: 29790753 DOI: 10.1021/acs.jproteome.8b00019] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Metabolite identification is a crucial step in mass spectrometry (MS)-based metabolomics. However, it is still challenging to assess the confidence of assigned metabolites. We report a novel method for estimating the false discovery rate (FDR) of metabolite assignment with a target-decoy strategy, in which the decoys are generated through violating the octet rule of chemistry by adding small odd numbers of hydrogen atoms. The target-decoy strategy was integrated into JUMPm, an automated metabolite identification pipeline for large-scale MS analysis and was also evaluated with two other metabolomics tools, mzMatch and MZmine 2. The reliability of FDR calculation was examined by false data sets, which were simulated by altering MS1 or MS2 spectra. Finally, we used the JUMPm pipeline coupled to the target-decoy strategy to process unlabeled and stable-isotope-labeled metabolomic data sets. The results demonstrate that the target-decoy strategy is a simple and effective method for evaluating the confidence of high-throughput metabolite identification.
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22
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Tebani A, Afonso C, Bekri S. Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery. J Inherit Metab Dis 2018; 41:393-406. [PMID: 28842777 PMCID: PMC5959951 DOI: 10.1007/s10545-017-0080-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 07/27/2017] [Accepted: 07/28/2017] [Indexed: 12/11/2022]
Abstract
This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Carlos Afonso
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France.
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France.
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23
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da Silva RR, Wang M, Nothias LF, van der Hooft JJJ, Caraballo-Rodríguez AM, Fox E, Balunas MJ, Klassen JL, Lopes NP, Dorrestein PC. Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput Biol 2018; 14:e1006089. [PMID: 29668671 PMCID: PMC5927460 DOI: 10.1371/journal.pcbi.1006089] [Citation(s) in RCA: 215] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 04/30/2018] [Accepted: 03/13/2018] [Indexed: 12/19/2022] Open
Abstract
The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.
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Affiliation(s)
- Ricardo R. da Silva
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
- NPPNS, Department of Physic and Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Louis-Félix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Justin J. J. van der Hooft
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
- Bioinformatics Group, Department of Plant Sciences, Wageningen University, Wageningen, The Netherlands
| | - Andrés Mauricio Caraballo-Rodríguez
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Evan Fox
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, United States of America
| | - Marcy J. Balunas
- Division of Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, United States of America
| | - Jonathan L. Klassen
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, United States of America
| | - Norberto Peporine Lopes
- NPPNS, Department of Physic and Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
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24
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Domingo-Almenara X, Montenegro-Burke JR, Benton HP, Siuzdak G. Annotation: A Computational Solution for Streamlining Metabolomics Analysis. Anal Chem 2018; 90:480-489. [PMID: 29039932 PMCID: PMC5750104 DOI: 10.1021/acs.analchem.7b03929] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.
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Affiliation(s)
- Xavier Domingo-Almenara
- Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - J Rafael Montenegro-Burke
- Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - H Paul Benton
- Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States
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Godzien J, Gil de la Fuente A, Otero A, Barbas C. Metabolite Annotation and Identification. COMPREHENSIVE ANALYTICAL CHEMISTRY 2018. [DOI: 10.1016/bs.coac.2018.07.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome. Anal Bioanal Chem 2017; 410:1287-1297. [PMID: 29256075 DOI: 10.1007/s00216-017-0768-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 11/13/2017] [Indexed: 12/17/2022]
Abstract
Although it is common in untargeted metabolomics to apply reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) methods that have been systematically optimized for lipids and central carbon metabolites, here we show that these established protocols provide poor coverage of semipolar metabolites because of inadequate retention. Our objective was to develop an RPLC approach that improved detection of these metabolites without sacrificing lipid coverage. We initially evaluated columns recently released by Waters under the CORTECS line by analyzing 47 small-molecule standards that evenly span the nonpolar and semipolar ranges. An RPLC method commonly used in untargeted metabolomics was considered a benchmarking reference. We found that highly nonpolar and semipolar metabolites cannot be reliably profiled with any single method because of retention and solubility limitations of the injection solvent. Instead, we optimized a multiplexed approach using the CORTECS T3 column to analyze semipolar compounds and the CORTECS C8 column to analyze lipids. Strikingly, we determined that combining these methods allowed detection of 41 of the total 47 standards, whereas our reference RPLC method detected only 10 of the 47 standards. We then applied credentialing to compare method performance at the comprehensive scale. The tandem method showed more than a fivefold increase in credentialing coverage relative to our RPLC benchmark. Our results demonstrate that comprehensive coverage of metabolites amenable to reversed-phase separation necessitates two reconstitution solvents and chromatographic methods. Thus, we suggest complementing HILIC methods with a dual T3 and C8 RPLC approach to increase coverage of semipolar metabolites and lipids for untargeted metabolomics. Graphical abstract Analysis of semipolar and nonpolar metabolites necessitates two reversed-phase chromatography (RPLC) methods, which extend metabolome coverage more than fivefold for untargeted profiling. HILIC hydrophilic interaction liquid chromatography.
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Scheubert K, Hufsky F, Petras D, Wang M, Nothias LF, Dührkop K, Bandeira N, Dorrestein PC, Böcker S. Significance estimation for large scale metabolomics annotations by spectral matching. Nat Commun 2017; 8:1494. [PMID: 29133785 PMCID: PMC5684233 DOI: 10.1038/s41467-017-01318-5] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 09/08/2017] [Indexed: 12/17/2022] Open
Abstract
The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.
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Affiliation(s)
- Kerstin Scheubert
- Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, 07743, Germany
| | - Franziska Hufsky
- Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, 07743, Germany
- RNA Bioinformatics and High Throughput Analysis, Friedrich Schiller University Jena, Jena, 07743, Germany
| | - Daniel Petras
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Louis-Félix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Kai Dührkop
- Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, 07743, Germany
| | - Nuno Bandeira
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Sebastian Böcker
- Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, 07743, Germany.
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Everett JR. NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2017; 102-103:1-14. [PMID: 29157489 DOI: 10.1016/j.pnmrs.2017.04.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 04/23/2017] [Accepted: 04/24/2017] [Indexed: 06/07/2023]
Abstract
Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.
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Affiliation(s)
- Jeremy R Everett
- Medway Metabonomics Group, University of Greenwich, Chatham Maritime, Kent ME4 4TB, UK.
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Mahieu NG, Patti GJ. Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites. Anal Chem 2017; 89:10397-10406. [PMID: 28914531 PMCID: PMC6427824 DOI: 10.1021/acs.analchem.7b02380] [Citation(s) in RCA: 209] [Impact Index Per Article: 26.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call "degenerate features", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ∼2961. We then applied an orthogonal approach to remove nonbiological features from the data using the 13C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5-fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database ( http://creDBle.wustl.edu ), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features.
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Affiliation(s)
- Nathaniel G. Mahieu
- Department of Chemistry, Washington University, St. Louis, Missouri 63130, United States
| | - Gary J. Patti
- Department of Chemistry, Washington University, St. Louis, Missouri 63130, United States
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Burgess KEV, Borutzki Y, Rankin N, Daly R, Jourdan F. MetaNetter 2: A Cytoscape plugin for ab initio network analysis and metabolite feature classification. J Chromatogr B Analyt Technol Biomed Life Sci 2017; 1071:68-74. [PMID: 29030098 PMCID: PMC5726607 DOI: 10.1016/j.jchromb.2017.08.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 08/07/2017] [Accepted: 08/13/2017] [Indexed: 01/19/2023]
Abstract
An update to the ab-initio network construction tool MetaNetter has been produced. The tool creates networks of masses from high resolution mass spectrometry data. The new plugin provides both chemical transformation and adduct mapping. Tables mapping adduct and transform counts across samples can be generated. Retention time windows are supported for both adduct and transform network generation.
Metabolomics frequently relies on the use of high resolution mass spectrometry data. Classification and filtering of this data remain a challenging task due to the plethora of complex mass spectral artefacts, chemical noise, adducts and fragmentation that occur during ionisation and analysis. Additionally, the relationships between detected compounds can provide a wealth of information about the nature of the samples and the biochemistry that gave rise to them. We present a biochemical networking tool: MetaNetter 2 that is based on the original MetaNetter, a Cytoscape plugin that creates ab initio networks. The new version supports two major improvements: the generation of adduct networks and the creation of tables that map adduct or transformation patterns across multiple samples, providing a readout of compound relationships. We have applied this tool to the analysis of adduct patterns in the same sample separated under two different chromatographies, allowing inferences to be made about the effect of different buffer conditions on adduct detection, and the application of the chemical transformation analysis to both a single fragmentation analysis and an all-ions fragmentation dataset. Finally, we present an analysis of a dataset derived from anaerobic and aerobic growth of the organism Staphylococcus aureus demonstrating the utility of the tool for biological analysis.
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Affiliation(s)
- K E V Burgess
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom; Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom.
| | - Y Borutzki
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom; Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - N Rankin
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - R Daly
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom
| | - F Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
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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: 45] [Impact Index Per Article: 5.6] [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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Randazzo GM, Tonoli D, Strajhar P, Xenarios I, Odermatt A, Boccard J, Rudaz S. Enhanced metabolite annotation via dynamic retention time prediction: Steroidogenesis alterations as a case study. J Chromatogr B Analyt Technol Biomed Life Sci 2017; 1071:11-18. [PMID: 28479067 DOI: 10.1016/j.jchromb.2017.04.032] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 03/07/2017] [Accepted: 04/20/2017] [Indexed: 10/19/2022]
Abstract
The development of metabolomics based on ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) now allows hundreds to thousands of metabolites to be simultaneously monitored in biological matrices. In that context, bioinformatics and multivariate data analysis (MVA) play a crucial role in the detection of relevant alteration patterns. However, sound biological interpretations must necessarily be supported by metabolite identifications to be definitive or at least have a high degree of confidence. Each compound, should be characterised by unique molecular properties. Among them, the exact mass and the chromatographic retention time are recognised as major and complementary criteria for compound identification. While the former is easily derived from the molecular structure, building generic and accurate retention time open databases still constitutes a critical issue because of the vast diversity of instruments, stationary phases and operating conditions in UHPLC-HRMS. Because several hits matching a molecular formula obtained from an exact mass and an isotopic pattern are often generated for each analyte, this methodology rarely allows a unique and unambiguous molecular identity to be gained. This work aims to provide a flexible solution to facilitate reliable compound annotation based on retention time in reversed-phase liquid chromatography (RPLC). It proposes an innovative approach based on the chromatographic linear solvent strength (LSS) theory, allowing retention times under any gradient conditions at fixed temperature, stationary phase and mobile phase type to be predicted. Starting from a subset of the Human Metabolite Database (HMDB), a new dynamic database involving LSS parameters was developed. A real case study involving steroidogenesis alterations due to forskolin exposure was conducted using the adrenal H295R OECD reference cell model for endocrine disruptor screening. The prediction of retention times was successfully achieved, facilitating steroid identification. An automated procedure which implements the compound annotation levels encouraged by the Metabolite Standard Initiative (MSI) and the Coordination of Standards in Metabolomics (COSMOS) was also developed to speed up the process and enhance the data reusability.
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Affiliation(s)
- Giuseppe Marco Randazzo
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, 1211 Geneva, Switzerland
| | - David Tonoli
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, 1211 Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, 4055 Basel, Switzerland; Human Protein Sciences Department, University of Geneva, 1211 Geneva, Switzerland
| | - Petra Strajhar
- Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, 4055 Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland
| | - Ioannis Xenarios
- Vital-IT/Swiss-Prot Groups, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Alex Odermatt
- Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, 4055 Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, 1211 Geneva, Switzerland
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, 1211 Geneva, Switzerland.
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Changes in the Profiles of Metabolites Originating from Protein Degradation During Ripening of Dry Cured Ham. FOOD BIOPROCESS TECH 2017. [DOI: 10.1007/s11947-017-1894-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Uppal K, Walker DI, Jones DP. xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data. Anal Chem 2017; 89:1063-1067. [PMID: 27977166 DOI: 10.1021/acs.analchem.6b01214] [Citation(s) in RCA: 231] [Impact Index Per Article: 28.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps.
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Affiliation(s)
- Karan Uppal
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30308, United States
| | - Douglas I Walker
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30308, United States.,Department of Civil and Environmental Engineering, Tufts University , Medford, Massachusetts 02153, United States
| | - Dean P Jones
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30308, United States
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Computational Strategies for Biological Interpretation of Metabolomics Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 965:191-206. [PMID: 28132181 DOI: 10.1007/978-3-319-47656-8_8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Biological interpretation of metabolomics data relies on two basic steps: metabolite identification and functional analysis. These two steps need to be applied in a coordinated manner to enable effective data understanding. The focus of this chapter is to introduce the main computational concepts and workflows during this process. After a general overview of the field, three sections will be presented: the first section will introduce the main computational methods and bioinformatics tools for metabolite identification using spectra from common analytical platforms; the second section will focus on introducing major bioinformatics approaches for functional enrichment analysis of metabolomics data; and the last section will discuss the three main workflows in current metabolomics studies, including the chemometrics approach, the metabolic profiling approach and the more recent chemo-enrichment analysis approach. The chapter ends with summary and future perspectives on computational metabolomics.
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Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C. Navigating freely-available software tools for metabolomics analysis. Metabolomics 2017; 13:106. [PMID: 28890673 PMCID: PMC5550549 DOI: 10.1007/s11306-017-1242-7] [Citation(s) in RCA: 150] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 07/25/2017] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools. OBJECTIVES To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics. METHODS The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for. RESULTS A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary. CONCLUSION This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.
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Affiliation(s)
- Rachel Spicer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Reza M. Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Daniel Cañueto
- Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007 Tarragona, Catalonia Spain
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
- Friedrich-Schiller-University Jena, Lessingstr. 8, Jena, 07743 Germany
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Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data. Metabolites 2016; 6:metabo6040037. [PMID: 27775610 PMCID: PMC5192443 DOI: 10.3390/metabo6040037] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 09/29/2016] [Accepted: 10/14/2016] [Indexed: 12/04/2022] Open
Abstract
Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92% of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0.
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Uppal K, Walker DI, Liu K, Li S, Go YM, Jones DP. Computational Metabolomics: A Framework for the Million Metabolome. Chem Res Toxicol 2016; 29:1956-1975. [PMID: 27629808 DOI: 10.1021/acs.chemrestox.6b00179] [Citation(s) in RCA: 185] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
"Sola dosis facit venenum." These words of Paracelsus, "the dose makes the poison", can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80-85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability-based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.
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Affiliation(s)
- Karan Uppal
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30322, United States
| | - Douglas I Walker
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30322, United States.,Hercules Exposome Research Center, Department of Environmental Health, Rollins School of Public Health, Emory University , Atlanta, Georgia 30322, United States.,Department of Civil and Environmental Engineering, Tufts University , Medford, Massachusetts 02155, United States
| | - Ken Liu
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30322, United States
| | - Shuzhao Li
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30322, United States.,Hercules Exposome Research Center, Department of Environmental Health, Rollins School of Public Health, Emory University , Atlanta, Georgia 30322, United States
| | - Young-Mi Go
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30322, United States
| | - Dean P Jones
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30322, United States.,Hercules Exposome Research Center, Department of Environmental Health, Rollins School of Public Health, Emory University , Atlanta, Georgia 30322, United States
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Mahieu NG, Spalding JL, Gelman SJ, Patti GJ. Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The Mz.unity Algorithm. Anal Chem 2016; 88:9037-46. [PMID: 27513885 PMCID: PMC6427821 DOI: 10.1021/acs.analchem.6b01702] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Analysis of a single analyte by mass spectrometry can result in the detection of more than 100 degenerate peaks. These degenerate peaks complicate spectral interpretation and are challenging to annotate. In mass spectrometry-based metabolomics, this degeneracy leads to inflated false discovery rates, data sets containing an order of magnitude more features than analytes, and an inefficient use of resources during data analysis. Although software has been introduced to annotate spectral degeneracy, current approaches are unable to represent several important classes of peak relationships. These include heterodimers and higher complex adducts, distal fragments, relationships between peaks in different polarities, and complex adducts between features and background peaks. Here we outline sources of peak degeneracy in mass spectra that are not annotated by current approaches and introduce a software package called mz.unity to detect these relationships in accurate mass data. Using mz.unity, we find that data sets contain many more complex relationships than we anticipated. Examples include the adduct of glutamate and nicotinamide adenine dinucleotide (NAD), fragments of NAD detected in the same or opposite polarities, and the adduct of glutamate and a background peak. Further, the complex relationships we identify show that several assumptions commonly made when interpreting mass spectral degeneracy do not hold in general. These contributions provide new tools and insight to aid in the annotation of complex spectral relationships and provide a foundation for improved data set identification. Mz.unity is an R package and is freely available at https://github.com/nathaniel-mahieu/mz.unity as well as our laboratory Web site http://pattilab.wustl.edu/software/ .
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Affiliation(s)
- Nathaniel G. Mahieu
- Department of Chemistry, Washington University, St. Louis, Missouri 63130, United States
- Department of Medicine, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Jonathan L. Spalding
- Department of Chemistry, Washington University, St. Louis, Missouri 63130, United States
- Department of Genetics
| | - Susan J. Gelman
- Department of Chemistry, Washington University, St. Louis, Missouri 63130, United States
- Department of Medicine, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Gary J. Patti
- Department of Chemistry, Washington University, St. Louis, Missouri 63130, United States
- Department of Medicine, Washington University School of Medicine, St. Louis, Missouri 63110, United States
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Sanchon-Lopez B, Everett JR. New Methodology for Known Metabolite Identification in Metabonomics/Metabolomics: Topological Metabolite Identification Carbon Efficiency (tMICE). J Proteome Res 2016; 15:3405-19. [PMID: 27490438 DOI: 10.1021/acs.jproteome.6b00631] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
A new, simple-to-implement and quantitative approach to assessing the confidence in NMR-based identification of known metabolites is introduced. The approach is based on a topological analysis of metabolite identification information available from NMR spectroscopy studies and is a development of the metabolite identification carbon efficiency (MICE) method. New topological metabolite identification indices are introduced, analyzed, and proposed for general use, including topological metabolite identification carbon efficiency (tMICE). Because known metabolite identification is one of the key bottlenecks in either NMR-spectroscopy- or mass spectrometry-based metabonomics/metabolomics studies, and given the fact that there is no current consensus on how to assess metabolite identification confidence, it is hoped that these new approaches and the topological indices will find utility.
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Affiliation(s)
- Beatriz Sanchon-Lopez
- Medway Metabonomics Research Group, University of Greenwich , Chatham Maritime, Kent ME4 4TB, United Kingdom
| | - Jeremy R Everett
- Medway Metabonomics Research Group, University of Greenwich , Chatham Maritime, Kent ME4 4TB, United Kingdom
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42
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Dona AC, Kyriakides M, Scott F, Shephard EA, Varshavi D, Veselkov K, Everett JR. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Comput Struct Biotechnol J 2016; 14:135-53. [PMID: 27087910 PMCID: PMC4821453 DOI: 10.1016/j.csbj.2016.02.005] [Citation(s) in RCA: 207] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 02/16/2016] [Accepted: 02/23/2016] [Indexed: 01/14/2023] Open
Abstract
Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC-MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.
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Affiliation(s)
- Anthony C Dona
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - Michael Kyriakides
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - Flora Scott
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, United Kingdom
| | - Elizabeth A Shephard
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, United Kingdom
| | - Dorsa Varshavi
- Medway Metabonomics Research Group, University of Greenwich, Chatham Maritime, Kent ME4 4TB, United Kingdom
| | - Kirill Veselkov
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - Jeremy R Everett
- Medway Metabonomics Research Group, University of Greenwich, Chatham Maritime, Kent ME4 4TB, United Kingdom
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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: 84] [Impact Index Per Article: 8.4] [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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Zhang T, Zhang A, Qiu S, Yang S, Wang X. Current Trends and Innovations in Bioanalytical Techniques of Metabolomics. Crit Rev Anal Chem 2015; 46:342-51. [DOI: 10.1080/10408347.2015.1079475] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Godzien J, Armitage EG, Angulo S, Martinez-Alcazar MP, Alonso-Herranz V, Otero A, Lopez-Gonzalvez A, Barbas C. In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics. Electrophoresis 2015; 36:2188-2195. [DOI: 10.1002/elps.201500016] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Revised: 02/08/2015] [Accepted: 02/08/2015] [Indexed: 01/16/2023]
Affiliation(s)
- Joanna Godzien
- Centre for Metabolomics and Bioanalysis (CEMBIO); Facultad de Farmacia; Universidad CEU San Pablo, Campus Montepríncipe; Boadilla del Monte Madrid Spain
| | - Emily G. Armitage
- Centre for Metabolomics and Bioanalysis (CEMBIO); Facultad de Farmacia; Universidad CEU San Pablo, Campus Montepríncipe; Boadilla del Monte Madrid Spain
| | - Santiago Angulo
- Centre for Metabolomics and Bioanalysis (CEMBIO); Facultad de Farmacia; Universidad CEU San Pablo, Campus Montepríncipe; Boadilla del Monte Madrid Spain
| | - Mari Paz Martinez-Alcazar
- Centre for Metabolomics and Bioanalysis (CEMBIO); Facultad de Farmacia; Universidad CEU San Pablo, Campus Montepríncipe; Boadilla del Monte Madrid Spain
| | - Vanesa Alonso-Herranz
- Centre for Metabolomics and Bioanalysis (CEMBIO); Facultad de Farmacia; Universidad CEU San Pablo, Campus Montepríncipe; Boadilla del Monte Madrid Spain
| | - Abraham Otero
- Department of Information and Communications Systems Engineering; Universidad CEU San Pablo, Campus Montepríncipe; Boadilla del Monte Madrid Spain
| | - Angeles Lopez-Gonzalvez
- Centre for Metabolomics and Bioanalysis (CEMBIO); Facultad de Farmacia; Universidad CEU San Pablo, Campus Montepríncipe; Boadilla del Monte Madrid Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO); Facultad de Farmacia; Universidad CEU San Pablo, Campus Montepríncipe; Boadilla del Monte Madrid Spain
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Wandy J, Daly R, Breitling R, Rogers S. Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets. Bioinformatics 2015; 31:1999-2006. [PMID: 25649621 PMCID: PMC4760236 DOI: 10.1093/bioinformatics/btv072] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2014] [Accepted: 01/28/2015] [Indexed: 11/24/2022] Open
Abstract
Motivation: The combination of liquid chromatography and mass spectrometry (LC/MS) has been widely used for large-scale comparative studies in systems biology, including proteomics, glycomics and metabolomics. In almost all experimental design, it is necessary to compare chromatograms across biological or technical replicates and across sample groups. Central to this is the peak alignment step, which is one of the most important but challenging preprocessing steps. Existing alignment tools do not take into account the structural dependencies between related peaks that coelute and are derived from the same metabolite or peptide. We propose a direct matching peak alignment method for LC/MS data that incorporates related peaks information (within each LC/MS run) and investigate its effect on alignment performance (across runs). The groupings of related peaks necessary for our method can be obtained from any peak clustering method and are built into a pair-wise peak similarity score function. The similarity score matrix produced is used by an approximation algorithm for the weighted matching problem to produce the actual alignment result. Results: We demonstrate that related peak information can improve alignment performance. The performance is evaluated on a set of benchmark datasets, where our method performs competitively compared to other popular alignment tools. Availability: The proposed alignment method has been implemented as a stand-alone application in Python, available for download at http://github.com/joewandy/peak-grouping-alignment. Contact:Simon.Rogers@glasgow.ac.uk Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Joe Wandy
- School of Computing Science, University of Glasgow, Glasgow, UK, School of Computing and Mathematical Sciences, Liverpool John Moores University, Merseyside, UK and Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Rónán Daly
- School of Computing Science, University of Glasgow, Glasgow, UK, School of Computing and Mathematical Sciences, Liverpool John Moores University, Merseyside, UK and Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Rainer Breitling
- School of Computing Science, University of Glasgow, Glasgow, UK, School of Computing and Mathematical Sciences, Liverpool John Moores University, Merseyside, UK and Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow, UK, School of Computing and Mathematical Sciences, Liverpool John Moores University, Merseyside, UK and Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
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47
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Affiliation(s)
- Caroline H. Johnson
- Scripps
Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Julijana Ivanisevic
- Scripps
Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California 92037, United States
| | - H. Paul Benton
- Scripps
Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Gary Siuzdak
- Scripps
Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California 92037, United States
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