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Park Y, Jin S, Noda I, Jung YM. Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part I. Yesterday and today. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 281:121573. [PMID: 35870431 DOI: 10.1016/j.saa.2022.121573] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/27/2022] [Indexed: 06/15/2023]
Abstract
This comprehensive survey review, as the first of three parts, compiles past developments and early concepts of two-dimensional correlation spectroscopy (2D-COS) and subsequent evolution, as well as its early applications in various fields for the last 35 years. It covers past review articles, books, proceedings, and numerous research papers published on 2D-COS. 2D-COS continues to evolve and grow with new significant developments and versatile applications in diverse scientific fields. The healthy, vigorous, and diverse progress of 2D-COS studies in many fields confirms that it is well accepted as a powerful analytical technique to provide the in-depth understanding of systems of interest.
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Affiliation(s)
- Yeonju Park
- Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon 24341, South Korea
| | - Sila Jin
- Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon 24341, South Korea
| | - Isao Noda
- Department of Materials Science and Engineering, University of Delaware, Newark, DE 19716, USA.
| | - Young Mee Jung
- Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon 24341, South Korea; Department of Chemistry, and Institute for Molecular Science and Fusion Technology, Kangwon National University, Chuncheon 24341, South Korea.
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Borges RM, Resende JVM, Pinto AP, Garrido BC. Exploring correlations between MS and NMR for compound identification using essential oils: A pilot study. PHYTOCHEMICAL ANALYSIS : PCA 2022; 33:533-542. [PMID: 35098600 DOI: 10.1002/pca.3107] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/27/2021] [Accepted: 12/28/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION In this era of 'omics' technology in natural products studies, the complementary aspects of mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based techniques must be taken into consideration. The advantages of using both analytical platforms are reflected in a higher confidence of results especially when using replicated samples where correlation approaches can be used to statistically link results from MS to NMR. OBJECTIVES Demonstrate the use of Statistical Total Correlation (STOCSY) for linking results from MS and NMR data to reach higher confidence in compound identification. METHODOLOGY Essential oil samples of Melaleuca alternifolia and M. rhaphiophylla (Myrtaceae) were used as test objects. Aliquots of 10 samples were collected for GC-MS and NMR data acquisition [proton (1 H)-NMR, and carbon-13 (13 C)-NMR as well as two-dimensional (2D) heteronuclear single quantum correlation (HSQC), heteronuclear multiple-bond correlation (HMBC), and HSQC-total correlated spectroscopy (TOCSY) NMR]. The processed data was imported to Matlab where STOCSY was applied. RESULTS STOCSY calculations led to the confirmation of the four main constituents of the sample-set. The identification of each was accomplished using; MS spectra, retention time comparison, 13 C-NMR data, and scalar correlations of the 2D NMR spectra. CONCLUSION This study provides a pipeline for high confidence in compound identification using a set of essential oils samples as test objects for demonstration.
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Affiliation(s)
- Ricardo Moreira Borges
- Instituto de Pesquisas de Produtos Naturais Walter Mors (IPPN), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - João Victor Mendes Resende
- Instituto de Pesquisas de Produtos Naturais Walter Mors (IPPN), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Açucena Pucu Pinto
- Instituto de Pesquisas de Produtos Naturais Walter Mors (IPPN), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Bruno Carius Garrido
- Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO), Rio de Janeiro, Brazil
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Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a major analytical method used in the growing field of metabolomics. Although NMR is relatively less sensitive than mass spectrometry, this analytical platform has numerous characteristics including its high reproducibility and quantitative abilities, its nonselective and noninvasive nature, and the ability to identify unknown metabolites in complex mixtures and trace the downstream products of isotope labeled substrates ex vivo, in vivo, or in vitro. Metabolomic analysis of highly complex biological mixtures has benefitted from the advances in both NMR data acquisition and analysis methods. Although metabolomics applications span a wide range of disciplines, a majority has focused on understanding, preventing, diagnosing, and managing human diseases. This chapter describes NMR-based methods relevant to the rapidly expanding metabolomics field.
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Abstract
The fast-growing field of metabolomics is impacting numerous areas of basic and life sciences. In metabolomics, analytical methods play a pivotal role, and nuclear magnetic resonance (NMR) and mass spectrometry (MS) have proven to be the most suitable and powerful methods. Although NMR exhibits lower sensitivity and resolution compared to MS, NMR's numerous important characteristics far outweigh its limitations. Some of its characteristics include excellent reproducibility and quantitative accuracy, the capability to analyze intact biospecimens, an unparalleled ability to identify unknown metabolites, the ability to trace in-cell and in-organelle metabolism in real time, and the capacity to trace metabolic pathways atom by atom using 2H, 13C, or 15N isotopes. Each of these characteristics has been exploited extensively in numerous studies. In parallel, the field has witnessed significant progress in instrumentation, methods development, databases, and automation that are focused on higher throughput and alleviating the limitations of NMR, in particular, resolution and sensitivity. Despite the advances, however, the high complexity of biological mixtures combined with the limitations in sensitivity and resolution continues to pose major challenges. These challenges need to be dealt with effectively to better realize the potential of metabolomics, in general. As a result, multifaceted efforts continue to focus on addressing the challenges as well as reaping the benefits of NMR-based metabolomics. This chapter highlights the current status with emphasis on the opportunities and challenges in NMR-based metabolomics.
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Madea D, Slanina T, Klán P. A 'photorelease, catch and photorelease' strategy for bioconjugation utilizing a p-hydroxyphenacyl group. Chem Commun (Camb) 2018; 52:12901-12904. [PMID: 27738680 DOI: 10.1039/c6cc07496k] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
A bioorthogonal 'catch and photorelease' strategy, which combines alkyne-azide cycloaddition between p-hydroxyphenacyl azide and alkyne derivatives to form a 1,2,3-triazole adduct and subsequent photochemical release of the triazole moiety via a photo-Favorskii rearrangement, is introduced. The first step can also involve photorelease of a strained alkyne and its Cu-free click reaction with azide.
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Affiliation(s)
- D Madea
- Department of Chemistry and RECETOX, Faculty of Science, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.
| | - T Slanina
- Department of Chemistry and RECETOX, Faculty of Science, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.
| | - P Klán
- Department of Chemistry and RECETOX, Faculty of Science, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.
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Amberg A, Riefke B, Schlotterbeck G, Ross A, Senn H, Dieterle F, Keck M. NMR and MS Methods for Metabolomics. Methods Mol Biol 2017; 1641:229-258. [PMID: 28748468 DOI: 10.1007/978-1-4939-7172-5_13] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.
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Affiliation(s)
| | - Björn Riefke
- Investigational Toxicology, Metabolic Profiling and Clinical Pathology, Bayer Pharma AG, Muellerstr. 178, Berlin, 13353, Germany.
| | - Götz Schlotterbeck
- School of Life Sciences, Institute for Chemistry and Bioanalytics, University of Applied Sciences, Northwestern Switzerland, Muttenz, Switzerland
| | - Alfred Ross
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Hans Senn
- Heythrop College UCL, Kensington Square, London W85HN, UK
| | - Frank Dieterle
- New Products and Medical, Near Patient Testing, Novartis, Basel, Switzerland
| | - Matthias Keck
- Analytical Development 1, Bayer Pharma AG, Wupperal, 42096, Germany
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Nagana Gowda GA, Raftery D. Can NMR solve some significant challenges in metabolomics? JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2015; 260:144-60. [PMID: 26476597 PMCID: PMC4646661 DOI: 10.1016/j.jmr.2015.07.014] [Citation(s) in RCA: 137] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2015] [Revised: 07/17/2015] [Accepted: 07/18/2015] [Indexed: 05/04/2023]
Abstract
The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact bio-specimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory.
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Affiliation(s)
- G A Nagana Gowda
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, United States; Department of Chemistry, University of Washington, Seattle, WA 98195, United States; Fred Hutchinson Cancer Research Center, Seattle, WA 98109, United States.
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Wolfender JL, Marti G, Thomas A, Bertrand S. Current approaches and challenges for the metabolite profiling of complex natural extracts. J Chromatogr A 2015; 1382:136-64. [DOI: 10.1016/j.chroma.2014.10.091] [Citation(s) in RCA: 352] [Impact Index Per Article: 39.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Revised: 10/23/2014] [Accepted: 10/26/2014] [Indexed: 12/11/2022]
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MS-based metabolomics facilitates the discovery of in vivo functional small molecules with a diversity of biological contexts. Future Med Chem 2014; 5:1953-65. [PMID: 24175746 DOI: 10.4155/fmc.13.148] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
In vivo small molecules as necessary intermediates are involved in numerous critical metabolic pathways and biological processes associated with many essential biological functions and events. There is growing evidence that MS-based metabolomics is emerging as a powerful tool to facilitate the discovery of functional small molecules that can better our understanding of development, infection, nutrition, disease, toxicity, drug therapeutics, gene modifications and host-pathogen interaction from metabolic perspectives. However, further progress must still be made in MS-based metabolomics because of the shortcomings in the current technologies and knowledge. This technique-driven review aims to explore the discovery of in vivo functional small molecules facilitated by MS-based metabolomics and to highlight the analytic capabilities and promising applications of this discovery strategy. Moreover, the biological significance of the discovery of in vivo functional small molecules with different biological contexts is also interrogated at a metabolic perspective.
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Trivedi DK, Iles RK. Do not just do it, do it right: urinary metabolomics -establishing clinically relevant baselines. Biomed Chromatogr 2014; 28:1491-501. [DOI: 10.1002/bmc.3219] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2014] [Revised: 03/17/2014] [Accepted: 03/25/2014] [Indexed: 12/11/2022]
Affiliation(s)
- Drupad K. Trivedi
- Eric Leonard Kruse Foundation for Health Research; Manchester UK
- Manchester Institute of Biotechnology and School of Chemistry; University of Manchester; M1 7DN UK
| | - Ray K. Iles
- Eric Leonard Kruse Foundation for Health Research; Manchester UK
- MAP Diagnostic Ltd; Ely Cambridgeshire UK
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11
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Nagana Gowda G, Raftery D. Advances in NMR-Based Metabolomics. FUNDAMENTALS OF ADVANCED OMICS TECHNOLOGIES: FROM GENES TO METABOLITES 2014. [DOI: 10.1016/b978-0-444-62651-6.00008-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Toumi I, Torrésani B, Caldarelli S. Effective Processing of Pulse Field Gradient NMR of Mixtures by Blind Source Separation. Anal Chem 2013; 85:11344-51. [DOI: 10.1021/ac402085x] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
- Ichrak Toumi
- Aix Marseille Université, CNRS, Centrale
Marseille, iSm2 UMR 7313, 13397, Marseille, France
| | - Bruno Torrésani
- Aix Marseille Université, CNRS, Centrale Marseille, LATP
UMR 7353, 13453, Marseille, France
| | - Stefano Caldarelli
- Aix Marseille Université, CNRS, Centrale
Marseille, iSm2 UMR 7313, 13397, Marseille, France
- CNRS UPR 2301 ICSN 91190, Gif-sur-Yvette, France
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13
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Kuehnbaum NL, Britz-McKibbin P. New Advances in Separation Science for Metabolomics: Resolving Chemical Diversity in a Post-Genomic Era. Chem Rev 2013; 113:2437-68. [DOI: 10.1021/cr300484s] [Citation(s) in RCA: 201] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Naomi L. Kuehnbaum
- Department of Chemistry
and Chemical Biology, McMaster University, Hamilton, Canada
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Claus SP, Swann JR. Nutrimetabonomics:applications for nutritional sciences, with specific reference to gut microbial interactions. Annu Rev Food Sci Technol 2013; 4:381-99. [PMID: 23297777 DOI: 10.1146/annurev-food-030212-182612] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Understanding the role of the diet in determining human health and disease is one major objective of modern nutrition. Mammalian biocomplexity necessitates the incorporation of systems biology technologies into contemporary nutritional research. Metabonomics is a powerful approach that simultaneously measures the low-molecular-weight compounds in a biological sample, enabling the metabolic status of a biological system to be characterized. Such biochemical profiles contain latent information relating to inherent parameters, such as the genotype, and environmental factors, including the diet and gut microbiota. Nutritional metabonomics, or nutrimetabonomics, is being increasingly applied to study molecular interactions between the diet and the global metabolic system. This review discusses three primary areas in which nutrimetabonomics has enjoyed successful application in nutritional research: the illumination of molecular relationships between nutrition and biochemical processes; elucidation of biomarker signatures of food components for use in dietary surveillance; and the study of complex trans-genomic interactions between the mammalian host and its resident gut microbiome. Finally, this review illustrates the potential for nutrimetabonomics in nutritional science as an indispensable tool to achieve personalized nutrition.
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Affiliation(s)
- Sandrine P Claus
- Department of Food and Nutritional Sciences, University of Reading, Whiteknights, Reading, RG6 6AP, UK
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15
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Resolving the problem of chromatographic overlap by 3D cross correlation (3DCC) processing of LC, MS and NMR data for characterization of complex glycan mixtures. Anal Bioanal Chem 2012; 404:1427-37. [DOI: 10.1007/s00216-012-6241-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 06/26/2012] [Accepted: 06/29/2012] [Indexed: 12/18/2022]
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Sheridan H, Krenn L, Jiang R, Sutherland I, Ignatova S, Marmann A, Liang X, Sendker J. The potential of metabolic fingerprinting as a tool for the modernisation of TCM preparations. JOURNAL OF ETHNOPHARMACOLOGY 2012; 140:482-491. [PMID: 22338647 DOI: 10.1016/j.jep.2012.01.050] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2011] [Revised: 01/30/2012] [Accepted: 01/31/2012] [Indexed: 05/31/2023]
Abstract
A vast majority Chinese herbal medicines (CHM) are traditionally administered as individually prepared water decoctions (tang) which are rather complicated in practice and their dry extracts show technological problems that hamper straight production of more convenient application forms. Modernised extraction procedures may overcome these difficulties but there is lack of clinical evidence supporting their therapeutic equivalence to traditional decoctions and their quality can often not solely be attributed to the single marker compounds that are usually used for chemical extract optimisation. As demonstrated by the example of the rather simple traditional TCM formula Danggui Buxue Tang, both the chemical composition and the biological activity of extracts resulting from traditional water decoction are influenced by details of the extraction procedure and especially involve pharmacokinetic synergism based on co-extraction. Hence, a more detailed knowledge about the traditional extracts' chemical profiles and their impact on biological activity is desirable in order to allow the development of modernised extracts that factually contain the whole range of compounds relevant for the efficacy of the traditional application. We propose that these compounds can be identified by metabolomics based on comprehensive fingerprint analysis of different extracts with known biological activity. TCM offers a huge variety of traditional products of the same botanical origin but with distinct therapeutic properties, like differentially processed drugs and special daodi qualities. Through this variety, TCM gives an ideal field for the application of metabolomic techniques aiming at the identification of active constituents.
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Affiliation(s)
- Helen Sheridan
- Trinity College, Dublin, School of Pharmacy and Pharmaceutical Sciences, East End Development 4/5, Dublin 2, Ireland
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Wei S, Zhang J, Liu L, Ye T, Nagana Gowda GA, Tayyari F, Raftery D. Ratio analysis nuclear magnetic resonance spectroscopy for selective metabolite identification in complex samples. Anal Chem 2011; 83:7616-23. [PMID: 21894988 PMCID: PMC3193582 DOI: 10.1021/ac201625f] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal-to-noise. In this study we present a new approach, RANSY (ratio analysis NMR spectroscopy), which identifies all the peaks of a specific metabolite on the basis of the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficients of variation derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to (1)H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.
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Affiliation(s)
- Siwei Wei
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907
| | - Jian Zhang
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907
| | - Lingyan Liu
- Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907
| | - Tao Ye
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907
| | - G. A. Nagana Gowda
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907
| | - Fariba Tayyari
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907
| | - Daniel Raftery
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907
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Kumari S, Stevens D, Kind T, Denkert C, Fiehn O. Applying in-silico retention index and mass spectra matching for identification of unknown metabolites in accurate mass GC-TOF mass spectrometry. Anal Chem 2011; 83:5895-902. [PMID: 21678983 PMCID: PMC3146571 DOI: 10.1021/ac2006137] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
One of the major obstacles in metabolomics is the identification of unknown metabolites. We tested constraints for reidentifying the correct structures of 29 known metabolite peaks from GCT premier accurate mass chemical ionization GC-TOF mass spectrometry data without any use of mass spectral libraries. Correct elemental formulas were retrieved within the top-3 hits for most molecular ion adducts using the "Seven Golden Rules" algorithm. An average of 514 potential structures per formula was downloaded from the PubChem chemical database and in-silico-derivatized using the ChemAxon software package. After chemical curation, Kovats retention indices (RI) were predicted for up to 747 potential structures per formula using the NIST MS group contribution algorithm and corrected for contribution of trimethylsilyl groups using the Fiehnlib RI library. When matching the range of predicted RI values against the experimentally determined peak retention, all but three incorrect formulas were excluded. For all remaining isomeric structures, accurate mass electron ionization spectra were predicted using the MassFrontier software and scored against experimental spectra. Using a mass error window of 10 ppm for fragment ions, 89% of all isomeric structures were removed and the correct structure was reported in 73% within the top-5 hits of the cases.
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Affiliation(s)
- Sangeeta Kumari
- UC Davis Genome Center, University of California-Davis, Davis, California 95616, United States
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Ebbels TMD, Lindon JC, Coen M. Processing and modeling of nuclear magnetic resonance (NMR) metabolic profiles. Methods Mol Biol 2011; 708:365-88. [PMID: 21207301 DOI: 10.1007/978-1-61737-985-7_21] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Modern nuclear magnetic resonance (NMR) spectroscopy generates complex and information-rich metabolic profiles. These require robust, accurate, and often sophisticated statistical techniques to yield the maximum meaningful knowledge. In this chapter, we describe methods typically used to analyze such data. We begin by describing seven goals of metabolic profile analysis, ranging from production of a data table to multi-omic integration for systems biology. Methods for preprocessing and pretreatment are then presented, including issues such as instrument-level spectral processing, data reduction and deconvolution, normalization, scaling, and transformations of the data. We then discuss methods for exploratory modeling and exemplify three techniques: principal components analysis, hierarchical clustering, and self-organizing maps. Moving to predictive modeling, we focus our discussion on partial least squares regression, orthogonal partial least squares regression, and genetic algorithm approaches. A typical set of in vitro metabolic profiles is used where possible to compare and contrast the methods. The importance of validating statistical models is highlighted, and standard techniques for doing so, such as training/test set and cross-validation are described. Finally, we discuss the contributions of statistical techniques such as statistical total correlation spectroscopy, and other correlation-based methods have made to the process of structural characterization for unknown metabolites.
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Affiliation(s)
- Timothy M D Ebbels
- Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.
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Sands CJ, Coen M, Ebbels TMD, Holmes E, Lindon JC, Nicholson JK. Data-driven approach for metabolite relationship recovery in biological 1H NMR data sets using iterative statistical total correlation spectroscopy. Anal Chem 2011; 83:2075-82. [PMID: 21323345 DOI: 10.1021/ac102870u] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Statistical total correlation spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite correlations in NMR metabonomic data sets. Here, the STOCSY approach is extended in a novel Iterative-STOCSY (I-STOCSY) tool in which correlations are calculated initially from a driver peak of interest and subsequently for all peaks identified as correlating with a correlation coefficient greater than a set threshold. Consequently, in a single automated run, the majority of information contained in multiple STOCSY calculations from all peaks recursively correlated to the original user defined driver peak of interest are recovered. In addition, highly correlating peaks are clustered into putative structurally related sets, and the results are presented in a fully interactive plot where each set is represented by a node; node-to-node connections are plotted alongside corresponding spectral data colored by the strength of connection, thus allowing the intuitive exploration of both inter- and intrametabolite connections. The I-STOCSY approach has been here applied to a (1)H NMR data set of 24 h postdose aqueous liver extracts from rats treated with the model hepatotoxin galactosamine and has been shown both to recover the previously deduced major metabolic effects of treatment and to generate new hypotheses even on this well-studied model system. I-STOCSY, thus, represents a significant advance in correlation based analysis and visualization, providing insight into inter- and intrametabolite relationships following metabolic perturbations.
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Affiliation(s)
- Caroline J Sands
- Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, SW7 2AZ, United Kingdom
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Dieterle F, Riefke B, Schlotterbeck G, Ross A, Senn H, Amberg A. NMR and MS methods for metabonomics. Methods Mol Biol 2011; 691:385-415. [PMID: 20972767 DOI: 10.1007/978-1-60761-849-2_24] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Metabonomics, also often referred to as "metabolomics" or "metabolic profiling," is the systematic profiling of metabolites in bio-fluids or tissues of organisms and their temporal changes. In the last decade, metabonomics has become increasingly popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabonomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabonomics, i.e., NMR, LC-MS, UPLC-MS, and GC-MS have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabonomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation, to determining the measurement details of all analytical platforms, and finally, to discussing the corresponding specific steps of data analysis.
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Affiliation(s)
- Frank Dieterle
- Molecular Diagnostics, Novartis Pharma AG, Basel, Switzerland
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22
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Coen M. A metabonomic approach for mechanistic exploration of pre-clinical toxicology. Toxicology 2010; 278:326-40. [DOI: 10.1016/j.tox.2010.07.022] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2010] [Revised: 07/29/2010] [Accepted: 07/30/2010] [Indexed: 12/17/2022]
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Dehaven CD, Evans AM, Dai H, Lawton KA. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J Cheminform 2010; 2:9. [PMID: 20955607 PMCID: PMC2984397 DOI: 10.1186/1758-2946-2-9] [Citation(s) in RCA: 452] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2009] [Accepted: 10/18/2010] [Indexed: 01/22/2023] Open
Abstract
Background Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Quantify Individual Components in a Sample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. Results LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. Conclusion The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.
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Affiliation(s)
- Corey D Dehaven
- Metabolon, Inc,, 800 Capitola Drive, Suite 1, Durham, NC 27713, USA.
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Durand S, Sancelme M, Besse-Hoggan P, Combourieu B. Biodegradation pathway of mesotrione: complementarities of NMR, LC-NMR and LC-MS for qualitative and quantitative metabolic profiling. CHEMOSPHERE 2010; 81:372-380. [PMID: 20692682 DOI: 10.1016/j.chemosphere.2010.07.017] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2010] [Revised: 07/04/2010] [Accepted: 07/09/2010] [Indexed: 05/29/2023]
Abstract
Enhanced knowledge of pesticide transformation products formed in the environment could lead to both accurate estimates of the overall effects of these compounds on environmental ecosystems and human health and improved removal processes. These compounds can present chemical and environmental behaviours completely different from the starting active ingredient. The difficulty lies on their identification or/and their quantification due to the lack of analytical reference standards. In this context, ex situ Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-NMR (LC-NMR) were used as complementary tools to LC-Mass Spectrometry (MS) to define the metabolic pathway of mesotrione, an emergent herbicide, by the bacterial strain Bacillus sp. 3B6. The complementarities of ex situ and LC-NMR allowed us to unambiguously identify six metabolites whereas the structures of only four metabolites were suggested by LC-MS. The presence of a new metabolic pathway was evidenced by NMR. These results demonstrate that NMR and LC-NMR spectroscopy provided unambiguous structural information for xenobiotic metabolic profiling, even at moderate magnetic field and allowed direct absolute quantification despite the lack of commercial or synthetic standards, required for LC-MS techniques.
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Affiliation(s)
- Stéphanie Durand
- Clermont Université, Université Blaise Pascal, Laboratoire de Synthèse et Etude de Systèmes à Intérêt Biologique, 63000 Clermont-Ferrand, France
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Alves AC, Rantalainen M, Holmes E, Nicholson JK, Ebbels TMD. Analytic properties of statistical total correlation spectroscopy based information recovery in 1H NMR metabolic data sets. Anal Chem 2010; 81:2075-84. [PMID: 19220030 DOI: 10.1021/ac801982h] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Structural assignment of resonances is an important problem in NMR spectroscopy, and statistical total correlation spectroscopy (STOCSY) is a useful tool aiding this process for small molecules in complex mixture analysis and metabolic profiling studies. STOCSY delivers intramolecular information (delineating structural connectivity) and in metabolism studies can generate information on pathway-related correlations. To understand further the behavior of STOCSY for structural assignment, we analyze the statistical distribution of structural and nonstructural correlations from 1050 (1)H NMR spectra of normal rat urine samples. We find that the distributions of structural/nonstructural correlations are significantly different (p < 10(-112)). From the area under the curve of the receiver operating characteristic (ROC AUC) we show that structural correlations exceed nonstructural correlations with probability AUC = 0.98. Through a bootstrap resampling approach, we demonstrate that sample size has a surprisingly small effect (e.g., AUC = 0.97 for a sample size of 50). We identify specific signatures in the correlation maps resulting from small matrix-derived variations in peak positions but find that their effect on discrimination of structural and nonstructural correlations is negligible for most metabolites. A correlation threshold of r > 0.89 is required to assign two peaks to the same metabolite with high probability (positive predictive value, PPV = 0.9), whereas sensitivity and specificity are equal at 93% for r = 0.22. To assess the wider applicability of our results, we analyze (1)H NMR spectra of urine from rats treated with 115 model toxins or physiological stressors. Across the data sets, we find that the thresholds required to obtain PPV = 0.9 are not significantly different and the degree of overlap between the structural and nonstructural distributions is always small (median AUC = 0.97). The STOCSY method is effective for structural characterization under diverse biological conditions and sample sizes provided the degree of correlation resulting from nonstructural associations (e.g., from nonstationary processes) is small. This study validates the use of the STOCSY approach in the routine assignment of signals in NMR metabolic profiling studies and provides practical benchmarks against which researchers can interpret the results of a STOCSY analysis.
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Affiliation(s)
- Alexessander Couto Alves
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, U.K
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Zhang S, Nagana Gowda GA, Ye T, Raftery D. Advances in NMR-based biofluid analysis and metabolite profiling. Analyst 2010; 135:1490-8. [PMID: 20379603 PMCID: PMC4720135 DOI: 10.1039/c000091d] [Citation(s) in RCA: 83] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Significant improvements in NMR technology and methods have propelled NMR studies to play an important role in a rapidly expanding number of applications involving the profiling of metabolites in biofluids. This review discusses recent technical advances in NMR spectroscopy based metabolite profiling methods, data processing and analysis over the last three years.
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Affiliation(s)
- Shucha Zhang
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
| | - G. A. Nagana Gowda
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
| | - Tao Ye
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
| | - Daniel Raftery
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
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Beckonert O, Coen M, Keun HC, Wang Y, Ebbels TMD, Holmes E, Lindon JC, Nicholson JK. High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat Protoc 2010; 5:1019-32. [PMID: 20539278 DOI: 10.1038/nprot.2010.45] [Citation(s) in RCA: 302] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Metabolic profiling, metabolomic and metabonomic studies require robust study protocols for any large-scale comparisons and evaluations. Detailed methods for solution-state NMR spectroscopy have been summarized in an earlier protocol. This protocol details the analysis of intact tissue samples by means of high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy and we provide a detailed description of sample collection, preparation and analysis. Described here are (1)H NMR spectroscopic techniques such as the standard one-dimensional, relaxation-edited, diffusion-edited and two-dimensional J-resolved pulse experiments, as well as one-dimensional (31)P NMR spectroscopy. These are used to monitor different groups of metabolites, e.g., sugars, amino acids and osmolytes as well as larger molecules such as lipids, non-invasively. Through the use of NMR-based diffusion coefficient and relaxation times measurements, information on molecular compartmentation and mobility can be gleaned. The NMR methods are often combined with statistical analysis for further metabonomics analysis and biomarker identification. The standard acquisition time per sample is 8-10 min for a simple one-dimensional (1)H NMR spectrum, giving access to metabolite information while retaining tissue integrity and hence allowing direct comparison with histopathology and MRI/MRS findings or the evaluation together with biofluid metabolic-profiling data.
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Affiliation(s)
- Olaf Beckonert
- Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, South Kensington, London, UK
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Abstract
Metabonomics is rapidly evolving through advances in analytical technologies together with the development of new hyphenated approaches that are increasingly being applied to analyze complex biological systems. Improvements in analytical performance, such as increased sensitivity and selectivity, are providing greater resolution to analytical datasets and the rich potential of metabonomics as a systems biology tool of choice is becoming clear. However, such improvements are resulting in datasets becoming increasingly demanding in terms of data handling and interpretation, and the degree to which metabonomics continues to develop will be dependent on how chemometrics and data-handling approaches keep pace with continually improving analytical technologies. This review provides an overview of the field of metabonomics, with a particular focus on the analytical techniques that are chiefly employed and the chemometric methods that have found most use. However, in addition, we mention less widely used analytical methods and suggest that advanced statistical methods will play a larger role in the future.
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Bohus E, Rácz A, Noszál B, Coen M, Beckonert O, Keun HC, Ebbels TMD, Cantor GH, Wijsman JA, Holmes E, Lindon JC, Nicholson JK. Metabonomic investigations into the global biochemical sequelae of exposure to the pancreatic toxin 1-cyano-2-hydroxy-3-butene in the rat. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2009; 47 Suppl 1:S26-S35. [PMID: 19639609 DOI: 10.1002/mrc.2485] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The time-related metabolic effects of 1-cyano-2-hydroxy-3-butene (CHB, crambene), a naturally occurring nitrile and experimental model toxin causing exocrine pancreatitis, have been investigated in rats using high-resolution NMR spectroscopy of urine and serum in combination with pattern recognition analysis. Rats were administered CHB subcutaneously in two doses, 15 mg/kg dose (n = 10) and 150 mg/kg (n = 10), and conventional histopathology and clinical chemistry assessments were performed. Urine samples were collected at - 16 and 0, 8, 24, 48, 72, 96, 120, 144 and 168 h postdosing and serum samples were collected at 48 and 168 h postdosing; these were analyzed using a range of 1D and 2D NMR spectroscopic methods. The metabolic profile perturbations seen throughout the time-course of the study are described, and the application of the spectral correlation technique Statistical TOtal Correlation SpectroscopY (STOCSY) to detect both structural and novel toxicological connectivities between xenobiotic and endogenous metabolite signals is illustrated for the first time. As a result, it is suggested that the STOCSY approach may be of wider application in the identification of toxic versus nontoxic metabolites in drug metabolism studies.
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Affiliation(s)
- Eszter Bohus
- Department of Pharmaceutical Chemistry, Semmelweis University, Högyes Endre u. 9. Budapest 1092, Hungary.
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Sands CJ, Coen M, Maher AD, Ebbels TMD, Holmes E, Lindon JC, Nicholson JK. Statistical Total Correlation Spectroscopy Editing of 1H NMR Spectra of Biofluids: Application to Drug Metabolite Profile Identification and Enhanced Information Recovery. Anal Chem 2009; 81:6458-66. [DOI: 10.1021/ac900828p] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Caroline J. Sands
- Department of Biomolecular Medicine, Sir Alexander Fleming Building, Division of Surgery, Oncology, Reproductive Biology and Anesthetics, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - Muireann Coen
- Department of Biomolecular Medicine, Sir Alexander Fleming Building, Division of Surgery, Oncology, Reproductive Biology and Anesthetics, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - Anthony D. Maher
- Department of Biomolecular Medicine, Sir Alexander Fleming Building, Division of Surgery, Oncology, Reproductive Biology and Anesthetics, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - Timothy M. D. Ebbels
- Department of Biomolecular Medicine, Sir Alexander Fleming Building, Division of Surgery, Oncology, Reproductive Biology and Anesthetics, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - Elaine Holmes
- Department of Biomolecular Medicine, Sir Alexander Fleming Building, Division of Surgery, Oncology, Reproductive Biology and Anesthetics, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - John C. Lindon
- Department of Biomolecular Medicine, Sir Alexander Fleming Building, Division of Surgery, Oncology, Reproductive Biology and Anesthetics, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
| | - Jeremy K. Nicholson
- Department of Biomolecular Medicine, Sir Alexander Fleming Building, Division of Surgery, Oncology, Reproductive Biology and Anesthetics, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom
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Loo RL, Coen M, Ebbels T, Cloarec O, Maibaum E, Bictash M, Yap I, Elliott P, Stamler J, Nicholson JK, Holmes E. Metabolic profiling and population screening of analgesic usage in nuclear magnetic resonance spectroscopy-based large-scale epidemiologic studies. Anal Chem 2009; 81:5119-29. [PMID: 19489597 PMCID: PMC2726443 DOI: 10.1021/ac900567e] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The application of a (1)H nuclear magnetic resonance (NMR) spectroscopy-based screening method for determining the use of two widely available analgesics (acetaminophen and ibuprofen) in epidemiologic studies has been investigated. We used samples and data from the cross-sectional INTERMAP Study involving participants from Japan (n = 1145), China (n = 839), U.K. (n = 501), and the U.S. (n = 2195). An orthogonal projection to latent structures discriminant analysis (OPLS-DA) algorithm with an incorporated Monte Carlo resampling function was applied to the NMR data set to determine which spectra contained analgesic metabolites. OPLS-DA preprocessing parameters (normalization, bin width, scaling, and input parameters) were assessed systematically to identify an optimal acetaminophen prediction model. Subsets of INTERMAP spectra were examined to verify and validate the presence/absence of acetaminophen/ibuprofen based on known chemical shift and coupling patterns. The optimized and validated acetaminophen model correctly predicted 98.2%, and the ibuprofen model correctly predicted 99.0% of the urine specimens containing these drug metabolites. The acetaminophen and ibuprofen models were subsequently used to predict the presence/absence of these drug metabolites for the remaining INTERMAP specimens. The acetaminophen model identified 415 out of 8436 spectra as containing acetaminophen metabolite signals while the ibuprofen model identified 245 out of 8604 spectra as containing ibuprofen metabolite signals from the global data set after excluding samples used to construct the prediction models. The NMR-based metabolic screening strategy provides a new objective approach for evaluation of self-reported medication data and is extendable to other aspects of population xenometabolome profiling.
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Affiliation(s)
- Ruey Leng Loo
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
| | - Muireann Coen
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
| | - Timothy Ebbels
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
| | - Olivier Cloarec
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
| | - Elaine Maibaum
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
| | - Magda Bictash
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
- Department of Epidemiology and Public Health, Imperial College London, St Mary's Campus, London, UK
| | - Ivan Yap
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
- Department of Epidemiology and Public Health, Imperial College London, St Mary's Campus, London, UK
| | - Paul Elliott
- Department of Epidemiology and Public Health, Imperial College London, St Mary's Campus, London, UK
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jeremy K. Nicholson
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
| | - Elaine Holmes
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, SW7 2AZ, UK
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Keifer PA. Chemical-shift referencing and resolution stability in gradient LC-NMR (acetonitrile:water). JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2009; 199:75-87. [PMID: 19423372 DOI: 10.1016/j.jmr.2009.04.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2008] [Revised: 03/14/2009] [Accepted: 04/07/2009] [Indexed: 05/27/2023]
Abstract
Wide ranges of solvent conditions are generated during solvent-gradient LC-NMR. This complicates the referencing of the chemical-shift scale of the resulting NMR data. The problems that arise when performing LC-NMR in acetonitrile:water - particularly when the mobile-phase composition can range anywhere from 0% to 100% - are examined here, and the reliability of the secondary reference signals are evaluated. It is shown that under these conditions the use of the acetonitrile signal is superior to the use of the water signal in any form (either the (1)H or the (2)H signal) as a secondary reference, a lock signal, and a signal for shimming. The limitations of the referencing methods and other experimental parameters, and the limitations on the solvent-gradient ramp parameters, primarily as they affect lineshapes, are all shown. These results are compared to the way some other publications have referenced the (1)H chemical-shift axis (when using acetonitrile:water mixtures to perform reversed-phase chromatography LC-NMR).
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Affiliation(s)
- Paul A Keifer
- Varian Inc., 3120 Hansen Way D-298, Palo Alto, CA 94304-1030, USA.
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Moco S, Schneider B, Vervoort J. Plant Micrometabolomics: The Analysis of Endogenous Metabolites Present in a Plant Cell or Tissue. J Proteome Res 2009; 8:1694-703. [DOI: 10.1021/pr800973r] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Sofia Moco
- Laboratory of Biochemistry, Wageningen University, Dreijenlaan 3, 6703 HA Wageningen, and Max-Planck-Institute for Chemical Ecology, Beutenberg Campus, Hans-Knöll-Str. 8, D-07745 Jena, Germany
| | - Bernd Schneider
- Laboratory of Biochemistry, Wageningen University, Dreijenlaan 3, 6703 HA Wageningen, and Max-Planck-Institute for Chemical Ecology, Beutenberg Campus, Hans-Knöll-Str. 8, D-07745 Jena, Germany
| | - Jacques Vervoort
- Laboratory of Biochemistry, Wageningen University, Dreijenlaan 3, 6703 HA Wageningen, and Max-Planck-Institute for Chemical Ecology, Beutenberg Campus, Hans-Knöll-Str. 8, D-07745 Jena, Germany
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Maher AD, Cloarec O, Patki P, Craggs M, Holmes E, Lindon JC, Nicholson JK. Dynamic biochemical information recovery in spontaneous human seminal fluid reactions via 1H NMR kinetic statistical total correlation spectroscopy. Anal Chem 2009; 81:288-95. [PMID: 19117456 DOI: 10.1021/ac801993m] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Human seminal fluid (HSF) is a complex mixture of reacting glandular metabolite and protein secretions that provides critical support functions in fertilization. We have employed 600-MHz (1)H NMR spectroscopy to compare and contrast the temporal biochemical and biophysical changes in HSF from infertile men with spinal cord injury compared to age-matched controls. We have developed new approaches to data analysis and visualization to facilitate the interpretation of the results, including the first application of the recently published K-STOCSY concept to a biofluid, enhancing the extraction of information on biochemically related metabolites and assignment of resonances from the major seminal protein, semenogelin. Principal components analysis was also applied to evaluate the extent to which macromolecules influence the overall variation in the metabolic data set. The K-STOCSY concept was utilized further to determine the relationships between reaction rates and metabolite levels, revealing that choline, N-acetylglucosamine, and uridine are associated with higher peptidase activity. The novel approach adopted here has the potential to capture dynamic information in any complex mixture of reacting chemicals including other biofluids or cell extracts.
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Affiliation(s)
- Anthony D Maher
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington, SW7 2AZ, United Kingdom.
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Xiayan L, Legido-Quigley C. Advances in separation science applied to metabonomics. Electrophoresis 2008; 29:3724-36. [PMID: 18850642 DOI: 10.1002/elps.200700851] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Metabonomics focuses on metabolite profile changes in diverse living systems caused by a biological perturbation. These metabolite signatures can be achieved with techniques such as gas chromatography, high-performance liquid chromatography (ultra-high-performance/pressure liquid chromatography and capHPLC), capillary electrophoresis, and capillary electrochromatography normally hyphenated with MS. In this review we present the latest developments of the abovementioned techniques applied in the field of metabonomics, with applications covering phytochemistry, toxicology and clinical research.
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Affiliation(s)
- Li Xiayan
- Pharmaceutical Sciences Research Division, King's College London, 150 Stamford Street, London, UK
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Nuclear magnetic resonance and liquid chromatography-mass spectrometry combined with an incompleted separation strategy for identifying the natural products in crude extract. Anal Chim Acta 2008; 632:221-8. [PMID: 19110097 DOI: 10.1016/j.aca.2008.11.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2008] [Revised: 10/29/2008] [Accepted: 11/02/2008] [Indexed: 11/23/2022]
Abstract
NMR and LC-MS combined with an incompleted separation strategy were proposed to the simultaneous structure identification of natural products in crude extracts, and a novel method termed as NMR/LC-MS parallel dynamic spectroscopy (NMR/LC-MS PDS) was developed to discover the intrinsic correlation between retention time (Rt), mass/charge (m/z) and chemical shift (delta) data of the same constituent from mixture spectra by the co-analysis of parallelly visualized multispectroscopic datasets from LC-MS and (1)H NMR. The extracted ion chromatogram (XIC) and (1)H NMR signals deriving from the same individual constituent were correlated through fraction ranges and intensity changing profiles in NMR/LC-MS PDS spectrum due to the signal amplitude co-variation resulted from the concentration variation of constituents in a series of incompletely separated fractions. NMR/LC-MS PDS was applied to identify 12 constituents in an active herbal extract including flavonol glycosides, which was separated into a series of fractions by flash column chromatography. The complementary spectral information of the same individual constituent in the crude extract was discovered simultaneously from mixture spectra. Especially, two groups of co-eluted isomers were identified successfully. The results demonstrated that NMR/LC-MS PDS combined with the incompleted separation strategy achieved the similar function of on-line LC-NMR-MS analysis in off-line mode and had the potential for simplifying and accelerating the analytical routes for structure identification of constituents in herbs or their active extracts.
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Maher AD, Crockford D, Toft H, Malmodin D, Faber JH, McCarthy MI, Barrett A, Allen M, Walker M, Holmes E, Lindon JC, Nicholson JK. Optimization of Human Plasma 1H NMR Spectroscopic Data Processing for High-Throughput Metabolic Phenotyping Studies and Detection of Insulin Resistance Related to Type 2 Diabetes. Anal Chem 2008; 80:7354-62. [DOI: 10.1021/ac801053g] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Anthony D. Maher
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Derek Crockford
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Henrik Toft
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Daniel Malmodin
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Johan H. Faber
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Mark I. McCarthy
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Amy Barrett
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Maxine Allen
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Mark Walker
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Elaine Holmes
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - John C. Lindon
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
| | - Jeremy K. Nicholson
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom, Novo Nordisk A/S, Novo Nordisk Park, DK-2760 Måløv, Denmark, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, United Kingdom, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom,
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Crockford DJ, Maher AD, Ahmadi KR, Barrett A, Plumb RS, Wilson ID, Nicholson JK. 1H NMR and UPLC-MS(E) statistical heterospectroscopy: characterization of drug metabolites (xenometabolome) in epidemiological studies. Anal Chem 2008; 80:6835-44. [PMID: 18700783 DOI: 10.1021/ac801075m] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Statistical HeterospectroscopY (SHY) is a statistical strategy for the coanalysis of multiple spectroscopic data sets acquired in parallel on the same samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecular fingerprints measured by multiple spectroscopic techniques across cohorts of samples. Here, the method is applied to 600-MHz (1)H NMR and UPLC-TOF-MS (E) data obtained from human urine samples ( n = 86) from a subset of an epidemiological population unselected for any relevant phenotype or disease factor. We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/ z data from MS, together with fragment analysis from MS (E) scans, leads not only to the detection of numerous endogenous urinary metabolites but also the identification of drug metabolites that are part of the latent use of drugs by the population. We show previously unreported positive mode ions of ibuprofen metabolites with their NMR correlates and suggest the detection of new metabolites of disopyramide in the population samples. This approach is of great potential value in the description of population xenometabolomes and in population pharmacology studies, and indeed for drug metabolism studies in general.
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Affiliation(s)
- Derek J Crockford
- Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Medicine and Anaesthetics, Sir Alexander Fleming Building, Imperial College London, UK.
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Graça G, Duarte IF, J Goodfellow B, Carreira IM, Couceiro AB, Domingues MDR, Spraul M, Tseng LH, Gil AM. Metabolite profiling of human amniotic fluid by hyphenated nuclear magnetic resonance spectroscopy. Anal Chem 2008; 80:6085-92. [PMID: 18564856 DOI: 10.1021/ac800907f] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The metabolic profiling of human amniotic fluid (HAF) is of potential interest for the diagnosis of disorders in the mother or the fetus. In order to build a comprehensive metabolite database for HAF, hyphenated NMR has been used, for the first time, for systematic HAF profiling. Experiments were carried out using reverse-phase (RP) and ion-exchange liquid chromatography (LC), in order to detect less and more polar compounds, respectively. RP-LC conditions achieved good separation of amino acids, some sugars, and xanthines. Subsequent NMR and MS analysis enabled the rapid identification of 30 compounds, including 3-methyl-2-oxovalerate and 4-aminohippurate identified in HAF for the first time, to our knowledge. Under ion-exchange LC conditions, a different set of 30 compounds was detected, including sugars, organic acids, several derivatives of organic acids, and amino acids. In this experiment, five compounds were identified for the first time in HAF: D-xylitol, amino acid derivatives (N-acetylalanine, N-acetylglycine, 2-oxoleucine), and isovalerate. The nonendogenous nature of some metabolites (caffeine, paraxanthine, D-xylitol, sorbitol) is discussed. Hyphenated NMR has allowed the rapid detection of approximately 60 metabolites in HAF, some of which are not detectable by standard NMR due to low abundance (microM) and signal overlap thus enabling an extended metabolite database to be built for HAF.
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Affiliation(s)
- Gonçalo Graça
- CICECO-Department of Chemistry, Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
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41
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Johnson CH, Athersuch TJ, Wilson ID, Iddon L, Meng X, Stachulski AV, Lindon JC, Nicholson JK. Kinetic andJ-Resolved Statistical Total Correlation NMR Spectroscopy Approaches to Structural Information Recovery in Complex Reacting Mixtures: Application to Acyl Glucuronide Intramolecular Transacylation Reactions. Anal Chem 2008; 80:4886-95. [DOI: 10.1021/ac702614t] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Caroline H. Johnson
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, U.K., Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire SK10 4TG, U.K., and Department of Chemistry, The Robert Robinson Laboratories, University of Liverpool, Liverpool L69 7ZD, U.K
| | - Toby J. Athersuch
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, U.K., Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire SK10 4TG, U.K., and Department of Chemistry, The Robert Robinson Laboratories, University of Liverpool, Liverpool L69 7ZD, U.K
| | - Ian D. Wilson
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, U.K., Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire SK10 4TG, U.K., and Department of Chemistry, The Robert Robinson Laboratories, University of Liverpool, Liverpool L69 7ZD, U.K
| | - Lisa Iddon
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, U.K., Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire SK10 4TG, U.K., and Department of Chemistry, The Robert Robinson Laboratories, University of Liverpool, Liverpool L69 7ZD, U.K
| | - Xiaoli Meng
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, U.K., Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire SK10 4TG, U.K., and Department of Chemistry, The Robert Robinson Laboratories, University of Liverpool, Liverpool L69 7ZD, U.K
| | - Andrew V. Stachulski
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, U.K., Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire SK10 4TG, U.K., and Department of Chemistry, The Robert Robinson Laboratories, University of Liverpool, Liverpool L69 7ZD, U.K
| | - John C. Lindon
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, U.K., Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire SK10 4TG, U.K., and Department of Chemistry, The Robert Robinson Laboratories, University of Liverpool, Liverpool L69 7ZD, U.K
| | - Jeremy K. Nicholson
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, U.K., Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire SK10 4TG, U.K., and Department of Chemistry, The Robert Robinson Laboratories, University of Liverpool, Liverpool L69 7ZD, U.K
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Wang Y, Wang J, Yao M, Zhao X, Fritsche J, Schmitt-Kopplin P, Cai Z, Wan D, Lu X, Yang S, Gu J, Häring HU, Schleicher ED, Lehmann R, Xu G. Metabonomics Study on the Effects of the Ginsenoside Rg3 in a β-Cyclodextrin-Based Formulation on Tumor-Bearing Rats by a Fully Automatic Hydrophilic Interaction/Reversed-Phase Column-Switching HPLC−ESI-MS Approach. Anal Chem 2008; 80:4680-8. [DOI: 10.1021/ac8002402] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Yuan Wang
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Jiangshan Wang
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Ming Yao
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Xinjie Zhao
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Jens Fritsche
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Philippe Schmitt-Kopplin
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Zongwei Cai
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Dafang Wan
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Xin Lu
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Shengli Yang
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Jianren Gu
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Hans Ulrich Häring
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Erwin D. Schleicher
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Rainer Lehmann
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
| | - Guowang Xu
- National Chromatography Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, 200032 Shanghai, China, Immatics Biotechnologies GmbH, 72076 Tuebingen, Germany, Helmholtz-Zentrum Muenchen—German Research Center for Environmental Health, Institute for Ecological Chemistry, Ingoldstaedter Landstrasse 1 D-85764 Neuherberg, Germany,
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Baidoo EEK, Benke PI, Neusüss C, Pelzing M, Kruppa G, Leary JA, Keasling JD. Capillary Electrophoresis-Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for the Identification of Cationic Metabolites via a pH-Mediated Stacking-Transient Isotachophoretic Method. Anal Chem 2008; 80:3112-22. [DOI: 10.1021/ac800007q] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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44
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Rezzi S, Vera FA, Martin FPJ, Wang S, Lawler D, Kochhar S. Automated SPE-RP-HPLC fractionation of biofluids combined to off-line NMR spectroscopy for biomarker identification in metabonomics. J Chromatogr B Analyt Technol Biomed Life Sci 2008; 871:271-8. [PMID: 18439883 DOI: 10.1016/j.jchromb.2008.04.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2008] [Revised: 04/01/2008] [Accepted: 04/03/2008] [Indexed: 01/03/2023]
Abstract
NMR-based metabonomics is a valuable and straightforward approach to measuring hundreds of metabolites in complex biofluids. However, metabolite identification is sometimes limited by overlapped signals in NMR spectra. We describe a new methodology using an automated hyphenation of solid phase extraction (SPE) with RP-HPLC combined to NMR spectroscopy, which allowed identification of 72 metabolites of various molecular classes in human urine. This methodology was also successfully applied to the fractionation of a cat urine sample to aid identification of aromatic compounds and felinine. The SPE-RP-HPLC method appears to be a reliable tool to support biomarker discovery in metabonomic studies.
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Affiliation(s)
- Serge Rezzi
- BioAnalytical Science, Nestlé Research Center, PO Box 44, CH-1000 Lausanne 26, Switzerland.
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45
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Lindon JC, Nicholson JK. Analytical technologies for metabonomics and metabolomics, and multi-omic information recovery. Trends Analyt Chem 2008. [DOI: 10.1016/j.trac.2007.08.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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46
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Beckonert O, Keun HC, Ebbels TMD, Bundy J, Holmes E, Lindon JC, Nicholson JK. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2008; 2:2692-703. [PMID: 18007604 DOI: 10.1038/nprot.2007.376] [Citation(s) in RCA: 1480] [Impact Index Per Article: 92.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery. The standard acquisition time per sample is 4-5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics.
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Affiliation(s)
- Olaf Beckonert
- Department of Biomolecular Medicine, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, UK
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47
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Keun HC, Athersuch TJ, Beckonert O, Wang Y, Saric J, Shockcor JP, Lindon JC, Wilson ID, Holmes E, Nicholson JK. Heteronuclear 19F-1H statistical total correlation spectroscopy as a tool in drug metabolism: study of flucloxacillin biotransformation. Anal Chem 2008; 80:1073-9. [PMID: 18211034 DOI: 10.1021/ac702040d] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.
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Affiliation(s)
- Hector C Keun
- Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
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48
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Coen M, Holmes E, Lindon JC, Nicholson JK. NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology. Chem Res Toxicol 2008; 21:9-27. [PMID: 18171018 DOI: 10.1021/tx700335d] [Citation(s) in RCA: 225] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
We have reviewed the main contributions to the development of NMR-based metabonomic and metabolic profiling approaches for toxicological assessment, biomarker discovery, and studies on toxic mechanisms. The metabonomic approach, (defined as the quantitative measurement of the multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification) was originally developed to assist interpretation in NMR-based toxicological studies. However, in recent years there has been extensive fusion with metabolomic and other metabolic profiling approaches developed in plant biology, and there is much wider coverage of the biomedical and environmental fields. Specifically, metabonomics involves the use of spectroscopic techniques with statistical and mathematical tools to elucidate dominant patterns and trends directly correlated with time-related metabolic fluctuations within spectral data sets usually derived from biofluids or tissue samples. Temporal multivariate metabolic signatures can be used to discover biomarkers of toxic effect, as general toxicity screening aids, or to provide novel mechanistic information. This approach is complementary to proteomics and genomics and is applicable to a wide range of problems, including disease diagnosis, evaluation of xenobiotic toxicity, functional genomics, and nutritional studies. The use of biological fluids as a source of whole organism metabolic information enhances the use of this approach in minimally invasive longitudinal studies.
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Affiliation(s)
- Muireann Coen
- Department of Biomolecular Medicine, Surgery, Oncology, Reproductive Biology and Anesthetics Division, Faculty of Medicine, Imperial College London, London, UK
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Lindon JC, Nicholson JK. Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2008; 1:45-69. [PMID: 20636074 DOI: 10.1146/annurev.anchem.1.031207.113026] [Citation(s) in RCA: 206] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Methods for generating and interpreting metabolic profiles based on nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and chemometric analysis methods are summarized and the relative strengths and weaknesses of NMR and chromatography-coupled MS approaches are discussed. Given that all data sets measured to date only probe subsets of complex metabolic profiles, we describe recent developments for enhanced information recovery from the resulting complex data sets, including integration of NMR- and MS-based metabonomic results and combination of metabonomic data with data from proteomics, transcriptomics, and genomics. We summarize the breadth of applications, highlight some current activities, discuss the issues relating to metabonomics, and identify future trends.
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Affiliation(s)
- John C Lindon
- Department of Biomolecular Medicine, Imperial College London, United Kingdom
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Van QN, Issaq HJ, Jiang Q, Li Q, Muschik GM, Waybright TJ, Lou H, Dean M, Uitto J, Veenstra TD. Comparison of 1D and 2D NMR Spectroscopy for Metabolic Profiling. J Proteome Res 2007; 7:630-9. [DOI: 10.1021/pr700594s] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Que N. Van
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Haleem J. Issaq
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Qiujie Jiang
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Qiaoli Li
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Gary M. Muschik
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Timothy J. Waybright
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Hong Lou
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Michael Dean
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Jouni Uitto
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
| | - Timothy D. Veenstra
- Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, and Laboratory of Genomic Diversity, National Cancer Institute at Frederick, Frederick, Maryland 21702
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