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Brennan L. NMR-based metabolomics: from sample preparation to applications in nutrition research. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2014; 83:42-9. [PMID: 25456316 DOI: 10.1016/j.pnmrs.2014.09.001] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 09/28/2014] [Accepted: 09/29/2014] [Indexed: 05/24/2023]
Abstract
Metabolomics is the study of metabolites present in biological samples such as biofluids, tissue/cellular extracts and culture media. Measurement of these metabolites is achieved through use of analytical techniques such as NMR and mass spectrometry coupled to liquid chromatography. Combining metabolomic data with multivariate data analysis tools allows the elucidation of alterations in metabolic pathways under different physiological conditions. Applications of NMR-based metabolomics have grown in recent years and it is now widely used across a number of disciplines. The present review gives an overview of the developments in the key steps involved in an NMR-based metabolomics study. Furthermore, there will be a particular emphasis on the use of NMR-based metabolomics in nutrition research.
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Affiliation(s)
- Lorraine Brennan
- UCD Institute of Food and Health, Belfield, UCD, Dublin 4, Ireland.
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Sogin EM, Anderson P, Williams P, Chen CS, Gates RD. Application of 1H-NMR metabolomic profiling for reef-building corals. PLoS One 2014; 9:e111274. [PMID: 25354140 PMCID: PMC4213140 DOI: 10.1371/journal.pone.0111274] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2014] [Accepted: 09/23/2014] [Indexed: 01/26/2023] Open
Abstract
In light of global reef decline new methods to accurately, cheaply, and quickly evaluate coral metabolic states are needed to assess reef health. Metabolomic profiling can describe the response of individuals to disturbance (i.e., shifts in environmental conditions) across biological models and is a powerful approach for characterizing and comparing coral metabolism. For the first time, we assess the utility of a proton-nuclear magnetic resonance spectroscopy (1H-NMR)-based metabolomics approach in characterizing coral metabolite profiles by 1) investigating technical, intra-, and inter-sample variation, 2) evaluating the ability to recover targeted metabolite spikes, and 3) assessing the potential for this method to differentiate among coral species. Our results indicate 1H-NMR profiling of Porites compressa corals is highly reproducible and exhibits low levels of variability within and among colonies. The spiking experiments validate the sensitivity of our methods and showcase the capacity of orthogonal partial least squares discriminate analysis (OPLS-DA) to distinguish between profiles spiked with varying metabolite concentrations (0 mM, 0.1 mM, and 10 mM). Finally, 1H-NMR metabolomics coupled with OPLS-DA, revealed species-specific patterns in metabolite profiles among four reef-building corals (Pocillopora damicornis, Porites lobata, Montipora aequituberculata, and Seriatopora hystrix). Collectively, these data indicate that 1H-NMR metabolomic techniques can profile reef-building coral metabolomes and have the potential to provide an integrated picture of the coral phenotype in response to environmental change.
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Affiliation(s)
- Emilia M. Sogin
- Hawaii Institute of Marine Biology, Kaneohe, Hawaii, United States of America
- University of Hawaii at Manoa, Honolulu, Hawaii, United States of America
- * E-mail:
| | - Paul Anderson
- College of Charleston, Charleston, South Carolina, United States of America
| | - Philip Williams
- University of Hawaii at Manoa, Honolulu, Hawaii, United States of America
| | | | - Ruth D. Gates
- Hawaii Institute of Marine Biology, Kaneohe, Hawaii, United States of America
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Metabolomics insights into pathophysiological mechanisms of interstitial cystitis. Int Neurourol J 2014; 18:106-14. [PMID: 25279237 PMCID: PMC4180160 DOI: 10.5213/inj.2014.18.3.106] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2014] [Accepted: 09/06/2014] [Indexed: 12/20/2022] Open
Abstract
Interstitial cystitis (IC), also known as painful bladder syndrome or bladder pain syndrome, is a chronic lower urinary tract syndrome characterized by pelvic pain, urinary urgency, and increased urinary frequency in the absence of bacterial infection or identifiable clinicopathology. IC can lead to long-term adverse effects on the patient's quality of life. Therefore, early diagnosis and better understanding of the mechanisms underlying IC are needed. Metabolomic studies of biofluids have become a powerful method for assessing disease mechanisms and biomarker discovery, which potentially address these important clinical needs. However, limited intensive metabolic profiles have been elucidated in IC. The article is a short review on metabolomic analyses that provide a unique fingerprint of IC with a focus on its use in determining a potential diagnostic biomarker associated with symptoms, a response predictor of therapy, and a prognostic marker.
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Hao J, Liebeke M, Astle W, De Iorio M, Bundy JG, Ebbels TMD. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nat Protoc 2014; 9:1416-27. [PMID: 24853927 DOI: 10.1038/nprot.2014.090] [Citation(s) in RCA: 125] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Data processing for 1D NMR spectra is a key bottleneck for metabolomic and other complex-mixture studies, particularly where quantitative data on individual metabolites are required. We present a protocol for automated metabolite deconvolution and quantification from complex NMR spectra by using the Bayesian automated metabolite analyzer for NMR (BATMAN) R package. BATMAN models resonances on the basis of a user-controllable set of templates, each of which specifies the chemical shifts, J-couplings and relative peak intensities for a single metabolite. Peaks are allowed to shift position slightly between spectra, and peak widths are allowed to vary by user-specified amounts. NMR signals not captured by the templates are modeled non-parametrically by using wavelets. The protocol covers setting up user template libraries, optimizing algorithmic input parameters, improving prior information on peak positions, quality control and evaluation of outputs. The outputs include relative concentration estimates for named metabolites together with associated Bayesian uncertainty estimates, as well as the fit of the remainder of the spectrum using wavelets. Graphical diagnostics allow the user to examine the quality of the fit for multiple spectra simultaneously. This approach offers a workflow to analyze large numbers of spectra and is expected to be useful in a wide range of metabolomics studies.
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Affiliation(s)
- Jie Hao
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Manuel Liebeke
- 1] Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK. [2] Present address: Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - William Astle
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Maria De Iorio
- Department of Statistical Science, University College London, London, UK
| | - Jacob G Bundy
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Timothy M D Ebbels
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
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Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites. Anal Bioanal Chem 2014; 406:3091-102. [DOI: 10.1007/s00216-014-7752-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2014] [Revised: 03/04/2014] [Accepted: 03/06/2014] [Indexed: 12/26/2022]
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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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Meyer H, Weidmann H, Lalk M. Methodological approaches to help unravel the intracellular metabolome of Bacillus subtilis. Microb Cell Fact 2013; 12:69. [PMID: 23844891 PMCID: PMC3722095 DOI: 10.1186/1475-2859-12-69] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Accepted: 07/01/2013] [Indexed: 11/16/2022] Open
Abstract
Background Bacillus subtilis (B. subtilis) has become widely accepted as a model organism for studies on Gram-positive bacteria. A deeper insight into the physiology of this prokaryote requires advanced studies of its metabolism. To provide a reliable basis for metabolome investigations, a validated experimental protocol is needed since the quality of the analytical sample and the final data are strongly affected by the sampling steps. To ensure that the sample analyzed precisely reflects the biological condition of interest, outside biases have to be avoided during sample preparation. Results Procedures for sampling, quenching, extraction of metabolites, cell disruption, as well as metabolite leakage were tested and optimized for B. subtilis. In particular the energy status of the bacterial cell, characterized by the adenylate energy charge, was used to evaluate sampling accuracy. Moreover, the results of the present study demonstrate that the cultivation medium can affect the efficiency of the developed sampling procedure. Conclusion The final workflow presented here allows for the reproducible and reliable generation of physiological data. The method with the highest qualitative and quantitative metabolite yield was chosen, and when used together with complementary bioanalytical methods (i.e., GC-MS, LC-MS and 1H-NMR) provides a solid basis to gather information on the metabolome of B. subtilis.
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Affiliation(s)
- Hanna Meyer
- Institute of Biochemistry, Ernst-Moritz-Arndt-University Greifswald, Felix-Hausdorff-Strasse 4, 17487 Greifswald, Germany
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Da Silva L, Godejohann M, Martin FPJ, Collino S, Bürkle A, Moreno-Villanueva M, Bernhardt J, Toussaint O, Grubeck-Loebenstein B, Gonos ES, Sikora E, Grune T, Breusing N, Franceschi C, Hervonen A, Spraul M, Moco S. High-resolution quantitative metabolome analysis of urine by automated flow injection NMR. Anal Chem 2013; 85:5801-9. [PMID: 23718684 PMCID: PMC3690541 DOI: 10.1021/ac4004776] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
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Metabolism is essential to understand
human health. To characterize
human metabolism, a high-resolution read-out of the metabolic status
under various physiological conditions, either in health or disease,
is needed. Metabolomics offers an unprecedented approach for generating
system-specific biochemical definitions of a human phenotype through
the capture of a variety of metabolites in a single measurement. The
emergence of large cohorts in clinical studies increases the demand
of technologies able to analyze a large number of measurements, in
an automated fashion, in the most robust way. NMR is an established
metabolomics tool for obtaining metabolic phenotypes. Here, we describe
the analysis of NMR-based urinary profiles for metabolic studies,
challenged to a large human study (3007 samples). This method includes
the acquisition of nuclear Overhauser effect spectroscopy one-dimensional
and J-resolved two-dimensional (J-Res-2D) 1H NMR spectra obtained on a 600 MHz spectrometer,
equipped with a 120 μL flow probe, coupled to a flow-injection
analysis system, in full automation under the control of a sampler
manager. Samples were acquired at a throughput of ∼20 (or 40
when J-Res-2D is included) min/sample. The associated
technical analysis error over the full series of analysis is 12%,
which demonstrates the robustness of the method. With the aim to describe
an overall metabolomics workflow, the quantification of 36 metabolites,
mainly related to central carbon metabolism and gut microbial host
cometabolism, was obtained, as well as multivariate data analysis
of the full spectral profiles. The metabolic read-outs generated using
our analytical workflow can therefore be considered for further pathway
modeling and/or biological interpretation.
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Affiliation(s)
- Laeticia Da Silva
- BioAnalytical Science, Nestle Research Center, Vers-chez-les-Blanc, P.O. Box 44, 1000 Lausanne 26, Switzerland
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Hao J, Astle W, De Iorio M, Ebbels TMD. BATMAN--an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics 2012; 28:2088-90. [PMID: 22635605 DOI: 10.1093/bioinformatics/bts308] [Citation(s) in RCA: 99] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain metabolite profiles in complex biological mixtures. Common methods used to assign and estimate concentrations of metabolites involve either an expert manual peak fitting or extra pre-processing steps, such as peak alignment and binning. Peak fitting is very time consuming and is subject to human error. Conversely, alignment and binning can introduce artefacts and limit immediate biological interpretation of models. RESULTS We present the Bayesian automated metabolite analyser for NMR spectra (BATMAN), an R package that deconvolutes peaks from one-dimensional NMR spectra, automatically assigns them to specific metabolites from a target list and obtains concentration estimates. The Bayesian model incorporates information on characteristic peak patterns of metabolites and is able to account for shifts in the position of peaks commonly seen in NMR spectra of biological samples. It applies a Markov chain Monte Carlo algorithm to sample from a joint posterior distribution of the model parameters and obtains concentration estimates with reduced error compared with conventional numerical integration and comparable to manual deconvolution by experienced spectroscopists. AVAILABILITY AND IMPLEMENTATION http://www1.imperial.ac.uk/medicine/people/t.ebbels/ CONTACT t.ebbels@imperial.ac.uk.
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Affiliation(s)
- Jie Hao
- Section of Biomolecular Medicine, Department of Surgery and Cancer, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK
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Ye T, Zheng C, Zhang S, Gowda GAN, Vitek O, Raftery D. "Add to subtract": a simple method to remove complex background signals from the 1H nuclear magnetic resonance spectra of mixtures. Anal Chem 2012; 84:994-1002. [PMID: 22221170 PMCID: PMC3282557 DOI: 10.1021/ac202548n] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Because of its highly reproducible and quantitative nature and minimal requirements for sample preparation or separation, (1)H nuclear magnetic resonance (NMR) spectroscopy is widely used for profiling small-molecule metabolites in biofluids. However (1)H NMR spectra contain many overlapped peaks. In particular, blood serum/plasma and diabetic urine samples contain high concentrations of glucose, which produce strong peaks between 3.2 ppm and 4.0 ppm. Signals from most metabolites in this region are overwhelmed by the glucose background signals and become invisible. We propose a simple "Add to Subtract" background subtraction method and show that it can reduce the glucose signals by 98% to allow retrieval of the hidden information. This procedure includes adding a small drop of concentrated glucose solution to the sample in the NMR tube, mixing, waiting for an equilibration time, and acquisition of a second spectrum. The glucose-free spectra are then generated by spectral subtraction using Bruker Topspin software. Subsequent multivariate statistical analysis can then be used to identify biomarker candidate signals for distinguishing different types of biological samples. The principle of this approach is generally applicable for all quantitative spectral data and should find utility in a variety of NMR-based mixture analyses as well as in metabolite profiling.
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Affiliation(s)
- Tao Ye
- Harvard-MIT Division of Health Sciences & Technology, Cambridge, MA 02139
| | - Cheng Zheng
- Novartis Pharmaceuticals Corporation, Oncology BU Biometrics and Data Management, Florham Park, NJ 07932
| | - Shucha Zhang
- Division of Clinical Research, Fred Hutchinson Cancer Research, Seattle, WA 98102
| | | | - Olga Vitek
- Department of Statistics, Purdue University, West Lafayette, IN 47907
- Department of Computer Science, Purdue University, West Lafayette, IN 47907
| | - Daniel Raftery
- Department of Chemistry, Purdue University, West Lafayette, IN 47907
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61
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Dumas ME. Metabolome 2.0: quantitative genetics and network biology of metabolic phenotypes. MOLECULAR BIOSYSTEMS 2012; 8:2494-502. [DOI: 10.1039/c2mb25167a] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Tulpan D, Léger S, Belliveau L, Culf A, Cuperlović-Culf M. MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures. BMC Bioinformatics 2011; 12:400. [PMID: 21999117 PMCID: PMC3213069 DOI: 10.1186/1471-2105-12-400] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2011] [Accepted: 10/14/2011] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND One-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research. RESULTS We introduce a web server application, called MetaboHunter, which can be used for automatic assignment of 1H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods. CONCLUSIONS MetaboHunter is a freely accessible, easy to use and user friendly 1H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.
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Affiliation(s)
- Dan Tulpan
- Institute for Information Technology, National Research Council of Canada, Moncton, New Brunswick, E1A 7R1, Canada.
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Allen GI, Maletić-Savatić M. Sparse non-negative generalized PCA with applications to metabolomics. ACTA ACUST UNITED AC 2011; 27:3029-35. [PMID: 21930672 DOI: 10.1093/bioinformatics/btr522] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
MOTIVATION Nuclear magnetic resonance (NMR) spectroscopy has been used to study mixtures of metabolites in biological samples. This technology produces a spectrum for each sample depicting the chemical shifts at which an unknown number of latent metabolites resonate. The interpretation of this data with common multivariate exploratory methods such as principal components analysis (PCA) is limited due to high-dimensionality, non-negativity of the underlying spectra and dependencies at adjacent chemical shifts. RESULTS We develop a novel modification of PCA that is appropriate for analysis of NMR data, entitled Sparse Non-Negative Generalized PCA. This method yields interpretable principal components and loading vectors that select important features and directly account for both the non-negativity of the underlying spectra and dependencies at adjacent chemical shifts. Through the reanalysis of experimental NMR data on five purified neural cell types, we demonstrate the utility of our methods for dimension reduction, pattern recognition, sample exploration and feature selection. Our methods lead to the identification of novel metabolites that reflect the differences between these cell types. AVAILABILITY www.stat.rice.edu/~gallen/software.html. CONTACT gallen@rice.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Genevera I Allen
- Department of Pediatrics-Neurology, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA.
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