1
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Mishra R, Yong JRJ, Jacquemmoz C, Lorandel B, Foroozandeh M, Dumez JN. Spatially encoded pure-shift diffusion-ordered NMR spectroscopy yielded by chirp excitation. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 359:107628. [PMID: 38301415 DOI: 10.1016/j.jmr.2024.107628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 01/17/2024] [Accepted: 01/20/2024] [Indexed: 02/03/2024]
Abstract
Spatially-encoded diffusion-ordered NMR spectroscopy (SPEN-DOSY) has emerged as a new time-efficient tool for the analysis of mixtures of small molecules in solution. Time efficiency is achieved using the concept of spatial parallelization of the effective gradient area, instead of the sequential incrementation used in conventional diffusion experiments. The data acquired with such sequences are then usually processed to extract diffusion coefficients, but cases when peak overlap in the 1H spectrum are difficult to address. Such limitation in conventional diffusion experiments is addressed via using the Pure Shift Yielded by CHirp Excitation (PSYCHE)-iDOSY sequence. Here we have adapted the PSYCHE-iDOSY sequence by using echo planar spectroscopic imaging (EPSI) to acquire SPEN-DOSY data. The pure shift mode of PSYCHE separates the overlapped components and a modified Stejskal-Tanner equation is used to extract the corresponding diffusion coefficient. The primary results obtained with the above-mentioned mixtures seem to open the possibility of separating complex mixtures in less time than PSYCHE-iDOSY.
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Affiliation(s)
- Rituraj Mishra
- Nantes Université, CNRS, CEISAM UMR6230, F-44000 Nantes, France
| | - Jonathan R J Yong
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK
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2
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Pomary PK, Eichau S, Amigó N, Barrios L, Matesanz F, García-Valdecasas M, Hrom I, García Sánchez MI, Garcia-Martin ML. Multifaceted Analysis of Cerebrospinal Fluid and Serum from Progressive Multiple Sclerosis Patients: Potential Role of Vitamin C and Metal Ion Imbalance in the Divergence of Primary Progressive Multiple Sclerosis and Secondary Progressive Multiple Sclerosis. J Proteome Res 2023; 22:743-757. [PMID: 36720471 PMCID: PMC9990127 DOI: 10.1021/acs.jproteome.2c00460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The progressive forms of multiple sclerosis (MS) primary progressive MS (PPMS) and secondary progressive MS (SPMS) are clinically distinguished by the rate at which symptoms worsen. Little is however known about the pathological mechanisms underlying the differential rate of accumulation of pathological changes. In this study, 1H NMR spectroscopy was used to measure low-molecular-weight metabolites in paired cerebrospinal fluid (CSF) and serum of PPMS, SPMS, and control patients, as well as to determine lipoproteins and glycoproteins in serum samples. Additionally, neurodegenerative and inflammatory markers, neurofilament light (NFL) and chitinase-3-like protein 1 (CHI3L1), and the concentration of seven metal elements, Mg, Mn, Cu, Fe, Pb, Zn, and Ca, were also determined in both CSF and serum. The results indicate that the pathological changes associated with progressive MS are mainly localized in the central nervous system (CNS). More so, PPMS and SPMS patients with comparable disability status are pathologically similar in relation to neurodegeneration, neuroinflammation, and some metabolites that distinguish them from controls. However, the rapid progression of PPMS from the onset may be driven by a combination of neurotoxicity induced by heavy metals coupled with diminished CNS antioxidative capacity associated with differential intrathecal ascorbate retention and imbalance of Mg and Cu.
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Affiliation(s)
- Precious Kwadzo Pomary
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA Plataforma BIONAND), Universidad de Málaga, C/Severo Ochoa, 35, 29590 Málaga, Spain
| | - Sara Eichau
- Unidad de Neurología, Hospital Universitario Virgen de la Macarena, Av. Dr. Fedriani, 3, 41009 Sevilla, Spain
| | - Núria Amigó
- Biosfer Teslab, 43201 Reus, Spain.,Department of Basic Medical Sciences, University Rovira I Virgili, IISPV, CIBERDEM, 43201 Reus, Spain
| | - Laura Barrios
- Statistics Department, Computing Center (SGAI-CSIC), Pinar 19, Madrid 28006, Spain
| | - Fuencisla Matesanz
- Instituto de Parasitologia y Biomedicina ″Lopez-Neyra″, Avda. del Conocimiento 17. P. T. Ciencias de la Salud, 18016 Granada, Spain
| | - Marta García-Valdecasas
- Unidad de Neurología, Hospital Universitario Virgen de la Macarena, Av. Dr. Fedriani, 3, 41009 Sevilla, Spain
| | - Ioana Hrom
- Unidad de Neurología, Hospital Universitario Virgen de la Macarena, Av. Dr. Fedriani, 3, 41009 Sevilla, Spain
| | - María Isabel García Sánchez
- Unidad de Neurología, Hospital Universitario Virgen de la Macarena, Av. Dr. Fedriani, 3, 41009 Sevilla, Spain
| | - Maria Luisa Garcia-Martin
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA Plataforma BIONAND), Universidad de Málaga, C/Severo Ochoa, 35, 29590 Málaga, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials & Nanomedicine (CIBER-BBN), 29590 Málaga, Spain
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3
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Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR. MOLECULES (BASEL, SWITZERLAND) 2023; 28:molecules28020792. [PMID: 36677850 PMCID: PMC9866129 DOI: 10.3390/molecules28020792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/15/2023]
Abstract
Resolving small molecule mixtures by nuclear magnetic resonance (NMR) spectroscopy has been of great interest for a long time for its precision, reproducibility, and efficiency. However, spectral analyses for such mixtures are often highly challenging due to overlapping resonance lines and limited chemical shift windows. The existing experimental and theoretical methods to produce shift NMR spectra in dealing with the problem have limited applicability owing to sensitivity issues, inconsistency, and/or the requirement of prior knowledge. Recently, we resolved the problem by decoupling multiplet structures in NMR spectra by the wavelet packet transform (WPT) technique. In this work, we developed a scheme for deploying the method in generating highly resolved WPT NMR spectra and predicting the composition of the corresponding molecular mixtures from their 1H NMR spectra in an automated fashion. The four-step spectral analysis scheme consists of calculating the WPT spectrum, peak matching with a WPT shift NMR library, followed by two optimization steps in producing the predicted molecular composition of a mixture. The robustness of the method was tested on an augmented dataset of 1000 molecular mixtures, each containing 3 to 7 molecules. The method successfully predicted the constituent molecules with a median true positive rate of 1.0 against the varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets.
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4
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Sinha Roy A, Srivastava M. Analysis of Small-Molecule Mixtures by Super-Resolved 1H NMR Spectroscopy. J Phys Chem A 2022; 126:9108-9113. [PMID: 36413171 DOI: 10.1021/acs.jpca.2c06858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Analysis of small molecules is essential to metabolomics, natural products, drug discovery, food technology, and many other areas of interest. Current barriers preclude from identifying the constituent molecules in a mixture as overlapping clusters of NMR lines pose a major challenge in resolving signature frequencies for individual molecules. While homonuclear decoupling techniques produce much simplified pure shift spectra, they often affect sensitivity. Conversion of typical NMR spectra to pure shift spectra by signal processing without a priori knowledge about the coupling patterns is essential for accurate analysis. We developed a super-resolved wavelet packet transform based 1H NMR spectroscopy that can be used in high-throughput studies to reliably decouple individual constituents of small molecule mixtures. We demonstrate the efficacy of the method on the model mixtures of saccharides and amino acids in the presence of significant noise.
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Affiliation(s)
- Aritro Sinha Roy
- Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853-0001,United States
| | - Madhur Srivastava
- Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853-0001,United States.,National Biomedical Resources for Advanced ESR Technologies (ACERT), Ithaca, New York 14853, United States
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5
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Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27123653. [PMID: 35744782 PMCID: PMC9227391 DOI: 10.3390/molecules27123653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/03/2022] [Accepted: 06/05/2022] [Indexed: 11/16/2022]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.
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6
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Lhoste C, Lorandel B, Praud C, Marchand A, Mishra R, Dey A, Bernard A, Dumez JN, Giraudeau P. Ultrafast 2D NMR for the analysis of complex mixtures. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2022; 130-131:1-46. [PMID: 36113916 DOI: 10.1016/j.pnmrs.2022.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 06/15/2023]
Abstract
2D NMR is extensively used in many different fields, and its potential for the study of complex biochemical or chemical mixtures has been widely demonstrated. 2D NMR gives the ability to resolve peaks that overlap in 1D spectra, while providing both structural and quantitative information. However, complex mixtures are often analysed in situations where the data acquisition time is a crucial limitation, due to an ongoing chemical reaction or a moving sample from a hyphenated technique, or to the high-throughput requirement associated with large sample collections. Among the great diversity of available fast 2D methods, ultrafast (or single-scan) 2D NMR is probably the most general and versatile approach for complex mixture analysis. Indeed, ultrafast NMR has undergone an impressive number of methodological developments that have helped turn it into an efficient analytical tool, and numerous applications to the analysis of mixtures have been reported. This review first summarizes the main concepts, features and practical limitations of ultrafast 2D NMR, as well as the methodological developments that improved its analytical potential. Then, a detailed description of the main applications of ultrafast 2D NMR to mixture analysis is given. The two major application fields of ultrafast 2D NMR are first covered, i.e., reaction/process monitoring and metabolomics. Then, the potential of ultrafast 2D NMR for the analysis of hyperpolarized mixtures is described, as well as recent developments in oriented media. This review focuses on high-resolution liquid-state 2D experiments (including benchtop NMR) that include at least one spectroscopic dimension (i.e., 2D spectroscopy and DOSY) but does not cover in depth applications without spectral resolution and/or in inhomogeneous fields.
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Affiliation(s)
- Célia Lhoste
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | | | - Clément Praud
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | - Achille Marchand
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | - Rituraj Mishra
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | - Arnab Dey
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
| | - Aurélie Bernard
- Nantes Université, CNRS, CEISAM UMR 6230, Nantes F-44000, France
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7
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Mishra R, Marchand A, Jacquemmoz C, Dumez JN. Ultrafast diffusion-based unmixing of 1H NMR spectra. Chem Commun (Camb) 2021; 57:2384-2387. [PMID: 33538725 DOI: 10.1039/d0cc07757g] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
We show that the NMR spectra of components in a mixture can be separated using 2D data acquired in less than one second, and an algorithm that is executed in just a few seconds. This NMR unmixing method is based on spatial encoding of the translational diffusion coefficients of the mixture's components, with multivariate processing of the data. This requires a new frequency swept pulse, which is designed and implemented to obtain quadratic spacing of the spatially parallelised gradient dimension. Ultrafast NMR unmixing may help in the analysis of mixtures that evolve in time.
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Affiliation(s)
- Rituraj Mishra
- Université de Nantes, CNRS, CEISAM UMR 6230, F-44000 Nantes, France.
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8
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Snyder DA. Covariance NMR: Theoretical concerns, practical considerations, contemporary applications and related techniques. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2021; 122:1-10. [PMID: 33632414 DOI: 10.1016/j.pnmrs.2020.09.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/23/2020] [Accepted: 09/29/2020] [Indexed: 06/12/2023]
Abstract
The family of resolution enhancement and spectral reconstruction techniques collectively known as covariance NMR continues to expand, along with the list of applications for these techniques. Recent advances in covariance NMR include the utilization of covariance to reconstruct pure shift NMR spectra, and the growing use of covariance NMR in processing non-uniformly sampled data, especially in solid state NMR and metabolomics. This review describes theoretical and practical considerations for direct and indirect covariance NMR techniques, and summarizes recent additions to the covariance NMR family. The review also outlines some of the applications of covariance NMR, and places covariance NMR in the larger context of methods that use statistical and algebraic approaches to enhance and combine various kinds of spectroscopic data, including tensor-based approaches for multidimensional NMR and heterocovariance spectroscopy.
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Affiliation(s)
- David A Snyder
- Department of Chemistry, College of Science and Health, William Paterson University of NJ, United States.
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9
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Crook AA, Powers R. Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications. Molecules 2020; 25:E5128. [PMID: 33158172 PMCID: PMC7662776 DOI: 10.3390/molecules25215128] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 12/19/2022] Open
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.
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Affiliation(s)
- Alexandra A. Crook
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
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10
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Huang T, Chen P, Liu B, Li X, Lv X, Hu K. NPid: an Automatic Approach to Rapid Identification of Known Natural Products in the Crude Extract of Crabapple Based on 2D 1H- 13C Heteronuclear Correlation Spectra of the Extract Mixture. Anal Chem 2020; 92:10996-11006. [PMID: 32686928 DOI: 10.1021/acs.analchem.9b05363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
An automatic approach to identification of natural products (NPid) in complex extracts by exploring pure shift HSQC (psHSQC) and H2BC spectra of the mixture is developed, which integrated information on chemical shifts (CS), adjacent relationships (AR) and peak intensities (PI) of 1H-13C groups for identification of candidate natural product in a customized NMR database. A weighted comprehensive score is calculated for each candidate from the values of CS, AR and PI to rate the likelihood of its existence in the complex mixture. Using the crude extract of crabapple (Malus fusca) as an example, a customized NMR database of natural products from plants of the genus Malus was constructed. The performance of NPid was first evaluated using simulated data in four scenarios, that is, for identification of structurally similar natural products, identification of natural products with part of peaks missing in psHSQC due to low concentration, without available adjacent relationship information, or without useful peak intensity information. The false positive and false negative rates of the natural products identified by NPid were estimated by Monte Carlo simulation. It shows that AR and PI can effectively reduce the false positive rate of identification. Proof of concept of the proposed method was elucidated on a model mixture consisting of 10 known natural products. Application of this method was then demonstrated on an authentic sample of crude extract of crabapple and 19 known natural products were successfully identified and confirmed by standard spiking.
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Affiliation(s)
- Tao Huang
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Pengyu Chen
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bin Liu
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xing Li
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinqiao Lv
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Kaifeng Hu
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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11
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Abstract
The major goal in plant metabolomics is to study complex extracts for the purposes of metabolic exploration and natural products discovery. To achieve this goal, plant metabolomics relies on accurate and selective acquisition of all possible chemical information, which includes maximization of the number of detected metabolites and their correct molecular assignment. Nuclear magnetic resonance (NMR) spectroscopy has been recognized as a powerful platform for obtaining the metabolite profiles of plant extracts. In this chapter, we provide a workflow for targeted and untargeted metabolite profiling of plant extracts using both 1D and 2D NMR methods. The protocol includes sample preparation, instrument operation, data processing, multivariate analysis, biomarker elucidation, and metabolite quantitation. It also addresses the annotation of plant metabolite peaks considering NMR's capabilities to cover a broad range of metabolites and elucidate structures for unknown compounds.
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Affiliation(s)
- Denise Medeiros Selegato
- Núcleo de Bioensaios, Biossíntese e Ecofisiologia de Produtos Naturais (NuBBE), Departamento de Química Orgânica, Instituto de Química, Universidade Estadual Paulista (UNESP), Araraquara, São Paulo, Brazil
| | - Alan Cesar Pilon
- Núcleo de Pesquisa em Produtos Naturais e Sintéticos (NPPNS), Departamento de Física e Química, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Fausto Carnevale Neto
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA, USA.
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12
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Abstract
In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.
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Affiliation(s)
- Wimal Pathmasiri
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA.
| | - Kristine Kay
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Susan McRitchie
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Susan Sumner
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
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13
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Giraudeau P. NMR-based metabolomics and fluxomics: developments and future prospects. Analyst 2020; 145:2457-2472. [DOI: 10.1039/d0an00142b] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Recent NMR developments are acting as game changers for metabolomics and fluxomics – a critical and perspective review.
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14
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Ranjan R, Sinha N. Nuclear magnetic resonance (NMR)-based metabolomics for cancer research. NMR IN BIOMEDICINE 2019; 32:e3916. [PMID: 29733484 DOI: 10.1002/nbm.3916] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 02/01/2018] [Accepted: 02/12/2018] [Indexed: 06/08/2023]
Abstract
Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.
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Affiliation(s)
- Renuka Ranjan
- Centre of Biomedical Research, SGPGIMS Campus, Raebarelly Road, Lucknow, India
- School of Biotechnology, Institute of Science Banaras Hindu University, Varanasi, India
| | - Neeraj Sinha
- Centre of Biomedical Research, SGPGIMS Campus, Raebarelly Road, Lucknow, India
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15
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Paudel L, Nagana Gowda GA, Raftery D. Extractive Ratio Analysis NMR Spectroscopy for Metabolite Identification in Complex Biological Mixtures. Anal Chem 2019; 91:7373-7378. [PMID: 31059230 DOI: 10.1021/acs.analchem.9b01235] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The complexity of biological mixtures continues to challenge efforts aimed at unknown metabolite identification in the metabolomics field. To address this challenge, we provide a new method to identify related peaks from individual metabolites in complex NMR spectra. Extractive ratio analysis NMR spectroscopy (E-RANSY) builds on our previously described ratio analysis method [ Wei et al. Anal. Chem. 2011 , 83 , 7616 - 7623 ] and exploits the simplified NMR spectra provided by the extraction of metabolites under varied pH conditions. Under such conditions, metabolites from the same biological specimen are extracted differentially, and the resulting NMR spectra exhibit characteristics favorable for unraveling unknown metabolite peaks using ratio analysis. We demonstrate the utility of the E-RANSY method by extracting carboxylic acid containing metabolites from human urine, one of the highly complex biological mixtures encountered in the metabolomics field. E-RANSY performs better than STOCSY and the original RANSY method and offers new avenues to identify unknown metabolites in complex biological mixtures.
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Affiliation(s)
| | | | - Daniel Raftery
- Fred Hutchinson Cancer Research Center , Seattle , Washington 98109 , United States
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16
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Charris-Molina A, Riquelme G, Burdisso P, Hoijemberg PA. Tackling the Peak Overlap Issue in NMR Metabolomics Studies: 1D Projected Correlation Traces from Statistical Correlation Analysis on Nontilted 2D 1H NMR J-Resolved Spectra. J Proteome Res 2019; 18:2241-2253. [PMID: 30916564 DOI: 10.1021/acs.jproteome.9b00093] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The identification of metabolites in complex biological matrices is a challenging task in 1D 1H-NMR-based metabolomics studies. Statistical total correlation spectroscopy (STOCSY) has emerged for aiding the structural elucidation by revealing the peaks that present a high correlation to a driver peak of interest (which would likely belong to the same molecule). However, in these studies, the signals from metabolites are normally present as a mixture of overlapping resonances, limiting the performance of STOCSY. As an alternative to avoid the overlap issue, 2D 1H homonuclear J-resolved (JRES) spectra were projected, in their usual tilted and symmetrized processed form, and STOCSY was applied on these 1D projections (p-JRES-STOCSY). Nonetheless, this approach suffers in cases where the signals are very close. In addition, STOCSY was applied to the whole JRES spectra (also tilted) to identify correlated multiplets, although the overlap issue in itself was not addressed directly and the subsequent search in databases is complicated in cases of higher order coupling. With these limitations in mind, in the present work, we propose a new methodology based on the application of STOCSY on a set of nontilted JRES spectra, detecting peaks that would overlap in 1D spectra of the same sample set. Correlation comparison analysis for peak overlap detection (COCOA-POD) is able to reconstruct projected 1D STOCSY traces that result in more suitable database queries, as all peaks are summed at their f2 resonances instead of the resonance corresponding to the multiplet center in the tilted JRES spectra. (The peak dispersion and resolution enhancement gained are not sacrificed by the projection.) Besides improving database queries with better peak lists obtained from the projections of the 2D STOCSY analysis, the overlap region is examined, and the multiplet itself is analyzed from the correlation trace at 45° to obtain a cleaner multiplet profile, free from contributions from uncorrelated neighboring peaks.
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Affiliation(s)
- Andrés Charris-Molina
- CIBION-CONICET, Centro de Investigaciones en Bionanociencias , NMR Group , Polo Científico Tecnológico , Ciudad Autónoma de Buenos Aires C1425FQD , Argentina.,Universidad de Buenos Aires , Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales , Ciudad Universitaria, Buenos Aires C1428EGA , Argentina
| | - Gabriel Riquelme
- CIBION-CONICET, Centro de Investigaciones en Bionanociencias , NMR Group , Polo Científico Tecnológico , Ciudad Autónoma de Buenos Aires C1425FQD , Argentina.,Universidad de Buenos Aires , Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales , Ciudad Universitaria, Buenos Aires C1428EGA , Argentina
| | - Paula Burdisso
- Instituto de Biología Molecular y Celular de Rosario (IBR-CONICET) , Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario and Plataforma Argentina de Biología Estructural y Metabolómica (PLABEM) , Rosario , Santa Fe S2002LRK , Argentina
| | - Pablo A Hoijemberg
- CIBION-CONICET, Centro de Investigaciones en Bionanociencias , NMR Group , Polo Científico Tecnológico , Ciudad Autónoma de Buenos Aires C1425FQD , Argentina.,ECyT-UNSAM , 25 de Mayo y Francia , San Martín, Buenos Aires B1650HMP , Argentina
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17
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Cañueto D, Gómez J, Salek RM, Correig X, Cañellas N. rDolphin: a GUI R package for proficient automatic profiling of 1D 1H-NMR spectra of study datasets. Metabolomics 2018; 14:24. [PMID: 30830320 DOI: 10.1007/s11306-018-1319-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Accepted: 01/03/2018] [Indexed: 11/25/2022]
Abstract
INTRODUCTION Adoption of automatic profiling tools for 1H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices. OBJECTIVES To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process. METHODS rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality. RESULTS The information and quality achieved in two public datasets of complex matrices are maximized. CONCLUSION rDolphin is an open-source R package ( http://github.com/danielcanueto/rDolphin ) able to provide the best balance between accuracy, reproducibility and ease of use.
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Affiliation(s)
- Daniel Cañueto
- Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007, Tarragona, Spain.
| | - Josep Gómez
- Intensive Care Unit, Joan XXIII University Hospital, IISPV, Carrer Dr. Mallafrè Guasch, 4, 43005, Tarragona, Spain
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK
| | - Xavier Correig
- Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007, Tarragona, Spain
- CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, 28029, Madrid, Spain
| | - Nicolau Cañellas
- Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007, Tarragona, Spain.
- CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, 28029, Madrid, Spain.
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18
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Ebbels TMD, Rodriguez-Martinez A, Dumas ME, Keun HC. Advances in Computational Analysis of Metabolomic NMR Data. NMR-BASED METABOLOMICS 2018. [DOI: 10.1039/9781782627937-00310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.
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Affiliation(s)
- Timothy M. D. Ebbels
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Andrea Rodriguez-Martinez
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Marc-Emmanuel Dumas
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Hector C. Keun
- Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London W12 0NN UK
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19
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Hoijemberg PA, Pelczer I. Fast Metabolite Identification in Nuclear Magnetic Resonance Metabolomic Studies: Statistical Peak Sorting and Peak Overlap Detection for More Reliable Database Queries. J Proteome Res 2017; 17:392-401. [PMID: 29135266 DOI: 10.1021/acs.jproteome.7b00617] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
A lot of time is spent by researchers in the identification of metabolites in NMR-based metabolomic studies. The usual metabolite identification starts employing public or commercial databases to match chemical shifts thought to belong to a given compound. Statistical total correlation spectroscopy (STOCSY), in use for more than a decade, speeds the process by finding statistical correlations among peaks, being able to create a better peak list as input for the database query. However, the (normally not automated) analysis becomes challenging due to the intrinsic issue of peak overlap, where correlations of more than one compound appear in the STOCSY trace. Here we present a fully automated methodology that analyzes all STOCSY traces at once (every peak is chosen as driver peak) and overcomes the peak overlap obstacle. Peak overlap detection by clustering analysis and sorting of traces (POD-CAST) first creates an overlap matrix from the STOCSY traces, then clusters the overlap traces based on their similarity and finally calculates a cumulative overlap index (COI) to account for both strong and intermediate correlations. This information is gathered in one plot to help the user identify the groups of peaks that would belong to a single molecule and perform a more reliable database query. The simultaneous examination of all traces reduces the time of analysis, compared to viewing STOCSY traces by pairs or small groups, and condenses the redundant information in the 2D STOCSY matrix into bands containing similar traces. The COI helps in the detection of overlapping peaks, which can be added to the peak list from another cross-correlated band. POD-CAST overcomes the generally overlooked and underestimated presence of overlapping peaks and it detects them to include them in the search of all compounds contributing to the peak overlap, enabling the user to accelerate the metabolite identification process with more successful database queries and searching all tentative compounds in the sample set.
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Affiliation(s)
- Pablo A Hoijemberg
- Department of Chemistry, Frick Chemistry Laboratory, Princeton University , Princeton, New Jersey 08544, United States.,NMR Group, Centro de Investigaciones en Bionanociencias, CIBION-CONICET, Polo Científico Tecnológico , 1425 Ciudad Autónoma de Buenos Aires, Argentina
| | - István Pelczer
- Department of Chemistry, Frick Chemistry Laboratory, Princeton University , Princeton, New Jersey 08544, United States
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20
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Wang C, He L, Li DW, Bruschweiler-Li L, Marshall AG, Brüschweiler R. Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry. J Proteome Res 2017; 16:3774-3786. [PMID: 28795575 DOI: 10.1021/acs.jproteome.7b00457] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Metabolite identification in metabolomics samples is a key step that critically impacts downstream analysis. We recently introduced the SUMMIT NMR/mass spectrometry (MS) hybrid approach for the identification of the molecular structure of unknown metabolites based on the combination of NMR, MS, and combinatorial cheminformatics. Here, we demonstrate the feasibility of the approach for an untargeted analysis of both a model mixture and E. coli cell lysate based on 2D/3D NMR experiments in combination with Fourier transform ion cyclotron resonance MS and MS/MS data. For 19 of the 25 model metabolites, SUMMIT yielded complete structures that matched those in the mixture independent of database information. Of those, seven top-ranked structures matched those in the mixture, and four of those were further validated by positive ion MS/MS. For five metabolites, not part of the 19 metabolites, correct molecular structural motifs could be identified. For E. coli, SUMMIT MS/NMR identified 20 previously known metabolites with three or more 1H spins independent of database information. Moreover, for 15 unknown metabolites, molecular structural fragments were determined consistent with their spin systems and chemical shifts. By providing structural information for entire metabolites or molecular fragments, SUMMIT MS/NMR greatly assists the targeted or untargeted analysis of complex mixtures of unknown compounds.
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Affiliation(s)
| | - Lidong He
- Department of Chemistry and Biochemistry, Florida State University , Tallahassee, Florida 32306, United States
| | | | | | - Alan G Marshall
- Department of Chemistry and Biochemistry, Florida State University , Tallahassee, Florida 32306, United States.,Ion Cyclotron Resonance Program, The National High Magnetic Field Laboratory, Florida State University , Tallahassee, Florida 32310, United States
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21
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Zhang B, Yuan J, Brüschweiler R. Differential Attenuation of NMR Signals by Complementary Ion-Exchange Resin Beads for De Novo Analysis of Complex Metabolomics Mixtures. Chemistry 2017; 23:9239-9243. [PMID: 28523725 DOI: 10.1002/chem.201701572] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Indexed: 11/06/2022]
Abstract
A primary goal of metabolomics is the characterization of a potentially very large number of metabolites that are part of complex mixtures. Application to biofluids and tissue samples offers insights into biochemical metabolic pathways and their role in health and disease. 1D 1 H and 2D 13 C-1 H HSQC NMR spectra are most commonly used for this purpose. They yield quantitative information about each proton of the mixture, but do not tell which protons belong to the same molecule. Interpretation requires the use of NMR spectral databases, which naturally limits these investigations to known metabolites. Here, a new method is presented that uses complementary ion exchange resin beads to differentially attenuate 2D NMR cross-peaks that belong to different metabolites. Based on their characteristic attenuation patterns, cross-peaks could be clustered and assigned to individual molecules, including unknown metabolites with multiple spin systems, as demonstrated for a metabolite model mixture and E. coli cell lysate.
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Affiliation(s)
- Bo Zhang
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio, 43210, USA
| | - Jiaqi Yuan
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio, 43210, USA
| | - Rafael Brüschweiler
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio, 43210, USA.,Campus Chemical Instrument Center, The Ohio State University, 460 W 12th Avenue, Columbus, Ohio, 43210, USA.,Department of Biological Chemistry and Pharmacology, The Ohio State University, 1645 Neil Avenue, Columbus, Ohio, 43210, USA
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22
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Guduff L, Kuprov I, van Heijenoort C, Dumez JN. Spatially encoded 2D and 3D diffusion-ordered NMR spectroscopy. Chem Commun (Camb) 2017; 53:701-704. [DOI: 10.1039/c6cc09028a] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The separation of 2D spectra of components in mixtures is accelerated with spatial encoding.
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Affiliation(s)
- Ludmilla Guduff
- Institut de Chimie des Substances Naturelles
- CNRS UPR2301
- Univ. Paris Sud
- Université Paris-Saclay
- 91190 Gif-sur-Yvette
| | - Ilya Kuprov
- School of Chemistry
- University of Southampton
- Southampton SO17 1BJ
- UK
| | - Carine van Heijenoort
- Institut de Chimie des Substances Naturelles
- CNRS UPR2301
- Univ. Paris Sud
- Université Paris-Saclay
- 91190 Gif-sur-Yvette
| | - Jean-Nicolas Dumez
- Institut de Chimie des Substances Naturelles
- CNRS UPR2301
- Univ. Paris Sud
- Université Paris-Saclay
- 91190 Gif-sur-Yvette
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23
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Diez-Castellnou M, Salvia MV, Springhetti S, Rastrelli F, Mancin F. Nanoparticle-Assisted Affinity NMR Spectroscopy: High Sensitivity Detection and Identification of Organic Molecules. Chemistry 2016; 22:16957-16963. [DOI: 10.1002/chem.201603578] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Indexed: 11/06/2022]
Affiliation(s)
- Marta Diez-Castellnou
- Dipartimento di Scienze Chimiche; Università degli Studi di Padova; via Marzolo 1 35131 Padova Italy
| | - Marie-Virginie Salvia
- Dipartimento di Scienze Chimiche; Università degli Studi di Padova; via Marzolo 1 35131 Padova Italy
- Laboratoire d'Excellence “CORAIL”; Université de Perpignan; 58 Avenue Paul Alduy 66860 Perpignan Cedex France
| | - Sara Springhetti
- Dipartimento di Scienze Chimiche; Università degli Studi di Padova; via Marzolo 1 35131 Padova Italy
| | - Federico Rastrelli
- Dipartimento di Scienze Chimiche; Università degli Studi di Padova; via Marzolo 1 35131 Padova Italy
| | - Fabrizio Mancin
- Dipartimento di Scienze Chimiche; Università degli Studi di Padova; via Marzolo 1 35131 Padova Italy
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24
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Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, Wishart DS. The future of NMR-based metabolomics. Curr Opin Biotechnol 2016; 43:34-40. [PMID: 27580257 DOI: 10.1016/j.copbio.2016.08.001] [Citation(s) in RCA: 518] [Impact Index Per Article: 64.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 08/05/2016] [Accepted: 08/08/2016] [Indexed: 12/15/2022]
Abstract
The two leading analytical approaches to metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Although currently overshadowed by MS in terms of numbers of compounds resolved, NMR spectroscopy offers advantages both on its own and coupled with MS. NMR data are highly reproducible and quantitative over a wide dynamic range and are unmatched for determining structures of unknowns. NMR is adept at tracing metabolic pathways and fluxes using isotope labels. Moreover, NMR is non-destructive and can be utilized in vivo. NMR results have a proven track record of translating in vitro findings to in vivo clinical applications.
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Affiliation(s)
- John L Markley
- Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI 53706, USA.
| | - Rafael Brüschweiler
- Department of Chemistry & Biochemistry, The Ohio State University, 151 W. Woodruff Ave., Columbus, OH 43210, USA; Department of Biological Chemistry & Pharmacology, The Ohio State University, 151 W. Woodruff Ave., Columbus, OH 43210, USA
| | - Arthur S Edison
- Department of Genetics and Biochemistry, Institute of Bioinformatics and Complex Carbohydrate Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, USA
| | - Hamid R Eghbalnia
- Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI 53706, USA
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, 722 Hamilton Hall, Lincoln, NE 68588, USA
| | - Daniel Raftery
- Department of Anesthesiology & Pain Medicine, 850 Republican St, University of Washington, Seattle, WA 98109, USA
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8; Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8
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25
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Hao J, Liebeke M, Sommer U, Viant MR, Bundy JG, Ebbels TMD. Statistical Correlations between NMR Spectroscopy and Direct Infusion FT-ICR Mass Spectrometry Aid Annotation of Unknowns in Metabolomics. Anal Chem 2016; 88:2583-9. [PMID: 26824414 DOI: 10.1021/acs.analchem.5b02889] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
NMR spectroscopy and mass spectrometry are the two major analytical platforms for metabolomics, and both generate substantial data with hundreds to thousands of observed peaks for a single sample. Many of these are unknown, and peak assignment is generally complex and time-consuming. Statistical correlations between data types have proven useful in expediting this process, for example, in prioritizing candidate assignments. However, this approach has not been formally assessed for the comparison of direct-infusion mass spectrometry (DIMS) and NMR data. Here, we present a systematic analysis of a sample set (tissue extracts), and the utility of a simple correlation threshold to aid metabolite identification. The correlations were surprisingly successful in linking structurally related signals, with 15 of 26 NMR-detectable metabolites having their highest correlation to a cognate MS ion. However, we found that the distribution of the correlations was highly dependent on the nature of the MS ion, such as the adduct type. This approach should help to alleviate this important bottleneck where both 1D NMR and DIMS data sets have been collected.
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Affiliation(s)
- Jie Hao
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, United Kingdom
| | - Manuel Liebeke
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, United Kingdom
| | - Ulf Sommer
- NERC Biomolecular Analysis Facility - Metabolomics Node (NBAF-B), School of Biosciences, University of Birmingham , Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Mark R Viant
- NERC Biomolecular Analysis Facility - Metabolomics Node (NBAF-B), School of Biosciences, University of Birmingham , Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Jacob G Bundy
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, United Kingdom
| | - Timothy M D Ebbels
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, United Kingdom
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26
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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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27
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Puchades-Carrasco L, Palomino-Schätzlein M, Pérez-Rambla C, Pineda-Lucena A. Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers. Brief Bioinform 2015; 17:541-52. [PMID: 26342127 DOI: 10.1093/bib/bbv077] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Indexed: 12/29/2022] Open
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28
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Larive CK, Barding GA, Dinges MM. NMR spectroscopy for metabolomics and metabolic profiling. Anal Chem 2014; 87:133-46. [PMID: 25375201 DOI: 10.1021/ac504075g] [Citation(s) in RCA: 151] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Cynthia K Larive
- Department of Chemistry, University of California-Riverside , Riverside, California 92521, United States
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29
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Potential of metabolomics in preclinical and clinical drug development. Pharmacol Rep 2014; 66:956-63. [PMID: 25443721 DOI: 10.1016/j.pharep.2014.06.010] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2014] [Revised: 06/03/2014] [Accepted: 06/10/2014] [Indexed: 12/29/2022]
Abstract
Metabolomics is an upcoming technology system which involves detailed experimental analysis of metabolic profiles. Due to its diverse applications in preclinical and clinical research, it became an useful tool for the drug discovery and drug development process. This review covers the brief outline about the instrumentation and interpretation of metabolic profiles. The applications of metabolomics have a considerable scope in the pharmaceutical industry, almost at each step from drug discovery to clinical development. These include finding drug target, potential safety and efficacy biomarkers and mechanisms of drug action, the validation of preclinical experimental models against human disease profiles, and the discovery of clinical safety and efficacy biomarkers. As we all know, nowadays the drug discovery and development process is a very expensive, and risky business. Failures at any stage of drug discovery and development process cost millions of dollars to the companies. Some of these failures or the associated risks could be prevented or minimized if there were better ways of drug screening, drug toxicity profiling and monitoring adverse drug reactions. Metabolomics potentially offers an effective route to address all the issues associated with the drug discovery and development.
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30
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Gowda GAN, Raftery D. Quantitating metabolites in protein precipitated serum using NMR spectroscopy. Anal Chem 2014; 86:5433-40. [PMID: 24796490 PMCID: PMC4045325 DOI: 10.1021/ac5005103] [Citation(s) in RCA: 135] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2014] [Accepted: 05/05/2014] [Indexed: 01/04/2023]
Abstract
Quantitative NMR-based metabolite profiling is challenged by the deleterious effects of abundant proteins in the intact blood plasma/serum, which underscores the need for alternative approaches. Protein removal by ultrafiltration using low molecular weight cutoff filters thus represents an important step. However, protein precipitation, an alternative and simple approach for protein removal, lacks detailed quantitative assessment for use in NMR based metabolomics. In this study, we have comprehensively evaluated the performance of protein precipitation using methanol, acetonitrile, perchloric acid, and trichloroacetic acid and ultrafiltration approaches using 1D and 2D NMR, based on the identification and absolute quantitation of 44 human blood metabolites, including a few identified for the first time in the NMR spectra of human serum. We also investigated the use of a "smart isotope tag," (15)N-cholamine for further resolution enhancement, which resulted in the detection of a number of additional metabolites. (1)H NMR of both protein precipitated and ultrafiltered serum detected all 44 metabolites with comparable reproducibility (average CV, 3.7% for precipitation; 3.6% for filtration). However, nearly half of the quantified metabolites in ultrafiltered serum exhibited 10-74% lower concentrations; specifically, tryptophan, benzoate, and 2-oxoisocaproate showed much lower concentrations compared to protein precipitated serum. These results indicate that protein precipitation using methanol offers a reliable approach for routine NMR-based metabolomics of human blood serum/plasma and should be considered as an alternative to ultrafiltration. Importantly, protein precipitation, which is commonly used by mass spectrometry (MS), promises avenues for direct comparison and correlation of metabolite data obtained from the two analytical platforms to exploit their combined strength in the metabolomics of blood.
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Affiliation(s)
- G. A. Nagana Gowda
- Northwest Metabolomics
Research Center, Anesthesiology and Pain Medicine, and Department of
Chemistry, University of Washington, Seattle, Washington 98109, United States
| | - Daniel Raftery
- Northwest Metabolomics
Research Center, Anesthesiology and Pain Medicine, and Department of
Chemistry, University of Washington, Seattle, Washington 98109, United States
- Fred Hutchinson
Cancer Research Center, Seattle, Washington 98109, United States
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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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Gu H, Gowda GAN, Neto FC, Opp MR, Raftery D. RAMSY: ratio analysis of mass spectrometry to improve compound identification. Anal Chem 2013; 85:10771-9. [PMID: 24168717 DOI: 10.1021/ac4019268] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The complexity of biological samples poses a major challenge for reliable compound identification in mass spectrometry (MS). The presence of interfering compounds that cause additional peaks in the spectrum can make interpretation and assignment difficult. To overcome this issue, new approaches are needed to reduce complexity and simplify spectral interpretation. Recently, focused on unknown metabolite identification, we presented a new approach, RANSY (ratio analysis of nuclear magnetic resonance spectroscopy; Anal. Chem. 2011, 83, 7616-7623), which extracts the (1)H signals related to the same metabolite based on peak intensity ratios. On the basis of this concept, we present the ratio analysis of mass spectrometry (RAMSY) method, which facilitates improved compound identification in complex MS spectra. RAMSY works on the principle that, under a given set of experimental conditions, the abundance/intensity ratios between the mass fragments from the same metabolite are relatively constant. Therefore, the quotients of average peak ratios and their standard deviations, generated using a small set of MS spectra from the same ion chromatogram, efficiently allow the statistical recovery of the metabolite peaks and facilitate reliable identification. RAMSY was applied to both gas chromatography/MS and liquid chromatography tandem MS (LC-MS/MS) data to demonstrate its utility. The performance of RAMSY is typically better than the results from correlation methods. RAMSY promises to improve unknown metabolite identification for MS users in metabolomics or other fields.
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Affiliation(s)
- Haiwei Gu
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican St., Seattle, WA 98109, United States
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Wang B, Shi Z, Weber GF, Kennedy MA. Introduction of a new critical p value correction method for statistical significance analysis of metabonomics data. Anal Bioanal Chem 2013; 405:8419-29. [PMID: 24026514 DOI: 10.1007/s00216-013-7284-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2013] [Revised: 07/16/2013] [Accepted: 07/30/2013] [Indexed: 01/22/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy-based metabonomics is of growing importance for discovery of human disease biomarkers. Identification and validation of disease biomarkers using statistical significance analysis (SSA) is critical for translation to clinical practice. SSA is performed by assessing a null hypothesis test using a derivative of the Student's t test, e.g., a Welch's t test. Choosing how to correct the significance level for rejecting null hypotheses in the case of multiple testing to maintain a constant family-wise type I error rate is a common problem in such tests. The multiple testing problem arises because the likelihood of falsely rejecting the null hypothesis, i.e., a false positive, grows as the number of tests applied to the same data set increases. Several methods have been introduced to address this problem. Bonferroni correction (BC) assumes all variables are independent and therefore sacrifices sensitivity for detecting true positives in partially dependent data sets. False discovery rate (FDR) methods are more sensitive than BC but uniformly ascribe highest stringency to lowest p value variables. Here, we introduce standard deviation step down (SDSD), which is more sensitive and appropriate than BC for partially dependent data sets. Sensitivity and type I error rate of SDSD can be adjusted based on the degree of variable dependency. SDSD generates fundamentally different profiles of critical p values compared with FDR methods potentially leading to reduced type II error rates. SDSD is increasingly sensitive for more concentrated metabolites. SDSD is demonstrated using NMR-based metabonomics data collected on three different breast cancer cell line extracts.
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Affiliation(s)
- Bo Wang
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH, 45056, USA
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Barding GA, Orr DJ, Sathnur SM, Larive CK. VIZR--an automated chemometric technique for metabolic profiling. Anal Bioanal Chem 2013; 405:8409-17. [PMID: 23912833 DOI: 10.1007/s00216-013-7254-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Revised: 07/08/2013] [Accepted: 07/11/2013] [Indexed: 11/26/2022]
Abstract
A chemometric technique, visual interpretation of z-score ratios (VIZR), written in the open source code R, has been developed to identify metabolic differences between individual biosamples and a control group. To demonstrate the capabilities of VIZR, 49 urine samples were collected from healthy volunteers: 41 samples were collected randomly following a normal dietary routine and 7 test samples were collected after dietary supplementation with either ibuprofen or alcoholic beverages. An eighth test sample was prepared by 50% dilution of a control sample. Sample analysis was conducted by (1)H nuclear magnetic resonance (NMR) spectroscopy and the collected data were subjected to VIZR analysis, which successfully discriminated each of the 8 test samples from the 41 control samples. In addition, VIZR analysis revealed the NMR spectral regions responsible for the disparity between the individual test samples and the control group. The self-normalizing nature of the VIZR calculation provides a robust analysis independent of dilution effects, which is especially important in urine analyses. Potential applications of VIZR include high-throughput data analysis for toxicological profiling, disease diagnosis, and biomarker identification in any type of biosample for which a control dataset can be established. Although demonstrated herein for the statistical analysis of (1)H NMR data, the VIZR program is platform independent and could be applied to digitized metabolic datasets acquired using other techniques including hyphenated mass spectrometry measurements.
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Affiliation(s)
- Gregory A Barding
- Department of Chemistry, University of California, Riverside, CA, 92521, USA
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Raffler J, Römisch-Margl W, Petersen AK, Pagel P, Blöchl F, Hengstenberg C, Illig T, Meisinger C, Stark K, Wichmann HE, Adamski J, Gieger C, Kastenmüller G, Suhre K. Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma. Genome Med 2013; 5:13. [PMID: 23414815 PMCID: PMC3706909 DOI: 10.1186/gm417] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Revised: 12/21/2012] [Accepted: 02/15/2013] [Indexed: 12/18/2022] Open
Abstract
Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in (1)H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.
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Affiliation(s)
- Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany ; Faculty of Biology, Ludwig-Maximilians-Universität, Großhaderner Straße 2, 82152 Planegg-Martinsried, Germany
| | - Werner Römisch-Margl
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Ann-Kristin Petersen
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Philipp Pagel
- numares GmbH, Josef-Engert-Str. 9, 93053 Regensburg, Germany
| | - Florian Blöchl
- numares GmbH, Josef-Engert-Str. 9, 93053 Regensburg, Germany
| | - Christian Hengstenberg
- Klinik und Poliklinik für Innere Medizin II, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany ; Hannover Unified Biobank, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany
| | - Christa Meisinger
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Klaus Stark
- Klinik und Poliklinik für Innere Medizin II, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Germany ; Department of Epidemiology and Preventive Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Germany
| | - H-Erich Wichmann
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany ; Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, P.O. BOX 24144, Doha, Qatar
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Abstract
Parkinson's disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergic neurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress are linked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolite changes in biofluids, cell lysates, tissues and tumors in order to correlate these metabolomic changes to a disease state. Thus, the application of metabolomics to investigate PD provides a systematic approach to understand the pathology of PD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This review will examine current research into PD mechanisms with a focus on mitochondrial dysfunction and oxidative stress. Neurotoxin-based PD animal models and the rationale for metabolomics studies in PD will also be discussed. The review will also explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis.
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Affiliation(s)
- Shulei Lei
- University of Nebraska-Lincoln, Department of Chemistry, 722
Hamilton Hall, Lincoln, NE 68588-0304
| | - Robert Powers
- University of Nebraska-Lincoln, Department of Chemistry, 722
Hamilton Hall, Lincoln, NE 68588-0304
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Reddy GNM, Caldarelli S. Identification and quantification of EPA 16 priority polycyclic aromatic hydrocarbon pollutants by Maximum-Quantum NMR. Analyst 2012; 137:741-6. [DOI: 10.1039/c1an16047h] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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