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Kumar N, Jaitak V. Recent Advancement in NMR Based Plant Metabolomics: Techniques, Tools, and Analytical Approaches. Crit Rev Anal Chem 2024:1-25. [PMID: 38990786 DOI: 10.1080/10408347.2024.2375314] [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: 07/13/2024]
Abstract
Plant metabolomics, a rapidly advancing field within plant biology, is dedicated to comprehensively exploring the intricate array of small molecules in plant systems. This entails precisely gathering comprehensive chemical data, detecting numerous metabolites, and ensuring accurate molecular identification. Nuclear magnetic resonance (NMR) spectroscopy, with its detailed chemical insights, is crucial in obtaining metabolite profiles. Its widespread application spans various research disciplines, aiding in comprehending chemical reactions, kinetics, and molecule characterization. Biotechnological advancements have further expanded NMR's utility in metabolomics, particularly in identifying disease biomarkers across diverse fields such as agriculture, medicine, and pharmacology. This review covers the stages of NMR-based metabolomics, including historical aspects and limitations, with sample preparation, data acquisition, spectral processing, analysis, and their application parts.
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Affiliation(s)
- Nitish Kumar
- Department of Pharmaceutical Science and Natural Products, Central University of Punjab, Bathinda, India
| | - Vikas Jaitak
- Department of Pharmaceutical Science and Natural Products, Central University of Punjab, Bathinda, India
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2
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Nawrocka EK, Jadwiszczak M, Leszczyński PJ, Kazimierczuk K. Supporting the assignment of NMR spectra with variable-temperature experiments. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2024; 62:479-485. [PMID: 38303612 DOI: 10.1002/mrc.5433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 02/03/2024]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful tools in analytical chemistry. An important step in the analysis of NMR data is the assignment of resonance frequencies to the corresponding atoms in the molecule being investigated. The traditional approach considers the spectrum's characteristic parameters: chemical shift values, internuclear couplings, and peak intensities. In this paper, we show how to support the process of assigning a series of spectra of similar organic compounds by using temperature coefficients, that is, the rates of change in chemical shift values associated with given changes in temperature.
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Affiliation(s)
- Ewa K Nawrocka
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
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3
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Lysak DH, Wolff WW, Soong R, Bermel W, Kupče ER, Jenne A, Biswas RG, Lane D, Gasmi-Seabrook G, Simpson A. Application of 15N-Edited 1H- 13C Correlation NMR Spectroscopy─Toward Fragment-Based Metabolite Identification and Screening via HCN Constructs. Anal Chem 2023; 95:11926-11933. [PMID: 37535003 DOI: 10.1021/acs.analchem.3c01362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
Many key building blocks of life contain nitrogen moieties. Despite the prevalence of nitrogen-containing metabolites in nature, 15N nuclei are seldom used in NMR-based metabolite assignment due to their low natural abundance and lack of comprehensive chemical shift databases. However, with advancements in isotope labeling strategies, 13C and 15N enriched metabolites are becoming more common in metabolomic studies. Simple multidimensional nuclear magnetic resonance (NMR) experiments that correlate 1H and 15N via single bond 1JNH or multiple bond 2-3JNH couplings using heteronuclear single quantum coherence (HSQC) or heteronuclear multiple bond coherence are well established and routinely applied for structure elucidation. However, a 1H-15N correlation spectrum of a metabolite mixture can be difficult to deconvolute, due to the lack of a 15N specific database. In order to bridge this gap, we present here a broadband 15N-edited 1H-13C HSQC NMR experiment that targets metabolites containing 15N moieties. Through this approach, nitrogen-containing metabolites, such as amino acids, nucleotide bases, and nucleosides, are identified based on their 13C, 1H, and 15N chemical shift information. This approach was tested and validated using a [15N, 13C] enriched Daphnia magna (water flea) metabolite extract, where the number of clearly resolved 15N-containing peaks increased from only 11 in a standard HSQC to 51 in the 15N-edited HSQC, and the number of obscured peaks decreased from 59 to just 7. The approach complements the current repertoire of NMR techniques for mixture deconvolution and holds considerable potential for targeted metabolite NMR in 15N, 13C enriched systems.
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Affiliation(s)
- Daniel H Lysak
- University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada
| | - William W Wolff
- University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada
| | - Ronald Soong
- University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada
| | - Wolfgang Bermel
- Bruker BioSpin GmbH, Rudolf-Plank-Str. 23, Ettlingen 76275, Germany
| | | | - Amy Jenne
- University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada
| | - Rajshree Ghosh Biswas
- University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada
| | - Daniel Lane
- University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada
| | | | - Andre Simpson
- University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada
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4
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Liu W, Zhang L, Bao L, Shen G, Feng J. Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure. J Proteome Res 2023; 22:758-767. [PMID: 36710647 DOI: 10.1021/acs.jproteome.2c00488] [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: 01/31/2023]
Abstract
The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.
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Affiliation(s)
- Wuping Liu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
| | - Lirong Zhang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
| | - Lijun Bao
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
| | - Guiping Shen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
| | - Jianghua Feng
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
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5
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Judge MT, Ebbels TMD. Problems, principles and progress in computational annotation of NMR metabolomics data. Metabolomics 2022; 18:102. [PMID: 36469142 PMCID: PMC9722819 DOI: 10.1007/s11306-022-01962-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/18/2022] [Indexed: 12/08/2022]
Abstract
BACKGROUND Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for 1H 1-dimensional (1H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards. AIM OF REVIEW This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions. KEY SCIENTIFIC CONCEPTS OF REVIEW We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.
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Affiliation(s)
- Michael T Judge
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, 131 Sir Alexander Fleming Building, South Kensington Campus, London, UK
| | - Timothy M D Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, 131 Sir Alexander Fleming Building, South Kensington Campus, London, UK.
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Bhinderwala F, Vu T, Smith TG, Kosacki J, Marshall DD, Xu Y, Morton M, Powers R. Leveraging the HMBC to Facilitate Metabolite Identification. Anal Chem 2022; 94:16308-16318. [PMID: 36374521 PMCID: PMC10948112 DOI: 10.1021/acs.analchem.2c02902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.
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Affiliation(s)
- Fatema Bhinderwala
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, United States
| | - Thao Vu
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska 68583-0963, United States
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045-2609
| | - Thomas G. Smith
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
| | - Julian Kosacki
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
| | - Darrell D. Marshall
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
| | - Yuhang Xu
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska 68583-0963, United States
- Department of Applied Statistics and Operations Research, Bowling Green State University, Bowling Green, Ohio 43403-0001
| | - Martha Morton
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln Nebraska 68588-0304
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7
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Sobolev AP, Ingallina C, Spano M, Di Matteo G, Mannina L. NMR-Based Approaches in the Study of Foods. Molecules 2022; 27:7906. [PMID: 36432006 PMCID: PMC9697393 DOI: 10.3390/molecules27227906] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/07/2022] [Accepted: 11/14/2022] [Indexed: 11/17/2022] Open
Abstract
In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.
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Affiliation(s)
- Anatoly P. Sobolev
- Magnetic Resonance Laboratory “Segre-Capitani”, Institute for Biological Systems, CNR, Via Salaria, Km 29.300, 00015 Monterotondo, Italy
| | - Cinzia Ingallina
- Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Mattia Spano
- Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Giacomo Di Matteo
- Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Luisa Mannina
- Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
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8
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Nawrocka EK, Urbańczyk M, Koziński K, Kazimierczuk K. Variable-temperature NMR spectroscopy for metabolite identification in biological materials. RSC Adv 2021; 11:35321-35325. [PMID: 35493175 PMCID: PMC9043013 DOI: 10.1039/d1ra05626c] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/21/2021] [Indexed: 01/28/2023] Open
Abstract
Nuclear magnetic resonance is a "workhorse technique" used in metabolomics, complementary to mass spectrometry. Unfortunately, only the most basic NMR methods are sensitive enough to allow fast medical screening. The most common of them, a simple 1H NMR, suffers from low dispersion of resonance frequencies, which often hampers the identification of metabolites. In this article we show that 1H NMR spectra contain previously overlooked parameters potentially helpful in metabolite identification, namely the rates of temperature-induced changes of chemical shifts. We prove that they are reproducible between various metabolite mixtures and can be determined quickly when Radon transform is used to process the data.
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Affiliation(s)
- Ewa K Nawrocka
- Centre of New Technologies, University of Warsaw ul. Banacha 2C 02-097 Warsaw Poland
- Faculty of Chemistry, University of Warsaw ul. Pasteura 1 02-093 Warsaw Poland
| | - Mateusz Urbańczyk
- Centre of New Technologies, University of Warsaw ul. Banacha 2C 02-097 Warsaw Poland
- Institute of Physical Chemistry, Polish Academy of Sciences Kasprzaka 44/52 01-224 Warsaw Poland
| | - Kamil Koziński
- Centre of New Technologies, University of Warsaw ul. Banacha 2C 02-097 Warsaw Poland
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9
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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10
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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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11
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Bhinderwala F, Evans P, Jones K, Laws BR, Smith T, Morton M, Powers R. Phosphorus NMR and Its Application to Metabolomics. Anal Chem 2020; 92:9536-9545. [PMID: 32530272 PMCID: PMC8327684 DOI: 10.1021/acs.analchem.0c00591] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphorus is a naturally abundant and NMR active isotope that does not require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments, but 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorus-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provide a measure of a cell's metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N-, and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N-, and 31P-labeled metabolites from a single NMR sample, and a data set of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.
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Affiliation(s)
- Fatema Bhinderwala
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln NE 68588-0304
| | - Paula Evans
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
| | - Kaleb Jones
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
| | - Benjamin R. Laws
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
| | - Thomas Smith
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln NE 68588-0304
| | - Martha Morton
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln NE 68588-0304
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln NE 68588-0304
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12
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Charris-Molina A, Riquelme G, Burdisso P, Hoijemberg PA. Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search. J Proteome Res 2020; 19:2977-2988. [PMID: 32450699 DOI: 10.1021/acs.jproteome.9b00872] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D 1H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D 1H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D 1H J-resolved (JRES) spectra, p-JRES, which are similar to 1D 1H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to "ConQuer ABC", which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.
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Affiliation(s)
- Andrés Charris-Molina
- Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina.,CIBION-CONICET, Centro de Investigaciones en Bionanociencias, NMR Group, Polo Científico Tecnológico, Ciudad Autónoma de Buenos Aires, Buenos Aires C1425FQD, Argentina
| | - Gabriel Riquelme
- Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina.,CIBION-CONICET, Centro de Investigaciones en Bionanociencias, NMR Group, Polo Científico Tecnológico, Ciudad Autónoma de Buenos Aires, Buenos Aires C1425FQD, 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, Buenos Aires C1425FQD, Argentina.,ECyT-UNSAM, 25 de Mayo y Francia, San Martín, Buenos Aires B1650HMP, Argentina
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13
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Salem MA, Perez de Souza L, Serag A, Fernie AR, Farag MA, Ezzat SM, Alseekh S. Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis. Metabolites 2020; 10:E37. [PMID: 31952212 PMCID: PMC7023240 DOI: 10.3390/metabo10010037] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 12/25/2019] [Accepted: 01/11/2020] [Indexed: 12/22/2022] Open
Abstract
Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating every single compound from a natural extract is not always possible due to the complex chemistry and presence of most secondary metabolites at very low levels. Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used to comprehensively annotate the constituents of plant natural products for screening, drug discovery as well as for quality control purposes such as those required for phytomedicine. In this review, the current advancements in plant sample preparation, sample measurements, and data analysis are presented alongside a few case studies of the successful applications of these processes in plant natural product drug discovery.
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Affiliation(s)
- Mohamed A. Salem
- Department of Pharmacognosy, Faculty of Pharmacy, Menoufia University, Gamal Abd El Nasr st., Shibin Elkom, Menoufia 32511, Egypt
| | - Leonardo Perez de Souza
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; (L.P.d.S.); (A.R.F.)
| | - Ahmed Serag
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, Cairo 11751, Egypt;
| | - Alisdair R. Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; (L.P.d.S.); (A.R.F.)
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv 4000, Bulgaria
| | - Mohamed A. Farag
- Pharmacognosy Department, Faculty of Pharmacy, Cairo University, Cairo 11562, Egypt; (M.A.F.); (S.M.E.)
- Chemistry Department, School of Sciences & Engineering, The American University in Cairo, New Cairo 11835, Egypt
| | - Shahira M. Ezzat
- Pharmacognosy Department, Faculty of Pharmacy, Cairo University, Cairo 11562, Egypt; (M.A.F.); (S.M.E.)
- Department of Pharmacognosy, Faculty of Pharmacy, October University for Modern Sciences and Arts (MSA), Giza 11787, Egypt
| | - Saleh Alseekh
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; (L.P.d.S.); (A.R.F.)
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv 4000, Bulgaria
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14
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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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15
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Bell M, Blais JM. "-Omics" workflow for paleolimnological and geological archives: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 672:438-455. [PMID: 30965259 DOI: 10.1016/j.scitotenv.2019.03.477] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 03/29/2019] [Accepted: 03/30/2019] [Indexed: 06/09/2023]
Abstract
"-Omics" is a powerful screening method with applications in molecular biology, toxicology, wildlife biology, natural product discovery, and many other fields. Genomics, proteomics, metabolomics, and lipidomics are common examples included under the "-omics" umbrella. This screening method uses combinations of untargeted, semi-targeted, and targeted analyses paired with data mining to facilitate researchers' understanding of the genome, proteins, and small organic molecules in biological systems. Recently, however, the use of "-omics" has expanded into the fields of geology, specifically petrology, and paleolimnology. Specifically, untargeted analyses stand to transform these fields as petroleomics, and sediment-"omics" become more prevalent. "-Omics" facilitates the visualization of small molecule profiles from environmental matrices (i.e. oil and sediment). Small molecule profiles can provide improved understanding of small molecules distributions throughout the environment, and how those compositions can change depending on conditions (i.e. climate change, weathering, etc.). "-Omics" also facilities discovery of next-generation biomarkers that can be used for oil source identification and as proxies for reconstructing past environmental changes. Untargeted analyses paired with data mining and multivariate statistical analyses represents a powerful suite of tools for hypothesis generation, and new method development for environmental reconstructions. Here we present an introduction to "-omics" methodology, technical terms, and examples of applications to paleolimnology and petrology. The purpose of this review is to highlight the important considerations at each step in the "-omics" workflow to produce high quality and statistically powerful data for petrological and paleolimnological applications.
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Affiliation(s)
- Madison Bell
- Laboratory for the Analysis of Natural and Synthetic Environmental Toxicants, Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Jules M Blais
- Laboratory for the Analysis of Natural and Synthetic Environmental Toxicants, Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
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16
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Geier FM, Leroi AM, Bundy JG. 13C Labeling of Nematode Worms to Improve Metabolome Coverage by Heteronuclear Nuclear Magnetic Resonance Experiments. Front Mol Biosci 2019; 6:27. [PMID: 31106208 PMCID: PMC6498324 DOI: 10.3389/fmolb.2019.00027] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Accepted: 04/04/2019] [Indexed: 11/29/2022] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is widely used as a metabolomics tool, and 1D spectroscopy is overwhelmingly the commonest approach. The use of 2D spectroscopy could offer significant advantages in terms of increased spectral dispersion of peaks, but has a number of disadvantages—in particular, heteronuclear 2D spectroscopy is often much less sensitive than 1D NMR. One factor contributing to this low sensitivity in 13C/1H heteronuclear NMR is the low natural abundance of the 13C stable isotope; as a consequence, where it is possible to label biological material with 13C, there is a potential enhancement of sensitivity of up to around 90fold. However, there are some problems that can reduce the advantages otherwise gained—in particular, the fine structure arising from 13C/13C coupling, which is essentially non-existent at natural abundance, can reduce the possible sensitivity gain and increase the chances of peak overlap. Here, we examined the use of two different heteronuclear single quantum coherence (HSQC) pulse sequences for the analysis of fully 13C-labeled tissue extracts from Caenorhabditis elegans nematodes. The constant time ct-HSQC had improved peak shape, and consequent better peak detection of metabolites from a labeled extract; matching this against reference spectra from the HMDB gave a match to about 300 records (although fewer actual metabolites, as some of these represent false positive matches). This approach gives a rapid and automated initial metabolome assignment, forming an ideal basis for further manual curation.
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Affiliation(s)
- Florian M Geier
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Armand M Leroi
- Department of Life Sciences, Imperial College London, South Kensington, London, United Kingdom
| | - Jacob G Bundy
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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17
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Yamada S, Ito K, Kurotani A, Yamada Y, Chikayama E, Kikuchi J. InterSpin: Integrated Supportive Webtools for Low- and High-Field NMR Analyses Toward Molecular Complexity. ACS OMEGA 2019; 4:3361-3369. [PMID: 31459550 PMCID: PMC6648201 DOI: 10.1021/acsomega.8b02714] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/24/2018] [Indexed: 05/06/2023]
Abstract
InterSpin (http://dmar.riken.jp/interspin/) comprises integrated, supportive, and freely accessible preprocessing webtools and a database to advance signal assignment in low- and high-field NMR analyses of molecular complexities ranging from small molecules to macromolecules for food, material, and environmental applications. To support handling of the broad spectra obtained from solid-state NMR or low-field benchtop NMR, we have developed and evaluated two preprocessing tools: sensitivity improvement with spectral integration, which enhances the signal-to-noise ratio by spectral integration, and peaks separation, which separates overlapping peaks by several algorithms, such as non-negative sparse coding. In addition, the InterSpin Laboratory Information Management System (SpinLIMS) database stores numerous standard spectra ranging from small molecules to macromolecules in solid and solution states (dissolved in polar/nonpolar solvents), and can be searched under various conditions using the following molecular assignment tools. SpinMacro supports easy assignment of macromolecules in natural mixtures via solid-state 13C peaks and dimethyl sulfoxide-dissolved 1H-13C correlation peaks. InterAnalysis improves the accuracy of molecular assignment by integrated analysis of 1H-13C correlation peaks and 1H-J correlation peaks of small molecules dissolved in D2O or deuterated methanol, which supports easy narrowing down of metabolite candidates. Finally, by enabling database interoperability, SpinLIMS's client software will ultimately support scientific discovery by facilitating sharing and reusing of NMR data.
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Affiliation(s)
- Shunji Yamada
- Graduate
School of Bioagricultural Sciences, Nagoya
University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kengo Ito
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Atsushi Kurotani
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yutaka Yamada
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Eisuke Chikayama
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Department
of Information Systems, Niigata University
of International and Information Studies, 3-1-1 Mizukino, Nishi-ku, Niigata-shi, Niigata 950-2292, Japan
| | - Jun Kikuchi
- Graduate
School of Bioagricultural Sciences, Nagoya
University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- E-mail: . Phone/Fax: +81-544039439
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18
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Edison AS, Le Guennec A, Delaglio F, Kupče Ē. Practical Guidelines for 13C-Based NMR Metabolomics. Methods Mol Biol 2019; 2037:69-95. [PMID: 31463840 DOI: 10.1007/978-1-4939-9690-2_5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
We present an overview of 13C-based NMR metabolomics. At first glance, the low sensitivity of 13C relative to 1H NMR might seem like too great an obstacle to use this approach. However, there are several advantages to 13C NMR, whether samples can be isotopically enriched or not. At natural abundance, peaks are sharp and largely resolved, and peak frequencies are more stable to pH and other sample conditions. Statistical approaches can be used to obtain C-C and C-H correlation maps, which greatly aid in compound identification. With 13C isotopic enrichment, other experiments are possible, including both 13C-J-RES and INADEQUATE, which can be used for de novo identification of metabolites not in databases.NMR instrumentation and software has significantly improved, and probes are now commercially available that can record useful natural abundance 1D 13C spectra from real metabolomics samples in 2 h or less. Probe technology continues to improve, and the next generation should be even better. Combined with new methods of simultaneous data acquisition, which allows for two or more 1D or 2D NMR experiments to be collected using multiple receivers, very rich datasets can be collected in a reasonable amount of time that should improve metabolomics data analysis and compound identification.
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Affiliation(s)
- Arthur S Edison
- Department of Biochemistry, University of Georgia, Athens, GA, USA. .,Department of Genetics, University of Georgia, Athens, GA, USA. .,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA.
| | - Adrien Le Guennec
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA.,NMR Facility, Guy's Campus, King's College London, London, UK
| | - Frank Delaglio
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, University of Maryland, Rockville, MD, USA
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19
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Asakura T, Date Y, Kikuchi J. Application of ensemble deep neural network to metabolomics studies. Anal Chim Acta 2018; 1037:230-236. [DOI: 10.1016/j.aca.2018.02.045] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 02/05/2018] [Accepted: 02/10/2018] [Indexed: 10/18/2022]
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20
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Wolfender JL, Nuzillard JM, van der Hooft JJJ, Renault JH, Bertrand S. Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics. Anal Chem 2018; 91:704-742. [DOI: 10.1021/acs.analchem.8b05112] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Jean-Luc Wolfender
- School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, CMU, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland
| | - Jean-Marc Nuzillard
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, Université de Reims Champagne Ardenne, 51687 Reims Cedex 2, France
| | | | - Jean-Hugues Renault
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, Université de Reims Champagne Ardenne, 51687 Reims Cedex 2, France
| | - Samuel Bertrand
- Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes, 44035 Nantes, France
- ThalassOMICS Metabolomics Facility, Plateforme Corsaire, Biogenouest, 44035 Nantes, France
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21
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Bingol K. Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods. High Throughput 2018; 7:E9. [PMID: 29670016 PMCID: PMC6023270 DOI: 10.3390/ht7020009] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 04/09/2018] [Accepted: 04/13/2018] [Indexed: 12/23/2022] Open
Abstract
Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.
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Affiliation(s)
- Kerem Bingol
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
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22
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Bhinderwala F, Lonergan S, Woods J, Zhou C, Fey PD, Powers R. Expanding the Coverage of the Metabolome with Nitrogen-Based NMR. Anal Chem 2018; 90:4521-4528. [PMID: 29505241 DOI: 10.1021/acs.analchem.7b04922] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Isotopically labeling a metabolite and tracing its metabolic fate has provided invaluable insights about the role of metabolism in human diseases in addition to a variety of other issues. 13C-labeled metabolite tracers or unlabeled 1H-based NMR experiments are currently the most common application of NMR to metabolomics studies. Unfortunately, the coverage of the metabolome has been consequently limited to the most abundant carbon-containing metabolites. To expand the coverage of the metabolome and enhance the impact of metabolomics studies, we present a protocol for 15N-labeled metabolite tracer experiments that may also be combined with routine 13C tracer experiments to simultaneously detect both 15N- and 13C-labeled metabolites in metabolic samples. A database consisting of 2D 1H-15N HSQC natural-abundance spectra of 50 nitrogen-containing metabolites are also presented to facilitate the assignment of 15N-labeled metabolites. The methodology is demonstrated by labeling Escherichia coli and Staphylococcus aureus metabolomes with 15N1-ammonium chloride, 15N4-arginine, and 13C2-acetate. Efficient 15N and 13C metabolite labeling and identification were achieved utilizing standard cell culture and sample preparation protocols.
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Affiliation(s)
| | | | | | - Chunyi Zhou
- Center for Staphylococcal Research, Department of Pathology and Microbiology , University of Nebraska Medical Center , Omaha , Nebraska 68198-5900 , United States
| | - Paul D Fey
- Center for Staphylococcal Research, Department of Pathology and Microbiology , University of Nebraska Medical Center , Omaha , Nebraska 68198-5900 , United States
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23
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Shiokawa Y, Date Y, Kikuchi J. Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet. Sci Rep 2018; 8:3426. [PMID: 29467421 PMCID: PMC5821832 DOI: 10.1038/s41598-018-20121-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 01/08/2018] [Indexed: 12/13/2022] Open
Abstract
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general “linear” multivariate analysis alone limits interpretations due to “non-linear” variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input–output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.
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Affiliation(s)
- Yuka Shiokawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
| | - Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan. .,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan. .,Graduate School of Bioagricultural Sciences and School of Agricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
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24
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Kikuchi J, Ito K, Date Y. Environmental metabolomics with data science for investigating ecosystem homeostasis. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 104:56-88. [PMID: 29405981 DOI: 10.1016/j.pnmrs.2017.11.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 11/19/2017] [Accepted: 11/19/2017] [Indexed: 05/08/2023]
Abstract
A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.
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Affiliation(s)
- Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan.
| | - Kengo Ito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
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25
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Date Y, Kikuchi J. Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables. Anal Chem 2018; 90:1805-1810. [DOI: 10.1021/acs.analchem.7b03795] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
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26
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Metabolomics: State-of-the-Art Technologies and Applications on Drosophila melanogaster. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1076:257-276. [PMID: 29951824 DOI: 10.1007/978-981-13-0529-0_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Metabolomics is one of the latest "omics" technology concerned with the high-throughput identification and quantification of metabolites, the final products of cellular processes. The revealed data provide an instantaneous snapshot of an organism's metabolic pathways, which can be used to explain its phenotype or physiology. On the other hand, Drosophila has shown its power in studying metabolism and related diseases. At this stage, we have the state-of-the-art knowledge in place: a potential candidate to study cellular metabolism (Drosophila melanogaster) and a powerful methodology for metabolic network decipherer (metabolomics). Yet missing is advanced metabolomics technologies like isotope-assisted metabolomics optimized for Drosophila. In this chapter, we will discuss on the current status and future perspectives in technologies and applications of Drosophila metabolomics.
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27
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Cheng J, Lan W, Zheng G, Gao X. Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration. Methods Mol Biol 2018. [PMID: 29536449 DOI: 10.1007/978-1-4939-7717-8_16] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.
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Affiliation(s)
- Jing Cheng
- Department of Medical Instrument, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Wenxian Lan
- State Key Laboratory of Bio-Organic and Natural Product Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Guangyong Zheng
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Xianfu Gao
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
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28
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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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29
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Li DW, Wang C, Brüschweiler R. Maximal clique method for the automated analysis of NMR TOCSY spectra of complex mixtures. JOURNAL OF BIOMOLECULAR NMR 2017; 68:195-202. [PMID: 28573376 PMCID: PMC7032946 DOI: 10.1007/s10858-017-0119-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 05/24/2017] [Indexed: 05/16/2023]
Abstract
Characterization of the chemical components of complex mixtures in solution is important in many areas of biochemistry and chemical biology, including metabolomics. The use of 2D NMR total correlation spectroscopy (TOCSY) experiments has proven very useful for the identification of known metabolites as well as for the characterization of metabolites that are unknown by taking advantage of the good resolution and high sensitivity of this homonuclear experiment. Due to the complexity of the resulting spectra, automation is critical to facilitate and speed-up their analysis and enable high-throughput applications. To better meet these emerging needs, an automated spin-system identification algorithm of TOCSY spectra is introduced that represents the cross-peaks and their connectivities as a mathematical graph, for which all subgraphs are determined that are maximal cliques. Each maximal clique can be assigned to an individual spin system thereby providing a robust deconvolution of the original spectrum for the easy extraction of critical spin system information. The approach is demonstrated for a complex metabolite mixture consisting of 20 compounds and for E. coli cell lysate.
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Affiliation(s)
- Da-Wei Li
- Campus Chemical Instrument Center, The Ohio State University, Columbus, OH, 43210, USA.
| | - Cheng Wang
- Department of Chemistry and Biochemistry, The Ohio State University, CBEC Building, Columbus, OH, 43210, USA
| | - Rafael Brüschweiler
- Campus Chemical Instrument Center, The Ohio State University, Columbus, OH, 43210, USA.
- Department of Chemistry and Biochemistry, The Ohio State University, CBEC Building, Columbus, OH, 43210, USA.
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, OH, 43210, USA.
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30
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Palanisamy SK, Rajendran NM, Marino A. Natural Products Diversity of Marine Ascidians (Tunicates; Ascidiacea) and Successful Drugs in Clinical Development. NATURAL PRODUCTS AND BIOPROSPECTING 2017; 7:1-111. [PMID: 28097641 PMCID: PMC5315671 DOI: 10.1007/s13659-016-0115-5] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Accepted: 12/14/2016] [Indexed: 06/06/2023]
Abstract
This present study reviewed the chemical diversity of marine ascidians and their pharmacological applications, challenges and recent developments in marine drug discovery reported during 1994-2014, highlighting the structural activity of compounds produced by these specimens. Till date only 5% of living ascidian species were studied from <3000 species, this study represented from family didemnidae (32%), polyclinidae (22%), styelidae and polycitoridae (11-12%) exhibiting the highest number of promising MNPs. Close to 580 compound structures are here discussed in terms of their occurrence, structural type and reported biological activity. Anti-cancer drugs are the main area of interest in the screening of MNPs from ascidians (64%), followed by anti-malarial (6%) and remaining others. FDA approved ascidian compounds mechanism of action along with other compounds status of clinical trials (phase 1 to phase 3) are discussed here in. This review highlights recent developments in the area of natural products chemistry and biotechnological approaches are emphasized.
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Affiliation(s)
- Satheesh Kumar Palanisamy
- Department of Chemical, Biological, Pharmaceutical and Environmental Science, University of Messina, 98166, Messina, Italy.
| | - N M Rajendran
- Key Laboratory of Engineering Plastics and Beijing National Laboratory for Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China
| | - Angela Marino
- Department of Chemical, Biological, Pharmaceutical and Environmental Science, University of Messina, 98166, Messina, Italy
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31
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Nuclear Magnetic Resonance Strategies for Metabolic Analysis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 965:45-76. [DOI: 10.1007/978-3-319-47656-8_3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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32
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Kikuchi J, Yamada S. NMR window of molecular complexity showing homeostasis in superorganisms. Analyst 2017; 142:4161-4172. [DOI: 10.1039/c7an01019b] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.
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Affiliation(s)
- Jun Kikuchi
- RIKEN Center for Sustainable Resource Science
- Yokohama
- Japan
- Graduate School of Bioagricultural Sciences
- Nagoya University
| | - Shunji Yamada
- RIKEN Center for Sustainable Resource Science
- Yokohama
- Japan
- Graduate School of Bioagricultural Sciences
- Nagoya University
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33
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Bingol K, Li DW, Zhang B, Brüschweiler R. Comprehensive Metabolite Identification Strategy Using Multiple Two-Dimensional NMR Spectra of a Complex Mixture Implemented in the COLMARm Web Server. Anal Chem 2016; 88:12411-12418. [PMID: 28193069 DOI: 10.1021/acs.analchem.6b03724] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Identification of metabolites in complex mixtures represents a key step in metabolomics. A new strategy is introduced, which is implemented in a new public web server, COLMARm, that permits the coanalysis of up to three two-dimensional (2D) NMR spectra, namely, 13C-1H HSQC (heteronuclear single quantum coherence spectroscopy), 1H-1H TOCSY (total correlation spectroscopy), and 13C-1H HSQC-TOCSY, for the comprehensive, accurate, and efficient performance of this task. The highly versatile and interactive nature of COLMARm permits its application to a wide range of metabolomics samples independent of the magnetic field. Database query is performed using the HSQC spectrum, and the top metabolite hits are then validated against the TOCSY-type experiment(s) by superimposing the expected cross-peaks on the mixture spectrum. In this way the user can directly accept or reject candidate metabolites by taking advantage of the complementary spectral information offered by these experiments and their different sensitivities. The power of COLMARm is demonstrated for a human serum sample uncovering the existence of 14 metabolites that hitherto were not identified by NMR.
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Affiliation(s)
- Kerem Bingol
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , Richland, Washington 99354, United States
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34
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Walker LR, Hoyt DW, Walker SM, Ward JK, Nicora CD, Bingol K. Unambiguous metabolite identification in high-throughput metabolomics by hybrid 1D 1 H NMR/ESI MS 1 approach. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2016; 54:998-1003. [PMID: 27539910 DOI: 10.1002/mrc.4503] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 07/08/2016] [Accepted: 08/15/2016] [Indexed: 06/06/2023]
Affiliation(s)
- Lawrence R Walker
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - David W Hoyt
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - S Michael Walker
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, 66045, USA
| | - Joy K Ward
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, 66045, USA
| | - Carrie D Nicora
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Kerem Bingol
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
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35
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Bingol K, Brüschweiler R. Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods. Curr Opin Biotechnol 2016; 43:17-24. [PMID: 27552705 DOI: 10.1016/j.copbio.2016.07.006] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 07/26/2016] [Accepted: 07/28/2016] [Indexed: 01/10/2023]
Abstract
Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.
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Affiliation(s)
- Kerem Bingol
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Rafael Brüschweiler
- Campus Chemical Instrument Center, The Ohio State University, Columbus, OH 43210, United States; Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, United States; Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, OH 43210, United States.
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36
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Ito K, Tsutsumi Y, Date Y, Kikuchi J. Fragment Assembly Approach Based on Graph/Network Theory with Quantum Chemistry Verifications for Assigning Multidimensional NMR Signals in Metabolite Mixtures. ACS Chem Biol 2016; 11:1030-8. [PMID: 26789380 DOI: 10.1021/acschembio.5b00894] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The abundant observation of chemical fragment information for molecular complexities is a major advantage of biological NMR analysis. Thus, the development of a novel technique for NMR signal assignment and metabolite identification may offer new possibilities for exploring molecular complexities. We propose a new signal assignment approach for metabolite mixtures by assembling H-H, H-C, C-C, and Q-C fragmental information obtained by multidimensional NMR, followed by the application of graph and network theory. High-speed experiments and complete automatic signal assignments were achieved for 12 combined mixtures of (13)C-labeled standards. Application to a (13)C-labeled seaweed extract showed 66 H-C, 60 H-H, 326 C-C, and 28 Q-C correlations, which were successfully assembled to 18 metabolites by the automatic assignment. The validity of automatic assignment was supported by quantum chemical calculations. This new approach can predict entire metabolite structures from peak networks of biological extracts.
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Affiliation(s)
- Kengo Ito
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehirocho, Tsurumi-ku, Yokohama 230-0045, Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama 235-0045, Japan
| | - Yu Tsutsumi
- Bruker BioSpin K.K., 3-9 Moriya-cho, Kanagawa-ku, Yokohama 221-0022, Japan
| | - Yasuhiro Date
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehirocho, Tsurumi-ku, Yokohama 230-0045, Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama 235-0045, Japan
| | - Jun Kikuchi
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehirocho, Tsurumi-ku, Yokohama 230-0045, Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama 235-0045, Japan
- Graduate
School of Bioagricultural Sciences and School of Agricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
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37
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Abstract
This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.
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38
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Shiokawa Y, Misawa T, Date Y, Kikuchi J. Application of Market Basket Analysis for the Visualization of Transaction Data Based on Human Lifestyle and Spectroscopic Measurements. Anal Chem 2016; 88:2714-9. [PMID: 26824632 DOI: 10.1021/acs.analchem.5b04182] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
With the innovation of high-throughput metabolic profiling methods such as nuclear magnetic resonance (NMR), data mining techniques that can reveal valuable information from substantial data sets are constantly desired in this field. In particular, for the analytical assessment of various human lifestyles, advanced computational methods are ultimately needed. In this study, we applied market basket analysis, which is generally applied in social sciences such as marketing, and used transaction data derived from dietary intake information and urinary chemical data generated using NMR and inductively coupled plasma optical emission spectrometry measurements. The analysis revealed several relationships, such as fish diets with high trimethylamine N-oxide excretion and N-methylnicotinamide excreted at higher levels in the morning and produced from a protein that was consumed one day prior. Therefore, market basket analysis can be applied to metabolic profiling to effectively understand the relationships between metabolites and lifestyle.
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Affiliation(s)
- Yuka Shiokawa
- Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Takuma Misawa
- Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yasuhiro Date
- Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Bioagricultural Sciences, Nagoya University , 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
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39
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Misawa T, Komatsu T, Date Y, Kikuchi J. SENSI: signal enhancement by spectral integration for the analysis of metabolic mixtures. Chem Commun (Camb) 2016; 52:2964-7. [DOI: 10.1039/c5cc09442a] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The method provided here can overcome the low S/N problem in 13C NMR by the integration of plural spectra to increase the resolution based on non-bucketing analysis without measurements.
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Affiliation(s)
- Takuma Misawa
- Graduate School of Medical Life Science
- Yokohama City University (YCU)
- Yokohama 230-0045
- Japan
- RIKEN Center for Sustainable Resource Science
| | - Takanori Komatsu
- Graduate School of Medical Life Science
- Yokohama City University (YCU)
- Yokohama 230-0045
- Japan
- RIKEN Center for Sustainable Resource Science
| | - Yasuhiro Date
- Graduate School of Medical Life Science
- Yokohama City University (YCU)
- Yokohama 230-0045
- Japan
- RIKEN Center for Sustainable Resource Science
| | - Jun Kikuchi
- Graduate School of Medical Life Science
- Yokohama City University (YCU)
- Yokohama 230-0045
- Japan
- RIKEN Center for Sustainable Resource Science
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40
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Kikuchi J, Tsuboi Y, Komatsu K, Gomi M, Chikayama E, Date Y. SpinCouple: Development of a Web Tool for Analyzing Metabolite Mixtures via Two-Dimensional J-Resolved NMR Database. Anal Chem 2015; 88:659-65. [PMID: 26624790 DOI: 10.1021/acs.analchem.5b02311] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
A new Web-based tool, SpinCouple, which is based on the accumulation of a two-dimensional (2D) (1)H-(1)H J-resolved NMR database from 598 metabolite standards, has been developed. The spectra include both J-coupling and (1)H chemical shift information; those are applicable to a wide array of spectral annotation, especially for metabolic mixture samples that are difficult to label through the attachment of (13)C isotopes. In addition, the user-friendly application includes an absolute-quantitative analysis tool. Good agreement was obtained between known concentrations of 20-metabolite mixtures versus the calibration curve-based quantification results obtained from 2D-Jres spectra. We have examined the web tool availability using nine series of biological extracts, obtained from animal gut and waste treatment microbiota, fish, and plant tissues. This web-based tool is publicly available via http://emar.riken.jp/spincpl.
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Affiliation(s)
- Jun Kikuchi
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Bioagricultural Sciences, Nagoya University , 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
| | - Yuuri Tsuboi
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Keiko Komatsu
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Masahiro Gomi
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Eisuke Chikayama
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Information Systems, Niigata University of International and Information Studies , 3-1-1 Mizukino, Nishi-ku, Niigata-shi, Niigata 950-2292, Japan
| | - Yasuhiro Date
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
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41
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Bingol K, Brüschweiler R. Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics. Curr Opin Clin Nutr Metab Care 2015; 18:471-7. [PMID: 26154280 PMCID: PMC4533976 DOI: 10.1097/mco.0000000000000206] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW This review describes some of the advances made over the past year in NMR-based metabolomics for the elucidation of known and unknown compounds, including new ways of how to combine this information with high-resolution mass spectrometry. RECENT FINDINGS A new method allows the back-calculation of mass spectra from NMR spectra that have been queried against databases improving the accuracy of the identified compounds by validation and consistency analysis. For the de-novo characterization of unknown compounds, an algorithm has been introduced that predicts all viable NMR spectra from accurate masses allowing, by comparison with experimental NMR data, the determination of the structures of new metabolites in complex mixtures. SUMMARY Recent advances in NMR and mass spectrometry-based metabolomics and their synergistic use promises to significantly improve metabolomics sample characterization both in terms of identification and quantitation, and accelerate metabolite discovery.
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Affiliation(s)
| | - Rafael Brüschweiler
- Department of Chemistry and Biochemistry
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio, USA
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42
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Zhang B, Xie M, Bruschweiler-Li L, Bingol K, Brüschweiler R. Use of Charged Nanoparticles in NMR-Based Metabolomics for Spectral Simplification and Improved Metabolite Identification. Anal Chem 2015; 87:7211-7. [PMID: 26087125 DOI: 10.1021/acs.analchem.5b01142] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Metabolomics aims at a complete characterization of all metabolites in biological samples in terms of both their identities and concentrations. Because changes of metabolites and their concentrations are a direct reflection of cellular activity, it allows for a better understanding of cellular processes and function to be obtained. Although NMR spectroscopy is routinely applied to complex biological mixtures without purification, overlapping NMR peaks often pose a challenge for the comprehensive and accurate identification of the underlying metabolites. To address this problem, we present a novel nanoparticle-based strategy that differentiates between metabolites based on their electric charge. By adding electrically charged silica nanoparticles to the solution NMR sample, metabolites of opposite charge bind to the nanoparticles and their NMR signals are weakened or entirely suppressed due to peak broadening caused by the slow rotational tumbling of the nanometer-sized nanoparticles. Comparison of the edited with the original spectrum significantly facilitates analysis and reduces ambiguities in the identification of metabolites. This method makes NMR directly sensitive to the detection of molecular charges at constant pH, as demonstrated here both for model mixtures and human urine. The simplicity of the approach should make it useful for a wide range of metabolomics applications.
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Affiliation(s)
- Bo Zhang
- †Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Mouzhe Xie
- †Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Lei Bruschweiler-Li
- †Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Kerem Bingol
- †Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Rafael Brüschweiler
- †Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
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43
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Abstract
The many advantages of (13)C NMR are often overshadowed by its intrinsically low sensitivity. Given that carbon makes up the backbone of most biologically relevant molecules, (13)C NMR offers a straightforward measurement of these compounds. Two-dimensional (13)C-(13)C correlation experiments like INADEQUATE (incredible natural abundance double quantum transfer experiment) are ideal for the structural elucidation of natural products and have great but untapped potential for metabolomics analysis. We demonstrate a new and semiautomated approach called INETA (INADEQUATE network analysis) for the untargeted analysis of INADEQUATE data sets using an in silico INADEQUATE database. We demonstrate this approach using isotopically labeled Caenorhabditis elegans mixtures.
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Affiliation(s)
- Chaevien S. Clendinen
- Department of Biochemistry & Molecular Biology,
University of Florida, Gainesville FL 32610-0245
- Southeast Center for Integrated Metabolomics, University of
Florida, Gainesville FL 32610-0245
| | | | - Ramadan Ajredini
- Department of Biochemistry & Molecular Biology,
University of Florida, Gainesville FL 32610-0245
- Southeast Center for Integrated Metabolomics, University of
Florida, Gainesville FL 32610-0245
| | - Arthur S. Edison
- Department of Biochemistry & Molecular Biology,
University of Florida, Gainesville FL 32610-0245
- Southeast Center for Integrated Metabolomics, University of
Florida, Gainesville FL 32610-0245
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44
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Bingol K, Brüschweiler R. NMR/MS Translator for the Enhanced Simultaneous Analysis of Metabolomics Mixtures by NMR Spectroscopy and Mass Spectrometry: Application to Human Urine. J Proteome Res 2015; 14:2642-8. [PMID: 25881480 DOI: 10.1021/acs.jproteome.5b00184] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A novel metabolite identification strategy is presented for the combined NMR/MS analysis of complex metabolite mixtures. The approach first identifies metabolite candidates from 1D or 2D NMR spectra by NMR database query, which is followed by the determination of the masses (m/z) of their possible ions, adducts, fragments, and characteristic isotope distributions. The expected m/z ratios are then compared with the MS(1) spectrum for the direct assignment of those signals of the mass spectrum that contain information about the same metabolites as the NMR spectra. In this way, the mass spectrum can be assigned with very high confidence, and it provides at the same time validation of the NMR-derived metabolites. The method was first demonstrated on a model mixture, and it was then applied to human urine collected from a pool of healthy individuals. A number of metabolites could be detected that had not been reported previously, further extending the list of known urine metabolites. The new analysis approach, which is termed NMR/MS Translator, is fully automated and takes only a few seconds on a computer workstation. NMR/MS Translator synergistically uses the power of NMR and MS, enhancing the accuracy and efficiency of the identification of those metabolites compiled in databases.
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Affiliation(s)
- Kerem Bingol
- †Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Rafael Brüschweiler
- †Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
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45
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Bingol K, Bruschweiler-Li L, Yu C, Somogyi A, Zhang F, Brüschweiler R. Metabolomics beyond spectroscopic databases: a combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures. Anal Chem 2015; 87:3864-70. [PMID: 25674812 PMCID: PMC5035699 DOI: 10.1021/ac504633z] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
A novel strategy is introduced that combines high-resolution mass spectrometry (MS) with NMR for the identification of unknown components in complex metabolite mixtures encountered in metabolomics. The approach first identifies the chemical formulas of the mixture components from accurate masses by MS and then generates all feasible structures (structural manifold) that are consistent with these chemical formulas. Next, NMR spectra of each member of the structural manifold are predicted and compared with the experimental NMR spectra in order to identify the molecular structures that match the information obtained from both the MS and NMR techniques. This combined MS/NMR approach was applied to Escherichia coli extract, where the approach correctly identified a wide range of different types of metabolites, including amino acids, nucleic acids, polyamines, nucleosides, and carbohydrate conjugates. This makes this approach, which is termed SUMMIT MS/NMR, well suited for high-throughput applications for the discovery of new metabolites in biological and biomedical mixtures, overcoming the need of experimental MS and NMR metabolite databases.
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Affiliation(s)
- Kerem Bingol
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Lei Bruschweiler-Li
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Cao Yu
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Arpad Somogyi
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Fengli Zhang
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, United States
| | - Rafael Brüschweiler
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, United States
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46
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Alonso A, Marsal S, Julià A. Analytical methods in untargeted metabolomics: state of the art in 2015. Front Bioeng Biotechnol 2015; 3:23. [PMID: 25798438 PMCID: PMC4350445 DOI: 10.3389/fbioe.2015.00023] [Citation(s) in RCA: 393] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 02/18/2015] [Indexed: 12/20/2022] Open
Abstract
Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.
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Affiliation(s)
- Arnald Alonso
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
- Department of Automatic Control (ESAII), Polytechnic University of Catalonia, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
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Johnson SR, Lange BM. Open-access metabolomics databases for natural product research: present capabilities and future potential. Front Bioeng Biotechnol 2015; 3:22. [PMID: 25789275 PMCID: PMC4349186 DOI: 10.3389/fbioe.2015.00022] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 02/14/2015] [Indexed: 12/24/2022] Open
Abstract
Various databases have been developed to aid in assigning structures to spectral peaks observed in metabolomics experiments. In this review article, we discuss the utility of currently available open-access spectral and chemical databases for natural products discovery. We also provide recommendations on how the research community can contribute to further improvements.
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Affiliation(s)
- Sean R Johnson
- Institute of Biological Chemistry, M.J. Murdock Metabolomics Laboratory, Washington State University , Pullman, WA , USA
| | - Bernd Markus Lange
- Institute of Biological Chemistry, M.J. Murdock Metabolomics Laboratory, Washington State University , Pullman, WA , USA
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48
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Bingol K, Li DW, Bruschweiler-Li L, Cabrera OA, Megraw T, Zhang F, Brüschweiler R. Unified and isomer-specific NMR metabolomics database for the accurate analysis of (13)C-(1)H HSQC spectra. ACS Chem Biol 2015; 10:452-9. [PMID: 25333826 PMCID: PMC4340359 DOI: 10.1021/cb5006382] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
A new
metabolomics database and query algorithm for the analysis
of 13C–1H HSQC spectra is introduced,
which unifies NMR spectroscopic information on 555 metabolites from
both the Biological Magnetic Resonance Data Bank (BMRB) and Human
Metabolome Database (HMDB). The new database, termed Complex Mixture
Analysis by NMR (COLMAR) 13C–1H HSQC
database, can be queried via an interactive, easy to use web interface
at http://spin.ccic.ohio-state.edu/index.php/hsqc/index. Our new HSQC database separately treats slowly exchanging isomers
that belong to the same metabolite, which permits improved query in
cases where lowly populated isomers are below the HSQC detection limit.
The performance of our new database and query web server compares
favorably with the one of existing web servers, especially for spectra
of samples of high complexity, including metabolite mixtures from
the model organisms Drosophila melanogaster and Escherichia coli. For such samples, our web server has on
average a 37% higher accuracy (true positive rate) and a 82% lower
false positive rate, which makes it a useful tool for the rapid and
accurate identification of metabolites from 13C–1H HSQC spectra at natural abundance. This information can
be combined and validated with NMR data from 2D TOCSY-type spectra
that provide connectivity information not present in HSQC spectra.
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Affiliation(s)
| | | | | | - Oscar A. Cabrera
- Department
of Biomedical Sciences, Florida State University College of Medicine, Tallahassee, Florida 32306, United States
| | - Timothy Megraw
- Department
of Biomedical Sciences, Florida State University College of Medicine, Tallahassee, Florida 32306, United States
| | - Fengli Zhang
- National
High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, United States
| | - Rafael Brüschweiler
- National
High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, United States
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Clendinen CS, Stupp GS, Ajredini R, Lee-McMullen B, Beecher C, Edison AS. An overview of methods using (13)C for improved compound identification in metabolomics and natural products. FRONTIERS IN PLANT SCIENCE 2015; 6:611. [PMID: 26379677 PMCID: PMC4548202 DOI: 10.3389/fpls.2015.00611] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Accepted: 07/23/2015] [Indexed: 05/11/2023]
Abstract
Compound identification is a major bottleneck in metabolomics studies. In nuclear magnetic resonance (NMR) investigations, resonance overlap often hinders unambiguous database matching or de novo compound identification. In liquid chromatography-mass spectrometry (LC-MS), discriminating between biological signals and background artifacts and reliable determination of molecular formulae are not always straightforward. We have designed and implemented several NMR and LC-MS approaches that utilize (13)C, either enriched or at natural abundance, in metabolomics applications. For LC-MS applications, we describe a technique called isotopic ratio outlier analysis (IROA), which utilizes samples that are isotopically labeled with 5% (test) and 95% (control) (13)C. This labeling strategy leads to characteristic isotopic patterns that allow the differentiation of biological signals from artifacts and yield the exact number of carbons, significantly reducing possible molecular formulae. The relative abundance between the test and control samples for every IROA feature can be determined simply by integrating the peaks that arise from the 5 and 95% channels. For NMR applications, we describe two (13)C-based approaches. For samples at natural abundance, we have developed a workflow to obtain (13)C-(13)C and (13)C-(1)H statistical correlations using 1D (13)C and (1)H NMR spectra. For samples that can be isotopically labeled, we describe another NMR approach to obtain direct (13)C-(13)C spectroscopic correlations. These methods both provide extensive information about the carbon framework of compounds in the mixture for either database matching or de novo compound identification. We also discuss strategies in which (13)C NMR can be used to identify unknown compounds from IROA experiments. By combining technologies with the same samples, we can identify important biomarkers and corresponding metabolites of interest.
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Affiliation(s)
- Chaevien S. Clendinen
- Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, USA
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
| | | | - Ramadan Ajredini
- Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, USA
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
| | - Brittany Lee-McMullen
- Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, USA
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
| | - Chris Beecher
- Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, USA
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
- IROA Technologies, Ann Arbor, MI, USA
| | - Arthur S. Edison
- Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, USA
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
- *Correspondence: Arthur S. Edison, Southeast Center for Integrated Metabolomics and Department of Biochemistry and Molecular Biology, University of Florida, 1600 Archer Road, Rm R3-226, Box 100245, Gainesville, FL 32610-0245, USA,
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50
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Clendinen C, Lee-McMullen B, Williams CM, Stupp GS, Vandenborne K, Hahn DA, Walter GA, Edison AS. ¹³C NMR metabolomics: applications at natural abundance. Anal Chem 2014; 86:9242-50. [PMID: 25140385 PMCID: PMC4165451 DOI: 10.1021/ac502346h] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 08/20/2014] [Indexed: 12/30/2022]
Abstract
(13)C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality (13)C NMR spectra obtained using a custom (13)C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D (13)C and (1)H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful (13)C-(13)C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of (13)C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The (13)C and (1)H data together led to 15 matches in the database compared to just 7 using (1)H alone, and the (13)C correlated peak lists had far fewer false positives than the (1)H generated lists. In addition, the (13)C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the D. melanogaster extracts and mouse serum.
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Affiliation(s)
- Chaevien
S. Clendinen
- Department of Biochemistry & Molecular Biology, Department of Entomology and Nematology, Department of Physical
Therapy, Department of Physiology and Functional Genomics, and Southeast Center for Integrated
Metabolomics, University of Florida, Gainesville, Florida 32610-0245, United States
| | - Brittany Lee-McMullen
- Department of Biochemistry & Molecular Biology, Department of Entomology and Nematology, Department of Physical
Therapy, Department of Physiology and Functional Genomics, and Southeast Center for Integrated
Metabolomics, University of Florida, Gainesville, Florida 32610-0245, United States
| | - Caroline M. Williams
- Department of Biochemistry & Molecular Biology, Department of Entomology and Nematology, Department of Physical
Therapy, Department of Physiology and Functional Genomics, and Southeast Center for Integrated
Metabolomics, University of Florida, Gainesville, Florida 32610-0245, United States
| | - Gregory S. Stupp
- Department of Biochemistry & Molecular Biology, Department of Entomology and Nematology, Department of Physical
Therapy, Department of Physiology and Functional Genomics, and Southeast Center for Integrated
Metabolomics, University of Florida, Gainesville, Florida 32610-0245, United States
| | - Krista Vandenborne
- Department of Biochemistry & Molecular Biology, Department of Entomology and Nematology, Department of Physical
Therapy, Department of Physiology and Functional Genomics, and Southeast Center for Integrated
Metabolomics, University of Florida, Gainesville, Florida 32610-0245, United States
| | - Daniel A. Hahn
- Department of Biochemistry & Molecular Biology, Department of Entomology and Nematology, Department of Physical
Therapy, Department of Physiology and Functional Genomics, and Southeast Center for Integrated
Metabolomics, University of Florida, Gainesville, Florida 32610-0245, United States
| | - Glenn A. Walter
- Department of Biochemistry & Molecular Biology, Department of Entomology and Nematology, Department of Physical
Therapy, Department of Physiology and Functional Genomics, and Southeast Center for Integrated
Metabolomics, University of Florida, Gainesville, Florida 32610-0245, United States
| | - Arthur S. Edison
- Department of Biochemistry & Molecular Biology, Department of Entomology and Nematology, Department of Physical
Therapy, Department of Physiology and Functional Genomics, and Southeast Center for Integrated
Metabolomics, University of Florida, Gainesville, Florida 32610-0245, United States
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